regional cooperation in strengthening the low-carbon green ...... · in this intraregional primary...

Regional Cooperation in Strengthening the

Low-Carbon Green Growth:

Challenges, Prospects & Policy Framework

by

Kazi Arif Uz Zaman

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

of the Australian National University

November 2018

ii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Candidate's Declaration

This thesis contains no material which has been accepted for the award of any other degree or

diploma in any university. To the best of the author’s knowledge, it contains no material

previously published or written by another person, except where due reference is made in the

text.

Kazi Arif Uz Zaman Date: 16/07/2018

iii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Acknowledgements

Ph.D. remains a passionate endeavor for me with immense opportunities and profound learning

experiences from many great people who have provided their unconditional support and

encouragements throughout my journey.

First and foremost, to my supervisor Professor Kaliappa Kalirajan. I am highly indebted to him

for his insightful knowledge, incomparable endurance and jovial munificence in the process of

guiding me till this point. Professor Kalirajan has always been a true inspiration for me with

his vast experience, depth of knowledge, and a world of motivation. Besides, he is such a

pleasant personality who not only took good care of my academic issues but with an amazingly

calm and positive mindset; he helped me to feel like a family. As a supervisor, Professor

Kalirajan is instrumental in inspiring the optimism, igniting the imagination, and instill a love

of learning- which helped me to come along this way with my dream.

My sincere and heartfelt thanks to the honorable panels, Professor Ligang Song and Dr. Moinul

Islam for their kind consent to share a momentous part of my journey. Both of them always

remained so encouraging and supportive to figuring out the pivotal issues of my work for

further enrichment and fine tunings.

I am profoundly grateful to some senior Professors of the Australian National University for

sharing their valuable comments, advice, and feedback as and when it was required for my

thesis. I would like to acknowledge the kind advice from Professor Raghbendra Jha, Professor

Prema-chandra Athukorala, Professor R. Quentin Grafton, and Professor David Stern in this

regard.

I would also like to take this opportunity to acknowledge the adequate support, logistics, and

services provided by the Crawford School of Public Policy throughout this four-year period.

Dr. Megan (Academic & Research Skills Advisor), Tracy McRae (Senior HDR

Administrator for Ph.D. Students), Mark Badger (Academic Skills Advisor), and

Shahandra Martino (CAP-IT service team), I highly appreciate their valuable supports

and help to make my working environment smooth and lively.

My research would not be possible without the aid and support from the Department of

Education and Training, Government of Australia. I am thoroughly grateful to this government

body to allow me with this lifetime opportunity through awarding the Endeavour Postgraduate

Scholarship, without which my dream would never turn into a reality. I would also like to

iv | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

acknowledge the kind cooperation of the different case managers of Scope Global to make my

life easier in Australia.

It is my utmost pleasure to express my gratitude to all friends and colleagues of the Stanner

Building of Crawford School of Public Policy for their unconditional supports, motivation, and

time. We always had very productive discussions to support each other not only regarding our

works but on any issues starting from politics, sports to the yummy dishes of everyone’s

favorite restaurants. Liyana, Elton, Panittra, Lam, Martha, Huong, and all of my friends, they

were always there to make things easier for me. Thank you, mates!

The list would not be completed without acknowledging the love, care, and encouragement

from my dearest family. I am not able to fully express my indebtedness to my mother who has

to make incredible sacrifice throughout her life to bring me up to this position. Her blessings

always remain the key for all of my achievements. My wholehearted thanks to my sisters,

relatives, and friends who were always there to cheer me up on every occasion.

I cannot but to say my heartiest appreciations to my lovely wife Zannatul who has to take all

the cares and responsibilities in the last four years to give me the most-possible comfort and

easing to make enough space for me to carry out my research. Tons of love to my cute little

daughter Maryam, more often who has to sacrifice her precious times and desire for play with

her Dad during my research works. I would like to dedicate this accomplishment to my wife

and my daughter.

All of a sudden, the calendar is reminding me that four years at the ANU is coming to an end.

It is indeed not only the journey of 1460 days but the accumulation of the infinite moments of

good memories, some valuable friendships and networking all over the world, the

belongingness of the faculties and their invaluable sharing of knowledge. It is the lifetime

attainments and thanks Australia, for handing me this precious time of my life.

v | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Abstract of the Thesis

Following the enduring changes in climate system, along with it’s universal and irreversible

impacts for the people and ecosystems, an explicit paradigm shift from the traditional growth

policy towards the ‘low-carbon green growth’ (LCGG) perspective is extolled in recent time.

Since environmental pollution and CO2 emission has become a cross-boundary issue, actions

taken by one nation have affected the development path of others, especially for the

neighboring countries. Therefore, countries should strategize the comprehensive regional or

sub-regional cooperation frameworks so that they can mutually overcome the impacts of such

environmental degradation and emission-related issues. This thesis attempts to develop

comprehensive economic analysis to elucidate how best the regional cooperation (RC) can

boost the LCGG implications in the countries.

It reveals that negotiation on environmental issues are often challenged by the socio-political

interests and power-domination factors while lacked adequate economic analysis. The thesis

introduces a plausible solution in form of Geo-Environmental Importance index by which a

country can quantify the potential risk and benefit resulted from the emission or environmental

degradation of all negotiating countries. Once the background information on the emission and

its impacts are precisely available, it would be easier for the countries to negotiate and make a

better decision. For empirical analysis, 20 South-through-East Asian countries: 10 ASEAN,

seven SAARC, China, Japan, and Korea are chosen. In short, the regional bloc is termed as

StEA. Results reveal that eight countries are identified as predominantly geoenvironmental risk

assimilator, one risk neutral while rest of the countries are identified as predominantly risk

disseminator. The top-5 geoenvironmental risk disseminating countries are China, India,

Singapore, Japan, and Indonesia. Conversely, Bhutan, Nepal, Laos, Cambodia, and Myanmar

are the top-5 countries most susceptible to the regional geoenvironmental risk.

The thesis subsequently analyzes the potential role of an RC in strengthening the countries’

LCGG in four key areas: energy, agriculture, trade, and natural resources management. For the

energy analysis, this study empirically evaluates the existing demand-supply gaps of energy in

StEA countries and explains how the gaps can be minimized most efficiently through

intraregional trade while making the transition towards the low-carbon system. Stochastic

Frontier (SF) models are used to estimate the country-specific intraregional trade efficiencies

in the primary energy and the Renewable Energy Goods (REG). The result implies that for

most of the countries, intraregional export of primary energy, as well as REG, are positively

influenced by GDP of the exporting and importing countries. Tariff and distance adversely

vi | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

affect the exports while the implication of cross exchange ratio seems minimal in both cases.

RTA is also found to have notable positive impact. China and Malaysia are the most-efficient

in this intraregional primary energy exports, while Bangladesh and Myanmar remain the least-

efficient. China and Japan are the most-efficient in REG exports, while Myanmar remains the

least-efficient. On average, the whole region has the weighted export efficiency of 58.4% in

intraregional primary energy trade and 65.4% in REG trade. Institutional quality, better

infrastructure, goods market efficiency, and technological readiness have reasonable impacts

to enhance the countries’ intraregional energy trade efficiencies.

It also examines the underlying factors which can explain the energy usage efficiency of the

countries both in aggregate and sectoral level. DEA Malmquist index model is used for the

sectoral level analysis. On average, the region’s energy use efficiency in agriculture is declined

by 19% while technology level is advanced by 42% during 1995-2013. Energy use efficiency

in industry sector is dropped by 24% while technology level is improved by 26% during this

time. For electricity sector, energy use efficiency is improved by 4% while the technology level

is dropped by 19%. Energy use efficiency in the transportation sector is declined by 12% while

technology is level improved by 4% during 1995-2013.

For agriculture, both the production efficiency and emission management efficiency are

estimated both at country-level and regional level by using the SF models. Result implies that

land, capital, energy, and FDI have positive impacts while labor and fertilizer have negative

impact on the region’s agriculture production. It also estimates that under regional cooperation

(RC) framework, on average, StEA region can add an untapped potential production of 16.3%

without deploying any additional resources. Forming an RC could have, on average, 34%

added impact (synergy effect) in improving the production closer to the potential.

Analysis for emission management reveals that more use of land, labor, fertilizer, and energy

will increase the emission while capital, and FDI will reduce it. On average, emission

management efficiency under the RC framework would be 52.6%. Synergy effect analysis

reveals that forming the regional bloc can have 2.6% added impact for potential emission level.

According to the combined Green Growth Index in Agriculture, China, Japan, and Korea have

the highest overall efficiency while Cambodia, Lao PDR, and Thailand, have the lowest.

In following chapter, the thesis uses a two-stage analysis for an in-depth understanding of the

factors affecting the sustainable natural resources management in the StEA region. The first

stage uses the extended Kaya identity approach to decompose the underlying factors for

vii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

resource consumption. The result implies that population growth has a positive but little

contribution to resource consumption growth in the StEA countries. GDP per capita has a

higher positive contribution to resource consumption growth for all countries. Emission

intensity also plays a substantial role to decline the resource consumption while the impact of

the resource intensity of emission remains ambiguous. Results also reveal that in aggregate,

1% increase in population, per capita income, emission intensity, and resource per emission

would lead to increase the resource consumption in the StEA region by 0.20%, 0.71%, 0.03%,

and 0.05% respectively. The second stage analysis uses the non-parametric Data Envelopment

Analysis (DEA) method to examine the role of technological change, efficiency change, and

input substitutability changes in resource productivity (RP) estimation. Results show that

Singapore, Korea, Malaysia, and the Philippines have the highest improvement in RP while

Laos, Bhutan, and Vietnam experienced the declining RP according to all three models. RP

changes for economic growth and combined goals are mostly influenced by the efficiency

changes while input substitutability factor dominates to the RP changes for limiting emission.

Japan, Korea, Singapore, and Malaysia remain the top-performing economies in technological

advancement in all three models.

Subsequent chapter examines the intra-regional export potential of the low-carbon goods

(LCG) for the StEA region. The study also investigates the implications of various

determinants of trade in the countries under three broad categories: core determinants, trade

environmental factors, and distributional efficiency factors. Stochastic Frontier Gravity model

estimates that untapped intra-regional export potential of the LCG for the StEA region is

34.8%. Korea has the highest intra-regional export efficiency while Myanmar has the lowest.

Among the core components, GDP of both exporting and importing countries, and trade

agreement factor have found to have positive influences on trade. Distance (between the

exporting and importing countries) and tariff rate are found to have an adverse impact on trade

while the role of exchange rate seems inconclusive. Trade environment factors, such as

institutional strength, quality infrastructure, and market efficiencies are found to have profound

influences on export efficiency. Distributional factors, conversely, seem to have reasonable

influence on intra-regional export efficiency.

Finally, the study provides some generalized guidelines and frameworks on how the RC can

be cohesively adopted to share the best-practices, knowledge, technology, and other resources

in strengthening the performances of all countries towards attaining the sustainable green

growth in this StEA region.

viii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Table of Contents Page

Candidate’s Declaration ii

Acknowledgements iii

Abstract of the Thesis v

Table of Contents viii

List of Tables xiii

List of Figures xv

List of Acronyms xvii

Chapter 1: Introduction

1.1 Background of the study 02

1.2 Objectives of the thesis 07

1.3 Scope of empirical analysis 08

1.4 Significance of the StEA region for this study 10

1.5 Organization of the thesis 13

Chapter 2: Negotiation phase of Regional Cooperation for Low-Carbon Green

Growth: Challenges, and a Plausible Solution

2.1 Preamble of the Chapter 15

2.2 Negotiation process for MEAs 15

2.3 Key challenges in negotiation 16

2.3.1 Political aspect 16

2.3.2 Technical aspect 16

2.3.3 Policy compatibility and countries’ multiple prioritizations 16

2.3.4 Specification of the countries’ roles 17

2.4 Suggested methodology 17

2.4.1 Index Formulation 18

2.4.2 Features of GEI Index 20

2.4.3 Static, 2-Country model 22

2.4.4 Dynamic Approach 23

2.5 Empirical analysis 27

2.5.1 Data sources 27

2.5.2 Results and findings 27

2.6 Potential applications of the GEI index 32

2.7 Limitations of the GEI 34

2.8 Concluding remarks 34

Chapter 3: The Economics of Regional cooperation in Low-Carbon Green

Growth


3.2 Designing the theoretical framework 36

ix | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

3.2.1 The Model 36

3.2.2 Production function 38

3.3 Implication of LCGG strategy adoption 39

3.4 Diffusion and efficient distrubution of recources 49

3.5 Optimal allocation of resources 50

3.5.1 Prospective Model outline 51

3.6 Implications of the proposed theoretical framework 53


Chapter 4: Regional Energy Cooperation for Low-carbon growth: A Demand-

Supply analysis


4.2 Regional statistics 59

4.3 Country-level statistics 61

4.4 Trade of energy 63

4.4.1 Regional level statistics of energy trade 63

4.4.2 Country level statistics of energy trade 65

4.4.2.1 HCE trade statistics 65

4.4.2.2 LCE trade statistics 67

4.5 Methodology: measuring efficiency in energy exports 69

4.5.1 Stochastic Frontier Model 71

4.5.2 Model specification 72

4.5.3 Description of Data 75

4.6 Results and Discussion 75

4.6.1 Trade in Primary energy (fossil fuel) 75

4.6.2 Trade in Renewable Energy Goods 81

4.7 Policy implications 85

4.8 Summary and Concluding remarks 86

Chapter 5: Sustainable Low-carbon Energy system through Regional

Cooperation: An efficiency-based approach


5.2 Analysis of Energy Demand Management 91

5.2.1 Methodology for efficiency estimation 91

5.2.2 Optimization of the goals 92

5.2.3 Model Specification 92

5.2.4 Hypothesis behind choosing the independent variables 93

5.2.5 Data sources 94

5.2.6 Results and Findings 94

5.2.6.1 Estimation of coefficients for the Demand for energy models 94

5.2.6.2 Efficiency measures in Energy Demand Management 96

5.2.7 Policy implications 97

x | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

5.3 End-use efficiency of energy assessment 98

5.3.1 Macro analysis 98

5.3.1.1 Methodology 98

5.3.1.2 Data sources 99

5.3.1.3 Results and findings 100

5.3.1.4 Performance of the countries 101

5.3.2 Sector wise use efficiency 104

5.3.2.1 Methodology 104

5.3.2.2 Model Specification 106

5.3.2.3 Data sources 106

5.3.2.4 Results and findings 107

5.3.3 Policy implications 113

5.4 Summary and Concluding remarks 113

Chapter 6: Sustainable Green Growth in Agriculture through Production

Efficiency: Role of regional cooperation


6.2 Areas of cooperation in targeting Low-carbon growth in agriculture 118

6.2.1 Efficient agricultural production 119

6.2.2 Efficient management of emission and environmental degradation

in agriculture

119

6.3 Objectives and organization of this chapter 119

6.4 Current status of agricultural production and emission in StEA 120

6.4.1 Inadequate production to meet regional agricultural demand 120

6.4.2 Growing emission from agriculture 120

6.4.3 Emission intensity 123

6.5 Methodology 124

6.5.1 Model specification for Production efficiency 125

6.5.2 Description of data 127

6.6 Results and findings 128

6.6.1 Summary statistics 128

6.6.2 Productivity of the inputs 129

6.6.3 Estimation of the coefficients 130

6.6.4 Estimations of the determinants of the Production efficiency of the

countries

131

6.6.5 Estimations of Production efficiency of the countries 132

6.6.6 Impact of Regional cooperation 133

6.6.7 Synergy Effect for potential gap reduction 134



xi | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Chapter 7: Regional Cooperation for Optimal Emission-Management in

Agriculture


7.2 Methodology 139


7.2.2 Description of the data 141


7.3.1 Summary statistics 141

7.3.2 Estimation of the coefficients 142

7.3.3 Estimations of the determinants of the emission management

efficiency

143

7.3.4 Estimations of the Emission-management efficiency of the

countries

144

7.3.5 Performance in managing the sources of emission 145

7.3.6 Impact of Regional cooperation 148

7.3.7 Synergy Effect for potential gap reduction 149

7.4 Green Growth Index in Agriculture: A Composite indicator 150



Chapter 8: Regional cooperation for Sustainable Natural Resources Management


8.2 Current states of Natural resources management in StEA countries 158

8.3 Areas of Cooperation for NRM 162

8.4 Methodology and Data 162

8.4.1 Methodology for analyzing the determinants of resource

consumption (demand-side)

162

8.4.2 Methodology for measuring resource use productivity and its

determinants

164

8.4.2.1 DEA and the decomposition framework 165

8.4.2.2 Models specification 170

8.5 Data sources 171

8.6 Results and Findings 172

8.6.1 Demand-side analysis 172

8.6.2 Productivity analysis 175

8.7 Policy issues 179

8.8 Concluding Remarks 180

Chapter 9: Regional cooperation for Sustainable Green Growth: Role of

intraregional trade on Low-Carbon Goods


9.2 Statistics of the LCG trade in StEA countries 183

xii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

9.2.1 Intraregional trade (aggregated) 183

9.2.2 Intraregional trade (category-wise) 184

9.3 Methodology 186


9.3.2 Description of Data 188


9.4.1 Estimations of intraregional export efficiency for each LCG

category

189

9.4.2 Estimations of intraregional export efficiency for aggregated LCG 190

9.4.3 Determinants of the export performances 192

9.4.3.1 Core components of trade 192

9.4.3.2 Trade environment factors 195

9.4.3.3 Distributional efficiency factors for export 200

9.4.4 Summary performances and implications of the various trade

determinants

202


Chapter 10: Policy Framework, Monitoring, Summary and Conclusion


10.2 Basic RC Framework 207

10.2.1 Policy Framework 208

10.2.2 Institutional Framework 209

10.2.3 Operational Framework 210

10.3 Monitoring process 211

10.4 Summary of this thesis 212

10.5 Contributions and Concluding remarks 218

References 221

Appendix 1: Explanation of GEI index (Proposition 2) 235

Appendix 2: List of the Renewable Energy Goods from the APEC 54 List 238

Appendix 3: Explanation of the Synergy Effect 239

xiii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

List of Tables

Serial Description Page

1.1 Average growth rate of GDP among regions/groups 11

1.2 Regional extraction and consumption of natural resource materials (in Giga

Tons)

12

1.3 Environmental Performance Index, by regions 13

2.1 Matrix of GEIs for the StEA countries 29

2.2 Priority list based on Geo-environmental Impact for each country 30

2.3 Categorization of the member countries based on risk dominance 31

2.4 Calculation of NR shares of the countries 32

2.5 Strategic focus matrix based on the country’s emission and risks features 33

2.6 Plausible roles and strategic focus of the StEA countries 33

3.1 Factors’ requirement and endowment 40

3.2 An example of the optimal resource allocation 52

4.1 Channels of regional energy cooperation for low-carbon growth 57

4.2 Growth of GDP, energy usage, and CO2 emission among the regions 58

4.3 CO2-factors of energy sources 60

4.4 Total energy production, usages, and sufficiency of the countries (2014) 62

4.5 Energy production and share of StEA countries (2014) 62

4.6 Intraregional primary energy trade volume (average, 2006-2016) 66

4.7 Average intraregional exposure share of Primary energy trade in (2010-

2016)

67

4.8 Intraregional REG trade volume (average, 2006-2015) 68

4.9 Average intraregional exposure share of REG trade in (2010-2015) 69

4.10 Estimates of coefficients of the SFG model for primary energy trade 76

4.11 Intraregional primary energy export efficiencies of each country 78

4.12 Estimations of coefficients of the SFG model for Renewable Energy Goods

trade

82

4.13 Intraregional REG export efficiencies of each country 83

5.1 SFP model estimation for HCE production 95

5.2 Technical efficiency for managing demand for HCE and LCE 96

5.3 Conversion efficiency of all StEA countries (1995-2013) 102

5.4 Growth efficiency of all StEA countries (1995-2013) 102

5.5 Direct carbonization efficiency of all StEA countries (1995-2013) 102

5.6 Overall Energy use efficiency of all StEA countries (1995-2013) 103

5.7 TFP change, efficiency change, and Technological changes for agricultural

sector’s energy use

108

5.8 TFP change, efficiency change, and Technological changes for industrial


109

5.9 TFP change, efficiency change, and Technological changes for electricity


111

5.10 TFP change, efficiency change, and Technological changes for transportation


112

xiv | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

6.1 Areas of regional cooperation, the channel of impact, and low-carbon growth

goal

118

6.2 Net food export position of the StEA countries (2011-2013 annual average) 122

6.3 Summary statistics of the input and output factors (annual average, 2000-

2013)

128

6.4 Ranking in various agricultural productivities (based on 2010-2013

average)

129

6.5 Estimation of coefficients for Production efficiency model 130

6.6 Country-wise technical efficiency in agricultural production (2000-2013) 132

6.7 Estimation of the link between optimal production efficiency and emission

reduction

133

6.8 Synergy effect measure, i.e., Impact of Regional cooperation to close the

potential gaps

135

7.1 Summary statistics of the input and output factors (annual average, 2000-

2013)

142

7.2 Estimation of coefficients for Emission management efficiency model 143

7.3 Country-wise technical efficiency in agricultural production (2000-2013) 144

7.4 Ranking in source-based emission intensity management (2010-2013

average)

145

7.5 Estimation of the link between optimal emission-management efficiency

and potential production level

148

7.6 Synergy effect measure, to close the potential gaps in emission 150

7.7 Countries overall performance regarding production efficiency, emission

management efficiency, and GGIA

152

8.1 Regional Extraction of natural resource materials (in Gigatons) 158

8.2 Regional Consumption of natural resource materials (in Giga Tons) 158

8.3 Regional per capita income and resource consumption trend 159

8.4 Country-wise extraction and consumption of NR (2010-2017 average) 160

8.5 Extraction and consumption rates in different time periods 161

8.6 Elasticities of the determinants of resource consumption 174

8.7 Decomposition of RP in economic growth 176

8.8 Decomposition of RP in resource use and limiting emission 177

8.9 Decomposition of RP in economic growth and limiting emission 178

9.1 Aggregated intraregional export and import of the StEA countries (2006-

2016)

184

9.2 Product categorization of the LCGs 184

9.3 Export efficiency of the countries, by category 189

9.4 Intraregional export efficiencies of each country (aggregated LCG) 191

9.5 Coefficients of explanatory variables (core components) 192

9.6 Average rating scales of the trade environment factors (2006-2016) 195

9.7 Summary performances and implications of the various trade determinants

of the exporting countries

204

A2.1 List of the Renewable Energy Goods from the APEC 54 List 238

xv | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

List of Figures

Serial Description Page

1.1 Schematic diagram of the Comprehensive RC framework model 07

1.2 Global GHG emission, by economic sector 08

2.1 Illustration of 𝜃𝑖,𝑗 20

3.1 Dynamics of economic transition towards Environmental Goods and

Services

39

3.2 Impact of increasing supply of G in production 41

3.3 Production Possibility Frontier and potential addition 41

3.4 Potential addition of PPF & Convergence of growth 42

3.5 Change in Consumer surplus with an increased supply of QE 42

3.6 Decomposition of the Malmquist Productivity Index 47

3.7 Schematic diagram of the Supply and Share of Resources phenomenon 49

3.8 Distributional relationship between Technological gap and diffusion 50

3.9 Flowchart for determining the optimal allocation of resources 51

4.1 Aggregated energy production and usage in the StEA region over 1995-

2014

59

4.2 Self-sufficiency in aggregated energy of the StEA region (1995-2014) 60

4.3 Demand-supply trend for HCE 61

4.4 Demand-supply trend for LCE 61

4.5 Energy production mix in StEA countries (2014) 63

4.6 Internal and external demand and supply of energy in StEA region (1995-

2014)

64

4.7 Export composition of StEA 64

4.8 Import composition of StEA 64

4.9 Coefficients of the determinants of the primary energy export efficiency 80

4.10 Coefficients of the determinants of the intraregional REG export efficiency 85

5.1 Performance of countries in energy use efficiency (1995-2013) 101

5.2 Sectoral shares in global energy usage and emission, 2013 104

5.3 Share of low-carbon energy in total energy use in Agriculture sector 107

5.4 Share of low-carbon energy in total energy use in the Industry sector 109

5.5 Share of low-carbon energy in total energy use in the Electricity sector 110

5.6 Share of low-carbon energy in total energy use in the Transportation sector 112

6.1 Average level and growth rate of emission from Agriculture in StEA

countries (2010-2013)

121

6.2 Aggregated emission intensity of the countries (2010-2013 average) 123


reduction

134

7.1 Emission intensities of different sources in countries, (2010-2013 average) 147


reduction

149

8.1 Growth rates in per capita income and resource consumption, by regions

(2000-2013)

159

xvi | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

8.2 Demand and supply of natural resource materials in StEA region (1990-

2017)

161

8.3 Illustration of the Output distance functions 166

8.4 Components of Kaya identity for natural resource consumption (2000-

2015)

172

9.1 LCG export and import product composition, by countries (2006-2016) 185

9.2 Shares of product categories in aggregated Intraregional LCG trade in

StEA region

186

9.3 Estimation of coefficients of institutional strengths 196

9.4 Trend of Pakistan’s Institutional Strengths (2006-2016) 197

9.5 Estimation of coefficients of infrastructure quality 198

9.6 Estimation of coefficients of Good market efficiency 199

9.7 Estimation of coefficients of Technological readiness and adoption 200

9.8 Distributional efficiency measures for the export efficiency 201

9.9 Distributional efficiency measures (DEF2) for demand-based distribution 202

10.1 Basic RC framework model 208

10.2 Institutional Multilateral Cooperation Framework for the 2030 Agenda 209

10.3 Chronological phases of actions for an operational framework 210

A1.1 Illustration of the example 236

xvii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

List of Acronyms

ABC Australian Broadcasting Corporation

ADB Asian Development Bank

ADBI Asian Development Bank Institute

APEC Asia-Pacific Economic Cooperation

ASEAN Association of Southeast Asian Nations

CAIT – WRI Climate Access Indicators Tool (data explorer)-World Resources Institute

CES Constant Elasticity of Substitution

CGE Computable General Equilibrium

CJK China, Japan, and Korea Republic

CO2 Carbon Dioxide

COP Conference of the Parties

CRS Constant Return to Scale

DEA Data Envelopment Analysis

DEF1 Distributional efficiency factor 1

DEF2 Distributional efficiency factor 2

DMC Domestic material consumption

DMUs Decision-making Units

ECOSOC Economic and Social Council of United Nations

EFFCH Efficiency Changes

EG Environment-friendly Goods

EPA Environmental Protection Agency

EPI Environmental Performance Index

EPP Environmentally Preferable Products

EU European Union

FAO Food and Agriculture Organization

FDI Foreign Direct Investment

FTA Free Trade Agreement

GCI Global Competitiveness Index

GEI Geo-environmental Impact

GGIA Green Growth Index in Agriculture

GHG Greenhouse gas

Gtoe Giga Tons of oil equivalent

HCE High-Carbon energy

ICT Information and Communications Technology

IEA International Energy Agency

IPCC Inter-governmental Panel on Climate Change

Lao PDR Lao People's Democratic Republic

LCE Low-Carbon energy

LCG Low-Carbon Goods

LCGG Low-Carbon Green Growth

LCOE Levelized Cost of Energy

xviii | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

LP Linear Programming

LRTAP Long-range Transboundary Air Pollution

MDG Millennium Development Goals

MEA Multilateral Environmental Agreement

MONIT Monitoring-based product

NR Natural Resources

NRM Natural Resources Management

NRS Net Risk Share

OECD Organization for Economic Co-operation and Development

PM Particulate Matter less than 5 microns

PM10 Particulate Matter less than 10 microns

PM2.5 Particulate Matter less than 2.5 microns

PMGT Proactive Management products

POLL Pollution and clean-up products

PPF Production Possibility Frontier

PRA Predominantly Risk Assimilator

PRD Predominant Risk Disseminators

PRPCH Potential Resource Productivity Changes

PTA Preferential Trade Agreement

RC Regional Cooperation

REC Regional Energy Cooperation

REG Renewable Energy Goods

RP Resource Productivity

RPCH Resource Productivity Changes

RTA Regional Trade Agreement

SAARC South Asian Association for Regional Cooperation

SDG Sustainable Development Goals

SFA Stochastic Frontier Analysis

SSR Supply and Share of Resources

StEA South-through-East Asian

TECH Technological Changes

TFPCH Total Factors Productivity Changes

TG Technological Gap

UN United Nations

UNCTAD United Nations Conference on Trade and Development

UN-DESA United Nations Department of Economic and Social Affairs

UNEP United Nations Environment Programme

UNFCCC UN Framework Convention on Climate Change

UNPD United Nations Population Division

USD United States Dollar

WDI World Development Indicators

WITS World Integrated Trade Solution

WTO World Trade Organization

1 | Regional Cooperation in Strengthening Low-Carbon Green Growth: Challenges, Prospects & Policy Framework

Chapter 1

Introduction


1.1 Background of the study

The World has been undergoing steady economic growth since the early twentieth century; that

has been gaining remarkable momentum, especially from the latter half of the century. Global

GDP had around seven-fold increase from 11.2 trillion to 77.6 trillion USD measured at

constant 2010 price during 1960-2016 (World Bank, 2017). Innovation and technological

progress, initiated by few of the countries like the US, Japan, Germany and some European

countries during this era, subsequently propelled the diffusion and adaptation of such

technologies by other emerging economies (Eaton & Kortum, 1999). With the persistent wave

of globalization and thrive for interconnectedness, global consolidation and integration process

has incentivized the institutional settings and human capital development on a larger scale after

the Second World War. It has also helped faster convergence of economies, especially in the

recent time (Rodrik, 2011). However, the compulsion of focusing only on growth has instigated

some notably adverse consequences. It results in mounting pressure on natural resources

extraction and energy exploitation which leads towards the unsustainable development.

According to the UNEP (2018), global resource extraction has increased by 3.3 times from

26.7 billion tons to 88.4 billion tons during the 1970-2017 period. Such growing unsustainable

economic activities have had an immense impact on the vast spreading environmental

degradation and subsequent climatic changes. Anthropogenic actions have induced the

emissions of GHG to reach the highest in history in 2010. Pachauri et al. (2014) reveals that

the atmospheric concentration of CO2 has risen at an alarming pace of 2.0 ± 0.1 ppm per year

during 2002– 2011. At this time, the levels of methane and nitrous oxide also record the highest

among the last 800,000 years. As a result, the world has been experiencing several

unprecedented disorders and havoc over the decades to millennia (UNEP, 2011).

Environmental degradation, increase in GHG emission, and climate change have resulted into

increased vulnerability in the primary sector, unwanted migration or displacement of people,

socio-economic imbalances, and persistent global warming (Fay, 2012).

Following the enduring changes in the climate system, along with it’s universal and irreversible


policy towards the ‘low-carbon green growth’ perspective is extolled in recent time (Barbier

2010). In short, Low-carbon Green Growth (LCGG) is the development phenomenon that

decouples economic growth from carbon emission and environmental degradation through the

sustained and efficient use of resources and environmental services while promoting the

environmental goods, industries, and business in a socio-inclusive manner. OECD, the


organization representing the advanced industrial nations, adopted this green growth strategy

on June 2009 “as part of their responses to the crisis and beyond, acknowledging that green

and growth can go hand-in-hand” (OECD, 2011). Multilateral development banks like the

ADB and the World Bank have also emphasized on “the need to transition toward green

growth as the key to sustainable development and prosperity” in the context of the Rio+20

Conference in June 2012 (Bowen, 2012). ADB expresses that “green growth is an imperative,

not a luxury, for developing Asia” (ADB-ADBI, 2012). The World Bank also supports the idea

that “Making development sustainable requires inclusive green growth” (World Bank, 2012).

With such waves of accumulating advocacies for low-carbon green growth, the UN has scaled

up the MDG into the SDG to set universal aspirational goals applicable to all the nations during

2015-2030. In fact, 10 out of 17 goals of the SDG have directly focused on these sustainability

factors. To espousing such essence, economic growth expects to be coupled with increased

social equity and environmental sustainability. However, ADB-ADBI (2013) explores that it

is not only the worrying impact of climate change which needs to be considered for adopting

LCGG but also the fact of getting locked into high-carbon infrastructure should be given the

preference. According to the study, the transformation from high-carbon to the low-carbon

infrastructure must be considered at earliest possible; else a delayed initiation would cost two

to five-fold increase in mitigation expenses per decade. Hence, it is the high time for every

nation to adopt and strengthen the LCGG strategy as the core element of their national

development outlooks.

In fact, few countries have already adopted LCGG as their key prioritized strategy and

formulated wide-scaled domestic policy frameworks to achieve the goals. However, given the

geo-environmental context, a country alone cannot manage to strengthen its LCGG policy

framework effectively (Wyes & Lewandowski, 2012). Since environmental pollution and CO2

emission has become a cross-boundary issue, actions taken by one nation have affected the

development path of others, especially for its neighboring countries. Hazardous particulate

matters like PM2.5, PM5, and PM 10 are found to drift from one place to another through the

air, sea, and other ecological interactions. Persistent haze problem originated from burning of

the land for producing pulp, paper, and palm oil in Sumatra, Borneo and some of the Western

Indonesian islands create serious environmental havoc. In turns, it incurs enormous economic

and human costs every year not only in Indonesia but also in many areas in Malaysia and

Singapore (McCafferty, 2015). Even in last few years, it reached far away to the southern

Philippines, resulted in air travel disruptions in this Southeast Asian region (ABC News, 2015).


Another example is the large-scale industrialization in China that has an adverse impact not

only within its boundary but also among its surrounding countries. In fact, massive industrial

pollution and overwhelming coal combustion in China have been indicted as the prime cause

of frequent acid rain in Japan and Korea (Nagase & Silva, 2007). Following such tenacious

transboundary air problems in Europe during the 1970s, 50 European countries and the EU

were compelled to adopt the Convention on Long-range Transboundary Air Pollution (LRTAP)

along with other protocols since early 1980s. Transboundary pollution can also spread through

water. Heavy metal contaminations in the rivers of Kura and Aras were the results of large-

scale mining activities in Turkey. When the rivers flow over the neighboring Georgia and

Azerbaijan, it induces severe environmental problem for the whole region. Under such an

interconnected scenario, countries should strategize the comprehensive regional or sub-

regional cooperation frameworks so that they can mutually overcome the impacts of this

environmental degradation and emission-related issues.

Obviously, like any other integration arrangement, regional cooperation in LCGG would help

to broaden the markets and input resources for the environmental goods and services. It would

also ensure better allocation of resources across the region that would lead towards higher

efficiency of resource usages (ADB, 2013). Members can expect mutual benefits by using their

comparative advantages in dealing with the tangible and intangible low-carbon resources,

goods and services (Schiff & Winters, 2002). Multilateral or regional cooperation may also

boost the regional energy and food security which should have an immense impact on the socio-

economic welfare of the common people. Anbumozhi (2015) also states that by using the

comparative advantages in sharing the technologies and investments across boundaries, it

would also be possible to achieve the least-cost abatement, which is so crucial to incentivize

the adoption of LCGG strategies. Risk-sharing is the other mutual benefit of regional

cooperation. Krugman & Venables (1996), Baldwin & Venables (1995), Fernandez & Portes

(1998), Acharya & Johnston (2007) and Sapir (2011) argue that broad-scale cooperation and

integration would attain a wider economies of scale, enhance competitiveness, and

specialization through technological changes and improved productivity within the dynamic

market frameworks. As discussed earlier, the composition of LCGG places significant

emphasis on the development challenges which are global (i.e., cross-boundary) in nature.

Therefore, the interlinkages and mutual dependencies between the countries, and multilateral

organizations are more relevant and the means to implement the LCGG in a region. Helgason

(2016) states that such multifaceted cooperation framework would enhance the impact and


cost-effectiveness of development actions of the countries more than the other small-scale

cooperation frameworks.

Despite such potential urge, no regional cooperation bloc is evident which is exclusively based

on the agenda of strengthening the LCGG. Most of the regional cooperations target the

economic welfare aspects of the countries. More specifically, intra-regional trade is considered

as the key tool for such economic cooperation and integration. Several other areas such as

transportation and physical connectivity, ICT, and financial market are also deemed to boost

such cooperation (ESCAP, 2004). There are, of course, some international and multilateral

initiatives, Multilateral Environmental Agreements (MEAs), Regional Environmental

Agreements, and Framework Conventions covering different geographical regions and sectors

to support the green growth (UNEP, 2011). However, most of these pledges are nonspecific,

outwardly under-scaled and taken into consideration with lesser significance (Wyes &

Lewandowski, 2012). As a result, the entire effectiveness and acceleration in the success of the

LCGG implementation are not entirely achieved (Hallegatte et al., 2012).

Historically, countries show lesser intent to cooperate with each other under a regional

cooperation framework for tackling climate change or controlling emissions. One of the main

reasons behind such intent is the mounting challenges perceived in the lengthy stretching

negotiation process in the early stages (Rauschmayer & Wittmer, 2006). Since each of the

countries, regions, and groups has its particular challenges and interests which are diverse in

nature, it is very difficult for each of them to converge those interests and to reach to an

agreement. It is true not only in the global platforms but also in regional or sub-regional levels.

Though the geopolitical context often influences these negotiations, there are also some

common challenges such as determining the basis for resources allocation, optimal share of

responsibilities and rights, and risks sharing mechanism between the actors (Gupta, 2012).

Standard economic analysis and suitable approaches to figuring out these issues are often

overlooked, not only in literature but also in the policy designs. This thesis is an attempt to

scrutinize these challenging areas of the regional cooperation (RC) frameworks for LCGG and

to find out the ways to overcome these challenges.

The decision-making approaches of the policymakers have been changing markedly in recent

time. Due to the advent of bottom-up, participatory and inclusive approach, now people are

more aware of receiving adequate information, analysis, and evidence before accepting those

policies (Marchi et al., 2016). The basic focus of this thesis to develop a Comprehensive RC

Framework model that would help to understand the significance and implication of several


inter-related issues involving at the different stages of an RC process. The fundamental idea is

to identify the strengths, challenges, and potentials of each country involved in this process.

Such analysis would provide substantial information and economic analysis to understand the

dynamics of the interconnectedness among the countries and subsequently, can formulate a

robust and rational evidence-based RC framework while reducing the uncertainties embedded

at different stages of the process (Greenhalgh & Russell, 2009).

In this thesis, the proposed framework would link the initial strategic (or negotiation) phase,

cooperation phase, and monitoring and review phase of an RC framework. Figure 1.1 illustrates

the schematic diagram of the Comprehensive RC Framework model.

The initial strategic (or negotiation) phase should be based on standard quantitative analysis

that can be used as an effective tool for explaining the motivation for the countries to cooperate.

For a proper understanding of the RC issue, it should find an analytic technique to evaluate

how the LCGG decision (i.e., mainly, the level of emission or environmental degradation) of

one country would affect other countries’ decisions within the region. Following such analysis

of dynamic interrelations among the countries, the indicative model would aid to differentiate

the broader-scale role of the countries in the regional bloc. Such quantification, in general,

would help in conducting effective negotiation and subsequent setting of policy prioritization

within the RC framework.

The cooperation phase of this Comprehensive RC Framework should deal with the supply of

resources, i.e., optimal allocation, distribution, and usages of all kind of resources and activity

flow that would strengthen the LCGG policy implementation through the RC. Several intra-

regional cooperation areas such as energy, agriculture, trade, and natural resources will be

examined to estimate the strengths, limitations, and potentials of the countries. Consequently,

the impact of the RC to adopt best-practice for all the countries in the region will also be

analyzed. The respective national policy of the countries, however, should combine the

domestic fiscal, monetary, and environmental instruments to strengthen its LCGG internally.

At the same time, synchronization between regional and domestic policies is also imperative.

This system is dynamic as the policy outcomes would result in constant changes in the level of

LCGG attainment of the countries. Such changes need to be monitored and reviewed on a

regular basis. Based on the analysis at this phase, a new set of policy agenda and outcomes

might result over time which would compel to revise the strategies in both the initial strategic

and cooperation phases accordingly.


Figure 1.1: Schematic diagram of the Comprehensive RC framework model

1.2 Objectives of the thesis

This thesis is an attempt to develop a comprehensive LCGG policy framework that would be

based on the economic analysis for an RC bloc. The current states and challenges of each

country in respective areas will be investigated. The ultimate goal of this thesis is to frame the

outlines at different phases to reveal how best an RC can help to strengthen the LCGG activities

in the member countries.

Therefore, the thesis has three key set of objectives:

i) Negotiation (or initial strategic) phase: To develop an effective tool that could be

used as a standard basis in the early negotiation process and for the set of strategies.

ii) Cooperation phase:

▪ To develop theoretical frameworks that would optimize the effective sharing

and allocation of resources through RC to strengthening the LCGG among the

countries.

▪ To empirically test how the theoretical implication can be adopted in various

key policy areas in a sample regional bloc.

Initial strategic phase

Area of analysis:

Countries’ motivation to

cooperate

Outcome of analysis:

• Policy prioritization of

the countries

• Role differentiation of

the countries

Cooperation phase

Area of analysis:

Supply and allocation of

resources among the countries


• Estimate the strengths,

limitations, and potentials of

the countries

• Impact of RC to adopt best-

practice for all the countries

in the region

Monitoring phase

Area of analysis:

Real outcomes of attaining

the LCGG goals


A new set of policy agenda

and outcomes for future

modification


iii) Monitoring and Reviewing phase: To suggest a mechanism to monitor the probable

outcomes of the LCGG activities and to reviewing them for future policy design.

Key policy areas are chosen based on the shares of the sectors in global GHG emission. As the

Figure 1.2 reveals, energy embedded with most of the sectors, such as electricity and heat

production, transportation, and other energy is primarily responsible for the global GHG

emission. Agriculture and relevant activities also share around one-fourth of the global

emission. The industrial process, too, contributes a substantial share in it. Industry, building

construction, and other economic activities frequently require various natural resources

including the metal ores, non-metallic minerals, and others. For encompassing the entire

emissions from the sectoral contributions, this thesis attempts to empirically study four broader

policy areas: energy, agriculture, industry (in the form of production and subsequent trade of

goods), and natural resources management.

Source: IPCC (2015) from https://www.ipcc.ch/report/ar5/wg3/

Figure 1.2: Global GHG emission, by economic sector

1.3 Scope of empirical analysis

The tremendous growth in the emerging economies led by China and India has laid the

foundation for the 21st century to be treated as the Asian Century1. Along with total GDP, per

capita GDP is also projected a six-fold increase in PPP terms from $6,700 in 2010 to $40,800

in 2050 (Kohli et al., 2011). Concurrently, the rate of resource extraction in Asia grew much

1 Asian Century refers to the scenario which predicts that current growth dominance of Asia will continue and its

GDP is expected to increase from $17 trillion in 2010 to $174 trillion in 2050 which would equivalent to 52% of

the world. Against the eight ‘poor’ countries in 2012, there will be no poor country in Asia by 2050.

Agriculture, Forestry &

other Land use24%

Industry21%

Transportation14%

Electricity & Heat

production25%

Other energy10%

Building6%


faster than the global average. Especially, due to the increasing industrialization that geared up

since the 1990s, the global share of non-renewable resource extraction has been increasing in

Asia (ADB, 2013). ADB, in few of its studies, has expressed the significance of large-scale

Asian integration for sustained development (ADB, 2011, and 2017). However, the larger-

scaled Asian integration framework may have faced substantial intrinsic challenges because of

varied geographical, socio-economic, and cultural divergences among the countries. In fact,

regional cooperation and integration is a very complex phenomenon. Effective implementation

of RC demands some critical common contextual characteristics among the members.

Considering these issues, the thesis, for its empirical analysis, chooses 20 countries as the

proposed regional bloc combining 10 ASEAN countries, seven SAARC countries, and three

East Asian countries-China, Japan, and the Republic of Korea. Following the geographical

location of these Asian countries, i.e., South-through-East Asia, the term StEA will be used

throughout the thesis to represent the sample regional bloc.

The thesis chooses the sample for following reasons:

• Though the StEA countries are from three separate subregions of Asia, i.e., South,

Southeast and East Asia, they have geographical proximity which is a crucial factor for

tying up in strengthening the LCGG.

• ASEAN and SAARC have been working for a long time to strengthen the

socioeconomic ties among the countries in South-east and South Asia respectively. In

1997, ASEAN established their strategic partnership with China, Japan, and Korea in

several areas of cooperation (e.g., energy, transport, and ICT) and treated the alliances

as ASEAN+3 (ASEAN, 2017). Besides, China’s interest in joining the SAARC has also

been discussed (Zongyi, 2014). China, Japan, and Korea are also the official observers

in SAARC which indicates their vested interest in this region. So, amalgamating all the

countries under a common bloc of StEA looks promising not only from the

socioeconomic aspects but also for dealing with strengthening the LCGG in this region.

• ADB’s Strategy 2020 also encourages strengthening the ties among Asian sub-regions.

It sets five core areas to refocus its operations and specializations that best support its

future agenda: (i) infrastructure, (ii) environment and climate change, (iii) regional

cooperation and integration, (iv) education, and (v) financial sector development (ADB,

2008). Hence, the institutional support from ADB would be much promising for

adopting LCGG policies in this region.


• StEA represents well balance between the emerging and developed countries. Two

largest emerging economies of the world- China, and India have an aggregate

population of 2.61 billion and cover 10% of the global land area. On the other hand,

there are some fast-growing countries like Bangladesh, Pakistan, Vietnam, and

Indonesia. Japan, Korea, and Singapore are the high-tech developed countries within

this proposed bloc. Since different levels of the economies have varied implication in

dealing with emission and environmental degradation, the well-balanced mix of the

StEA countries might be an interesting sample to study the implications of RC in

strengthening the LCGG. The following section provides more details on the LCGG

factors in the StEA bloc.

• Sustained growth in SAARC (led by India) and in East Asia (led by China) has been

considered as the backbone of development in Asia. Besides, the immensely extended

production network in ASEAN has offered the tremendous growth opportunities not

only within its territory (Salim & Kabir, 2010) but also for the neighboring sub-regions

like SAARC and East Asia. Such encouraging features of these regions would result in

enhanced intra-regional trade and higher investments in cost-competitive infrastructure

while the stronger regional links would help lowering transaction costs through efficient

resource sharing (Chia & Plummer, 2015; Athukorala 2010).

1.4 Significance of the StEA region for this study

Here are few important features of the StEA region that would describe why the adaptation of

the LCGG strategies is so crucial for this region:

i) Sustain the ongoing economic growth and production network

StEA countries comprise 52.8% of global population and share 29.1% of the world GDP in

2016 (World Development Indicators, 2017). The growth rate of GDP in this region is amongst

the highest in the world. According to the regional comparison depicted in Table 1.1, the

average GDP growth rate of ASEAN, SAARC, and CJK (i.e., China, Japan & Korea) during

2005-2016 are 6.06%, 10.11%, and 6.03% respectively. On average, the rate for the StEA

region is much higher at 6.54% against the global average growth rate of 3.04%. Such rapid

growth in this region has some remarkable eco-political significance in the global arena.


Table 1.1: Average growth rate of GDP among regions/groups

Regions GDP Growth

(2005-2016)

Per capita Energy

usage growth

(2005-2014)

CO2 growth

(2005-2014)

OECD members 1.51% -1.13% -0.89%

North America 1.61% -1.20% -0.97%

Latin America & Caribbean 3.02% 1.10% 2.81%

Sub-Saharan Africa 5.68% 0.18% 2.68%

Middle East & North Africa 4.54% 3.31% 4.60%

European Union 1.15% -1.64% -2.19%

Europe & Central Asia 1.53% -0.65% -1.00%

ASEAN 6.06% 2.01% 4.54%

SAARC 10.11% 3.91% 8.57%

CJK 6.03% 4.64% 6.57%

World 3.04% 0.97% 2.50%

StEA 6.54% 3.80% 6.65% Source: World Development Indicators (2017)

ii) Increasing usage of energy

The region has also been experiencing the fastest growth in per capita energy usage. Against

the world’s average growth rate of 0.97% per annum during the 2005-2014 period, StEA region

has the growth rate of 3.80% in per capita energy consumption as depicted in Table 1.1. Such

rapid growth in this region has some remarkable eco-political significance in the global

perspective. Inadequate energy policy, higher energy intensity and increased dependency on

non-renewable energy in the emerging economies of these regions can be treated as the primary

concerns in this regard.

iii) CO2 emission trend

Rising economic growth is coupled with increased CO2 emission in the StEA countries.

While the global average growth rate of CO2 emission over 2005-2014 was only 2.50%,

the rate in the StEA countries was alarmingly higher at 6.65%. Table 1.1 depicts that

this rate is indeed the highest amongst all regional blocs of the world. Excessive reliance

on fossil-fuel energy, lower efficiency and lack of carbon policy have exposed this

region with such high level of emission.

iv) Resource extraction and consumption

StEA region has the fastest extraction of natural resources among the regions. It has almost a

six-fold increase in natural resource extraction since 1980. Role of two emerging economies,


China, and India, remains the key in this regard. StEA region extracted 24.5% of global

resources in 1980, which is increased to 54.1% in 2017. The recent trend, as presented in Table

1.2, shows that StEA region has an average annual growth rate in the extraction of 3.7% per

annum during the 2010-2017 period which is much faster than the global average of 2.5% per

annum.

Table 1.2: Regional extraction and consumption of natural resource materials (in Giga Tons)

Regions

Growth in resource

material extraction

(2010-2017)

Growth in resource

material consumption

(2010-2017)

Africa 1.7% 2.2%

Asia and the Pacific 3.7% 3.8%

Europe 0.9% 0.7%

Latin America and the Caribbean 1.7% 1.7%

North America -0.6% -1.4%

Sub-Saharan Africa (M49) 2.2% 2.2%

West Asia 3.1% 3.4%

World 2.5% 2.5%

StEA 3.7% 3.9%

Source: UNEP (2018)

Like the resource extraction, StEA region is also the fastest region in natural resource

consumption. It’s consumption increased from 8.79 Gigatons in 1980 to 51.15 Gigatons in

2017. Global share of resource consumption increased from 26% to 58% during this period.

The recent trend, as depicted in Table 1.2, shows an average annual growth rate in the

extraction of 3.9% per annum during the 2010-2017 period, much faster than the global average

of 2.5% per annum.

v) Environment Performance Index

Environmental Performance Index (EPI) also implies that the StEA region performs below par

at the global level. EPI, prepared by the Columbia University, uses two objective components:

environmental health and ecosystem volatility to measure the composite index. Out of total 178

countries considered for ranking, only four of the SEA countries are placed on the top-50 list.

13 countries are ranked over 100th in the list. Among all StEA countries, Singapore performed

best with 4th in global ranking, while Bangladesh came out as the worst performer with 168th

in the ranking. In all cases, the performances of ASEAN and SAARC countries are found below

the global average as shown in Table 1.3.


Table 1.3: Environmental Performance Index, by regions

Region/Group EPI Score 10-Year

Percent

Change

Environmental

Health

Ecosystem

Vitality

Global average 50.68 5.96 65.31 40.93

top-10 average 81.13 2.02 95.51 71.55

bottom-10 average 21.62 7.88 32.88 14.12

ASEAN average 49.02 3.23 63.52 39.35

SAARC average 38.19 5.43 40.60 36.59

CJK average 59.71 4.05 73.03 50.84

StEA average 47.29 4.05 57.78 40.29

Source: Environmental Performance Index (2014)

1.5 Organization of the thesis

Chapter 2 devises a model for standard negotiation basis and subsequent strategies. Chapter 3

focuses on developing a theoretical framework which can explain the implications of an RC in

attaining LCGG within a region. Chapter 4 to 8 analyze the role of RC in few key areas which

are pivotal in strengthening the LCGG in the countries and the region, as a whole. Chapter 4

and 5 empirically investigate the role of RC in attaining sustainable low-carbon energy.

Chapter 6 and 7 evaluate the implications for sustainable and green agriculture; Chapter 8

studies the implications of regional cooperation for sustainable natural resources management.

Chapter 9 explicates the implications of intraregional trade of environmental goods in LCGG.

Chapter 10 ends-up the thesis by briefly explaining the outlines of a standard RC framework,

mechanism of monitoring and reviewing the strategies, followed by the summary and

concluding remarks.


Chapter 2

Negotiation phase of Regional Cooperation for Low-Carbon

Green Growth: Challenges, and a Plausible Solution

15 | Regional Cooperation in Strengthening Low Carbon Green Growth: Challenges, Prospects & Policy Framework

2.1 Preamble of the Chapter

Historically, countries show lesser intent to cooperate with each other for the RC framework

for tackling climate change or controlling emissions. One of the main reasons behind such

intent is the mounting challenges perceived in those lengthy stretching negotiation process

(Rauschmayer & Wittmer, 2006). Especially after the introduction of the UN Framework

Convention on Climate Change (UNFCCC) in December 1990 and subsequent adoption of the

convention in May 1992, a series of negotiation under the banner of Conference of the Parties

(COP) has been going on on a regular basis. There has always been the debate and heated

discussion from the least developed or the deprived groups those are perceived to be severely

affected by the carbonization of the developed countries. Despite lots of enthusiasm, most of

the COP meetings ended with a frustrating outcome with no specific binding resolution except

the latest one at COP-21 held in Paris in 2015. Since each of the countries, regions, and groups

has own challenges and interests which are diverse in nature, it is utmost challenging for each

of them to converge the discussion and reach towards an agreement. As mentioned earlier, the

standard economic analysis and suitable approaches to figuring out these issues are often

overlooked, in economic literature as well as in policy papers. In addressing this deficiency,

this chapter is an attempt to dig out more on the real-ground challenges in this initial strategic

phase of cooperation for LCGG among the nations. As an effective and plausible policy

solution, this chapter also develops a quantitative tool that can measure the probable risks

through quantifying the geo-environmental impact of each country on others within a region.

Once the countries can quantify their respective risks, they are in a better state to settle down

with an efficient outcome from that early phase negotiation.

2.2 Negotiation process for MEAs

There are few stages in an MEA negotiation process: pre-negotiation, negotiation, adoption

and signature, ratification and accession, and entry into force (UNEP, 2007). Each stage has

different implication and therefore, requires differential approaches. The pre-negotiation stage

is probably the most critical juncture among all since it sets the basis for the eventual outcome

of the total process. At this phase, the policymakers consult on any environmental or climatic

issue(s) that would have profound implications beyond national-boundaries. They try to sort

out the answers: whether any action is required to deal with the issues; and whether any

multilateral cooperation would be feasible.


2.3 Key challenges in negotiation

2.3.1 Political aspect

Probably the biggest hurdle in environmental agreement or climatic negotiation is the political

commitments or strategies among the governments. In other words, it is due to the game of

balancing the power within a region. Rather than acting proportionately, influential members

under the conventional RCs often try to dominate in designing the strategies, which in most

occasions are in their best interest (ADB, 2013). Therefore, in practice, integration often

paybacks more towards some members than the others. Venables (2009) reveals that, within

such an arrangement, some members may even lose. It is evident as this game theoretical

approach is based merely on the bargaining power of the individual member country rather

than the degree of significance of the country to obtain a particular goal (e.g., emission

reduction) in the regional context.

2.3.2 Technical aspect

According to the UNEP (2007), scientific analysis often plays the key role at this pre-

negotiation phase. Evidence reveals that most of the scientific analysis to date target to measure

the macro-level impact of any environmental or climatic issue. For instance, first assessment

report placed by Inter-governmental Panel on Climate Change (IPCC) assesses the scientific

information related to the different climate change issue, but on a global scale. The report, in

fact, admits its limitation in predicting the potential behavior of GHG emission and subsequent

impacts on different countries. Multiple simulation models use many historical data to make a

forecast of various climate variability on a macro scale. However, some models utilized for the

scientific analysis are too complicated to convince. For instance, the First report of IPCC

(1990) uses Atmospheric general circulation model which requires several decades of

variability data to estimate equilibrium response. It also refers to Global Ocean Model that

requires millennia to reach the equilibrium. Sinclair & Diduck (2017) also argues that scientific

uncertainty into the model followed by the complex interpretation of environmental concerns

lead towards the overextended non-convincing negotiation process.

2.3.3 Policy compatibility and countries’ multiple prioritizations

Simultaneous ratification of WTO in the mid-90s along with the evolution of MEAs created a

complicated scenario for both the developed and the developing countries to adopt the optimum

policy mix which would be better for the common interest (Biermann, 2017). Many literature


affirms that diverging interests of actors make the negotiation difficult in international

environmental diplomacy (Sinclair & Diduck, 2017). Bhaskar & Glyn (2014) highlights the

north versus south geopolitical differences in environmental concerns. O'neill (2017) reveals

that the improper design of incentives also affects the interests of each country and shapes the

countries short and long-term behavior accordingly.

2.3.4 Specification of the countries’ roles

One major factor that defies the successful negotiation is the improper way of assigning the

functions and responsibilities of the countries involved in the cooperation arrangement. Larger

countries are often accused as significant emitters, which is the fact to some extent. However,

there is always a devious debate on to what extent the emission from one country affects the

other countries. This discussion may end up with a plausible solution if the countries are able

to define the emission dissemination and emission assimilation profile of each member country.

2.4 Suggested methodology

In accomplishing a fruitful negotiation on MEA-based RC, each country must have concrete

quantitative and analytic information on how cooperating the others would, in turn, benefit its

own cause. In other words, a country must have a clear picture of the emission or environmental

degradation trends of its partner countries and must have the analytical tool to measure the

probable risks associated with those emissions or environmental degradations. If a country can

realize the quantifiable potential risk imposed on it due to the emission or environmental

deterioration from other countries, it would be easier to adopt proper strategies for cooperation.

Specifically, a negotiation for cooperation should answer the following questions:

a. How much would it benefit a country if its partner countries could reduce the

emission or environmental degradation to a certain level? Alternatively, vice

versa.

b. What should be the costs and payoffs of the countries’ decisions under a game-

theoretic approach?

c. Under a regional cooperation framework, how the roles and liabilities of each

country can be quantified?


For this purpose, this chapter develops an index to quantify the geo-environmental impact of

each country on the rest of the negotiating countries within the region. In simple words, the

index will specify how much risk a country is for rest of the countries in a proposed regional

bloc. The geo-environmental impact (i.e., a risk factor) is assumed to be influenced by the

countries’ geographic location and environmental emission factors. Following the analysis of

dynamic interrelations among the countries, the model expects to identify the contribution of

each country in environmental risk dissemination and assimilation for the whole region. Such

quantification would help to conduct effective negotiation and policy prioritization among the

countries under an RC framework.

The suggestive model, therefore, expects to have the following attributes:

• Simplified assumptions that would lead towards easily measurable and understandable

outcomes;

• It should establish a standard quantitative framework that will act as the platform for

the initial negotiation basis.

2.4.1 Index Formulation

CO2 is the most significant contributor to GHG emission resulted from human activities. It has

an immense adverse impact on climate and environment (EPA, 2016). More fossil fuel

combustion, industrial production, and deforestation increase CO2 concentration in the

environment of a country. Being a part of the air, CO2 diffuses from the point of sources.

Hence, it can not only create an environmental impact on the country of its origin but also on

the neighboring countries. Such transboundary characteristic of emission needs to be taken into

account while measuring the geo-environmental impact of one country concerning another.

To simplify the model, following assumptions are made:

i) CO2 emission (from combustion) indicates a countries degree of carbon-intensive

economic activities. More the CO2 emission from combustion, the more will be the

carbon-intensive economic activities which will, in turn, propagate higher

environmental risk.

ii) On average, the emission and environmental pollution spreads equally at 360 degrees

from the source;

iii) The proposed index should only measure the geo-environmental impact or risk factor

rather than the real vulnerability impact on the countries. It is mentioned earlier that the


real impact or vulnerability of the affected country, along with the geo-environmental

impacts, also depends on its socioeconomic strength and resilience to minimize the

adverse outcomes (e.g., mitigation and adaptation process);

iv) Model based on the proposed index concentrates on the short-term implication on

environment and pollution rather than the long run climate change issues.

Based on consideration mentioned above, the geo-environmental impact of a country is

modeled with the following components:

1) The level of emission: More the level of CO2 emission of a country, more it is a

matter of concern for other countries2. Hence, a higher level of emission in country-

i would imply the higher geo-environmental impact (i.e., environmental risk factor)

of country-i for the reference country (say, country-j), ceteris paribus.

2) The growth rate of emission: Not only the level but also the growth rate of emission

is an important criterion to measure the impact. Apparently, a higher rate of increase

in emission of a country would pose more concern for the others.

3) Land area: Bigger the area of a country, lesser risky it will be in diffusing the

emission to the others. It is assumed that a bigger country is more likely to disperse

most of its emission within the own boundary. Therefore, ceteris paribus, a bigger

country would have a lesser geo-environmental impact on other countries.

4) Proximity: A country is expected to be affected more by its neighbors’ CO2

emission or environmental hazard than the far-distant countries. In this case,

proximity should refer both the distance between countries as well as the respective

areas of the vicinity. Following the geometric concept, the circular angle with arc

measured by the Radian can be considered to define the proximity in this regard.

Top of that, for considering the aerial flows of pollutant in emission, this angular

distance measure rather than the conventional physical distance measure should be

more appropriate.

By using the components discussed above, Geo-environmental Impact (GEI) index is

constructed in the following manner:

2 Crago and Stranlund (2015) state that other perilous flow pollutants such as sulfur dioxide (SO2), nitrous dioxide

(NO2), fine and coarse particulate matters in forms of PM2.5 and PM10 are also emitted as the co-pollutant of

CO2 during the combustion process. Hence, CO2 is considered to represent the overall risk factor owing to the

carbonization activities of a country.


GEI of country-i on country-j

𝐺𝐸𝐼𝑖,𝑗= 𝐴𝑗

𝐴𝑖×

𝑚𝑖

𝑚𝑗×

𝑔 (𝑚𝑖)

𝑔 (𝑚𝑗)× 𝜃𝑖,𝑗 (2.1)

Where,

𝐺𝐸𝐼𝑖,𝑗 = Geo-environmental impact of country-i on country-j

𝐴𝑖 = Area of country-i

𝐴𝑗 = Area of country-j

𝑚𝑖 = CO2 emission level in country-i

𝑚𝑗 = CO2 emission level in country-j

𝑔 (𝑚𝑖) = growth factor for emission in country-i = (1+ growth rate)

𝑔 (𝑚𝑗) = growth factor for emission in country-j

𝜃𝑖,𝑗 = angle (in radian) measured from the centre of country-j and covering

the maximum arc over country-i, as illustrated in Figure 2.1

Similarly, GEI of country-j on country-i

𝐺𝐸𝐼𝑗,𝑖= 𝐴𝑖

𝐴𝑗×

𝑚𝑗

𝑚𝑖×

𝑔(𝑚𝑗)

𝑔(𝑚𝑖)× 𝜃𝑗,𝑖 (2.2)

Figure 2.1: Illustration of 𝜽𝒊,𝒋

Note: light-blue color refers to country-j and brown color refers to country-i

2.4.2 Features of GEI Index

Proposition-1: Country will be interested to cooperate with the countries which have a

relatively profound Geo-environmental impact on it.

If the partner country can reduce its emission level, the GEI (i.e., risk) from that country will

decrease. For instance, in equation (2.1), if mi declines, ceteris paribus, GEIi,j will also go down,

which is desirable for country-j.

𝜃𝑖,𝑗

Reference country-j Partner

Country –i


Proposition-2: Country with proximity should matter in RC for LCGG

According to the GEI index, a country with the closer proximity, ceteris paribus, should have

given more importance than the distant countries. For instance, if we consider country-j as

India, then it should give more importance to country-i, e.g., Bhutan, which is closer rather

than relatively far Australia. More explanation of this proposition is enclosed in Appendix 1.

Proposition-3: Through negotiation for RC, each country will try to reduce the current impact

(i.e., risk) of other countries on it.

The ultimate goal of an LCGG strategy is to reduce the particular geo-environmental impact.

If country-j is considered as the reference country and if it ties with country-i, then the goal for

country-j is to reduce 𝐺𝐸𝐼𝑖,𝑗.

As equation (1) reveals


𝐴𝑖×

𝑚𝑖

𝑚𝑗×

𝑔 (𝑚𝑖)

𝑔 (𝑚𝑗)× 𝜃𝑖,𝑗

Among the factors, areas A and angle 𝜃 cannot be changed. Hence the probable strategies for

reducing 𝐺𝐸𝐼𝑖,𝑗 are:

Strategy-1: Reduce mi or growth of (mi)

Strategy-2: Increase own m or growth of (mj)

The second straegy is against the essence of strengthening LCGG. So, it is required to focus

on the first strategy that would indeed call for the bilateral cooperation, or in larger scale, an

RC.

Proposition-4: Through RC, conflicting (relative) strategic dilemma will arise.

By multiplying equation (1) and (2),

𝐺𝐸𝐼𝑖,𝑗 × 𝐺𝐸𝐼𝑗,𝑖 = 𝜃𝑖,𝑗 × 𝜃𝑗,𝑖

⇒ 𝐺𝐸𝐼𝑖,𝑗 =𝜃𝑖,𝑗×𝜃𝑗,𝑖

𝐺𝐸𝐼𝑗,𝑖 (2.3)

Since, 𝜃𝑖,𝑗 and 𝜃𝑗,𝑖 are fixed, reducing 𝐺𝐸𝐼𝑖,𝑗 would, therefore, increase 𝐺𝐸𝐼𝑗,𝑖. This

phenomenon can be portraits as the Conflicting strategic dilemma. The phenomenon is

relative. Impacts of reducing country i’s emission are followings:

1. Country-i itself will be directly better-off due to lesser emission within the country;

2. Country-j will also be directly benefitted as its geo-environmental risk goes down;


3. Country-i will, however, indirectly increase its relative risks as it becomes relatively

more low-carbon as compared to its neighboring country-j. Since the emission level

of the country-j is assumed to be unchanged, country-i is now exposed to relatively

higher emission levels of the country-j. It can be explained from the game theories’

perspective. If under an RC arrangement, any country acts to reduce its emission level

while the other countries do not, then strategically the action-taking country has to pay

more. In such a case, the action-taking country would expect relatively higher

emission risk flows into the country from the rest of non-action-taking countries.

However, it is a static approach, i.e., here we consider that only one country acts towards

reducing the emission while the other country does not. The following section explains more

on the static 2-country model.

2.4.3 Static, 2-Country model

Let’s consider, Country-i is Bhutan and Country-j is India. Hence, reducing CO2 emission in

India would result in a reduction of 𝐺𝐸𝐼𝑗,𝑖, i.e., the geo-environmental risk from India to

Bhutan. On the contrary, it would simultaneously increase 𝐺𝐸𝐼𝑖.𝑗, i.e., the geo-environmental

impact of Bhutan on India. Similarly, reducing CO2 emission in Bhutan would result into a

reduction of 𝐺𝐸𝐼𝑖,𝑗, i.e., geo-environmental risk from Bhutan to India, but would also increase

𝐺𝐸𝐼𝑗,𝑖 , i.e., the impact of India on Bhutan. In fact, the ‘increase in impact’ in these cases are

conceptually relative.

To make an inference, it is, therefore, essential to calculate the resultant impact of these. Let’s

calculate how much impact (i.e., geo-environmental risk) will be reduced due to the reduction

of emission by country-i


𝐴𝑖×

𝑚𝑖

𝑚𝑗×

𝑔 (𝑚𝑖)


𝜕𝐺𝐸𝐼𝑖,𝑗

𝜕𝑚𝑖= [

𝐴𝑗

𝐴𝑖×

𝜃𝑖,𝑗

𝑚𝑗.𝑔 (𝑚𝑗)][𝑚𝑖.

𝜕𝑔 (𝑚𝑖)

𝜕𝑚𝑖+ 𝑔(𝑚𝑖)]

= [𝑘 × 𝜃𝑖,𝑗]. [𝑚𝑖.𝜕𝑔 (𝑚𝑖)

𝜕𝑚𝑖+ 𝑔(𝑚𝑖)]

[+] [+] [ +] [+]

So, reduction in 𝒎𝒊 will reduce 𝑮𝑬𝑰𝒊,𝒋


Again, 𝐺𝐸𝐼𝑗,𝑖= 𝐴𝑖

𝐴𝑗×

𝑚𝑗

𝑚𝑖×

𝑔 (𝑚𝑗)

𝑔 (𝑚𝑖)× 𝜃𝑗,𝑖

𝜕𝐺𝐸𝐼𝑗,𝑖

𝜕𝑚𝑖= [

𝐴𝑖

𝐴𝑗× 𝑚𝑗 × 𝑔(𝑚𝑗) × 𝜃𝑗,𝑖][

−𝑔(𝑚𝑖).𝑚𝑖2 −

1

𝑚𝑖.𝜕𝑔(𝑚𝑖)

𝜕𝑚𝑖

𝑔(𝑚𝑖)2 ]

= [1

𝑘 × 𝜃𝑗,𝑖]. [

−𝑔(𝑚𝑖).𝑚𝑖2 −

1

𝑚𝑖.𝜕𝑔(𝑚𝑖)

𝜕𝑚𝑖

𝑔(𝑚𝑖)2

]

[+] [-]

So, reduction in 𝒎𝒊will increase 𝑮𝑬𝑰𝒋,𝒊

Resultant impact of reducing 𝑚𝑖 will be equal to:

−𝑚𝑖 − 𝑑(𝐺𝐸𝐼𝑖,𝑗) + 𝑑(𝐺𝐸𝐼𝑗,𝑖)

= −𝑚𝑖 −[𝑘 × 𝜃𝑖,𝑗]. [𝑚𝑖.𝜕𝑔

𝜕𝑚𝑖+ 𝑔]𝑑𝑚𝑖 +[

1

𝑘 × 𝜃𝑗,𝑖]. [

−𝑔.𝑚𝑖2 −

1

𝑚𝑖.𝜕𝑔

𝜕𝑚𝑖

𝑔2] 𝑑𝑚𝑖

where 𝑔 = growth factor of mi

Hence the condition for effective cooperation between country-i and country-j is:

𝑚𝑖 +[𝑘 × 𝜃𝑖,𝑗]. [𝑚𝑖 .𝜕𝑔

𝜕𝑚𝑖+ 𝑔]𝑑𝑚𝑖 ≫ [

1

𝑘 × 𝜃𝑗,𝑖]. [

−𝑔.𝑚𝑖2 −

1

𝑚𝑖.𝜕𝑔

𝜕𝑚𝑖

𝑔2] 𝑑𝑚𝑖

However, as mentioned, it is a static approach, i.e., here we consider that only one country acts

towards reducing the emission while the other country does not. For an RC, it is imperative to

expect a dynamic approach where all the member countries will act together to reduce their

respective emission levels simultaneously. The next section will explain this approach in detail.

2.4.4 Dynamic Approach

a. 2-country model

Unlike the static approach, the dynamic approach assumes that both country-i and country-j

will work on reducing their respective levels of emission.

We have, 𝐺𝐸𝐼𝑖,𝑗= 𝐴𝑗

𝐴𝑖×

𝑚𝑖

𝑚𝑗×

𝑔 (𝑚𝑖)


Using simplified notations as 𝐺𝐸𝐼𝑖,𝑗 = 𝐺1, 𝐴𝑗

𝐴𝑖= 𝐴, 𝑔 (𝑚𝑖) = 𝑔𝑖, 𝑔 (𝑚𝑗) = 𝑔𝑗, and 𝜃𝑖,𝑗 =

𝜃1, equation (2.1) can be rewritten as


𝐺1= 𝐴 × 𝑚𝑖

𝑚𝑗×

𝑔𝑖

𝑔𝑗× 𝜃1

Using natural logarithm in the equation

ln(𝐺1) = ln𝐴 + ln(𝑚𝑖) − ln(𝑚𝑗) + ln(𝑔𝑖) − ln(𝑔𝑗) + ln 𝜃1

From total differentiation

1

𝐺1 . 𝑑(𝐺1) =

1

𝑚𝑖 . 𝑑(𝑚𝑖) −

1

𝑚𝑗 . 𝑑(𝑚𝑗) +

1

𝑔𝑖 .

𝜕𝑔𝑖

𝜕𝑚𝑖. 𝑑(𝑚𝑖) −

1

𝑔𝑗 .

𝜕𝑔𝑗

𝜕𝑚𝑗. 𝑑(𝑚𝑗)

𝑑(𝐺1)

𝐺1= (

1

𝑚𝑖+

1

𝑔𝑖 .

𝜕𝑔𝑖

𝜕𝑚𝑖 ) 𝑑(𝑚𝑖) − (

1

𝑚𝑗+

1

𝑔𝑗 .

𝜕𝑔𝑗

𝜕𝑚𝑗 ) 𝑑(𝑚𝑗) (2.4)

Hence, the growth rate of geo-environmental risk from country-i to country-j consists of two

parts. The first part of the right-hand side of equation (2.4) reflects the Direct effect while the

second part depicts the Indirect effect on the 𝐺1.

Sign of 𝑑(𝐺1)

𝐺1 : considering both country-i and j reduce their emission

𝑑(𝐺1)

𝐺1= (

1

𝑚𝑖+

1

𝑔𝑖 .

𝜕𝑔𝑖

𝜕𝑚𝑖 ) 𝑑(𝑚𝑖) − (

1

𝑚𝑗+

1

𝑔𝑗 .

𝜕𝑔𝑗

𝜕𝑚𝑗 ) 𝑑(𝑚𝑗)

= {(+ve).(-ve)} - {(+ve).(-ve)}

= -ve+ve

Hence, the overall sign depends on the dominance between the direct and indirect effects. If

the direct factor outweighs the indirect factor, then the geo-environmental risk, G1 will

decrease. On the contrary, the dominance of indirect factor will increase the geo-environmental

risk from country-i to country-j.

b. Multi-country model

If there are more than two countries, overall impact and changes in GEIs (due to any emission

reduction action) would be simultaneous for all the countries. For example, if there are k

number of countries in an RC bloc, then any action taken by country-i would affect:

1. Geo-environmental risk disperses from country-i to rest of the (k-1) countries: Let's

term it as Risk dissemination by country-i

2. Geo-environmental risk perceived to country-i from other countries: Let’s term it

Risk assimilation into country-i


Risk Dissemination (RD):

Risk dissemination refers to the aggregated geo-environmental threats that a country

disseminates to the other countries of the RC bloc. It is, therefore, measured by summing up

all the GEIs of that country to the rest.

RD of country-i = ∑ 𝐺𝐸𝐼𝑖,𝑗𝑘𝑗≠𝑖 (2.5)

Risk Dissemination Share is needed to calculate to see the contribution of each country in

disseminating the risks within the region.

RD share of country-i = ∑ 𝐺𝐸𝐼𝑖,𝑗

𝑘𝑗≠𝑖

∑ ∑ 𝐺𝐸𝐼𝑖,𝑗𝑘𝑗≠𝑖

𝑘𝑖=1

(2.6)

Example: (for k=20)

𝐺𝐸𝐼1,2 + 𝐺𝐸𝐼1,3 + 𝐺𝐸𝐼1,4 + ⋯……………+ 𝐺𝐸𝐼1,20 = ∑𝐺𝐸𝐼𝑖=1,𝑗

𝑘

𝑗≠𝑖


𝑘

𝑗≠𝑖

…………………………………………


𝑘

𝑗≠𝑖

Hence, R/D share of country-i = ∑ 𝐺𝐸𝐼𝑖,𝑗

20𝑗≠𝑖

∑ ∑ 𝐺𝐸𝐼𝑖,𝑗20𝑗≠𝑖

20𝑖=1

Risk Assimilation (RA):

Risk assimilation refers to the aggregated geo-environmental threats that are assimilated into a

country from rest of the countries of the RC bloc. It is, therefore, measured by summing up all

the GEIs of other countries to the particular country.

RA of country-i = ∑ 𝐺𝐸𝐼𝑗,𝑖𝑘𝑗≠𝑖 (2.7)

Again, like RD share, Risk Assimilation Share is needed to calculate to determine which

country or group of countries are more susceptible towards the geo-environmental risk resulted

from the other member countries in the region. It can be calculated as:

RA share of country-i = ∑ 𝐺𝐸𝐼𝑗,𝑖

𝑘𝑗≠𝑖

∑ ∑ 𝐺𝐸𝐼𝑗,𝑖𝑘𝑗≠𝑖

𝑘𝑖=1

(2.8)


Example: (for k=20)

𝐺𝐸𝐼2,1 + 𝐺𝐸𝐼3,1 + 𝐺𝐸𝐼4,1 + ⋯……………+ 𝐺𝐸𝐼20,1 = ∑𝐺𝐸𝐼𝑗,𝑖=1

𝑘

𝑗≠𝑖


𝑘

𝑗≠𝑖

…………………………………………


𝑘

𝑗≠𝑖

Hence, R/A share of country-i = ∑ 𝐺𝐸𝐼𝑗,𝑖

20𝑖≠𝑗

∑ ∑ 𝐺𝐸𝐼𝑗,𝑖20𝑗≠𝑖

20𝑖=1

Net Risk Share:

When a country’s RD share exceeds its RA share within the RC group, the country is more

likely to be treated as risk disseminator. On the other hand, if a country’s RA share exceed its

RD share, the country is more likely to be treated as risk assimilator country. However, only

the shares are not enough to explain the actual state. Hence, to realize the real geo-

environmental risk sharing status of a country, Net Risk Share (NRS) is needed to calculate.

Weighted risk factors are required to incorporate in such computation as described below:

Weighted Risk in = 𝑅𝐴 share ×ΣCO2 emission of other countries

𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒𝑑 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

Weighted Risk out = 𝑅𝐷 share × own CO2 emission

𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒𝑑 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

Hence,

𝑁𝑅𝑆𝑖 = Weighted Risk in − Weighted Risk out (2.9)

NRS gives an idea about the country’s degree of possessing geo-environmental risk. If the

country is geo-environmental risk disseminator, then NRS will indicate to what extent it is

responsible for disseminating the risk within the RC bloc. On the other hand, if the country is

geo-environmental risk assimilator, then NRS will show to what extent it is prone to assimilate

the risk from that RC bloc.


2.5 Empirical analysis

In the following sections, the GEI index and its subsequent implications are empirically tested

for the StEA countries3.

2.5.1 Data sources

CO2 emission data is collected from CAIT - WRI’s Climate Data Explorer. Latest four years

(2009-2012) emission data are used to calculate average g(m). World Development Indicators

(WDI) of World Bank provides land area data. For calculating θ, the online measuring tools of

SunEarthTools.com and LatLong.net are used.

2.5.2 Results and findings

a) Summary of the GEIs

Using the GEI index, the matrix of GEIs is constructed as shown in Table 2.1. Column–wise

element represents the geo-environmental impact of other countries on the reference country,

i.e., how much impact or geo-environmentally risk the other countries impose to the reference

country. For example, in the case of Bangladesh, India and China should be given the highest

importance among all countries as the indices points refer the highest impact of these two

countries on Bangladesh with 4.57 and 4.37 respectively. On the other hand, Bhutan and

Cambodia are found to exert lowest geo-environmental threat for Bangladesh with GEI index

points of 0.02 and 0.06 respectively.

The row-wise element represents how much geo-environmental risks the reference country

poses towards the rest of the countries. For example, Bangladesh poses highest threats to

Bhutan and Nepal with GEI index points of 67.61 and 6.17 respectively. On the contrary, it

poses least threat to Singapore and Korea with GEI index points of 0.002 and 0.02 respectively.

The right-most column, therefore, represents the total amount of risk disseminated by the

reference countries. In the model, this is termed as RD. Calculation shows the top-5 geo-

environmental risk disseminating countries are China, India, Singapore, Japan, and Indonesia.

Such a list is quite obvious since China, India, Japan, and Indonesia are the countries with

3 As mentioned in previous chapter, the proposed grand regional bloc is chosen with 20 countries: ten ASEAN

and seven SAARC countries, China, Japan and South Korea. By looking at the geographical location, we labelled

the grand regional bloc as South-through-East Asia or StEA.

http://www.wri.org/resources/data-sets/cait-historical-emissions-data-countries-us-states-unfccc

http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators

http://www.sunearthtools.com/tools/distance.php

http://www.latlong.net/


larger-scaled economic activities in the region. Singapore’s inclusion is due to its extremely

high CO2 concentration of 69490 MtCO2/square km, which is the highest in the StEA region.

The average concentration of StEA is 4513 MtCO2/square km.

Conversely, the bottom-most row represents the amount of risk assimilated into the reference

countries, which is defined as RA of the countries. From Table 2.1, it is evident that the top-5

countries which are most susceptible to the regional geo-environmental risk are Bhutan, Nepal,

Laos, Cambodia, and Myanmar. This listing is also much expected since Bhutan and Nepal

locate within the closest proximity of the top polluters China and India. Laos, Cambodia, and

Myanmar have been mostly exposed due to their least concentrations of CO2 per area. In fact,

all these top-5 countries also possess the least-5 CO2 concentration among the StEA regional

bloc.


Table 2.1: Matrix of GEIs for the StEA countries*

*Country-code: A3 (UN) style. Referred at: http://www.worldatlas.com/aatlas/ctycodes.htm [BGD: Bangladesh, BTN: Bhutan, BRN: Brunei Darussalam,

KHM: Cambodia, CHN: China, IND: India, INA: Indonesia, JPN: Japan, KOR: Korea, Republic, LAO: Lao PDR, MYS: Malaysia, MDV: Maldives, MMR:

Myanmar, NPL: Nepal, PAK: Pakistan, PHL: Philippines, SGP: Singapore, LKA: Sri Lanka, THA: Thailand, VNM: Vietnam]

Source: Author’s calculation

BGD BTN BRN KHM CHN IND IDN JPN KOR LAO MYS MDV MMR NPL PAK PHL SGP LKA THA VNM SUM

BGD 67.605 0.058 1.210 0.252 0.603 0.103 0.032 0.021 2.545 0.120 0.048 4.639 6.172 0.736 0.341 0.002 0.648 0.453 0.329 85.914

BTN 0.024 0.001 0.015 0.003 0.004 0.002 0.000 0.000 0.028 0.002 0.000 0.041 0.093 0.007 0.004 0.000 0.007 0.006 0.004 0.240

BRN 0.088 3.429 1.049 0.057 0.058 0.189 0.019 0.011 0.994 0.130 0.013 0.611 0.696 0.135 0.709 0.002 0.176 0.181 0.278 8.825

KHM 0.061 2.014 0.038 0.032 0.043 0.045 0.005 0.003 1.157 0.068 0.005 0.467 0.426 0.091 0.131 0.001 0.075 0.250 0.329 5.241

CHN 4.367 198.060 0.655 12.008 3.088 1.156 0.615 0.540 24.214 1.235 0.295 20.559 41.185 4.617 5.137 0.017 3.819 3.698 3.775 329.037

IND 4.568 146.227 0.290 5.400 0.494 0.612 0.091 0.053 8.676 0.689 0.172 10.783 34.843 6.553 1.446 0.009 3.657 1.816 1.381 227.760

IDN 1.038 37.578 1.424 12.685 0.767 0.642 0.188 0.121 12.189 2.983 0.094 7.297 7.434 1.427 5.284 0.048 1.094 2.523 3.076 97.893

JPN 1.304 53.223 0.374 2.944 1.577 1.175 0.558 0.971 7.375 0.397 0.118 6.232 12.281 3.523 3.255 0.006 1.335 0.514 1.127 98.290

KOR 1.102 40.907 0.412 5.747 1.257 0.887 0.655 0.925 8.903 0.621 0.123 6.113 9.757 2.236 3.539 0.009 1.419 1.484 1.840 87.938

LAO 0.074 2.109 0.014 1.117 0.042 0.053 0.034 0.011 0.006 0.054 0.008 0.764 0.424 0.093 0.166 0.001 0.114 0.431 0.622 6.136

MYS 0.918 31.257 2.437 13.579 0.671 0.565 1.712 0.123 0.081 10.889 0.049 6.034 6.391 1.257 3.733 0.041 0.828 2.774 2.981 86.318

MDV 1.037 31.042 0.348 5.274 0.251 0.545 0.827 0.104 0.059 6.846 0.875 5.312 6.926 0.516 1.809 0.012 7.671 1.339 1.408 72.202

MMR 0.345 11.305 0.049 1.428 0.069 0.198 0.049 0.018 0.011 3.009 0.060 0.020 1.887 0.358 0.356 0.001 0.298 0.667 0.409 20.536

NPL 0.080 3.282 0.005 0.079 0.022 0.118 0.012 0.002 0.002 0.104 0.013 0.006 0.167 0.094 0.017 0.000 0.079 0.025 0.021 4.128

PAK 0.221 8.374 0.037 0.482 0.087 0.401 0.090 0.015 0.009 0.967 0.091 0.033 0.943 2.713 0.205 0.001 0.362 0.193 0.150 15.373

PHL 0.141 5.145 0.181 1.459 0.070 0.132 0.167 0.037 0.015 1.540 0.161 0.021 0.915 1.083 0.293 0.003 0.268 0.296 0.435 12.361

SGP 1.510 56.126 0.769 17.968 0.697 1.009 4.319 0.144 0.089 15.868 8.209 0.159 7.067 11.604 2.161 3.542 2.304 2.581 3.361 139.488

LKA 0.095 2.908 0.023 0.391 0.030 0.075 0.047 0.007 0.004 0.531 0.057 0.048 0.483 0.638 0.122 0.120 0.001 0.114 0.095 5.789

THA 0.570 17.901 0.261 11.031 0.232 0.467 0.315 0.054 0.030 15.138 0.490 0.077 4.593 4.009 0.790 1.347 0.006 1.069 1.454 59.835

VNM 0.632 18.978 0.244 9.198 0.131 0.451 0.169 0.057 0.037 15.854 0.341 0.063 4.913 4.097 0.899 1.611 0.003 0.854 2.369 60.900

SUM 18.175 737.470 7.619 103.066 6.742 10.511 11.060 2.448 2.061 136.825 16.596 1.354 87.933 152.658 25.909 32.752 0.163 26.076 21.713 23.074 1424.204

http://www.worldatlas.com/aatlas/ctycodes.htm


Table 2.2: Priority list based on Geo-environmental Impact for each country

Priority

sl. BGD BTN BRN KHM CHN IND IDN JPN KOR LAO MYS MDV MMR NPL PAK PHL SGP LKA THA VNM

1 IND CHN MYS SGP JPN CHN SGP KOR JPN CHN SGP CHN CHN CHN IND IDN IDN MDV CHN CHN

2 CHN IND IDN MYS KOR JPN MYS CHN CHN SGP IDN IND IND IND CHN CHN MYS CHN MYS SGP

3 SGP BGD SGP IDN IDN SGP CHN IDN IDN VNM CHN SGP IDN JPN JPN MYS CHN IND SGP IDN

4 JPN SGP CHN CHN SGP KOR MDV SGP SGP THA MDV KOR SGP SGP KOR SGP MDV SGP IDN MYS

5 KOR JPN KOR THA MYS IDN KOR MYS MYS IDN IND JPN JPN KOR SGP KOR KOR KOR VNM KOR

6 IDN KOR JPN VNM IND BGD IND MDV MDV MYS KOR IDN KOR IDN IDN JPN IND JPN IND THA

7 MDV IDN MDV KOR BGD MYS JPN IND IND KOR THA THA MYS MDV MYS MDV JPN IDN KOR MDV

8 MYS MYS IND IND MDV MDV THA VNM VNM IND JPN VNM MDV MYS VNM VNM THA THA MDV IND

9 VNM MDV THA MDV THA THA BRN THA THA JPN VNM MYS VNM BGD THA IND VNM VNM MMR JPN

10 THA VNM VNM JPN VNM VNM VNM PHL BGD MDV PHL BGD BGD VNM BGD THA PHL MYS JPN LAO

11 MMR THA PHL PHL PAK PAK PHL BGD PHL MMR BRN LKA THA THA MDV BRN BRN BGD BGD PHL

12 PAK MMR BGD MMR PHL MMR BGD BRN BRN BGD BGD PAK PAK PAK MMR MMR BGD PAK LAO MMR

13 PHL PAK MMR BGD MMR PHL PAK MMR MMR PHL PAK PHL PHL MMR PHL BGD PAK MMR PHL KHM

14 LKA PHL KHM LAO BRN NPL MMR PAK PAK KHM KHM MMR LAO PHL BRN PAK KHM PHL KHM BGD

15 BRN BRN PAK BRN LAO LKA LKA LAO LAO BRN MMR BRN BRN BRN LKA LAO MMR BRN PAK BRN

16 NPL NPL LKA PAK KHM BRN KHM LKA LKA PAK LKA LAO LKA LKA NPL KHM LKA LAO BRN PAK

17 LAO LKA LAO LKA LKA LAO LAO KHM KHM LKA LAO NPL KHM KHM LAO LKA LAO NPL LKA LKA

18 KHM LAO NPL NPL NPL KHM NPL NPL NPL NPL NPL KHM NPL LAO KHM NPL NPL KHM NPL NPL

19 BTN KHM BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN BTN



b) Categorization of the countries

The matrix in Table 2.1 provides some valuable insights as follows:

i) Each country can identify which countries have a more adverse geo-environmental

impact on it and, accordingly, it can define its priority list. Based on that, a priority-

matrix can be constructed as presented in Table 2.2.

ii) Since few countries on the list are predominantly risk disseminator while few are risk-

assimilators and rest are seemingly risk-neutral, the matrix can also be used to

distinguishing the sub-groups based on the resultant impacts of the geo-environmental

risks. For instance, we can categorize the countries in the way as shown in Table 2.3.

Table 2.3: Categorization of the member countries based on risk dominance

Category4 Condition Countries

1. Predominantly Risk

Assimilator

𝑅𝐴

𝑅𝐷> 1.2 Bhutan, Cambodia, Laos, Myanmar,

Nepal, Pakistan, Philippines, Sri

Lanka.

2. Seemingly Risk

Neutral 0.8 ≤

𝑅𝐴

𝑅𝐷≤ 1.2 Brunei

3. Predominantly Risk

Disseminator

𝑅𝐴

𝑅𝐷< 0.8

Bangladesh, China, India, Indonesia,

Japan, Korea, Malaysia, Maldives,

Singapore, Thailand, Vietnam Source: Author’s calculation

c) Net Risk Sharing states

Regarding Risk share, Bhutan comprises more than half of the region’s RA share, followed by

Nepal as depicted in Table 2.4. It seems quite rational as both the countries have lowest CO2

concentration and both have closest proximities with China and India- the two biggest emitters

of this region. Such geo-environmental settings of Bhutan and Nepal have thus exposed them

to high RA shares. On the other hand, China is responsible for about one-fourth of total risk

dissemination for the whole StEA region. China is followed by India, Singapore, Japan, and

Indonesia. In aggregate, these top-5 disseminating countries comprise about 63% of total RD

of this region.

To realizing the actual risk sharing the state of a country, NRS of each country is subsequently

calculated as shown in Table 2.4. It reveals that only China, India, Japan, and Korea experience

the negative NRSs implying their dominance as risk disseminators within this grand RC bloc.

4 Theoretically, the single risk neutral point should refer where RA equals RD. To provide flexibility owing to

the risk dynamics, mentioned ranges are arbitrarily defined for each category.


Table 2.4: Calculation of NR shares of the countries

R/A R/A share R/D R/D share Emission NR Share

Bangladesh 18.17 1.28% 85.91 6.03% 93.31 1.2%

Bhutan 737.47 51.78% 0.24 0.02% 0.60 51.8%

Brunei 7.62 0.53% 8.82 0.62% 10.42 0.5%

Cambodia 103.07 7.24% 5.24 0.37% 26.78 7.2%

China 6.74 0.47% 329.04 23.10% 9020.82 -13.1%

India 10.51 0.74% 227.76 15.99% 1946.32 -1.3%

Indonesia 11.06 0.78% 97.89 6.87% 1668.58 0.0%

Japan 2.45 0.17% 98.29 6.90% 1111.94 -0.3%

Korea 2.06 0.14% 87.94 6.17% 585.31 -0.1%

Laos 136.83 9.61% 6.14 0.43% 24.94 9.6%

Malaysia 16.60 1.17% 86.32 6.06% 352.85 1.0%

Maldives 1.35 0.10% 72.20 5.07% 1.12 0.1%

Myanmar 87.93 6.17% 20.54 1.44% 89.55 6.1%

Nepal 152.66 10.72% 4.13 0.29% 10.40 10.7%

Pakistan 25.91 1.82% 15.37 1.08% 173.79 1.8%

Philippines 32.75 2.30% 12.36 0.87% 86.18 2.3%

Singapore 0.16 0.01% 139.49 9.79% 49.76 0.0%

Sri Lanka 26.08 1.83% 5.79 0.41% 22.47 1.8%

Thailand 21.71 1.52% 59.83 4.20% 271.34 1.4%

Vietnam 23.07 1.62% 60.90 4.28% 159.60 1.6%

Aggregate 1424.20 100.0% 1424.20 100.0% 15706.08 82.3%


2.6 Potential applications of the GEI index

a. Climate change negotiations

This model presents a standard quantitative framework that will act as the platform for the

negotiation basis at the initial phase of cooperation. A country can use this analytical tool to

measure the probable risks associated with the emissions or environmental degradations of

other negotiating countries. Simultaneously, each country can have the concrete quantitative

and analytic information on how cooperating the others would, in turn, benefit its own cause.

If a country can realize the quantifiable potential risk and benefit resulting from the emission

or environmental degradation of all negotiating countries, it would be easier to negotiate and

make a better decision.

b. Distinguishing the roles in RC settings

It would be exquisitely worthy for the policymakers if the in-depth background information on

the emission and its impacts are precisely available prior the policy formulation stage. It would


not only enhance better understanding of the overall emission issues in the region but would

also help to differentiate the country-specific roles in LCGG policy design. GEI index model,

therefore, can be a significant step forward in this regard. Following the analysis, few of the

countries are characterized as Predominant Risk Disseminators (PRD) while few others are

found as Predominantly Risk Assimilator (PRA) countries. Moreover, the extent of risk

dissemination or risk assimilation can also be determined. As a result, policymakers would be

better able to map the current scenario and the areas of strength and weakness for each country

under the sub-groups of an RC arrangement.

Alongside, countries will be able to define own strategic focus more specifically. For instances,

a PRA country with lower emission intensity (i.e., emission per unit area) requires more

adaptation strategies to cope up the risks disseminated from others. If a PRA country possesses

higher emission intensity, it should focus both on adaptation as well as mitigation policies.

Conversely, a PRD country with higher emission intensity must set mitigation as its priority

strategic focus. However, if a PRD country retains lower emission intensity, it may adopt the

slow-going strategy for its adaptation or mitigation policies. Table 2.5 refers the probable

strategic focus of the countries based on its emission and risks features.

Table 2.5: Strategic focus matrix based on the country’s emission and risks features

Predominantly Risk

Assimilator

Predominantly Risk

Disseminator

Lower Emission intensity Adaptation Slow going

Higher Emission intensity Adaptation and Mitigation Mitigation

Source: Author

Through following this analysis and empirical findings, the plausible roles and strategic focus

of the StEA countries can be suggested as referred to in Table 2.6.

Table 2.6: Plausible roles and strategic focus of the StEA countries

Predominantly Risk

Assimilator

Predominantly Risk

Disseminator

Lower Emission intensity Bhutan, Nepal, Laos, Myanmar,

Cambodia

India, Bangladesh, Indonesia,

China, Malaysia

Higher Emission intensity Pakistan, Philippines, Sri

Lanka, Viet Nam, Thailand

Singapore, Korea, Japan,

Brunei, Maldives



c. Formulating national strategic plans on LCGG

Since LCGG has transboundary impacts, it would be, therefore, more effectual to consider the

geo-environmental impacts from the neighbors while adopting countries’ national strategic

plans. Following the dynamics of the geo-environmental risks, a country can set its target more

realistically and can act accordingly. Over-ambitious or under-stated policy actions would not

result in the optimal outcome in this process. From the regional cooperation point of views, it

would also help the national strategic plans to synchronize with the RC policies at a faster pace.

d. Easy to establish political control over environmental decision-making

Since the roles and responsibilities are defined through quantitative analysis, it will be of much

ease for the political leaderships to establish more control over their respective environmental

decision-makings. It would be easier to make common people understand the situation more

concretely and thereby, would be conducive to motivate them to act in favor of the decision.

2.7 Limitations of the GEI

Like any other index, GEI also has its limitation to some extent. As mentioned, GEI is a

quantitative tool that can measure the probable risks through quantifying the geo-

environmental impact of each country on others within a region. Because of its probabilistic

feature, there is always a chance of some deviation from the real geo-environmental impact.

For capturing the real impacts of the countries’ environmental degradations on other countries,

few factors other than geographical and emission need to be added. For now, this is left as a

reference for a detail work in future on this issue.

2.8 Concluding remarks

The suggestive model can play a significant part is in early stage negotiating, adopting, and

strengthening the LCGG strategies under an RC arrangement. By differentiating the member

countries into risk assimilators and risk disseminators, it would help to formulate more

appropriate policies for the RC bloc. From an optimistic point of view, this index, therefore,

can be used as a crucial quantifying tool in intense discussion and negotiation on global climate

change issues: for instances, shared responsibility, contribution, and allocation of climate

finance.


Chapter 3

The Economics of Regional cooperation in

Low-Carbon Green Growth



As mentioned in the previous chapter, holistic and comprehensive regional or sub-regional

cooperation frameworks for sustainable LCGG are necessary for the countries to mutually

overcome the impacts of the common environmental degradation and emission-related issues.

Few international and multilateral initiatives are working in different geographical regions and

sectors to support the green growth. However, most of these pledges are broad-based,

seemingly under-scaled and are often underrate the significance and intensity they should have

(Wyes & Lewandowski, 2012). Top of that, environment-related negotiators, diplomats, and

policymakers concentrated more on the scientific analysis and legal framework of any

Multilateral Environmental Agreement (MEA) and Regional Cooperation (RC) while

frequently overlooking the economics of such cooperation frameworks. A comprehensive

theoretical framework supported by the economics principles are, therefore, necessary to

understand the implications of an RC for LCGG. This chapter is an attempt to design a

resource-based theoretical framework that can answer the following questions:

a. What sorts of resources countries can supply under the RC arrangement for LCGG?

b. How best the available resources can be supplied by and shared among the member

countries to achieve the LCGG goals optimally?

c. How would the sharing of resources bring enhanced welfare in attaining the LCGG

goals?

3.2 Designing the theoretical framework

In general, under an RC arrangement, three types of resources can be supplied and shared: the

factors of production, goods, and services, and the technology. The fundamental hypothesis of

this approach would consider that a broader resource base should induce higher environmental

as well as economic welfare which can be enhanced more under an RC arrangement.

3.2.1 The Model

Let’s start with an economy with two goods and two set of inputs.

Two goods: E and NE

Where, E: Environmental goods and services

NE: Non-environmental goods and services

Two sets of inputs: G and B


Where G: Green resources (including ‘green’ capital and ‘green’ labor)

B: Brown resources (including ‘brown’ or conventional capital and labor)

Assumption-1: Total capital of an economy is assumed to be the sum of green capital and brown

capital. Similarly, total labor is the sum of green labor and brown labor

Assumption-2: Green resources are assumed to have a lesser adverse impact on the environment

as compared to the brown resources.

The definition of environmental goods and services varies in existing literature. OECD (2005)

defines E as “the activities which produce goods and services to measure, prevent, limit,

minimize, or correct environmental damage to water, air, and soil, as well as problems related

to waste, noise, and ecosystems.” Broadly it classifies all environmental goods and services

into three categories: Pollution management, cleaner technologies and products, and resources

management. In the WTO negotiations, coverage of E is, however, constricted to those goods

and services which would result in positive environmental impacts. In other words, E is defined

as those goods and services whose end-result refers to the proportionate containment or

abatement of pollution (Cosbey et al., 2010). UNCTAD (2004) defines environmentally

preferable products (EPP) as products which cause “significantly less environmental harm at

some stage of their life cycle” as compared to the substitute goods and services which would

be used for similar purpose. The definition also refers to those products, the production and

consumption of which significantly aids to the conservancy of the environment. Few examples

of EPPs comprises jute and biofuels (as a lesser environmentally harmful product); organic

coffee, cocoa, tea, chemical free cotton (for being produced through pro-environmental

production process); and bio-pesticides (for contributing to environmental preservation).

By considering the purpose of the model, a broader definition of E is used in this chapter. The

production aspect is incorporated into the definition. Thereby, in this proposed model, E is

defined as those goods and services whose consumption or disposal would result in relatively

more positive impact or relatively less adverse impact on environmental well-beings and whose

production process can be termed as more environment-friendly and sustainable. Green capital

can be considered as those capitals that include green technologies, patent, R&D which are

mostly used for a sustainable production process (Rozenberg et al. 2013). Brown capital, on

the other hand, incorporates those capitals used for the conventional unsustainable production

process. Green labor, similarly, refers to those labor involves into green jobs or activities.

According to the UNEP (2008), green jobs are the “positions in agriculture, manufacturing,


construction, installation, and maintenance, as well as scientific and technical, administrative,

and service-related activities that contribute substantially to preserving or restoring

environmental quality. Specifically, but not exclusively, this includes jobs that help to protect

and restore ecosystems and biodiversity; reduce energy, materials, and water consumption

through high-efficiency and avoidance strategies; de-carbonize the economy; and minimize or

altogether avoid generation of all forms of waste and pollution.” Brown labor, on the contrary,

indicates the labor in the conventional production process.

3.2.2 Production function

Environmental goods and services are assumed to be less inclined to environmental

degradation and CO2 emission5, however, not wholly carbon-free throughout their production

or operational process. We can, therefore, assume that along with the green resources, some

brown resources will also be required to embed with the production of E. Similarly, non-

environmental goods and services are supposed not entirely to be carbon-emitter throughout

their production or operational process. So, they can also be considered to use the input mix of

both G and B.

𝑄𝐸 = 𝑓𝐸(𝐺𝐸 , 𝐵𝐸)

𝑄𝑁𝐸 = 𝑓𝑁𝐸(𝐺𝑁𝐸 , 𝐵𝑁𝐸)

Resource Constraints:

𝐺𝐸 + 𝐺𝑁𝐸 = �̅�

𝐵𝐸 + 𝐵𝑁𝐸 = �̅�

Assume that, per unit cost of G is g and per unit cost of B is b.

Environmental goods and services are G-intensive while the conventional non-environmental

goods and services are B-intensive.

The Edgeworth box presented in Figure 3.1 illustrates the interactions among the factor

endowments (GE, BE, GNE, BNE) and their relative prices along the ‘efficiency path’ between

the production of E and NE as depicted by OEMNONE in the diagram.

5 Emission and environmental degradation, both affects the sustainable and green growth process. These two terms

are interchangeably used throughout this chapter.


Figure 3.1: Dynamics of economic transition towards Environmental Goods and Services

It is assumed that prior taking the LCGG strategy, an economy stays closer to the OE origin

where environmental goods and services output is very low. The objective of an LCGG strategy

adoption is to move from the low-scale production of E to higher-scaled production. More

precisely, to maintain efficiency conditions in production, the economy needs to move along

the efficiency curve from OE to ONE.

3.3 Implication of LCGG strategy adoption

With the enactment of the LCGG strategy, following phenomenon are expected:

a) Demand for E will increase in that economy.

To meeting the rising demand for QE, the economy has to move from M to N. Since

Environmental goods and services are G-intensive, such a transition from M to N will

require more of G as compared to B resources. However, assuming a closed economy with

fixed factor endowments, increasing production of QE will lead to a corresponding decrease

in QNE. As the non-environmental goods and services are B-intensive, it can supply lesser

of green resources but more brown resources. Therefore, there will be a demand-supply

mismatch during such transition. More demand of G as compared to its supply will lead to

an increase in this factor’s price, while the proportionately excess supply of B will tend to

experience a decline in its price. The slopes drawn as the tangent lines for the isoquants of

OE

GE

GNE

Slope = b/g

ONE

BE

BNE

M

N

Slope = b/g


E and NE at point M and N in Figure 1 refer to the phoenomenon. According to the H-O

theorem, more demand of E will also increase the price ratio of PE/PNE.

b) Supply of G will increase

For matching the increasing demand of QE, the economy would like to manage more supply

of green resources. Excess supply may be added in three ways:

i) Domestically through increased extraction of green resources including the natural

capital.

ii) The stock of green resources may also be increased by using better technology.

iii) Excess supply can also be met by importing the green resources from other

countries.

An obligation and arrangement of RC would act as a driving force behind each of these

cases.

Implications of increasing supply of G on RC

Table 3.1 refers to the amount of factors requirement for producing environmental and non-

environmental goods and services. It also indicates the endowment constraints level.

Table 3.1: Factors’ requirement and endowment

Amount of G

required to

produce 1 unit

Amount of B

required to

produce 1 unit

Total

endowment of G

Total endowment

of B

E 𝑎1 b1 �̅� �̅�

NE 𝑎2 b2 �̅� �̅�

The constraint equation for the endowments are:

�̅� = 𝑎1𝑄𝐸 + 𝑎2𝑄𝑁𝐸

�̅� = 𝑏1𝑄𝐸 + 𝑏2𝑄𝑁𝐸

Now, with the increased supply of G, G-constraint line will be outwardly shifted as shown in

Figure 3.2. It will also result in a shift of the PPF skewed towards the environmental goods and

services. The potential addition of production is represented by the filled grey area as depicted

in Figure 3.2.


Figure 3.2: Impact of increasing supply of G in production

It is important to note that economy’s potentiality of added production for environmental goods

and services would be higher if the country’s initial level of QE is lower and vice-versa. For

instance, the potentiality of increased production for environmental goods and services at point

C will be higher than that of point B, and such potentiality at point B will be higher than that

of point A, as shown in Figure 3.3.

Figure 3.3: Production Possibility Frontier and potential addition

Increased supply of G and a subsequent outward shift in PPF has three crucial impacts that are

so crucial in any RC arrangement. The impacts are:

QNE

QE O

Potential

addition in total

Production

A

B

C


i) 2-Dimensional growth: With the increased room for potential addition to production, it is

possible to increase the production for both E and NE. Considering the individual potential

increase in production for E and NE, the resultant probable shift in output mix is shown in

Figure 3.4.

Figure 3.4: Potential addition of PPF & Convergence of growth

ii) Convergence of growth: For each economic state with lower E, it is evident that the

growth of E is higher than the growth of NE; i.e., ΔE > ΔNE. On the other hand, for each

economic state with lower NE, the growth of NE is higher than the growth of E; i.e., ΔNE

> ΔE. Such implication indicates that it may have likely convergences for both E and NE

among different economic states under this circumstance as illustrated in Figure 3.4.

iii) Increased consumer surplus: Price ratio of PE/PNE would, however, decreases with the

increased supply of E as shown in Figure 3.5. Consumer surplus will be increased with this

process, which would be a significant inference for the RC.

Figure 3.5: Change in Consumer surplus with an increased supply of QE

Assuming PNE is

normalized

QNE

QE O

PE

QE O

Demand for QE

Supply of QE

increases


It is, therefore, evident that increase in G would be beneficial to economic wellbeing. The

additional supply, as mentioned earlier, may come through domestic policy measures or the

RC arrangement. Along with the increase in the volume of resources, technology can also play

a crucial role in achieving the objective.

c) Technological advancement will take place.

Through the adoption of LCGG as the RC obligation, it is anticipated that the technological

progress will be held in producing environmental goods and services. Improved technology

would ensure lesser use of inputs for a given QE. It would also reduce the cost of producing

QE. However, it is a rationale to think that there should be a limit to reduce the level of inputs

a1 and b1 for a given QE. It can be treated as the Technology Frontier in this case.

Considering the Cobb-Douglas production functions for environmental and non-environmental

goods and services:

𝑄𝐸 = 𝐴𝐸𝑎1𝛼 𝑏1

1−𝛼

𝑄𝑁𝐸 = 𝐴𝑁𝐸𝑎2𝛽 𝑏2

1−𝛽

where, 𝐴𝐸 and 𝐴𝑁𝐸 represent the Technology used in the production process of E

and NE respectively.

Implications of technological advancement:

For environmental goods and services,

Profit 𝜋𝐸 = 𝑃𝐸𝑄𝐸 − 𝐶𝐸

𝜋𝐸 = 𝑃𝐸𝐴𝐸𝑎1𝛼 𝑏1

1−𝛼 − 𝑎1𝑔 − 𝑏1𝑏

Where, 𝑃𝐸 = unit price for E

𝐶𝐸 = per unit cost for producing E

Using First Order Condition with respect to green endowment a1

𝜕𝜋𝐸

𝜕𝑎1= 𝛼𝑃𝐸𝐴𝐸𝑎1

𝛼−1 𝑏11−𝛼 − 𝑔 = 0 (3.1)

Similarly, for the non-environmental goods and services, we have


𝜋𝑁𝐸 = 𝑃𝑁𝐸𝑄𝑁𝐸 − 𝐶𝑁𝐸

𝜋𝑁𝐸 = 𝑃𝑁𝐸𝐴𝑁𝐸𝑎2𝛽 𝑏2

1−𝛽 − 𝑎2𝑔 − 𝑏2𝑏

Using First Order Condition with respect to green endowment a2

𝜕𝜋𝑁𝐸

𝜕𝑎2= 𝛽𝑃𝑁𝐸𝐴𝑁𝐸𝑎2

𝛽−1 𝑏21−𝛽 − 𝑔 = 0 (3.2)

Therefore,

𝛼𝑃𝐸𝐴𝐸𝑎1𝛼−1 𝑏1

1−𝛼 = 𝛽𝑃𝑁𝐸𝐴𝑁𝐸𝑎2𝛽−1 𝑏2

1−𝛽

∴𝑃𝐸

𝑃𝑁𝐸=

𝛽

𝛼∗

𝐴𝑁𝐸

𝐴𝐸∗ (

𝑎1

𝑏1)1−𝛼

∗ (𝑏2

𝑎2)1−𝛽

𝑃𝐸

𝑃𝑁𝐸= 𝛽. 𝐴𝑁𝐸 ∗ (

𝑏2

𝑎2)1−𝛽

∗ (𝑎1

𝑏1)1−𝛼

∗1

𝛼∗

1

𝐴𝐸 (3.3)

Assuming that

i) for non-environmental goods and services: a2, b2, ANE will remain constant

ii) for environmental goods and services: QE, b1 will remain constant

iii) Per unit endowment prices: g, b will be unchanged.

Hence, the primary interest is to determine the impact of technological changes (for

environmental goods and service) on the reduction of green resource inputs for a given output

and prices.

Let’s consider 𝑃𝐸

𝑃𝑁𝐸= 𝑃 and 𝛽. 𝐴𝑁𝐸 ∗ (

𝑏2

𝑎2)1−𝛽

= 𝑅 = constant

Rewriting equation (3.3) using log form

ln 𝑃 = ln 𝑅 + (1 − 𝛼) ln𝑎1

𝑏1− ln 𝛼 − ln 𝐴𝐸

Since α represents the share of G in total production, we can replace 𝛼 =𝑎1𝑔

𝑄𝐸

ln 𝑃 = ln 𝑅 + (1 −𝑎1𝑔

𝑄𝐸) ln

𝑎1

𝑏1− ln 𝑎1 − ln 𝑔 + ln𝑄𝐸 − ln𝐴𝐸

ln 𝑃 = ln 𝑅 + (1 −𝑎1𝑔

𝑄𝐸) ln 𝑎1 − (1 −

𝑎1𝑔

𝑄𝐸) ln 𝑏1

− ln 𝑎1 − ln𝑔 + ln𝑄𝐸 − ln𝐴𝐸 (3.4)


By using total differentiation of equation (3.4), we have

1

𝑃𝑑𝑝 = (1 −

𝑎1𝑔

𝑄𝐸)

1

𝑎1𝑑𝑎1 −

𝑔

𝑄𝐸ln 𝑎1 𝑑𝑎1 +

𝑔

𝑄𝐸ln 𝑏1 𝑑𝑎1 −

1

𝑎1𝑑𝑎1 −

1

𝐴𝐸𝑑𝐴𝐸

1

𝑃𝑑𝑝 = (

1

𝑎1−

𝑔

𝑄𝐸−

𝑔

𝑄𝐸ln 𝑎1 +

𝑔

𝑄𝐸ln 𝑏1 −

1

𝑎1) 𝑑𝑎1 −

1

𝐴𝐸𝑑𝐴𝐸

1

𝑃𝑑𝑝 =

𝑔

𝑄𝐸(ln

𝑏1

𝑎1− 1)𝑑𝑎1 −

1

𝐴𝐸𝑑𝐴𝐸 (3.5)

If the output prices remain constant, 𝑑𝑝 = 0

The equation becomes,

𝑔

𝑄𝐸(ln

𝑏1

𝑎1− 1) 𝑑𝑎1 −

1

𝐴𝐸𝑑𝐴𝐸 = 0

𝑑𝑎1

𝑑𝐴𝐸= −

𝑄𝐸

𝑔. 𝐴𝐸(1 − ln

𝑎1

𝑏1) (3.6)

The negative sign in equation (3.6) implies that advancement in technology would require

lesser a1 for a given production of QE. Equation reveals that increase in technology by 1 unit

would reduce the input requirement by 𝑄𝐸

𝑔.𝐴𝐸(1 − ln

𝑎1

𝑏1). Therefore, doubling the technology,

i.e., increase in technology by AE unit would reduce the input requirement by 𝑄𝐸

𝑔(1 − ln

𝑎1

𝑏1).

Similarly, increase in technology by S unit would reduce the requirement of green inputs by

𝑄𝐸


𝑎1

𝑏1) 𝑆. Theoretically, if this is treated as maximum possible technological

advancement, then

𝑎1 = 𝑄𝐸


𝑎1

𝑏1) 𝑆

∴ 𝑆 = 𝑎1. 𝑔. 𝐴𝐸

𝑄𝐸 (1 − ln𝑎1

𝑏1)

(3.7)

Maximum possible technological advancement, i.e., Technology Frontier under this

framework equals [𝑎1.𝑔

𝑄𝐸(1−ln𝑎1𝑏1

) − 1] times of current technology.

Again, we have already revealed that more demand for E will also increase the price ratio of

PE/PNE. Therefore, we can set 𝑑𝑝 > 0

It would lead to,

𝑑𝑎1

𝑑𝐴𝐸> −

𝑄𝐸


𝑎1

𝑏1) (3.8)


The relationship states that though the slope 𝑑𝑎1

𝑑𝐴𝐸 is already proven to be negative, but there

could be a minimum value of the slope, which equals to 𝑄𝐸


𝑎1

𝑏1).

The impact of technological advancement on Cost

𝜕𝐶𝐸

𝜕𝐴𝐸=

𝜕𝐶𝐸

𝜕𝑎1∗

𝜕𝑎1

𝜕𝐴𝐸

𝜕𝐶𝐸

𝜕𝐴𝐸= −𝑔 ∗

𝑄𝐸


𝑎1

𝑏1)

∴ 𝜕𝐶𝐸

𝜕𝐴𝐸= −

𝑄𝐸

𝐴𝐸(1 − ln

𝑎1

𝑏1) (3.9)

Remarks:

• If the initial technological state is lower, the impact of technological advancement on

reducing the cost will be higher. It implies that convergence process in technological

impact will take place.

• Lower the ratio of green and brown input mix, higher will be the cost reduction effect.

In fact, with technological progress, green input requirement will be reduced, so as the

ratio of green and brown input mix. Consequently, the cost will follow the path.

Impact of technological advancement on production efficiency

Technological advancement also leads towards the efficiency in producing environmental as

well as non-environmental goods and services in an economy. Such efficiency needs to be

analyzed for formulating effective national LCGG policy framework. Eventually, an RC

process would effectively establish the linkages among these domestic policies of the members.

For measuring the efficiency and technical changes, Data Envelopment Analysis (DEA)

method is widely used. The basic concept can be explained graphically by a simplified case

with one input and one output using constant returns to scale (CRS) technology. Points D and

E in Figure 3.6 represent the input-output combinations of a production unit in period t and t+1

respectively. In both cases, it is operating below the production possibility frontier.


Hence, we will have two technical efficiency factors:

1. In period t with input xt, it should be able to produce ya if it has full technical efficiency.

Then the technical efficiency is measured by 𝑦𝑡

𝑦𝑎.

2. Similarly, in period t+1 with input xt+1, it should be able to produce yc if it has full

technical efficiency. Then the technical efficiency is measured by 𝑦𝑡+1

𝑦𝑐.

Again, the impact of technology change would result in different frontiers of production. Two

factors could be used to explain the inter-frontier movement.

1. Next period’s inputs and output considering the current period’s technology level; we

can express it as 𝑦𝑡+1

𝑦𝑏

2. current inputs and output measured at the next period’s technology level; we can

express it as 𝑦𝑡

𝑦𝑏

yt+1

y

yt

E

D

0

yb

yc

xt+1 x xt

Frontier in

period t

Frontier in

period t+1

ya

Figure 3.6: Decomposition of the Malmquist Productivity Index

In fact, when we have panel data, we can use Malmquist TFP index to measure productivity

change and proceed further to decompose this productivity change into technical change and

technical efficiency change.

Fare et al. (1994) defined an output-based Malmquist productivity change index as:

𝑚0(𝑦𝑡+1. 𝑥𝑡+1. 𝑦𝑡 . 𝑥𝑡) = [𝑑0

𝑡 (𝑥𝑡+1, 𝑦𝑡+1)

𝑑0𝑡 (𝑥𝑡, 𝑦𝑡)

𝑋𝑑0

𝑡+1 (𝑥𝑡+1, 𝑦𝑡+1)

𝑑0𝑡+1 (𝑥𝑡, 𝑦𝑡)

]

1/2


It can be decomposed as:

= 𝑑0

𝑡+1 (𝑥𝑡+1,𝑦𝑡+1)

𝑑0𝑡 (𝑥𝑡,𝑦𝑡)

𝑋 [𝑑0

𝑡 (𝑥𝑡+1,𝑦𝑡+1)

𝑑0𝑡+1 (𝑥𝑡+1,𝑦𝑡+1)

𝑋𝑑0

𝑡 (𝑥𝑡,𝑦𝑡)

𝑑0𝑡+1 (𝑥𝑡,𝑦𝑡)

]1/2

(3.10)

In Figure 6, the two components of the Malmquist Index as in Equation (1) is represented by:

Efficiency change = 𝑑0

𝑡+1 (𝑥𝑡+1,𝑦𝑡+1)

𝑑0𝑡 (𝑥𝑡,𝑦𝑡)

= 𝑦𝑡+1

𝑦𝑐⁄

𝑦𝑡𝑦𝑎

⁄ (3.11)

Technical change =[𝑑0

𝑡 (𝑥𝑡+1,𝑦𝑡+1)

𝑑0𝑡+1 (𝑥𝑡+1,𝑦𝑡+1)

𝑋𝑑0

𝑡 (𝑥𝑡,𝑦𝑡)

𝑑0𝑡+1 (𝑥𝑡,𝑦𝑡)

]1/2

= [𝑦𝑡+1

𝑦𝑏⁄

𝑦𝑡+1𝑦𝑐

⁄ 𝑋

𝑦𝑡𝑦𝑎

⁄𝑦𝑡

𝑦𝑏⁄

]

12⁄

(3.12)

To calculate equation (3.10), we must calculate the four component distance functions which

will involve four Linear Programming (LP) problems. Assuming the CRS technology, we have

the LPs as:

LP-1:

[𝑑0𝑡 (𝑥𝑡. 𝑦𝑡)]

−1 = 𝑚𝑎𝑥𝜙,𝜆 𝜙

Subject to: −𝜙𝑦𝑖𝑡 + 𝑌𝑡 𝜆 ≥ 0

𝑥𝑖𝑡 − 𝑋𝑡𝜆 ≥ 0

𝜆 ≥ 0

LP-2:

[𝑑0𝑡+1 (𝑥𝑡+1. 𝑦𝑡+1)]


Subject to: −𝜙𝑦𝑖,𝑡+1 + 𝑌𝑡+1𝜆 ≥ 0

𝑥𝑖,𝑡+1 − 𝑋𝑡+1𝜆 ≥ 0

𝜆 ≥ 0

LP-3:

[𝑑0𝑡 (𝑥𝑡+1. 𝑦𝑡+1)]


Subject to: −𝜙𝑦𝑖,𝑡+1 + 𝑌𝑡 𝜆 ≥ 0

𝑥𝑖,𝑡+1 − 𝑋𝑡𝜆 ≥ 0

𝜆 ≥ 0

LP-4:

[𝑑0𝑡+1 (𝑥𝑡. 𝑦𝑡)]


Subject to: −𝜙𝑦𝑖𝑡 + 𝑌𝑡+1 𝜆 ≥ 0

𝑥𝑖𝑡 − 𝑋𝑡+1𝜆 ≥ 0

𝜆 ≥ 0

So above 4 LPs to be calculated for each member in the RC arrangement.


The φ-parameter provides information on the technical efficiency score for the i-th country,

and the λ-vector contains information on the peers of the (inefficient) i-th country.

Figure 3.7 depicts the schematic diagram of the SSR phenomenon

Figure 3.7: Schematic diagram of the Supply and Share of Resources phenomenon

3.4 Diffusion and efficient distrubution of recources

The previous section explains the implication of aggregate resource sharing in strengthening

the LCGG through RC. More the resources available would increase the probability of

enhanced welfare for the member countries. However, availability of resources is not the only

important factor in an RC framework. Besides, how the available resources are diffused or

distributed most efficiently is also of much importance for an efficient RC arrangement. There

are few theories available in practice, but most of those focus mainly on the diffusion of

innovation or technology from firm-to-firm or over the product life-cycle (Korres, 2012,

Aghion et al., 2013, Karakaya et al., 2014, Skinner& Staiger, 2015).

Imapcts/ outcomes

ChannelsObjective of

RCI

StrengtehingLCGG

Creating and

increasing the

demand for E

1. Increase in domesticproduction of E anddecrease in productionof NE.

2. Increase the priceratio of PE/PNE.

Providing

increased

supply of G

1. Outward shift of PPFskewed towards E.

2.Convergence ingrowth for both E andNE

Technological advancement

1.Reduce the use of Ginputs

2.Reduce the production cost

3. Lead to efficient useof resources


In practice, it is evident that if two countries are in an equal state of technological advancement

or efficient in producing particular goods or sector, there is seemingly little need of diffusion

and sharing of the technologies between those countries. On the other extreme, if the

technological gap is so vast that the receiving country is not well-adaptive to use that

technology, no diffusion of technology will take place between those countries too. Hence, a

reasonable technological gap (TG) between countries is essential for the diffusion and sharing

process. In fact, this phenomenon of the technological gap and diffusion of technology can be

better captured if TG is considered as a ‘bell-shaped’ equation with regards to the rate of

diffusion (RoT) as shown in Figure 3.8.

As a bell-shaped equation, it can be presented by the following equation:

𝑓(𝑥|𝜇, 𝜎) =1

𝜎√2𝜋𝑒

−(𝑥−𝜇)2

2𝜎2 (3.13)

Hence, we may consider: x = TG

Figure 3.8: Distributional relationship between Technological gap and diffusion

3.5 Optimal allocation of resources

Sharing and subsequent diffusion of resources are the underlying motivations behind any RC

arrangement. In previous sections, it is revealed that sharing of resources could enhance the

overall welfare of the economies through increased production, technological upgradation, and

efficiency improvement. However, it is to note that the structure of the economies, the growth

rates and the quality of institutions would also determine this sharing process (Bhide et al.,

2006). The rate of diffusion is also necessary to understand the dynamics of the RC and to

frame the policies accordingly. By combining the sharing and diffusion phenomenon, the

ultimate objective is to ensure the optimal allocation of resources among the member countries

under an RC arrangement.

𝑇𝐺

𝑅𝑜𝐷


3.5.1 Prospective Model outline

To determine the optimal allocation of resources to strengthen the LCGG within an RC

arrangement, following model outline as illustrated in Figure 3.9 can be taken into

consideration for each member state. The process of optimal allocation of distributable

resources can be determined for each of the sectors, such as energy, agriculture, natural

resources, and industry. The term distributable is used since it is reasonable that not all of the

domestic resources are available to distribute under an RC framework. A country may be in a

position to share only a portion of its resources, products, technologies, and other supports for

the regional cooperation, but not its entire resource-base.

Figure 3.9: Flowchart for determining the optimal allocation of resources

Average efficiency of a country in using input resources for attaining LCGG (i.e., increasing

output while containing the emission) needs to be calculated first.

Having abundant resources do not necessarily imply the quality implication of those resources

into producing the desirable scale of output. Effective resource, therefore, can be introduced to

capture the quality of resources from an efficiency point of view.

Effective resources in sector-s of country-i can be calculated as:

∈𝑠,𝑖= 𝑅𝑠,𝑖 × 𝜂 𝑠,𝑖

where, 𝑅 𝑠,𝑖 = aggregated resources in sector-s of country-i

𝜂𝑠,𝑖 = average efficiency of using the resources in sector-s of country-i over the period

Relative effective resource share in sector-s of country-i can be calculated by using the

following formula:

Calculate the average efficiency of each

country

Calculate the relative effective resource

share of each country under the RC

arrangement

Allocate aggregate distributable

resources in proportion to the

countries' relative efficiencies


𝑅𝐸𝑅𝑆𝜖𝑠,𝑖=

𝑅𝑠,𝑖 × 𝜂 𝑠,𝑖

∑ (𝑅𝑠,𝑖 × 𝜂 𝑠,𝑖)𝑘𝑖=1

(3.14)

where k = total number of member countries in the RC bloc

Then optimal allocation of distributable resources (from the aggregated potential distributable

resources of the regional bloc) in sector-s of country-i can be determined as

𝑃𝑠,𝑖∗ = 𝑅𝐸𝑅𝑆𝜖𝑠,𝑖

× 𝑃𝑠

where, 𝑅𝐸𝑅𝑆𝜖𝑠,𝑖= relative effective resource share in sector-s of country-i

𝑃𝑠= potential regional aggregated amount of distributable resources in

sector-s

The method can be illustrated with an example as shown in Table 3.2.

Table 3.2: An example of the optimal resource allocation

Country Average

resource

Average

efficiency

Effective

resource

Relative effective

resource share

Optimal

sharing,

if 𝑷𝒔=150

1 200 0.4 80 33% 50

2 100 0.8 80 33% 50

3 50 0.8 40 17% 25

4 100 0.4 40 17% 25

Total 450 240 100% 150


The objective of a country within the RC arrangement is to increase its optimal resource

allocation for better implementing the LCGG policy. According to the prospective model

outline explained here, the channels for gaining higher allocation are:

i) Increase in the average efficiency of using resources

ii) Increase in average resource base through higher domestic stock as well as diffusion

inflows

iii) Increase in the resources available for sharing (i.e., distributable resources) within the

RC arrangement.

One thing is to keep in mind that the average efficiency, relative effective resource share and

optimal allocation are all dynamic measures rather than static. Each of these indicators is

expected to be changed over time, and therefore, the subsequent allocation decisions need to

be adjusted accordingly.


3.6 Implications of the proposed theoretical framework

Environment or green growth issues are still amongst some of those strategic areas which are

more stringent to reach any unanimous consensus. Hence, a thorough understanding of the

economics of RC for LCGG is of much significance, especially for the policymakers and

negotiators of the Multilateral environment agreements (MEAs) and RC framework. If the core

principles efficiently work, such an analysis would greatly help them to realize the significance

of cooperation.

As the theoretical framework implies, a more extensive resource-base induces higher

environmental as well as economic welfare which, within a competitive and functional market

system, can be enhanced further under an RC arrangement. The economic transition towards

LCGG should increase the demand for environmental goods and services. To matching the

increasing demand, the supply of green resources would also increase in an economy. Any

shortage of such supply would be met from the other members of an RC bloc. Increased supply

of green resources would subsequently shift the PPF outwardly. The analysis also reveals that

if the country’s initial level of production (of environmental goods and services) is lower, its

potentiality for increased production of environmental goods and services would be higher. It

indicates that there should be possible convergences among the countries’ productions of

environmental goods and services over time. Increased production of environmental goods and

services would also lead towards additional consumer surplus, which is a significant inference

for an RC.

Adoption of LCGG strategy under an RC arrangement would upwardly push the countries’

technology frontier. Advancement in technology will thereby reduce the requirement of green

resources and inputs in the production of environmental goods. However, theoretically, there

is a maximum limit in technological advancement (relative to current state of technology), and

hence, it determines the minimum threshold of green input requirement. Technological

progress also found to have a profound impact on cost reduction. If the initial technological

state is lower, the impact of technological advancement on reducing the cost will be higher. It

implies that convergence process in technological impact will also take place. Lower the ratio

of green to brown input mix, higher will be the cost reduction effect. In fact, with technological

progress, green input requirement will be reduced, so as the ratio of green and brown input

mix. Consequently, the cost will follow the path. The impact of technological changes would

also result in different frontiers of production, and subsequently, will lead towards the efficient

use of resources in production.


However, availability of resources is not the only factor matters in this regard. Instead, how the

available resources are diffused or distributed most efficiently is of much importance for an

effective RC arrangement. This chapter explains the distributional relationship between

technological gap and diffusion. It states that a reasonable technological gap between the

countries is essential for diffusion to take place.

To determining the optimal allocation and use of resources to strengthen the LCGG among the

member states under an RC arrangement, inter-relations and dynamics among the factors are

explained. This chapter outlines a prospective model of calculating average efficiency and

relative effective resource share of each country under an RC arrangement. It also suggests the

allocation of aggregate distributable resources in proportion to the countries' relative

efficiencies.


The theoretical framework developed in this chapter covers various aspects of an RC for

LCGG. Starting with the supply of resources by a country, the chapter uses a two-goods, two-

input model to explain the dynamics of economic transition towards environmental goods and

services. Then it analyzes the implications of adopting LCGG strategy with a particular focus

on share and accumulation of resources under an RC arrangement. Implications of

technological advancement under an RC arrangement are expounded. The chapter

mathematically formulates the maximum possible technological advancement, i.e., Technology

Frontier. Next, it determines the impact of technological advancement on production

efficiency. To making an in-depth analysis of resource sharing and diffusions of resources, the

chapter concludes by outlining the model for explaining the optimal utilization of resources to

ensure maximum welfare from the LCGG policies under an RC arrangement. In this sense, this

framework presented in this chapter is a comprehensive one which can be applied extensively

to policy practices.

In the next six chapters, the key essence of this theoretical framework will be empirically

examined for energy, agriculture, natural resources, and trade sectors.


Chapter 4

Regional Energy Cooperation for Low-carbon growth:

A Demand-Supply analysis



Energy has the most significant implications in the reinforcement of the low-carbon green

growth. In one hand, it is one of the critical elements for economic growth; while in contrast,

the use of energy is responsible for around two-third of the global greenhouse gas emissions

and 80% of the global CO2 emission (IEA, 2016). Because of the increasing population and

economic growth led by the emerging economies of South and East Asia, aggregate demand

for energy has been increasing more rapidly than ever (Anbumozhi & Yao, 2015; Lee et al.,

2014). To ensure the sustainable energy in a more sustained way, it requires new approaches

and new systems that would help this transformation optimally. OECD/IEA (2011) states that

such makeover should initiate the effective new policies towards the way we produce, deliver

and consume energy.

One of the key challenges is to shift away from the conventional fossil fuels on which the

countries have huge dependencies. Replacing fossil fuels by the low-carbon renewable energies

requires considerable time, investment, technological soundness, and efficient policy reforms

in the countries. However, to attain the sustainability of the energy system, only focusing on

the production aspect may not be adequate. The efficiency at the end-use of energy also has

the significant role in conquering the low-carbon target (Lorek & Spangenberg, 2014). There

is, of course, considerable debate on whether the enhanced efficiency of energy usage can offset

the rebound and backfire. Nevertheless, there is little dispute that it could be a useful tool to

strengthen the footings for the transition towards the sustainable growth (Chakravarty et al.,

2013, Broberg et al., 2015). The IEA recommends 25 policy agendas for improving energy

efficiency in the scope of buildings, appliances, lighting, transport, and industry. IEA estimates

that if the recommendations were adopted, global energy consumption could be reduced by

17% (at 2010 Business-as-usual) while CO2 emission can be reduced by 7.6 giga tons (Gt) of

CO2 a year by 2030 (OECD, 2017). Ayres et al. (2007) state that enhancing the energy usage

efficiency would supplement the production efficiency as it would not require radical new

investment in technologies, but merely improved regulations (and deregulations, in few cases)

will help to adopt the strategy. To support these multi-dimensional challenges, the transition

towards a sustainable low-carbon economy requires concerted efforts from both producers and

users of energy, not only within a country but also within a regional cooperation framework

(Kalirajan et al., 2016).

A Regional Energy Cooperation (REC) can facilitate low-carbon growth through three

channels as shown in Table 4.1:


Table 4.1: Channels of regional energy cooperation for low-carbon growth

Channels of

cooperation Objectives

Tools and

Probable outcomes

Intraregional

energy trade

Managing the demand-

supply gap of energy in

the countries to attain the

low-carbon growth

Prudent management of demand-supply would

strengthen the energy security of the region while

providing reasonable flexibility in energy

diversification for the countries. Price fluctuation

management of the countries would also improve

(Hafeez et al., 2011). Yu (2003) states that such

diversification under regional cooperation would

substitute the conventional fossil fuels by the low-

carbon energies. To reinforce the effective regional

cooperation framework for sustained low-carbon

economies, trade creation in different energy sources

can play a significant role. As Kalirajan (2016) states,

reinforcement of REC would also promote the low-

carbon energy products in the countries.

Demand

management

Restraining demand for

the High-Carbon energy

(HCE) and promoting

demand for the Low-

Carbon energy (LCE)6

Analyzing the demand functions and measuring

countries’ efficiency in demand management.

Sharing experience and knowledge, technology

transfer, technical assistance, and capacity building.

Usage

efficiency

Increasing micro level

energy usage efficiency

Estimating countries’ sectoral end-use efficiencies.

Sharing experience and knowledge, technology

transfer, technical assistance, and capacity building.

Source: Author

One of the critical areas for REC is to optimize the intraregional external demand-supply

matching. An REC) can facilitate meeting the growing energy demand in a region through

energy trade, thereby ensuring higher energy security and more reliable supply from

intraregional energy trade, particularly in the crisis period (Cho et al., 2015). Such cooperation

also enhances energy efficiency through technology transfer and technical assistance.

It is imperative that production of energy largely depends on the country’s available energy

reserve, and relevant geological attributes. Therefore, a country cannot maximize its energy

production at will. Estimation of production efficiency (i.e., energy supply management) is,

thus, not plausible. A distinctive methodology which the policymakers are currently using is

the Levelized Cost of Energy (LCOE). LCOE considers the capital costs, fuel costs, labor cost,

operations and maintenance costs, and investment costs to calculate the net present value of

the unit-cost of producing that energy over the lifetime of a generating plant. However, the

estimation of LCOEs are based on project-to-project data and not encompassing the whole

6 Definition of HCE and LCE is cited in section 4.2


country’s energy production data. Hence, we skipped this LCOE analysis for this thesis.

Instead, the study attempts to analyze the demand-management aspects of energy, i.e., to

contain the demand for HCE while promoting the demand for LCE. It also estimates the

sectoral energy-usage efficiencies of the country so that countries can investigate how best they

can cooperate with each other to attain the goals of LCGG through these channels under an

RC framework.

This study, therefore, attempts to address all three channels mentioned in Table 4.1. This

chapter highlights different aspects of existing demand-supply gaps of energy in the StEA

countries. It then analyzes how those gaps can be minimized most efficiently through

intraregional energy trade while making the transition towards the LCGG. The following

chapter measures countries’ efficiencies in managing the demand for the HCE and LCE from

the low-carbon aspect. The chapter also estimates energy-usage efficiency both in macro level

as well as in different sectors of the StEA countries. Both the chapters conclude with the policy

implications of the estimation results on attaining the low-carbon growth though the REC.

Table 4.2: Growth of GDP, energy usage, and CO2 emission among the regions

Regions Per capita Energy usage growth

(2005-2014)

CO2 growth

(2005-2014)

OECD members -1.13% -0.89%

North America -1.20% -0.97%

Latin America & Caribbean 1.10% 2.81%

Sub-Saharan Africa 0.18% 2.68%

Middle East & North Africa 3.31% 4.60%

European Union -1.64% -2.19%

Europe & Central Asia -0.65% -1.00%

ASEAN 2.01% 4.54%

SAARC 3.91% 8.57%

CJK 4.64% 6.57%

World 0.97% 2.50%

StEA 3.80% 6.65%

Source: World Development Indicators (2017)

As presented in Table 4.2, the StEA region has been experiencing the fastest growth in per

capita energy usage. Against the world’s average growth rate of 0.97% per annum during the

2005-2014 period, StEA region has the growth rate of 3.80% in per capita energy consumption.

Such rapid growth in this region has some remarkable eco-political significance in the global

perspective. However, such rapid growth has also turned this region into the fastest CO2

emitting region of the world. The average growth of CO2 emission in StEA region is 6.65%

over the 2005-2014 period, more than double of the world’s average growth of 2.50%.


Considering this fastest growth in economies, energy use, and subsequent CO2 emission in this

region, the demand-supply analysis for energy in this StEA region seems to have a great

significance for developing policies for sustainable low-carbon growth from a regional

cooperation context.

This chapter is organized into eight sections. Section 2 and 3 investigate the existing demand

and supply of energy sources at regional and country levels respectively in the StEA region.

Section 4 examines the intraregional energy trade exposures of the countries. Section 5 explains

the methodology to estimate the intraregional trade performances of the countries. Section 6

analyzes the outcomes from the empirical models. Section 7 and 8 end up with relevant policy

implications and concluding remarks respectively.

4.2 Regional statistics

a) Aggregated Energy production and usage in StEA

Figure 4.1 illustrates the aggregated energy production and usage in the StEA region over the

1995-2014 period. Total energy production in the StEA region indicates an increasing trend

throughout the 1995-2014 period with an average growth rate of 5.76% per annum. Total

production rose from 1.96 Gtoe (Giga ton oil equivalent) in 1995 to 4.1 Gtoe in 2014. Energy

usage at macro-level is referred to by Total primary energy supply (TPES) which is the sum of

production and imports subtracting exports and storage changes. Total energy usage in the

StEA region also indicates an increasing trend throughout the 1995-2014 period with an

average growth rate of 6.21% per annum. Total TPES rose from 2.46 Gtoe in 1995 to 5.36 Gtoe

in 2014.

Source: IEA (2017)

Figure 4.1: Aggregated energy production and usage in the StEA region over 1995-2014

0

1000

2000

3000

4000

5000

6000

1995 2000 2005 2010 2014

(in

Mto

e)

Total Energy Production Total energy usage


Self-sufficiency measures the region’s production capacity to meet its usage demand. As

indicated in Figure 4.2, the self-sufficiency was 79.7% in 1995 and drops to the lowest in 2000,

then reaches at peak (over 80%) in 2008, and falls again to 76.5% in 2014.

Source: Authors’ calculation

Figure 4.2: Self-sufficiency in aggregated energy of the StEA region (1995-2014)

b) Energy composition by carbonization type

The study carries out the analysis for both the High-carbon Energy (HCE) as well as the Low-

Carbon Energy (LCE) systems. HCE refers to those energy sources containing a higher range

of CO2-factor (i.e., kg CO2 per million BTU). Table 4.3 refers to the list of sources concerning

CO2-factors. Based on this Table, we have categorized coal, oil, gas, and biofuel and waste as

HCE and nuclear, hydro, geothermal, electricity, and heat energies as LCE.

Table 4.3: CO2-factors of energy sources

Energy source The range of CO2 Factor

(kg CO2 per million BTU) *

Coal 200-220

Oil 150-160

Gas 100-120

Nuclear 10

Hydro 10

Geothermal 10

Biofuel & waste 100

Electricity 10

Heat 10

Source: EIA (2016) https://www.eia.gov/environment/emissions/co2_vol_mass.cfm

Both the demand for and supply of HCE increased at a much faster pace, especially since 2000.

The rate is faster for the demand (i.e., usage) as compared to the supply (i.e., production) as

74%

75%

76%

77%

78%

79%

80%

81%

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

https://www.eia.gov/environment/emissions/co2_vol_mass.cfm


revealed in Figure 4.3. Self-sufficiency in HCE was at 79.8%, which gradually dropped since

2005 and reached 74.6% in 2014. Figure 4.4 illustrates that LCE demand grew slowly during

that time. The supply (i.e., production) also increased but at a declining rate. Self-sufficiency

in LCE, interestingly, showed a positive trend, which is an encouraging sign for strengthening

the LCE system in this region. From the self-sufficiency of 78.5% in 1995, the region attained

full self-sufficiency in LCE during 2001-2002, and then continuously rising to reach 115.7%

in 2014.

Source: IEA (2017) Source: IEA (2017)

Figure 4.3: Demand-supply trend for HCE Figure 4.4: Demand-supply trend for LCE

4.3 Country-level statistics

a) Aggregated energy production and usage in the countries

Table 4.4 presents the aggregated demand and supply of energy for each country in 2014.

China, as expected, leads both in production as well as in energy usage. Against the production

share of 63.3%, it uses 57.0% of total energy consumed in this StEA region. India, Indonesia,

Malaysia, and Thailand are the other top producers after China. Singapore remains the smallest

of energy producing country, followed by Cambodia, Sri Lanka, Nepal, and Brunei.

Concerning energy usage, China is followed by India, Japan, Korea, and Indonesia in the top-

5 users’ list. Brunei, on the other hand, remains the smallest energy consuming country,

followed by Cambodia, Sri Lanka, Nepal, and Myanmar.

According to Table 4.4, five countries: Brunei, Indonesia, Malaysia, Myanmar, and Viet Nam

are net producers of energy, i.e., self-sufficiency over 100%, while the rests are net users.

0

1000

2000

3000

4000

5000

6000

1995 2000 2005 2010 2015

(in

Mto

e)

HCE Prod HCE Use

0

50

100

150

200

250

300

1995 2000 2005 2010 2015

(in

Mto

e)

LCE Prod LCE Use


Table 4.4: Total energy production, usages, and sufficiency of the countries (2014)

Country Production Usage Self-sufficiency

Bangladesh 29.5 35.4 83%

Brunei 16.3 3.6 457%

Cambodia 4.3 6.4 67%

China 2593.1 3051.5 85%

India 541.8 824.7 66%

Indonesia 458.0 225.5 203%

Japan 26.6 441.7 6%

Korea 49.1 268.4 18%

Malaysia 94.6 89.7 106%

Myanmar 25.7 19.3 133%

Nepal 9.7 11.7 83%

Pakistan 68.2 89.9 76%

Philippines 25.9 47.7 54%

Singapore 0.6 28.0 2%

Sri Lanka 5.3 10.7 50%

Thailand 78.7 134.8 58%

Viet Nam 71.2 66.6 107% (Units of production and use are in Mtoe)

Source: IEA (2017)

b) Energy composition by carbonization type

Table 4.5 refers to the actual production of HCE and LCE along with their regional share for

each country. China and India produce more than three-fourths of the regional HCE. China

produces 63.7%, and India 13.6%. China also dominates in LCE production with 58.0%,

followed by Korea and India with 14.6%, and 8.7% respectively.

Table 4.5: Energy production and share of StEA countries (2014)

HCE LCE

Production Regional share Production Regional share

Bangladesh 29393 0.77% 64 0.02%

Brunei 16256 0.43% 0 0.00%

Cambodia 4099 0.11% 159 0.06%

China 2427513 63.66% 165600 58.03%

India 516911 13.56% 24900 8.73%

Indonesia 439435 11.52% 18563 6.51%

Japan 14256 0.37% 12331 4.32%

Korea 7443 0.20% 41667 14.60%

Malaysia 93470 2.45% 1171 0.41%

Myanmar 24917 0.65% 759 0.27%

Nepal 9414 0.25% 326 0.11%

Pakistan 64141 1.68% 4063 1.42%


Philippines 16192 0.42% 9662 3.39%

Singapore 646 0.02% 3 0.00%

Sri Lanka 4910 0.13% 416 0.15%

Thailand 78118 2.05% 622 0.22%

Vietnam 66157 1.73% 5042 1.77%

Source: IEA (2017) (Units of production and use are in Mtoe)

Figure 4.5 illustrates the composition of HCE and LCE production in each country in 2014.

Korea possesses the greenest energy mix having 85% of its total energy production as LCE.

Japan and Philippines follow Korea with 46% and 37% respectively. Top three energy

producers- China, India, and Indonesia, in contrast, possesses only 6.4%, 4.6%, and 4.0% LCE

in their production-mix. It also implies that there is an enormous opportunity for these countries

to go green with their energy production.

Source: IEA (2017)

Figure 4.5: Energy production mix in StEA countries (2014)

4.4 Trade of energy

4.4.1 Regional level statistics of energy trade

Due to the existence of the demand-supply gap in the region, international trade in energy is

inevitable. Figure 4.6 illustrates the trends of faster increasing aggregated imports over exports

of energy in StEA region over the 1995-2014 period. The annual growth rate of energy exports

was 6.67% against the imports growth of 7.51%. The prime concern is the widening of this

gap, which implies the growing dependence of the region on energy imports. The gap, indeed,

increased by 151% during the 1995-2014 period.

0%10%20%30%40%50%60%70%80%90%

100%

LCE within country HCE within country


Source: IEA (2017)

Figure 4.6: Internal and external demand and supply of energy in StEA region (1995-2014)

Aggregated Export mix

Export and import of energy are mostly based on fossil-fuels or HCE. As shown in Figure 4.7,

oil products remain the top energy source for export by the StEA region as a whole. In 2014,

oil products shared 43.2% of total energy export, followed by coal (38.1%) and gas (6.7%).

Small volumes of biofuels and wastes, and electricity are also exported. However, there is no

export of nuclear, hydro, and geothermal energy. The figure also implies that the export of coal

and oil products have been increasing while it has been decreasing for the gases.

[

Source: IEA (2017) Source: IEA (2017)

Figure 4.7: Export composition of StEA Figure 4.8: Import composition of StEA

Aggregated Import mix

Like exports, import of energy is also based on fossil-fuels or HCE. Figure 4.8 indicates that

the countries of StEA region import the crude oil most, i.e., 45.3%. It is followed by coal, oil

products, and gas with 25.3%, 17.7%, and 11.5% respectively. Small volumes of biofuels and

0

1000

2000

3000

4000

5000

6000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(in

Mto

e)

export import Prod TPES

0%

10%

20%

30%

40%

50%

coal crudeoil

oil prod gas biofuel& waste

elect

1995 2000 2005 2010 2014

0%

10%

20%

30%

40%

50%

60%

70%

coal crudeoil

oil prod gas biofuel& waste

elect

1995 2000 2005 2010 2014


wastes, and electricity are also imported. There is no import of nuclear, hydro, and geothermal

energy evident in the StEA countries. The figure also implies that the import of coal and gas

have been increasing over time while it has been decreasing for the crude oil and oil products.

4.4.2 Country level statistics of energy trade

From an RC perspective, the country-level analysis is so pivotal to assess the strengths,

weaknesses, and potentials of the countries to formulate the cooperation framework.

Especially, it is needed to measure how much exposure a country has in the intraregional trade

and to what extent the country’s contributions (i.e., intraregional exports) can help to meet

other countries’ demand (i.e., intraregional imports).

To drawing different inferences, this study separately analyzes the trade of HCE (in the form

of the primary energy fuels and mineral oils) and trade of LCE (in the form of the renewable

energy goods, discussed later in details) at the country level. The trade data are extracted from

World Integrated Trade Solution (WITS) dataset; details are described in section 4.5.3.

4.4.2.1 HCE trade statistics

a) Intraregional Exports and imports

Table 4.6 represents the intraregional exports and imports volumes for HCE (primary energy).

The average of the 2006-2016 period reveals that Indonesia is the largest intraregional primary

energy exporter in the StEA region. On average, the country exports 34.7 billion USD per

annum. Indonesia is followed by Malaysia, Singapore, and Korea. Singapore is the country,

which imports a large volume of raw primary energy, processes those, and then exports to other

countries. That is why, even though the country itself consumes a small volume of energy, its

exports and imports volume is very high. Sri Lanka, Bangladesh, and Pakistan have the lowest

intraregional exports among the countries.

Japan, Singapore, and China remain the top intraregional importers of primary energy with

annual average imports of USD 39.5 billion, USD 32.5 billion, and USD 27.3 billion

respectively. On the contrary, Brunei, Pakistan, and Myanmar are identified as the lowest

intraregional importers.

The average matching capacity of each country refers to the country’s capacity to meet its

intraregional imports with its intraregional exports, i.e., the ratio of intraregional exports


divided by imports. If a country’s intraregional export is higher than its intraregional import,

that country can be considered as a Net exporter. Conversely, if a country’s intraregional import

is higher than its intraregional export, that country can be regarded as a Net importer. As Table

4.6 shows, Brunei, India, Indonesia, Malaysia, and Myanmar are found to be Net exporters,

while the rest of the countries are found to be Net importers.

Table 4.6: Intraregional primary energy trade volume (average, 2006-2016)

Country* Export

(in million USD)

Import

(in million USD) Matching capacity

Bangladesh 63.78 1463.93 0.04

Brunei 6569.52 173.81 37.80

China 9391.46 27342.87 0.34

India 11122.63 9233.43 1.20

Indonesia 34733.45 21227.90 1.64

Japan 6289.72 39503.31 0.16

Korea, Rep. 20248.70 23227.98 0.87

Malaysia 33672.25 12172.43 2.77

Myanmar 2715.56 981.60 2.77

Pakistan 360.21 691.37 0.52

Philippines 1020.74 4461.99 0.23

Singapore 24449.47 32459.76 0.75

Sri Lanka 34.85 1514.38 0.02

Thailand 6353.53 9013.01 0.70

Vietnam 5443.31 7023.52 0.78

* Trade volume of Bhutan, Cambodia, Lao PDR, Maldives, and Nepal are very small. Hence, those are not included.

Source: Author’s calculation based on World Integrated Trade Solution (2017)

b) Intraregional trade exposure

This study calculates the Intraregional trade exposure of a country by the following ratio:

=𝐶𝑜𝑢𝑛𝑡𝑟𝑦′𝑠 𝐻𝐶𝐸 𝑡𝑟𝑎𝑑𝑒 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑤𝑖𝑡ℎ𝑖𝑛 𝑆𝑡𝐸𝐴

𝐶𝑜𝑢𝑛𝑡𝑟𝑦′𝑠 𝐻𝐶𝐸 𝑡𝑟𝑎𝑑𝑒 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑤𝑜𝑟𝑙𝑑

This exposure indicates the extent of regional connectedness of a country through its trade.

The average intraregional exposures of the countries’ HCE (i.e., primary energy) exports are

calculated over the 2010-2016 period and presented in Table 4.7.

Interestingly, the low-scale exporting countries are found to have more intraregional exposure

in HCE exports. Myanmar, Bhutan, Cambodia, and the Maldives have 100% of their HCE

exports within the StEA region. Conversely, the larger exporting nations, such as China, India,

Japan, and Korea have lower export exposures of 45.1%, 28.4%, 56.0%, and 63.6%


respectively within the StEA. Singapore and Indonesia, the largest two HCE exporting

countries, have higher intraregional exposure of 69.7% and 84.4% respectively.

Like the HCE exports exposure, the low importing countries are found to have more

intraregional exposure in HCE imports. Bhutan, Brunei, Cambodia, Lao, Myanmar, and Nepal-

each country has over 95% of their HCE imports from the StEA region. Conversely, the larger

importing nations, such as China, Japan, India, and Korea each has import exposures below

20% in the StEA.

Table 4.7: Average intraregional exposure share of Primary energy trade in (2010-2016) Export exposure share Import exposure share

Bangladesh 34.8% 57.2%

Bhutan 100.0% 99.9%

Brunei 84.2% 98.2%

Cambodia 100.0% 98.4%

China 45.1% 13.9%

India 28.4% 8.3%

Indonesia 84.8% 62.1%

Japan 56.0% 18.9%

Korea, Rep. 63.6% 15.2%

Lao PDR 96.5% 99.9%

Malaysia 80.3% 59.6%

Maldives 99.8% 23.8%

Myanmar 100.0% 95.6%

Nepal 68.1% 96.7%

Pakistan 18.0% 6.1%

Philippines 91.2% 44.1%

Singapore 69.8% 40.2%

Sri Lanka 44.8% 49.8%

Thailand 86.3% 25.7%

Vietnam 77.7% 74.4%

Source: Authors’ calculation based on World Integrated Trade Solution (2017)

4.4.2.2 LCE trade statistics

Since it is realistically not plausible to trade the primary LCE sources, to strengthen the low-

carbon economy, it would be prudent to emphasize on the trades of renewable energy-related

products among the nations. As Kalirajan and Liu (2016) have argued, regional cooperation is

so crucial in the area of renewable resources and energy goods in Asia to attain untapped

renewable resources, meeting the rising demand for energy, and most importantly, mitigating

the greenhouse gas emissions from fossil fuel use. For this purpose, a list of 15 Renewable


Energy Goods (REG) from the APEC-54 environmental goods list is used (Kalirajan & Liu,

2016).

a) Intraregional Exports and imports

Table 4.8 represents the intraregional exports and imports volumes for REG. The average of

the 2006-2015 period reveals that Korea, Japan, and China are the three largest intraregional

REG exporters in the StEA region. On average, these countries export USD 15.1 billion, USD

10.0 billion, and USD 8.6 billion annually respectively. Pakistan, Myanmar, and Sri Lanka

have the lowest intraregional exports among the sample countries.

China, by far, remains the top intraregional importer of REG. Its average annual import is

USD 26.8 billion, followed by Korea and Japan with an import value of USD 5.3 billion and

USD 5.1 billion. Conversely, Cambodia, Sri Lanka, and Myanmar are among the lowest

intraregional importers.

Table 4.8: Intraregional REG trade volume (average, 2006-2015)

Country* Export

(in million USD)

Import

(in million USD) Matching capacity

Bangladesh 9.03 120.29 0.08

Cambodia 22.45 16.29 1.38

China 8628.17 26757.61 0.32

India 170.34 2096.67 0.08

Indonesia 285.84 1002.47 0.29

Japan 9981.34 5066.45 1.97

Korea, Rep. 15090.84 5258.81 2.87

Malaysia 1460.46 1153.92 1.27

Myanmar 2.12 88.85 0.02

Pakistan 0.82 261.45 0.00

Philippines 661.92 478.25 1.38

Singapore 479.83 1296.62 0.37

Sri Lanka 8.84 42.52 0.21

Thailand 890.71 1026.91 0.87

Vietnam 132.28 911.21 0.15 * Trade volume of Bhutan, Brunei, Lao PDR, Maldives, and Nepal are very small. Hence, those are not included.

Source: Authors’ calculation based on World Integrated Trade Solution (2017)

The average matching capacity calculation reveals that Japan, Korea, Malaysia, Philippines,

and Cambodia are found to be the Net exporters of REG, while the rest of the countries are

found as the Net importers.


b) Intraregional trade exposure

The average intra-regional exposures of the countries’ REG exports are calculated over the

2010-2015 period and presented in Table 4.9. Three big exporting countries: China, Korea,

and Japan’s average intra-regional exposure of REG exports is calculated at 27.3%, 69.3%, and

60.9% respectively. Exposure range varies among the countries. Myanmar, Lao, and Cambodia

have the highest exposures among the countries. In contrary, Bhutan, Maldives, and Pakistan

have the lowest. On aggregate, the StEA region’s exports exposure is 44.3%. The average

intra-regional exposures of the countries’ REG imports are also presented in Table 4.9. On

average, the regional REG imports exposure is 61.9%. Nepal and Cambodia have the highest

intra-regional exposure shares in imports, while Brunei and Singapore have the lowest

exposure.

Table 4.9: Average intraregional exposure share of REG trade in (2010-2015) Export exposure share Import exposure share


Bhutan 0.0% 60.1%

Brunei 59.3% 24.2%

Cambodia 82.0% 89.9%

China 27.3% 60.0%

India 21.5% 56.9%


Japan 60.9% 69.9%

Korea, Rep. 69.3% 71.8%

Lao PDR 98.7% 65.1%


Maldives 0.0% 67.9%

Myanmar 98.1% 74.9%

Nepal 5.4% 95.7%

Pakistan 1.4% 53.6%



Sri Lanka 49.7% 69.9%


Vietnam 58.5% 76.3%


4.5 Methodology: measuring efficiency in energy exports

Facilitating the regional cooperation through trade necessitates understanding the dynamics of

the energy trade. More specifically, identifying the determinants and constraints, and


measuring export efficiencies of the countries are so vital from the trade policy perspective. As

explained by Kalirajan and Anbumozhi (2014), the methodology used in the literature for

examining the export performance may be broadly classified into three groups: input-output

analysis, computable general equilibrium (CGE) approach, and various gravity models

methods. A key limitation of the input-output analysis is the assumption of fixed coefficients

of production, which ignores any likelihood of the factor substitution. This approach is based

on linear equations. Hence the results may not be realistic. CGE modeling is a very

commanding tool to numerically handle long ranges of issues at a time that may not be possible

with conventional econometric estimations. However, CGE simulations are not based on

absolute predictions; rather they are assumed experimentations about what the world would be

like if the assumed policy change had been implemented (Hertel et al., 2007). Hence, CGE

modeling takes an ex-ante approach based on some robust assumptions such as balanced budget

and market clearing conditions, which may not hold good especially for the developing

countries (Kalirajan & Anbumozhi, 2014). Conventional gravity models are a popular

empirical ex-post approach to analyze the impact of various trade policy issues on bilateral

trade flows. Despite its considerable empirical applications, these conventional gravity models

were criticized for a long time for not having the strong theoretical basis (Ivus & Strong, 2007).

Later, since the early 80s, several theory-based gravity models have been introduced in the

trade literature. For instances, Anderson (1979) used constant elasticity of substitution (CES);

Bergstrand (1989) derived monopolistic trade model; Eaton and Kortum (2002) developed

micro-foundation; Anderson and van Wincoop (2003) used trade resistance model; and

Deardorff (2011) applied Heckscher-Ohlin model to explain the impact of relative factor

endowments in trade;. However, all these conventional gravity models assume that bilateral

trade flows depend only on those economic factors of the given pair of trading countries, which

are included in the models (Ivus & Strong, 2007). In other words, the expected value of the

error term is considered as zero. However, in reality, there may be substantial differences

between the actual and potential trade volume because of numerous trade-dominant factors

such as political, institutional, and environmental factors that are not captured into the model.

Omission or misspecification of these variables may lead to heterogeneity bias which is a big

drawback of estimating the conventional gravity model. This limitation is later overcome by

applying the stochastic frontier production function method with composed error term to

estimate the gravity model (Kalirajan, 2007).


This chapter, therefore, uses the Stochastic Frontier Gravity model to measure the potential

level of exports for each country, where export efficiency is defined as the ratio of the actual

level of exports to the potential level of exports.

4.5.1 Stochastic Frontier Model

The Stochastic Frontier Gravity (SFG) model with panel data set is generally expressed as:

𝑌𝑖𝑡 = 𝛽𝑋𝑖𝑡 + (𝑉𝑖𝑡 − 𝑈𝑖𝑡)

𝑖 = 1,2,3, …… . . , 𝑛 𝑎𝑛𝑑 𝑡 = 1,2,3, … . . , 𝑇

Where, 𝑌𝑖𝑡 : exports (actual value or logarithm value) of the i-th country in t-th time period

𝑋𝑖𝑡 : (𝑘 × 1) vector of input values of the i-th country in t-th time period

𝛽 : vector of the estimation parameters

𝑉𝑖𝑡 : Statistical error term, assumed to follow a normal distribution with 𝑁 (0, 𝜎𝑉2)

and independent of 𝑈𝑖𝑡

𝑈𝑖𝑡 = 𝑈𝑖exp (−𝜂(𝑡 − 𝑇)); where 𝑈𝑖 represents non-price and organizational variable

that captures production (or export) inefficiency of the country; assumed to follow

independent and identically distributed as truncated at zero of the |𝑁 (𝜇, 𝜎𝑈2)|

distribution. Parameter 𝜂 is to be estimated for time-varying inefficiency model.

Parameter γ is then defined as

𝛾 =𝜎𝑈

2

𝜎𝑉2 + 𝜎𝑈

2

The idea behind such a calculation of γ is to see how important is the influence of country-

specific non-price and organizational factors on the variations in exports. The value of γ lies

between 0 and 1. If the null hypothesis of γ = 0 cannot be rejected, which implies that 𝜎𝑈2 = 0.

Hence, 𝑈𝑖𝑡 term has no implication into the model and therefore, can be removed to follow

the conventional gravity model framework of empirical analysis. Conversely, a higher (closer

to one) and significant value of γ would refer to the validity of using the stochastic frontier

gravity model framework.

Now, export efficiency is measured from the above SFG model as

𝑋𝐸𝑖𝑡 =𝐸(𝑌𝑖𝑡|𝑈𝑖𝑡 , 𝑋𝑖𝑡)

𝐸(𝑌𝑖𝑡|𝑈𝑖𝑡 = 0, 𝑋𝑖𝑡)

The value of 𝑋𝐸𝑖𝑡 lies between 0 and 1.


4.5.2 Model specification

Drawing on Nguyen and Kalirajan (2016), it is rational to argue that the SFG model for exports

needs to incorporate the influences of natural determinants, mutually induced determinants,

explicit beyond the border determinants, and behind the border constraints for a given level of

existing implicit beyond the border determinants.

• Usually, the demand and supply of tradable goods, which are proxied by GDPs of

importing and exporting countries, along with their population, and geographical

distances between trading countries are considered as the natural determinants.

• Implementation of any bilateral or multilateral agreement on trade policy may also

impact the trade potential between the countries. It is termed as the mutually induced

determinant.

• The relative price of imported goods, which is primarily influenced by the tariff

structure of the importing country, and its real exchange rate, both are termed as explicit

beyond the border determinants.

• Some non-price, institutional, and infrastructural settings of the importing country may

also affect the trade facilitation between the countries, on which full information is not

available to researchers. The influence of these importing countries’ factors is named

as implicit beyond the border constraints because these are beyond the control of the

exporting country. The impact is included in the ‘V’ variable.

• Some non-price, institutional, and infrastructural settings of the exporting countries

may largely influence the trade facilitation. These factors are referred to as behind the

border determinants, which are under the control of the exporting country. Nguyen and

Kalirajan (2016) acknowledged the aggregated impact of such factors on export

inefficiency (i.e., modeled as the ‘-U’ variable) assuming that full information on these

variables is not known to the researchers.

This paper uses the following SF Gravity model:

ln𝑋𝑖𝑗,𝑡 = 𝛽0 + 𝛽1ln𝐺𝐷𝑃𝑋𝑡 + 𝛽1ln𝐺𝐷𝑃𝑀𝑡 + 𝛽3 ln 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖𝑗 + 𝛽4 𝑇𝑎𝑟𝑖𝑓𝑓𝑗𝑖,𝑡

+ 𝛽5 𝑅𝑎𝑡𝑖𝑜𝑖𝑗,𝑡 + 𝛽6 𝑅𝑇𝐴𝑖𝑗,𝑡 − 𝑈𝑖𝑗,𝑡 + 𝑉𝑖𝑗,𝑡

where, 𝑋𝑖𝑗,𝑡 : Value of export from country-i to country-j at time-t

𝐺𝐷𝑃𝑋𝑡 : Gross Domestic Product of the exporting country at time-t

𝐺𝐷𝑃𝑀𝑡 : Gross Domestic Product of the importing country at time-t


𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖𝑗: Distance between country-i and country-j

𝑇𝑎𝑟𝑖𝑓𝑓𝑗𝑖,𝑡 : Tariff imposed by the importing country (country-j) on country-i at time-t

𝑅𝑎𝑡𝑖𝑜𝑖𝑗,𝑡 : Cross exchange rate ratio is measured as the exchange rate of the importing

country divided by the exchange rate of the exporting country

𝑅𝑇𝐴𝑖𝑗,𝑡 : Dummy variables for Regional Trade Agreement between country-i and

country-j at time-t. RTA=1, if there is any trade agreement between the

countries at time t; else equals to zero.

𝑈𝑖𝑗,𝑡 : Single-sided error term for the combined effects of the ‘behind the border’

constraints on which full information is not available

𝑉𝑖𝑗,𝑡 : Normal statistical error term, captures the effect of inadvertently omitted

variables, and implicit beyond the border constraints.

This study further attempts to identify the determinants of export efficiency. In other words,

the study has endeavored to explore how much of the export inefficiency can be explained by

the available behind the border factors. Contemporary trade policy reveals that the exporting

country may provide financial and non-financial support to the importing country to develop

institutional and infrastructural settings of the importing country. For instance, India granted

$4.5 billion financial credit line to improve the infrastructure of Bangladesh to facilitate it’s

exports to Bangladesh (Mishra & Roche, 2017). China, through the ‘Belt and Road Initiative’,

has been helping its partner countries to improve their infrastructure to facilitate its trade in

those countries (Griffiths, 2017). Therefore, it is not only those conventional behind the border

factors which shape the efficiency of exporting country, rather, all across the border factors

which the exporting country may control (or at least, influence) should be considered to model

the inefficiency variable ‘U’. Hence, this study modifies the categorization of trade facilitation

factors. The global standard data on trade facilitation factors comprises the institutional

strength, infrastructure quality, market efficiency, and technological soundness of both

exporting and importing countries. These factors may not have a direct influence on the realized

bilateral trade volume and value, yet, they can support the trade environment and management

process, which is pivotal in determining the export efficiency (Galle et al., 2017, Grossman et

al., 2017, Baccini et al., 2017).


Therefore, to estimate the effects of these trade facilitation factors on export efficiency, the SF

Gravity model is extended to the export inefficiency modeling as follows:

𝑈𝑖𝑡 = 𝑧𝑖𝑡𝛿 + 𝑤𝑖𝑡

where 𝑤𝑖𝑡 is defined by the truncation of the normal distribution having zero mean and variance

of 𝜎2 such that 𝑤𝑖𝑡 ≥ −𝑧𝑖𝑡𝛿. This remains consistent with the assumption of 𝑈𝑖𝑡 being a non-

negative truncation of the (𝑧𝑖𝑡𝛿, 𝜎2 ) distribution.

Technically, while considering intraregional trade and using bilateral trade model, 𝑈𝑖𝑡 will be

replaced by 𝑈𝑖𝑗,𝑡, i.e., country-pair inefficiency. Accordingly, the explanatory 𝑧𝑖𝑡 variables also

need to modify in country-pair factors, otherwise, it would have an exclusion-bias problem. To

resolve the problem, this study converts the country-specific 𝑧𝑖𝑡 variables to country-pair 𝑧𝑖𝑗,𝑡

variables while upholding the across the border implication for the exporting country. For

instance, infrastructure quality of both the exporting and importing countries should impact the

bilateral trade performance. As mentioned earlier, under the new trade practice, the exporting

country may have influenced the infrastructure quality of its partner country too. Efficiency of

the exporting country, thereby, would depend on how efficiently it is able to choose the partner

country (having higher infrastructure quality, for example) or how it can extend its cooperation

to improve the infrastructure of the partner country.

Export efficiency is modeled as follows:

𝑈𝑖𝑗,𝑡 = 𝛿0 + 𝛿1𝐼𝑁𝑆𝑇𝑖𝑗,𝑡 + 𝛿1𝐼𝑁𝐹𝑅𝑖𝑗,𝑡 + 𝛿1𝑀𝐾𝑇𝐸𝐹𝐹𝑖𝑗,𝑡 + 𝛿1𝑇𝐸𝐶𝐻𝑖𝑗,𝑡 + 𝑤𝑖𝑗,𝑡

where 𝐼𝑁𝑆𝑇𝑖𝑗,𝑡 denotes the average of the strengths of institutions in exporting and importing

countries at time t

𝐼𝑁𝐹𝑅𝑖𝑗,𝑡 denotes the average of the quality of infrastructures in exporting and importing

countries at time t

𝑀𝐾𝑇𝐸𝐹𝐹𝑖𝑗,𝑡 denotes the average level of the goods market efficiency in exporting and

importing countries at time t

𝑇𝐸𝐶𝐻𝑖𝑗,𝑡 denotes the average of the state of technological adoption and readiness in

exporting and importing countries at time t


The Maximum Likelihood Estimation (MLE) is used to estimate the coefficients of the model

using joint density functions of 𝑈𝑖𝑗,𝑡 and 𝑉𝑖𝑗,𝑡. While the parameter γ is found significant, it

implies that behind the border determinants are important factors for the model.

FRONTIER 4.1 software developed by Coelli (1996) is used to estimate the model.

4.5.3 Description of Data

All trade data were extracted from World Integrated Trade Solution (WITS), a trade database

provided by the World Bank. For fossil fuel data, HS Code Chapter 27 (Harmonized System

Code of Mineral fuels, mineral oils, and products of their distillation; bituminous substances;

mineral waxes) was used. For Renewable Energy Goods (REG), a list of HS codes shown in

Appendix 2 was used. Export data were collected for all 20 countries of the StEA region over

2006-2016 (for primary fossil energy) and 2006-2015 (for REG). GDP at constant 2010 and

exchange rate data were collected from World Development Indicators of the World Bank. The

distance between the capital cities was extracted from Bertoli et al. (2016). Regional Trade

Agreement (RTA) data are collected from Asia Regional Integration Center dataset. In

determining the trade agreement between countries, only the agreements signed & in-effect is

considered. The earlier agreement is considered in case there is more than one agreement. Free

trade agreement (FTA), preferential trade agreement (PTA), Comprehensive Economic or

Trade cooperation all are considered. For any bilateral trade, the effective year of a trade

agreement between those countries is followed to place the respective value of the dummy

variable.

The data for the institutional strengths, quality of infrastructure, goods market efficiency, and

technological adoption and readiness were extracted from the Global Competitiveness Index

(GCI) 2006-2016, prepared by the World Economic Forum in an annual basis. In most cases,

indicators derived from the survey were presented on a 1–7 scale, with 7 being the most

desirable outcome. Units were omitted for the sake of readability for these indicators.

4.6 Results and Discussion

4.6.1 Trade in Primary energy (fossil fuel)

As discussed earlier, the value of gamma (γ) is crucial to determine whether or not the country-

specific data suits the SF model. Higher the value of gamma, better the model would be.

Considering this constraint, the model does not fit well with Bhutan, Cambodia, Lao PDR,

https://halshs.archives-ouvertes.fr/halshs-01288748/document

https://aric.adb.org/fta-country


Maldives, and Nepal as the primary energy exporters. Besides, these countries also have a tiny

share in overall regional primary energy trade; they are excluded from further analysis of this

section.

a) Discussion of the SF coefficients’ estimates

Table 4.10 shows the estimation of the coefficients of the six explanatory variables of primary

energy exports for each country. The estimates that were significant at least at the 10% level

are discussed below.

Table 4.10: Estimates of coefficients of the SFG model for primary energy trade

Exporting

country lnGDPX lnGDPM lnDistance Tariff

Exchange

ratio RTA Gamma

log

likelihood

function

Total

obser-

vation

Bangladesh -7.969*** -1.068 5.077 -0.435*** 0.0154* -5.612*** 0.999*** -113.29 47

Brunei 9.014*** 0.337* -1.991** -0.140* 0.0004*** 0.150 0.999*** -68.05 79

China 0.266** 1.184*** 0.086 -0.119*** 0.001*** -1.040*** 0.999*** -275.54 144

India 1.602*** 0.467*** -1.224* 0.123*** -0.003* 0.577 0.999*** -303.16 138

Indonesia 1.242*** 1.370*** -0.984*** -0.214*** -0.502** 0.854*** 0.987*** -226.59 139

Japan -1.064*** 1.533*** -1.105*** -0.042*** 0.008*** 1.192* 0.999*** -261.21 134

Korea, Rep. 3.951*** 1.696*** 0.420 -0.114*** 0.198*** 1.609*** 0.796*** -284.39 138

Malaysia 0.548** 0.879*** -0.839*** 0.002 -0.001 0.567*** 0.999*** -197.68 137

Myanmar 5.785*** -2.892*** 8.115 -0.837*** -0.003*** 2.664 0.999*** -108.80 40

Pakistan 4.145*** 0.635*** -1.544*** 0.629*** -0.015*** 5.557*** 0.999*** -216.81 87

Philippines 0.566* 1.106*** -3.249*** 0.010 -0.004*** 2.631*** 0.999*** -245.33 111

Singapore 1.156*** 0.680*** -0.835*** 0.005 0.0001*** 0.649*** 0.999*** -227.98 140

Sri Lanka 5.820*** 1.908*** -6.015*** 0.210*** -0.025*** 2.178*** 0.999*** -186.01 82

Thailand 1.529*** 1.272*** -3.579*** -0.017 0.002*** 1.963*** 0.560*** -243.17 144

Vietnam 0.774*** 1.140*** -4.134*** 0.056 3.236*** 1.498*** 0.999*** -219.11 110

Source: Authors’ estimations

* refers to the level of significance (* for 10%, ** for 5%, and *** for 1% significance level)

• Results imply that all countries but Japan and Bangladesh have positive intraregional

export elasticities of their GDP. Brunei has the highest elasticity of 9.01, followed by

Sri Lanka and Myanmar with 5.82 and 5.78 respectively. The larger exporting countries

such as Indonesia, Malaysia, and Singapore have the export elasticity of GDP of 1.24,

0.55, and 1.16 respectively.

• Other than Myanmar all countries are found to have positive demand elasticity of their

intraregional primary energy exports, i.e., increasing demand for energy in importing

country drives the exports positively for those countries. Sri Lanka has the highest

elasticity with 1.91, followed by Korea and Japan with 1.70 and 1.53 respectively.


Brunei, Pakistan, and Singapore have the lowest demand elasticities of their primary

energy exports.

• The impact of the tariff was negative for seven exporting countries, while it came

positive for three countries. The positive coefficient may be the result of the exporting

countries’ special arrangement with the importing countries, or the nature of the traded

goods are such that even with higher tariff the exporting countries appear to export

more. The negative impact is more prominent for Myanmar, Bangladesh, and Indonesia

as compared to the other exporting countries. The countries with positive coefficients

are India, Pakistan, and Sri Lanka.

• The impact of cross exchange rate ratio on export performances is mostly positive.

However, for most countries, this coefficient is found to be very low. Nonetheless, as

the impacts are too little, the implication of cross exchange ratio seems minimal for the

trade in this region. Vietnam has the highest positive coefficients, while Indonesia has

the most negative impact of this cross-exchange rate ratio on respective primary energy

exports.

• Other than Bangladesh and China, regional trade agreements are found to have a

positive impact on exports for all the countries. The impact is higher for the smaller

exporting countries such as Pakistan, Myanmar, and the Philippines.

• Values of log likelihood function represent the degree of goodness of fit of the models.

According to the values, all country-specific models are well fitted.


Table 4.11: Intraregional primary energy export efficiencies of each country

Exporting Countries

Ban

gla

des

h

Bru

nei

Ch

ina

Ind

ia

Ind

on

esia

Jap

an

Ko

rea,

Rep

.

Mal

aysi

a

My

anm

ar

Pak

ista

n

Ph

ilip

pin

e

Sin

gap

ore

Sri

Lan

ka

Th

aila

nd

Vie

tnam

Imp

ort

ing

co

un

trie

s

Bangladesh 0.81 0.35 0.55 0.35 0.71 0.76 0.50 0.81 0.75 0.49 0.86 0.66

Bhutan 0.39 0.23 0.12 0.59 0.00 0.72

Brunei 0.13 0.01 0.01 0.39 0.43 0.48 0.00 0.61 0.44 0.00

Cambodia 0.57 0.11 0.78 0.26 0.58 0.03 0.53 0.24 0.83 0.66

China 0.00 0.05 0.34 0.14 0.35 0.41 0.30 0.78 0.43 0.65 0.62 0.01 0.74 0.48

India 0.08 0.11 0.40 0.55 0.25 0.42 0.40 0.00 0.57 0.60 0.64 0.31 0.76 0.33

Indonesia 0.01 0.63 0.56 0.33 0.31 0.60 0.79 0.99 0.81 0.30 0.72 0.01 0.75 0.04

Japan 0.59 0.61 0.74 0.27 0.39 0.59 0.68 0.00 0.38 0.06 0.14 0.63 0.49 0.57

Korea, Rep. 0.34 0.64 0.88 0.51 0.82 0.36 0.88 0.05 0.71 0.38 0.28 0.11 0.72 0.73

Lao PDR 0.55 0.09 0.07 0.02 0.34 0.00 0.00 0.70 0.58

Malaysia 0.01 0.36 0.53 0.32 0.42 0.40 0.60 0.00 0.16 0.32 0.65 0.77 0.71 0.72

Maldives 0.73 0.15 0.20 0.39 0.50 0.66 0.01 0.64 0.47 0.78

Myanmar 0.58 0.14 0.01 0.24 0.48 0.25 0.68 0.86 0.06

Nepal 0.19 0.59 0.53 0.69 0.40 0.59 0.00 0.02 0.86 0.77 0.00

Pakistan 0.55 0.21 0.48 0.25 0.56 0.25 0.20 0.33 0.01 0.82 0.35

Philippines 0.15 0.16 0.80 0.22 0.63 0.31 0.73 0.37 0.00 0.71 0.81 0.03

Singapore 0.52 0.21 0.76 0.73 0.66 0.70 0.74 0.78 0.03 0.67 0.66 0.56 0.50 0.63

Sri Lanka 0.00 0.65 0.40 0.41 0.30 0.60 0.17 0.00 0.01 0.72 0.81 0.05

Thailand 0.00 0.73 0.36 0.33 0.47 0.32 0.53 0.53 0.28 0.03 0.48 0.17 0.00 0.08

Vietnam 0.35 0.68 0.33 0.47 0.36 0.59 0.44 0.21 0.02 0.62 0.48 0.93 0.79

Aggregated

export efficiency 31.5% 57.4% 73.0% 56.0% 48.9% 49.1% 56.6% 67.8% 33.7% 58.1% 53.0% 61.5% 56.3% 62.1% 56.3%



b) Export efficiencies of the countries

Each country has typically different export efficiencies for different importing countries. Since

the value of exports to each country may also differ substantially, using the mean export

efficiency (XE) would not represent the exporter’s effective level of efficiency. Weighted XE

is, therefore, employed in this study. Weighted XE is calculated by assigning the weight

according to the share of exports to the importing countries.

Weighted XE of the country –i is calculated as

=∑ (𝑋𝐸𝑖𝑗×𝑋𝑖𝑗)𝑗

∑ 𝑋𝑖𝑗𝑗

where, 𝑋𝐸𝑖𝑗 = Export efficiency of exports from country-i to country-j

𝑋𝑖𝑗 = Total Export value from country-i to country-j

j = all importing countries

Table 4.11 presents the weighted export efficiency in primary energy exports for each country.

China has the highest export efficiency in this intraregional primary energy exports with 73.0%.

China is followed by Malaysia, Thailand, and Singapore with 67.8%, 62.1%, and 61.5%

respectively. Bangladesh and Myanmar have the lowest export efficiencies of 31.5% and

33.7% respectively. On average, the whole region has the weighted export efficiency of 58.4%,

which implies that given the current settings, there is still an untapped intraregional export

potential of 41.6% in this region for the primary energy.

c) Identification of the determinates of export efficiency

Figure 4.9 shows the plotting of only the coefficients with at least 10% significance level. It

indicates that institution has a positive impact on export performance for all the countries but

Japan. It could be that Japan already has a good institutional framework that is conducive to

trade and increasing it further may, in fact, act as a constraint to its export performance due to

the ‘crowding effect.’ Also, Japan’s intraregional primary energy export has been declining,

especially in recent times. The impact is higher for other larger exporting countries such as

Singapore, Korea, and China. The impact is also substantial for the other big exporters like

Indonesia, India, and Thailand.


Figure 4.9: Coefficients of the determinants of the primary energy export efficiency

Other than Sri Lanka and Korea, infrastructure exerts a positive influence on primary energy

export performances of the countries. Countries such as Malaysia, Vietnam, and Indonesia are

mostly influenced by this factor.

Since the primary energy has a substantial influence on the overall goods market of any

economy, the market efficiency is a pivotal determinant for the primary energy export

performances. Other than the marginal negative impact on China and Sri Lanka, the effect is

positive for other countries’ export performances. The efficiency of Malaysia, Indonesia, and

Pakistan are highly influenced by this factor than the rest of the countries.

-5 0 5 10 15 20

Brunei

China

India

Indonesia

Japan

Korea, Rep.

Malaysia

Pakistan

Philippines

Singapore

Sri Lanka

Thailand

Institution

-5 0 5 10 15

Bangladesh

Brunei

India

Indonesia

Japan

Korea, Rep.

Malaysia

Pakistan

Philippines

Singapore

Sri Lanka

Thailand

Vietnam

Infrastructure

-5 0 5 10 15

Bangladesh

Brunei

China

India

Indonesia

Japan

Malaysia

Pakistan

Sri Lanka

Vietnam

Market efficiency

-10 -5 0 5 10 15

Bangladesh

Brunei

China

India

Indonesia

Japan

Korea, Rep.

Malaysia

Pakistan

Philippines

Sri Lanka

Technology readiness


Other than the Philippines, the technological adoption has positive implications for the export

performances for all countries. The impact is higher for Malaysia, China, and Indonesia than

the others.

One thing to note that, coefficient does not indicate the country’s level of robustness with that

determinants, rather the value shows how sensitive the determinant is for the country’s export

performance.

4.6.2 Trade in Renewable Energy Goods

Table 4.12 presents the results of the estimation of coefficients for the export of REG for each

country and StEA region. Because of the low (close to zero) values of gamma, the model is not

suitable for Bhutan, Lao PDR, and the Maldives. Besides, for a better comparison and

estimation, countries with an average annual REG trade of below 50 million USD have been

excluded from further analysis of this section. Therefore, along with the abovementioned three

countries, Brunei and Nepal have also not been considered in further analysis. Nevertheless, as

these five countries have a very small share (0.15%) in overall regional REG trade, it would

not have any substantial impact in comprehending the trade performances and impact of driving

factors in this StEA region.

a) Estimation of the SF coefficients

Table 4.12 shows the estimation of the SF coefficients of the six explanatory variables of REG

exports. Estimates that are significant at least at the 10% level are discussed below.

• Results imply that all countries except Indonesia have positive intraregional export

elasticities with respect to their respective GDPs. Cambodia seems to have the highest

elasticity of 4.95. Some other large exporting countries such as China, India, and

Singapore have the export elasticity of GDP of 3.46, 0.79, and 1.93 respectively. For

Japan and Korea, the results were not statistically significant.

• Noticeably, all countries are found to have positive demand elasticity of their REG

exports, i.e., increasing demand for energy (proxied by the GDP in importing country)

drives the exports positively for all countries. Cambodia has the highest elasticity of

2.97. China, Japan, Korea, and India, some larger exporters have elasticities of 1.26,

1.15, 1.62, and 0.26 respectively.


• Distance has a negative impact that is more prominent for the smaller exporters like

Cambodia, Bangladesh, and Myanmar. However, this coefficient for China, Korea, and

India is not statistically significant.

• Impact of the tariff also has a negative impact on REG trade for all but Singapore. The

impact is more prominent in India and Thailand than in other countries. The positive

coefficient for Singapore may be the result of Singapore’s special arrangement with the

importing countries, or the nature of the traded goods are such that even with higher

tariff Singapore exports more of REG goods.

• The impact of cross exchange rate ratio on export performances is mostly positive.

However, for most countries, this coefficient is found to be very low. Cambodia and

Indonesia have the highest positive impact, while Vietnam and Sri Lanka have the most

negative impact of this cross-exchange rate ratio on their REG exports.

• Other than Pakistan, and Cambodia, RTAs are found to have a positive impact on

exports from all countries. The impact is higher for the smaller exporting countries. Sri

Lanka, Myanmar, and Bangladesh have experienced a higher impact of RTA than the

others.

• Values of log likelihood function represent the degree of goodness of fit of the models.

According to the values, all country-specific models are well fitted.

Table 4.12: Estimations of coefficients of the SFG model for Renewable Energy Goods trade

Exporting

country lnGDPX lnGDPM lnDistance Tariff

Exchange

ratio RTA Gamma

log

likelihood

function

Total

observ-

ation

Bangladesh 0.303 1.866*** -5.046*** 0.0367 0.011*** 1.219*** 0.99*** -142.94 75

Cambodia 4.952*** 2.968*** -5.936*** -0.009 1.619*** -1.406*** 0.92*** -84.00 41

China 3.455*** 1.255*** -0.054 0.013 0.001*** 0.607*** 0.57*** -212.33 136

India 0.793*** 0.258*** 0.197 -0.099*** -0.002* 0.199 0.99*** -239.37 136

Indonesia -0.209*** 0.734*** -1.528*** -0.030*** 0.230*** 0.654*** 0.99*** -240.95 126

Japan -3.119 1.146*** -1.174*** -0.068*** 0.002* 0.579*** 0.93*** -210.30 134

Korea, Rep. -2.019 1.619*** -0.220 0.024 0.108*** 0.635** 0.98*** -240.58 130

Malaysia 0.548*** 1.064*** -1.007*** -0.019 0.001** 0.967*** 0.90*** -193.53 129

Myanmar -1.557 1.737*** -4.348*** -0.002 -0.001*** 3.075*** 0.99*** -82.24 41

Pakistan 5.525* 0.444** -3.081*** -0.064 -0.002 -1.663** 0.92*** -139.99 69

Philippines 1.843*** 1.204*** -1.541*** -0.079*** -0.001 0.392* 0.99*** -198.41 106

Singapore 1.930*** 0.608*** -0.599*** 0.072*** 0.0001** 0.711*** 0.99*** -205.40 132

Sri Lanka -1.571 0.665 0.260 -0.026 -0.039* 3.616** 0.94*** -167.06 79

Thailand 0.793 0.814*** -0.913*** -0.080*** -0.001 0.758** 0.91*** -238.92 134

Vietnam 3.053*** 0.892*** -1.081** -0.011 -1.695* 0.829** 0.98*** -197.11 107

Source: Authors’ estimations



Table 4.13: Intraregional REG export efficiencies of each country


Exporting Countries

Ban

gla

des

h

Cam

bo

dia

Ch

ina

Ind

ia

Ind

on

esia

Jap

an

Ko

rea,

Rep

.

Mal

aysi

a

My

anm

ar

Pak

ista

n

Ph

ilip

pin

e

Sin

gap

ore

Sri

Lan

ka

Th

aila

nd

Vie

tnam

Imp

ort

ing

co

un

trie

s

Bangladesh 0.75 0.68 0.31 0.68 0.38 0.03 0.23 0.00 0.72 0.05 0.77 0.42

Bhutan 0.38 0.14 0.23 0.01 0.04

Brunei 0.28 0.02 0.00 0.76 0.01 0.16 0.00 0.56 0.54 0.02

Cambodia 0.74 0.21 0.10 0.50 0.65 0.14 0.99 0.21 0.02 0.11 0.06 0.60

China 0.10 0.73 0.68 0.68 0.70 0.65 0.51 0.01 0.25 0.33 0.61 0.15 0.61 0.52

India 0.10 0.64 0.64 0.29 0.49 0.06 0.30 0.53 0.14 0.72 0.74 0.01 0.42 0.66

Indonesia 0.01 0.55 0.58 0.62 0.06 0.06 0.03 0.26 0.01 0.62 0.11 0.22 0.44

Japan 0.63 0.56 0.71 0.33 0.69 0.06 0.39 0.16 0.50 0.55 0.53 0.51 0.59

Korea,

Rep. 0.63 0.42 0.88 0.56 0.64 0.70

0.68 0.86 0.10 0.26 0.47 0.17 0.61 0.76

Lao PDR 0.74 0.19 0.04 0.03 0.34 0.01 0.33 0.34

Malaysia 0.00 0.69 0.58 0.55 0.06 0.72 0.66 0.37 0.46 0.57 0.61 0.08 0.72 0.57

Maldives 0.64 0.30 0.48 0.73 0.17 0.63 0.21 0.64 0.63 0.50 0.59 0.02

Myanmar 0.08 0.66 0.27 0.08 0.47 0.43 0.02 0.48 0.62 0.01

Nepal 0.91 0.62 0.79 0.74 0.96 0.79 0.73 0.29 0.21 0.69 0.51 0.00 0.19 0.08

Pakistan 0.18 0.75 0.42 0.19 0.50 0.44 0.03 0.10 0.05 0.00 0.16 0.73

Philippines 0.02 0.78 0.43 0.12 0.60 0.27 0.76 0.11 0.77 0.00 0.46 0.28

Singapore 0.77 0.62 0.76 0.46 0.64 0.73 0.52 0.54 0.49 0.22 0.80 0.37 0.60 0.75

Sri Lanka 0.00 0.46 0.74 0.04 0.59 0.10 0.14 0.16 0.05 0.26 0.44 0.32

Thailand 0.04 0.50 0.57 0.65 0.29 0.63 0.31 0.70 0.10 0.60 0.23 0.60 0.76 0.31

Vietnam 0.13 0.57 0.71 0.63 0.59 0.67 0.58 0.40 0.46 0.01 0.61 0.51

Aggregated export

efficiency 59.3% 59.1% 74.3% 58.5% 63.1% 69.1% 60.6% 51.5% 39.5% 50.0% 45.1% 61.3% 49.9% 60.4% 63.0%


b) REG Export efficiencies of the countries

Table 4.13 presents the weighted export efficiency in REG exports for each country. China and

Japan, two largest exporters, have been the most efficient in intraregional REG trade among the

selected countries. They enjoy export efficiencies of 74.3%, and 69.1% respectively. The other

three large exporters India, Korea, and Singapore have efficiencies of 58.5%, 60.6%, and 61.3%

respectively. Myanmar has the lowest export efficiency in REG trade with 39.5%. On average, the

whole region has the weighted export efficiency of 65.4%, which implies that given the current

technology and settings, there is still an untapped intraregional REG export potential of 34.6% in

this region.

c) Identification of the determinates of REG export efficiency

Figure 4.10 shows the coefficients with at least 10% of significance level. It indicates that

institution has a positive impact on export performance for most countries. The impact is higher

for India, Indonesia, and China revealed that the institutional factor largely influences REG exports

of these countries. The impact is found negative only for Vietnam, Cambodia, and Sri Lanka. It is

because these countries’ institutional strength concerning REG exports experienced a declining

trend in recent times.

Other than China and Myanmar, the infrastructure has a positive influence on REG export

performances of the countries. The REG export efficiency appears to be heavily influenced by

infrastructure, particularly in India, Vietnam, and Sri Lanka.

Market efficiency is also a key determinant for REG export performances, especially for the

smaller exporting countries. The effect is either low or negative for the large exporting countries

such as China, India, and Korea.

Other than India and Pakistan, the technological adoption has positive implications for the export

performances of other countries. It could be due to the low volume of foreign direct investment

that has been coming into India and Pakistan until very recently.


Figure 4.10: Coefficients of the determinants of the intraregional REG export efficiency

4.7 Policy implications

The empirical analysis shows that most of the countries in StEA region have to rely on imports to

a large extent to meet their respective energy demands. In some cases, the countries do import

from the countries outside this regional bloc. Even, few countries also export their surplus energy

both within as well as outside this regional bloc. Therefore, the matching capacity of each country

along with the region as a whole would provide fruitful insights for the policymakers. From a

regional cooperation perspective, it will also induce the effective cross-border investment decision

on cleaner energy (Singh, 2013).

-40 -20 0 20 40

Bangladesh

Cambodia

China

India

Indonesia

Korea, Rep.

Myanmar

Sri Lanka

Vietnam

Institution

-40 -20 0 20 40

Bangladesh

China

India

Korea, Rep.

Malaysia

Myanmar

Pakistan

Philippines

Sri Lanka

Thailand

Vietnam

Infrastructure

-20 -10 0 10 20 30 40

Bangladesh

China

India

Korea, Rep.

Malaysia

Myanmar

Pakistan

Philippines

Sri Lanka

Thailand

Vietnam

Market efficiency

-10 0 10 20 30

Bangladesh

China

India

Indonesia

Malaysia

Myanmar

Pakistan

Philippines

Vietnam

Technology readiness


REC helps to develop competitive energy market through the integration process. Subsequently,

through prudent regulation, legal capacity, and experience, domestic energy plants attempt to attain

a competitive edge over time that is so critical for the sustainability of the energy sector (UN-

DESA, 2005). Competitiveness amplifies further as the focus widens from single domestic issues

towards a broader regional proposition. Therefore, the power of single-interest or vested groups

are diluted in this process, and market structure becomes better competitive (Kessides et al. 2008).

This phenomenon is particularly important for the low-carbon green energy (LCGE) plants since

the enormous untapped potential of the global demand for LCGE is yet to be explored.

In primary HCE trade, the trade barriers need to ease out over time so that efficiency levels improve

for the countries. The large-scale intraregional trade of fossil-fuels would ensure energy security

and price stability for the nations. All these would lead towards better energy management and

lower energy cost. It would thus persuade for more savings to invest in LCGG technologies.

Removal of the trade barriers for renewable products would provide more choice for the people.

Both consumer and producer surplus would enhance the overall economic welfare in the region.

Institutional framework for technology transfer must be designed so that effective backward and

forward linkages of renewable energy system can be established.

4.8 Summary and Concluding remarks

Regional energy management plays a crucial role in adapting and strengthening the energy security

and the low-carbon growth in the region. Regional cooperation can instigate marked improvement

in that process. This chapter examines the demand-supply analysis under a REC in South-through-

East Asian countries. The entire analysis is divided into three broad sections: Internal demand-

supply (of energy) analysis, external demand-supply analysis, and efficiency (in meeting the

intraregional demands) analysis. The empirical study covers both regional levels as well as

country-level analysis.

Total energy production and usage of energy in the StEA region showed an increasing trend

throughout the 1995-2014 period with an average growth rate of 5.76% per annum in production,

and 6.21% per annum in usage. Self-sufficiency in meeting aggregated demand within this region

has been declining since 2008. China, as expected, leads both in production and energy usage.


India, Indonesia, Malaysia, and Thailand remain among the top-5 producers. In energy usage,

China is followed by India, Japan, Korea, and Indonesia in the top-5 users list.

StEA region experiences faster increasing aggregated imports over the exports during the 1995-

2014 period. The primary concern is the widening of this gap, which implies the growing

dependency of the region on energy imports. Indonesia is the largest intraregional primary energy

(HCE) exporter in the StEA region; followed by Malaysia, Singapore, and Korea. Japan,

Singapore, and China remain the top intraregional importers of primary energy in this region.

Interestingly, the low-scaled exporting and importing countries are found to have higher

intraregional trade exposure for this primary energy trade. For LCE trade, this study uses APEC-

54 Renewable Energy Goods (REG) list. Korea, Japan, and China are the three largest intraregional

REG exporters in the StEA region. China, by far, remains the top intraregional importer of REG,

followed by Korea and Japan.

To understand the dynamics of the trade, a Stochastic Frontier Gravity model is used for both

primary energy and REG. To further investigate the determinants of country-level efficiency, the

inefficiency effect model is also incorporated. Estimation result implies that for most of the

countries, intraregional export of primary energy as well as REG both are positively influenced by

supply-side and demand-side drivers, i.e., GDP of the exporting and importing countries

respectively. Tariff and distance adversely affect both types of export performances, while the

implication of cross exchange ratio seems minimal in both cases, in general. RTA also found to

have a notable positive impact on intraregional trade for both the cases.

This chapter also measures the weighted export efficiency in trade. China has the highest export

efficiency in this intraregional primary energy exports with 73.0%. China is followed by Malaysia,

Thailand, and Singapore. Bangladesh and Myanmar have the lowest export efficiencies of 31.5%

and 33.7% respectively. On average, the whole region has the weighted export efficiency of 58.4%,

which implies that given the current settings, there is still an untapped intraregional export

potential of 41.6% in this region for the primary energy.

In the case of REG trade, China, and Japan, two largest exporters have been the most efficient in

intraregional REG trade with 74.3%, and 69.1% of export efficiency respectively. Myanmar has

the lowest export efficiency in REG trade with 39.5%. On average, the whole region has the


weighted export efficiency of 65.4%, which implies that given the current technology and settings,

there is still an untapped intraregional REG export potential of 34.6% in this region.

The inefficiency effect model for both primary energy and REG trade reveals that institutional

quality, better infrastructure, goods market efficiency, and technological readiness- all the factors

have reasonable impacts to enhance the countries’ intraregional energy trade.

From the RC perspective, the comprehensive analysis of internal and external demand and supply

of energy presented in this chapter would provide significant intuitions to the policymakers of this

region. By measuring countries’ capacity to meet specific energy demands, long-term energy

management regarding energy security, availability, energy price, and most importantly, the

transition towards low-carbon economy would be plausible. Estimating the determinants of trade

performance would help the countries to take necessary measures to improve trade facilitation. On

a broader scale, the regional institutional framework can be established to make the necessary

reforms. The measures of export efficiency can be considered as the yardstick to help the countries

setting an optimal target based on its resources, endowments, and infrastructures. From the

regional perspective, such measures would encourage an effective cooperation framework to scale-

up the trade through plausible trade creation, technology transfer, and capacity building of the

countries. Higher efficiency in energy trade, in turn, ensures the higher welfare for the consumers

as well as the producers in this region. It would also widen up the scope of intensifying the

production and usage of low-carbon energies for the countries which is so crucial in strengthening

green growth in the region.


Chapter 5

Sustainable Low-carbon Energy system through Regional

Cooperation: An efficiency-based approach



It is already mentioned that Regional Energy Cooperation (REC) is so vital for the transition

towards green growth. In the previous chapter, optimization of the intra-regional demand-supply

matching of energy is examined under a proposed REC. Countries’ demands and supplies of

energy are measured, and subsequently, the chapter explained how best the intraregional energy

trade can be used as a tool to match the demand-supply gap while attaining the objective of low-

carbon growth.

For developing the comprehensive framework, the next question comes, which countries should

better manage their respective energy demands, which would foster to further matching the intra-

regional demand-supply gap while transiting towards the LCGG. Owing to the comparative

advantages, some countries may be more efficient to manage their demands in some forms of

energy as compared to the other countries. Though for fossil fuels, the potential reserve would be

the key to future production and demand decision; however, the demand-management efficiency

would remain a vital factor in this regard.

However, to attain the sustainability with the energy system, only focusing on the demand-

management efficiency may not be adequate. The efficiency at the end-use of energy also has the

significant role in conquering the target (Lorek & Spangenberg, 2014). There is, of course, a

considerable debate on whether the enhanced efficiency of energy usage can offset the rebound

and backfire. Nevertheless, there is little dispute that it could be a useful tool to strengthen the

footings for the transition towards the sustainable growth (Chakravarty et al., 2013, Broberg et al.,

2015). The International Energy Agency (IEA) recommends 25 policy agendas for improving

energy efficiency in the scope of buildings, appliances, lighting, transport, and industry. IEA

estimates that if the recommendations were adopted, global energy consumption could be reduced

by 17% (at 2010 business-as-usual) while CO2 emission can be reduced by 7.6 giga tons (Gt) of

CO2 a year by 2030 (OECD, 2017). Ayres et al. (2007) state that enhancement of the energy usage

efficiency would supplement the production efficiency as it does not require radical new

investment in technologies, but merely improved regulations (and deregulations, in few cases) will

help to adopt the strategy.

This Chapter is designed to analyze two abovementioned channels of REC which can instigate the

sustainable low-carbon energy system under a regional cooperation framework in the StEA

countries. First part of this chapter investigates how efficiently countries are managing their energy


demand towards attaining the LCGG objectives. The later part examines the macro and sectoral

level efficiencies of the countries in energy usage.

5.2 Analysis of Energy Demand Management

As mentioned in the previous chapter, demand for energy has been increasing at a much faster rate

than the production of energy in the StEA region. Hence, prudent demand management would

have significant implication in adopting the low-carbon energy system through limiting the usage

of HCE while fostering the LCE in this region. Consequently, demand side drivers would adjust

the potential production outcome of HCE and LCE.

5.2.1 Methodology for efficiency estimation

The implication of energy efficiency in containing energy consumption and emission is a key

policy agenda across the globe. Hence, the concept of aggregate energy demand function is

developed. Various models are applied to estimate the energy demand functions. Filippini and

Hunt (2011) use Stochastic Frontier Analysis to successfully estimate the aggregate energy

demand function for 29 OECD countries. Later Evans et al. (2013) developed the study further

with extended control variables. The model is also applied to estimate the residential sector’s

energy demand efficiency in 48 states of the US from over 1995 to 2007 (Filippini and Hunt, 2012)

and 27 EU member states from 1996 to 2009 (Filippini et al., 2014). The approach helps to

distinguish the impacts of ‘underlying energy efficiency’, explicit demand-driven control variables

such as, income, population, price effects, economic composition, weather, and climate. The SFA

model attempts to estimate the best-practice impacts of each variable and determine the respective

efficiency of the countries (or economic units) in energy usages based on their performance with

respect to the benchmark frontier. For analyzing the role of demand-driven factors in the energy

consumption, and subsequent estimation of technical efficiency, this study also uses the Stochastic

Frontier model for both HCE and LCE consumption. Such model is widely used in determining

the efficiency in energy literature (Hu & Honma, 2014, Shui et al., 2015, Boyd & Lee, 2016, Zhou

et al., 2012). As mentioned earlier, this parametric model suits better when it is essential to estimate

the relative performance of the countries while explaining the relationships between the input

driving factors and the outcomes.

The basic framework of the Stochastic Frontier model is explained in the previous chapter (section

4.5.1).


5.2.2 Optimization of the goals

The transition towards a low-carbon green growth should have a significant inference in setting

the optimization goal for efficiency estimation. For any production function, the obvious choice

should be the maximization of production with the given endowments and resources. In contrast,

demand management under a low-carbon policy framework would suggest the minimization of

HCE and maximization of LCE with the given demand-driving factors.

5.2.3 Model Specification

For High-carbon Energy (HCE) demand function, the following model is used:

Minimizing

𝑙𝑛𝐻𝐶𝐸𝑖,𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝐺𝐷𝑃𝑃𝐶𝑖,𝑡 + 𝛽2𝑙𝑛𝑃𝑂𝑃𝑖,𝑡 + 𝛽3 𝑙𝑛 𝐸𝑛𝑒𝑟𝑔𝑦𝑖,𝑡−1 + 𝛽4 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡

+ 𝛽5 𝑙𝑛𝑂𝐼𝐿𝑃𝑡 + 𝛽6𝑙𝑛𝐺𝐴𝑆𝑃𝑡 + 𝛽7𝑙𝑛𝐶𝑂𝐴𝐿𝑃𝑡 + 𝛽3 𝑙𝑛 𝐶𝑂2𝑖,𝑡−1 + 𝑇𝑖,𝑡 + 𝑈𝑖,𝑡 + 𝑉𝑖,𝑡

For Low-carbon Energy (LCE) demand function, the following model is used:

Maximizing

𝑙𝑛𝐿𝐶𝐸𝑖,𝑡 = 𝛽0 + 𝛽1𝑙𝑛𝐺𝐷𝑃𝑃𝐶𝑖,𝑡 + 𝛽2𝑙𝑛𝑃𝑂𝑃𝑖,𝑡 + 𝛽3 𝑙𝑛 𝐸𝑛𝑒𝑟𝑔𝑦𝑖,𝑡−1 + 𝛽4 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡

+ 𝛽5 𝑙𝑛𝑂𝐼𝐿𝑃𝑡 + 𝛽6𝑙𝑛𝐺𝐴𝑆𝑃𝑡 + 𝛽7𝑙𝑛𝐶𝑂𝐴𝐿𝑃𝑡 + 𝛽3 𝑙𝑛 𝐶𝑂2𝑖,𝑡−1 + 𝑇𝑖,𝑡 − 𝑈𝑖,𝑡 + 𝑉𝑖,𝑡

where, 𝐻𝐶𝐸𝑖,𝑡 : Consumption of HCE in country-i at time-t

𝐿𝐶𝐸𝑖,𝑡 : Consumption of LCE in country-i at time-t

𝐺𝐷𝑃𝑃𝐶𝑖,𝑡 : Per capita real GDP in PPP term of country-i at time-t

𝑃𝑂𝑃𝑖,𝑡 : Population in country-i at time-t

𝐸𝑛𝑒𝑟𝑔𝑦𝑖,𝑡−1 : total energy use (demand for energy) in country-i at time-(t-1)

𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝑖,𝑡 : Share of industry to GDP in country-i at time-t

𝑂𝐼𝐿𝑃𝑡 : Oil price at time-t (global market price)

𝐺𝐴𝑆𝑃𝑡 : Gas price at time-t (global market price)

𝐶𝑂𝐴𝐿𝑃𝑡 : Coal price at time-t (global market price)

𝐶𝐶𝑂2𝑖,𝑡−1 : CO2 concentration per area in country-i at time-(t-1)

𝑇𝑖,𝑡 : Time variable for country-i at time-t


𝑈𝑖,𝑡 : Single-sided error term for the combined effects of inefficiency, on which complete

information is not available

𝑉𝑖,𝑡: Normal statistical error term which captures the effect of inadvertently omitted

variables.

5.2.4 Hypothesis behind choosing the independent variables

▪ Higher per capita lead to higher living standard and higher demand for energy. According

to the Environmental Kuznets’s Curve hypothesis, when the economies are at lower per

capita income, the environmental degradation and emission would have an increasing

trend; but as the income level increases, countries would prefer more pro-economic

activities to lower the environmental degradation and emission (Kuznets, 1955).

▪ More the population, more would be the demand for both HCE and LCE (Salim & Shafiei,

2014).

▪ More energy use in a previous year would indicate that people are inclined to more energy-

intensive life-style. Hence, a higher demand can be expected in the current year.

▪ Higher the industry share in GDP, higher would be the industrialization which

subsequently induces the demand for more energy (Salim et al., 2014).

▪ Prices of oil, gas, and coal in global market may have varied impact on their demand. It

would depend on the price elasticity of demand, especially in the energy-import countries.

▪ As explained by Environmental Kuznet’s Curve (EKC) phenomenon, higher per capita

GDP would create the demand for pro-environmental energy. Besides, higher per capita

GDP would enable a country to invest more in low-carbon energy technological adoption

and consumption (He, 2016).

▪ CO2 concentration should have a reasonable impact on the countries’ environmental

policy, i.e., their approaches towards the LCGG. In an ideal scenario, higher CO2

concentration should induce more awareness about the emission, and subsequent policy-

reforms would instigate the demand for LCE (Salim & Rafiq, 2012).

▪ People may have a changing life-style which would shape their varying energy demand

over time. Hence, the demand for energy can be considered as a function of time.


5.2.5 Data sources

Energy usage data is not available for Bhutan, Lao PDR, and the Maldives. Hence, these countries

have not been considered and a panel dataset consists of the rest 17 countries over the 1991-2014

period is used.

▪ Data on energy consumption is extracted from the IEA’s country-wise Energy Balance

dataset.

▪ Data on Per capita GDP, population, CO2 emission, and land area are extracted from World

Development Indicators of the World Bank.

▪ Data on industry share of GDP is collected from ADB Key Indicators dataset

▪ Price data for oil, gas, and coal is collected from British Petroleum dataset.

Software FRONTIER 4.1 is used to perform the estimation with maximum likelihood method as

introduced by Coelli (1996). Cost function model is used for minimization of HCE consumption

while production function model is used for maximization of LCE consumption.

5.2.6 Results and Findings

5.2.6.1 Estimation of coefficients for the Demand for energy models

Estimation of the coefficients for the HCE and LCE-demand model is presented in Table 5.1.

Results imply that per capita income has a negative effect on HCE demand and positive effect on

LCE demand. It indicates that higher the income, countries are preferring to shift away from the

HCE consumption towards the LCE consumption. On average, 1% increase in per capita income

would result in 0.18% decrease in HCE consumption and 0.38% increase in LCE consumption,

ceteris paribus. Population growth and energy used in the previous year have positive effects on

both the demand for HCE and LCE. Coefficients of industry share are not found significant for

any of these demand models. The price of oil has negative demand elasticity for HCE, i.e., higher

the price of oil, lower would be the consumption of HCE, ceteris paribus. The higher price of gas,

conversely, is found to have a positive drive for HCE demand. The coal price, however, is found

to have no significant impact of any of these demand models. Higher CO2 concentration of

previous year also seems to have a positive impact for both HCE and LCE demand models. It is

expected for LCE demand to increase due to the higher CO2 concentration. However, positive

effect on HCE demand indicates that some of the countries still heavily relying on fossil-fuel based


energy system in this region. On average, 1% increase in CO2 concentration would result in

0.061% increase in demand for LCE, ceteris paribus. Values of log likelihood function represent

the degree of goodness of fit of the models. According to the values, both HCE as well as LCE

models are well fitted.

Table 5.1: SFP model estimation for HCE production

Explanatory variables and

model indicators

HCE

consumption

LCE

consumption

Differential

impact towards

LCE (elasticity)

ln(GDPPC) -0.182*** 0.380*** 0.562

ln(Population) 0.044* 0.395*** 0.351

ln(Energy_t-1) 0.864*** 0.685*** -0.179

Industry share 0.0000018 0.000009 n/a

ln(Oil price) -0.051* 0.054 n/a

ln(Gas price) 0.054*** -0.022 n/a

ln(Coal price) 0.029 -0.055 n/a

ln (CO2 concentration_t-1) 0.128*** 0.061** -0.067

time 0.038*** 0.061*** 0.023

Gamma 0.752*** 0.941*** -

eta 0.041*** 0.033*** -

log likelihood function 210.6614 380.8524 -

LR test of the one-sided error 371.1533 396.2023 -

No. of observation: 408

n/a: not appropriate for comparison (due to some statistically non-significant estimations)

Source: Author’s estimations

Differential impact indicates that on average, 1% increase in per capita income would induce

0.56% higher demand for LCE as compared to the demand for HCE. It is an encouraging

indication, especially for the emerging economies. 1% increase in the population of the StEA

region would have 0.35% higher demand for LCE as compared to the HCE. It divulges that

countries are focusing more on the consumption of LCEs to meet the growing demand from

increasing population. Differential impact for 1% increase in previous year’s energy consumption

is 0.18% in favor of the HCE demand. Improvement in energy productivity and efficiency would

be pivotal to reduce the energy consumption level which could subsequently result this differential

impact in favor of the LCE, i.e., 1% decrease in previous year’s energy consumption would result

in 0.18% more demand for LCE than the HCE. Differential impact for previous year’s CO2


concentration is also in favor of HCE-demand by 0.07%. Thus, lowering the CO2 concentration

would further instigate the LCE demand to have a higher differential impact on its favor. 1%

decline in previous year’s CO2 concentration would have 0.07% higher demand for LCE as

compared to the HCE. Time trend shows that the growth rate of HCE demand is 3.8% while it is

6.1% for the LCE demand.

Higher values of gamma indicate that both the models are robust and fit well with the explanatory

variables. Highly significant value of eta (η) refers that the efficiency estimation is time-variant.

5.2.6.2 Efficiency measures in Energy Demand Management

Table 5.2 presents the technical efficiency of containing the impacts of the demand-driven factors

for HCE in the StEA countries. It also shows the efficiency of the countries in enhancing the

impacts of the demand-driven factors for LCE.

Table 5.2: Technical efficiency of the countries in managing demand for HCE and LCE

Efficiency estimation

Containment of HCE-demand Increase in LCE-demand


Brunei 98.5% 77.3%

Cambodia 42.2% 35.8%

China 39.2% 96.2%

India 44.5% 79.1%


Japan 35.3% 74.8%

Korea, Rep. 41.4% 92.5%


Myanmar 37.0% 52.4%

Nepal 34.6% 34.2%

Pakistan 40.6% 85.3%



Sri Lanka 38.2% 62.9%


Vietnam 43.2% 88.1%

StEA (average) 47.5% 87.9%


Interestingly, the larger HCE consumers such as China, India, Japan, Korea, and Indonesia all have

lower technical efficiencies to contain the demand-driven factors for HCE. On the contrary, small-


scaled HCE consumers such as Brunei and Singapore have high technical efficiencies for the

containment of demand-driven factors for HCE. Notably, these large-scale HCE consuming

economies are either at emerging state or advanced development state. These are also the larger

industrial and manufacturing countries in this region. Since these countries are more settled with

their existing energy composition and infrastructures (which are also driving their growth), it is,

thus, relatively more challenging for them to contain the demand-side forces of HCE as compared

to the small-scaled consumers. Results show that Brunei has the highest efficiency of 98.5% in

HCE-demand containment, followed by Singapore with 92.6%. Brunei is primarily crude oil and

gas exporting nation which manufacturing sector contributes around 11% in its GDP (ADB, 2017).

The country highly relies on the import of food, manufacturing, and other goods. Singapore is also

a service-based economy. 74% of its GDP comes from the service sector (ADB, 2017). Hence, it

is relatively easier for Brunei and Singapore to contain the HCE consumption. On average, the

StEA region has the average weighted efficiency of 47.5%, indicating that it can still improve the

untapped technical efficiency of containing HCE-demand by 52.5% by adopting the best-practices

within this region.

The efficiency of the demand-driven factors for LCE shows better performance from the countries.

Malaysia, China, Thailand, and Korea have the highest efficiency levels at 96.7%, 96.2%, 95.7

and 92.5% respectively. Other than Indonesia, all top LCE consumers have the higher level of

efficiency in boosting the LCE-demand factors further. On average, the whole region has the

efficiency of 87.9% implying that there is still some room to increase the LCE-demand by 12.1%,

given the existing demand-side settings.

5.2.7 Policy implications

From the results estimated through the optimization process, following strategic decision can be

considered:

i. Larger energy-consuming countries should take necessary actions to improve their

respective efficiency levels of energy demand management.

ii. High efficient countries should enhance their demand composition scale as much as

possible towards the LCE.


iii. Low efficient and smaller consuming countries should focus more on improving technical

efficiency, else shift towards the production of other types of energy with higher potential.

The following section discusses the approaches to measure the productive end-use efficiency of

energy.

5.3 End-use efficiency of energy assessment

Energy usage efficiency requires in-depth critical analysis especially when it is measured from the

low-carbon green growth perspective. Energy use efficiency can be estimated at different phases:

starting from the loss of input energy during the primary energy production process, it

encompasses how the usable energy can lead towards higher output while containing emission

level at the minimum. To diagnosis further, sectoral efficiency measures can also be incorporated.

This chapter takes the comprehensive analysis techniques on the end-use efficiency of energy for

the StEA countries. The first methodology determines the macro level efficiency by using a

suggestive index while the second methodology digs out the sectoral energy usage efficiencies in

details.

5.3.1 Macro analysis

From the primary supply to the end-use of energy, efficiency can be measured in two phases. For

the first step, it is inferred that some energy losses occur during the conversion process to make

the primary energy usable for the end-users. The efficiency of this stage depicts how well the

conversion process is carried out to extract maximum useable energy from the Total Primary

Energy Supply (TPES).

In the second phase, the dilemma of low-carbon green growth is considered. In one hand, energy

usage induces economic growth; while on the other hand, it results in emission. Hence, we consider

both the energy intensity (i.e., GDP per unit of energy) and carbonization of energy- which

assumes to be mainly derived from these end-usages of the energy. Rather than considering CO2

emission only, carbonization considers the impact of aggregated GHG emission (but excluding the

Land-Use Change and Forestry) resulted from the economic activities which are primarily driven

by the use of different kinds of energy.


5.3.1.1 Methodology

By considering the facts mentioned above, this study formulates Energy use efficiency of country-

i at time t with following components:

i. 𝑈𝑠𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦𝑖,𝑡

𝑇𝑃𝐸𝑆𝑖,𝑡 : Conversion efficiency factor

ii.

(𝐺𝐷𝑃

𝑢𝑠𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦)𝑖,𝑡

(𝐺𝐷𝑃

𝑢𝑠𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦)𝐵,𝑡

: Growth efficiency factor; and

iii. (

𝐺𝐻𝐺


(𝐺𝐻𝐺


: Direct Carbonization efficiency factor.

Here the subscript B refers to the benchmark country, i.e., the country with highest GDP-to-usable

energy ratio and country with lowest GHG-to-energy ratio among the sample countries.

In formulating the overall energy use efficiency, conversion loss of energy is assumed to be a

common phenomenon attached to both goals of higher output and lower emission through using

of energy. From an LCGG viewpoint, how much weight to put on the growth factor and the

carbonization efficiency factor would depend on the policy decision? For simplicity, we assume

equal weights to both the factors in the formula. Hence, the overall energy use efficiency of

country-i at time-t is formulated as:

𝐸𝑛𝑒𝑟𝑔𝑦 𝑢𝑠𝑒 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖,𝑡

=𝑈𝑠𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦𝑖,𝑡

𝑇𝑃𝐸𝑆𝑖,𝑡×

[ 1

2×

(𝐺𝐷𝑃


(𝐺𝐷𝑃


+ 1

2×

(𝐺𝐻𝐺


(𝐺𝐻𝐺

𝑢𝑠𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦)𝑖,𝑡 ]

Since we use several years data, normalization technique is used so that efficiencies of the

countries can be compared both vertically (i.e., comparison among the countries for a particular

year) as well as horizontally (i.e., countries’ own efficiency over different time). For such

calculation, the highest GDP-to-usable energy ratio among all the countries over all the period is

taken as the benchmark for growth efficiency factor while the lowest GHG-to-usable energy is

used for benchmarking the direct carbonization efficiency factor.


5.3.1.2 Data sources

GHG emission data is extracted from CAIT-WRI data set for the 1995-2013 period. GDP (in

constant 2010 USD), and energy data are extracted from the IEA data set. Usable energy is

calculated by deducting conversation loss from the energy supply, i.e., TPES7.

5.3.1.3 Results and findings

Table 5.3 shows the conversion efficiency of the countries in 1995, 2000, 2005, 2010, and 2013.

Over time, most of the countries seem to adopt better technologies to reduce conversion loss of

energy. As a result, all the countries’ efficiency levels in this factor reach within 86.2% to 99.7%

range. Hence, the differences among the countries found very marginal in conversion efficiency

factor. In 2013, Brunei had the least conversion efficiency of 86.2%.

Table 5.4 shows the growth efficiency of energy usage. Other than Brunei, Malaysia, and Thailand,

all the countries have improved their growth efficiency over the 1995-2013 period. Japan leads the

list with average growth efficiency of 86.3%, followed by Singapore and Brunei with 64.4%, and

42.1% respectively. Sri Lanka and the Philippines are the other two in top-5 efficient countries.

Nepal, Cambodia, China, Pakistan, and Vietnam constitute the bottom-5 efficient countries. On

average, the StEA region’s growth efficiency has also increased from 28.8% to 29.1% over the

1995-2013 period. Such lower aggregated growth efficiency indicates that most of the countries

lag far behind from the pioneer country (i.e., Japan) by a considerable margin in this energy

intensity factor.

Table 5.5 refers to the direct carbonization efficiency of the countries over the 1995-2013 period.

Direct carbonization efficiency in all the countries except Japan, Myanmar, Pakistan, and the

Philippines have improved during this time. Singapore, Japan, and Korea are Estimation of mean

efficiency imply that Singapore, Japan, Korea, Thailand, and Nepal are amongst the top-5 efficient

countries to manage their carbonization efficiency. Singapore tops with direct carbonization

efficiency of 93.2%. Conversely, Brunei, Myanmar, Bangladesh, Cambodia, and Vietnam are

bottom-5 in that list with efficiencies ranging 27-45%. Two significant economies: China, and

7 The accounting follows: Production + Import – Export + Stock change = TPES;

TPES – supply to plants (electricity/ gas/ oil refinery/ etc. ) – Losses = Final consumption at Industry/ residential/

agriculture/ Transport and others


India have the efficiencies of 49.5%, and 50.8% respectively. In aggregate, the whole StEA region

has the mean carbonization efficiency level of 54.2%.

Overall energy use efficiency, as illustrated in Table 5.6, shows that all of the countries have

improved their overall energy use efficiency over the 1995-2013 period. Japan was the leader at

an earlier stage which has taken over by Singapore in the recent time. In 2013, Singapore led in

overall efficiency measure with 89.8%, followed by Japan, and Korea with 82.1%, and 56.2%

respectively. On the contrary, Brunei, Cambodia, and Myanmar have the least overall efficiency

with 30.3%, 30.7%, and 30.8% respectively. In aggregate, the StEA region has the overall energy

use efficiency of 40.9%.

5.3.1.4 Performance of the countries

Figure 5.1 illustrates the performances of the countries regarding their energy use efficiency over

the time. According to the mean energy-use efficiency, top-5 efficient countries are Japan,

Singapore, Korea, Sri Lanka, and Thailand. Bottom-5 countries include Cambodia, Myanmar,

Brunei, Vietnam, and Bangladesh. Encouragingly, all the countries have improved their efficiency

level. Improvement rate is higher for Singapore, Sri Lanka, and Cambodia while the rate is lowest

for Pakistan, Vietnam, and Malaysia.


Figure 5.1: Performance of countries in energy use efficiency (1995-2013)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Mean Growth


Table 5.3: Conversion efficiency of all StEA countries (1995-2013)

Year BGD BRN KHM CHN IND INA JPN KOR MAL MMR NPL PAK PHL SGP LKA THA VNM Grand

1995 97.5 78.2 99.8 99.2 98.2 99.0 99.2 99.5 98.7 98.9 99.7 96.8 98.5 99.6 98.8 98.9 98.8 98.9

2000 97.8 78.8 99.9 99.0 97.0 98.2 99.2 99.4 98.2 98.9 99.6 96.3 98.6 99.5 98.4 98.9 98.9 98.6

2005 98.0 83.1 99.7 99.0 97.0 98.7 99.2 99.4 97.6 98.6 99.3 96.9 98.5 99.5 98.5 98.9 98.7 98.7

2010 97.3 98.1 99.5 99.0 97.6 97.6 99.2 99.4 97.1 99.2 99.1 96.7 98.3 99.6 98.7 99.1 98.6 98.7

2013 97.9 92.8 99.3 99.0 97.6 97.8 99.1 99.4 97.6 98.6 99.1 96.4 98.5 99.6 99.0 99.2 98.4 98.7

Mean 97.7 86.2 99.7 99.0 97.5 98.3 99.2 99.4 97.9 98.9 99.4 96.6 98.5 99.6 98.7 99.0 98.7 98.7


Table 5.4: Growth efficiency of all StEA countries (1995-2013)


1995 27.2 45.9 10.3 11.3 13.5 27.0 79.6 30.1 30.1 8.5 10.3 15.4 25. 43.5 36.1 27.4 16.1 28.8

2000 30.0 45.9 12.2 15.8 15.5 23.7 79.1 30.3 27.0 11.7 10.8 15.2 25.4 57.8 33.4 24.3 17.2 30.1

2005 30.7 51.8 19.0 15.7 18.3 25.7 83.8 34.2 25.5 18.6 11.3 16.5 32.7 63.5 37.5 23.1 16.7 28.4

2010 31.0 31.1 17.0 18.6 20.2 29.1 88.8 35.2 28.6 28.5 12.6 17.2 40.1 74.6 47.1 23.3 15.9 28.5

2013 33.3 35.9 18.7 20.6 21.6 33.7 100.0 36.4 27.6 29.7 12.9 18.4 42.7 82.5 55.8 22.5 18.0 29.8

Mean 30.4 42.1 15.4 16.4 17.8 27.8 86.3 33.2 27.8 19.4 11.6 16.6 33.3 64.4 42.0 24.1 16.8 29.1


Table 5.5: Direct carbonization efficiency of all StEA countries (1995-2013)


1995 32.0 25.2 31.4 48.6 50.6 53.4 77.2 66.4 48.4 35.1 55.8 54.1 55.7 90.1 46.3 53.6 45.3 54.3

2000 33.2 24.0 37.0 49.5 50.2 56.0 79.2 72.3 51.7 34.0 57.7 55.1 57.7 78.8 52.6 58.3 42.9 55.2

2005 36.2 22.2 31.3 48.9 50.2 57.5 78.0 75.3 52.9 33.6 62.2 55.4 52.3 99.5 49.8 63.3 44.1 53.9

2010 38.4 34.4 39.7 50.5 51.5 58.8 79.6 76.0 50.4 29.7 60.8 52.8 50.6 100.0 52.6 67.3 46.3 54.3

2013 40.1 29.3 43.1 50.0 51.4 56.4 65.7 76.8 55.8 32.7 61. 51.7 50.8 97.9 51. 71.9 46.7 53.0

Mean 36.0 27.0 36.5 49.5 50.8 56.4 76.0 73.4 51.8 33.0 59.5 53.8 53.4 93.2 50.5 62.9 45.0 54.2



Table 5.6: Overall Energy use efficiency of all StEA countries (1995-2013)



1995 28.9 27.8 20.8 29.7 31.5 39.8 77.8 48.0 38.7 21.5 33.0 33.7 40.0 66.5 40.7 40.0 30.3 41.1

2000 30.9 27.5 24.6 32.3 31.9 39.1 78.5 51.0 38.6 22.6 34.1 33.9 41.0 67.9 42.3 40.8 29.7 42.1

2005 32.8 30.8 25.0 32.0 33.2 41.0 80.2 54.4 38.3 25.7 36.5 34.8 41.9 81.1 43.0 42.8 30.0 40.6

2010 33.8 32.1 28.2 34.2 35.0 42.9 83.5 55.2 38.3 28.9 36.4 33.9 44.6 86.9 49.2 44.9 30.7 40.9

2013 35.9 30.3 30.7 34.9 35.6 44.0 82.1 56.2 40.7 30.8 36.6 33.7 46.1 89.8 52.9 46.8 31.8 40.9


5.3.2 Sector wise use efficiency

For a comprehensive analysis of energy use efficiency, it is essential to investigate the efficiency

in sectoral level. This study examines the underlying factors that can explain the energy usage

efficiency in for major energy-consumed sectors: industry, transportation, electricity, and

agriculture. Figure 5.2 illustrates that these four sectors jointly use 85% of global energy and result

in 80% of global emission.

Source: IEA (2017) and World Resources Institute (2017)

Figure 5.2: Sectoral shares in global energy usage and emission, 2013

5.3.2.1 Methodology

Usually, in any efficiency model, data on the input resources and output are required (Meng et al.

2016). While choosing the method to determine the technical efficiency in sectoral level energy

usage, a significant challenge is the lack of standard data of input endowments used at the sectoral

level. For instance, labor and capital data for each sector of an economy are not found in a standard

global-scale data set. In such a constraint scenario, rather than using the parametric models, the

non-parametric model like the Data Envelopment Analysis (DEA) is extensively used in country

and regional level (Wang et al., 2016; Oh, 2010; Zhang et al., 2013).

DEA designs the linear programming problems for multiple outputs and multiple inputs model to

measure the relative efficiencies among the peer decision-making units (DMUs). Some outputs are

desirable (we want to maximize) while few are undesirable (we want to minimize). If both

desirable, as well as undesirable outputs, are incorporated in a model, which is the case of this

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Industry Electricity Transport Agriculture Others

Energy use Emission


study, it becomes more complicated (Li & Lin, 2015). Conventional DEA in the absence of any

undesirable output, both the inputs and the outputs are assumed to be strongly disposable, i.e., a

DMU, with its current input level, can produce any output level less than the current level (Zhou

et al., 2008a, 2008b). The inclusion of undesirable output does not support this assumption. As a

solution, three approaches are adopted in literature:

i. Treating undesirable output as another input because of their property “the less, the better”

(Ramanathan, 2005; Seiford & Zhu, 2002).

ii. Assuming the weak-disposable property proposed by Färe et al. (1989) which affirms that

a proportional reduction in desirable and undesirable outputs is plausible, but solely the

reduction of undesirable output is not feasible (Wang & Wei, 2014).

iii. Data transformation for undesirable outputs and applying on traditional DEA model

(Seiford & Zhu, 2002).

However, the choice of emission as an input to the production process is often found to contradict

the causal rationality as emission is a by-product of energy use rather than the input (Li & Lin,

2015). Therefore, it is assumed to provide a distorted evaluation of the fact (Wang et al., 2013).

The implication of weak-disposability for undesirable output is also challenged since the recent

phenomenon depicts that emission can be reduced without reducing the GDP at least at the sectoral

level of an economy (Pearce, 2016). Data transformation technique is also frequently used mostly

in the form of Slacks-based measures (SBM) of efficiency where the undesirable output is jointly

used with the desired output, and all the slacks are maximized (Zhang & Choi, 2013, Guo et al.,

2017). Scheel (2001) prefer to use the absolute value of negative (undesirable) output as input and

the absolute value of negative input as output. Portela et al. (2004) suggested to Range Directional

Measure (RDM) to quantify the negative output and desired output within a range that yield

efficiency between 0 and 1. However, such transformations are widely influenced by the user-

specified weights and biased towards the individual preferences (Guo et al., 2017).

In this chapter, we will use a different technique to incorporate undesired sectoral GHG emission

with the desired sectoral GDP as outputs resulted from the sector-wise energy use as input. We

assume that the emission never comes at zero level. In whatever case, there must be a minimum

amount of emission released from sectoral energy use. Hence, we take reciprocal of emission as

the transformed (desired) output. Lower the emission; higher will be the reciprocal value that


would aid the aggregated outcome performance with GDP. One advantage of taking reciprocal is

that we do not need to deal with any negative values.

We use the DEA Malmquist-index model for Panel data8. It computes the TFP Malmquist index

to measure the changes in productivity, which is further decomposed into technological change

and efficiency change factors to explain the dynamics. The Malmquist-index model is already

explained in Chapter 3 (section 3.3, part (c)).

5.3.2.2 Model Specification

Model 1 is designed to estimate the sectoral output maximization resulted from the use of energy

in that sector. It is, therefore, a one-output, one-input model, where sectoral GDP is considered as

the output and energy usage in that sector is treated as input.

Model 2 is framed to explore countries’ performances in containing the emission resulted from

energy usage for each sector. It is also a one-output, one-input model, where total GHG emission

from a sector is considered as the output and energy usage in that sector is treated as input. The

model takes the cost-function approach, i.e., minimization of input for the given level of output.

Model 3 tries to capture the combined effects of GDP (desirable) and emission (undesirable)

outcomes resulted from the energy usage for each sector. As discussed in the previous section, this

model uses two-output, one-input approach where sectoral GDP and reciprocal of emission is

considered as two outputs and the energy usage as the input. The model takes the production-

function approach, i.e., maximization of outputs for the given level of input.

5.3.2.3 Data sources

Data on Sectoral GDP is extracted from ADB’s Key Indicators for Asia and the Pacific Reports

over the 1995-2013 period. GHG emission data in each sector is collected from CAIT Climate

Data Explorer. Sectoral energy use data are mined from IEA data set.

8 Malmquist productivity index is the same as the Hicks-Moorsteen index, if the technology exhibits global constant returns to scale and inverse

homotheticity, which is assumed in this study. The Hicks-Moorsteen index is also intransitive like the Malmquist index. From the axiomatic index

literature, we know that transitivity is impossible to combine with the other most desirable properties of an ideal index, such as the Malmquist index. Hence, the Malmquist index that has all the desirable properties is used in this study, while acknowledging its intransitivity characteristic.

(Ref: Drechsler, L. (1973): “Weighting of index numbers in multilateral international comparisons”, Review of Income and Wealth 19, 17-34).


5.3.2.4 Results and findings

Agriculture sector

Figure 5.3 illustrates the shares of LCE in countries’ total energy consumption in the agriculture

sector. Pakistan, Philippines, and Malaysia have the highest shares of LCE mainly due to the

dominance of electricity use in agriculture. Conversely, Myanmar, Sri Lanka, and Thailand

possess the least proportion of LCE in agriculture. No data of energy use in the agriculture sector

was available for Brunei, Cambodia, and Singapore.

Source: Author’s calculation and using MS-Excel mapping

Figure 5.3: Share of low-carbon energy in total energy use in Agriculture sector

Table 5.7 presents the estimation results of DEA analysis, i.e., how efficiently energy is used for

agriculture in different countries to attain green growth objectives. The results of the combined

effect (i.e., higher output and lower emission) reveals that Japan and Korea attained highest total

factor productivity improvement (for energy use in the agriculture sector) of 59% and 56%

respectively during 1995-2013. On the contrary, Vietnam and China’s TFP have degraded most

by 25% and 16% during this time. Though all of the countries made significant advancement in

technological changes, efficiency levels have reduced in all countries but Japan and Korea. China,

India, and Japan all gained highest of 54% technological progress during this period. Efficiencies

of China, Pakistan, and Vietnam have dropped the most. On average, the region’s energy use

efficiency in agriculture is declined by 19% while technology is advanced by 42% during 1995-

2013.


Table 5.7: TFP change, efficiency change, and Technological changes for agricultural sector’s energy use

Country Model 1 (GDP maximize) Model 2 (Emission minimize) Model 3 (Combined effect)

effch techch sech tfpch effch techch sech tfpch effch techch sech tfpch

Bangladesh 0.69 1.24 0.61 0.86 0.78 1.68 0.95 1.31 0.88 1.54 0.88 1.35

China 1.38 1.24 1.23 1.72 0.71 1.68 0.99 1.18 0.55 1.54 0.55 0.84

India 0.88 1.24 1.01 1.09 0.89 1.68 1.03 1.49 0.79 1.54 0.79 1.21

Indonesia 1.00 1.24 0.75 1.23 0.99 1.68 1.11 1.66 0.70 1.54 0.70 1.07

Japan 0.72 1.24 0.72 0.89 1.24 1.68 1.11 2.08 1.04 1.54 1.04 1.59

Korea 0.84 1.24 0.88 1.04 0.72 1.68 0.83 1.20 1.01 1.54 1.01 1.56

Malaysia 0.95 1.24 0.95 1.17 0.53 1.68 0.85 0.88 0.68 1.51 0.68 1.02

Myanmar 1.15 1.24 0.86 1.43 0.58 1.68 0.88 0.97 0.72 1.48 0.72 1.06

Nepal 0.66 1.24 0.80 0.81 0.60 1.68 0.60 1.00 0.97 1.44 0.97 1.40

Pakistan 1.24 1.24 0.79 1.53 0.59 1.68 0.66 0.99 0.65 1.38 0.65 0.90

Philippines 0.62 1.24 0.77 0.76 0.81 1.68 0.83 1.37 0.98 1.31 0.98 1.29

Sri Lanka 1.37 1.24 1.01 1.69 0.75 1.68 0.81 1.26 0.74 1.21 0.74 0.90

Thailand 0.68 1.24 0.71 0.84 0.79 1.68 0.82 1.33 0.92 1.16 0.92 1.07

Vietnam 1.04 1.24 0.69 1.29 0.84 1.68 0.81 1.42 0.67 1.13 0.67 0.75

Mean (StEA) 0.94 1.24 0.84 1.16 0.77 1.68 0.88 1.29 0.81 1.42 0.81 1.14


Industry Sector

Figure 5.4 demonstrates the shares of LCE in countries’ total energy consumption in the industry

sector. Korea, Malaysia, and Bangladesh have the highest shares of LCE due to their higher

dominance of electricity over the fossil fuels in industry. Contrary wise, Cambodia, Brunei, and

Pakistan possess the least proportion of LCE in industry.

Table 5.8 reveals that Japan and Nepal attained highest TFP gain (for energy use in the industry

sector to attain the combined green growth objectives) of 42% and 21% respectively during 1995-

2013. On the contrary, Sri Lanka and Myanmar’s TFP have degraded most by 33% and 32% during

this time. Like agriculture sector, all of the countries made significant advancement in

technological changes in the industry sector. However, efficiency levels have reduced in all

countries except Japan and Nepal. Efficiencies of China, Myanmar, and Sri Lanka have dropped

the most. On average, the StEA region’s energy use efficiency level in industry sector is decreased

by 24% while technology is advanced by 26% during 1995-2013.



Figure 5.4: Share of low-carbon energy in total energy use in the Industry sector

Table 5.8: TFP change, efficiency change, and Technological changes for industrial sector’s energy use



Bangladesh 1.10 0.84 1.06 0.92 0.96 0.98 1.01 0.94 0.82 1.39 0.82 1.14

Brunei 1.28 0.84 1.28 1.08 1.08 0.98 1.08 1.05 0.88 1.39 0.88 1.21

Cambodia 1.55 0.84 0.99 1.30 1.45 0.98 1.52 1.42 0.69 1.39 0.69 0.96

China 1.90 0.84 1.18 1.60 1.00 0.98 1.00 0.98 0.51 1.40 0.51 0.71

India 1.33 0.84 1.08 1.12 1.09 0.98 1.00 1.07 0.75 1.41 0.75 1.05

Indonesia 1.56 0.84 1.23 1.31 1.03 0.98 1.00 1.00 0.73 1.40 0.73 1.02

Japan 1.24 0.84 1.24 1.04 1.04 0.98 1.00 1.02 1.02 1.39 1.02 1.42

Korea 1.35 0.84 1.23 1.14 0.88 0.98 1.00 0.86 0.79 1.36 0.79 1.07

Malaysia 1.44 0.84 1.20 1.22 0.97 0.98 1.00 0.95 0.72 1.31 0.72 0.94

Myanmar 1.71 0.84 0.97 1.44 1.15 0.98 1.02 1.12 0.54 1.26 0.54 0.68

Nepal 0.88 0.84 1.07 0.74 1.08 0.98 1.05 1.06 1.00 1.21 1.00 1.21

Pakistan 1.53 0.84 1.21 1.29 1.01 0.98 1.00 0.99 0.68 1.16 0.68 0.79

Philippines 1.18 0.84 1.18 0.99 0.97 0.98 1.00 0.95 0.93 1.12 0.93 1.04

Singapore 0.90 0.84 0.97 0.76 1.37 0.98 1.05 1.33 0.77 1.08 0.77 0.84

Sri Lanka 1.40 0.84 0.97 1.18 0.92 0.98 1.04 0.90 0.63 1.06 0.63 0.67

Thailand 1.16 0.84 1.22 0.98 1.00 0.98 1.00 0.97 0.87 1.05 0.87 0.91

Vietnam 1.41 0.84 1.18 1.19 1.09 0.98 1.00 1.07 0.61 1.04 0.61 0.64

Mean (StEA) 1.35 0.84 1.13 1.14 1.06 0.98 1.05 1.04 0.76 1.26 0.76 0.96



Electricity Sector

Figure 5.5 illustrates the shares of LCE in countries’ total energy consumption in the electricity

sector. Nepal, Myanmar, and Sri Lanka have the highest shares of LCE primarily due to their

dependencies on hydro power. On the contrary, Brunei, Bangladesh, and Malaysia possess the

least share of LCE in their electricity sector.


Figure 5.5: Share of low-carbon energy in total energy use in the Electricity sector

The estimation results presented in Table 5.9. The result of combined effect model reveals that

Bangladesh and Japan attained highest total factor productivity improvement (for energy use in

the electricity sector to attain green growth objectives) of 23% and 19% respectively during 1995-

2013. Though all of the countries made consistent improvement in energy usage efficiency in the

electricity sector, their technological changes level dropped almost for all. Japan and the

Philippines advanced the highest at 53%, and 24% efficiency during this period. On average, the

StEA region’s energy use efficiency in electricity sector is improved by 4% while the technology

is dropped by 19% during 1995-2013.


Table 5.9: TFP change, efficiency change, and Technological changes for electricity sector’s energy use



Bangladesh 0.83 0.92 0.99 0.76 0.94 0.99 0.97 0.93 1.17 1.05 1.17 1.23

Brunei 1.29 0.92 0.98 1.19 1.00 0.99 1.00 0.99 1.08 1.02 1.08 1.10

Cambodia 0.97 0.92 0.97 0.89 0.91 0.99 0.91 0.90 1.00 0.90 1.00 0.90

China 1.44 0.92 0.89 1.32 0.98 0.99 0.98 0.97 0.72 0.82 0.72 0.59

India 1.13 0.92 0.94 1.03 1.00 0.99 0.98 0.99 1.02 0.79 1.02 0.81

Indonesia 1.14 0.92 0.97 1.05 0.90 0.99 0.98 0.89 0.93 0.78 0.93 0.73

Japan 0.97 0.92 0.97 0.89 1.11 0.99 0.98 1.10 1.53 0.78 1.53 1.19

Korea 1.03 0.92 0.97 0.95 1.02 0.99 0.98 1.00 1.09 0.78 1.09 0.85

Malaysia 1.05 0.92 0.98 0.97 1.02 0.99 0.98 1.01 1.04 0.77 1.04 0.80

Myanmar 1.39 0.92 0.99 1.28 0.90 0.99 0.98 0.89 0.86 0.77 0.86 0.66

Nepal 0.96 0.92 0.96 0.89 0.59 0.99 0.48 0.59 1.15 0.77 1.15 0.88

Pakistan 1.17 0.92 0.98 1.08 1.00 0.99 0.98 0.99 1.06 0.76 1.06 0.81

Philippines 0.97 0.92 0.98 0.89 1.06 0.99 0.98 1.05 1.24 0.76 1.24 0.94

Singapore 1.32 0.92 1.00 1.21 0.88 0.99 0.98 0.87 0.98 0.76 0.98 0.74

Sri Lanka 1.10 0.92 1.00 1.01 1.39 0.99 1.05 1.37 0.98 0.76 0.98 0.74

Thailand 1.06 0.92 0.97 0.98 1.02 0.99 0.98 1.01 1.12 0.76 1.12 0.85

Vietnam 1.23 0.92 0.98 1.13 1.05 0.99 0.97 1.04 0.69 0.76 0.69 0.52

Mean (StEA) 1.12 0.92 0.97 1.03 0.99 0.99 0.95 0.98 1.04 0.81 1.04 0.84


Transportation Sector

Figure 5.6 maps the shares of LCE in countries’ total energy consumption in the transportation

sector. Usually, this sector uses the least LCE while highly-dependent on fossil fuels. Singapore

and Japan have the highest shares of LCE (8%, and 2% respectively) mainly due to introducing

electricity-driven vehicles.

Table 5.10 reveals that towards attaining the combined objectives of green growth, Brunei and

Japan achieved highest TFP improvement (for energy use to attain green growth objectives) of

31% and 29% respectively during the 1995-2013 in the transportation sector. Conversely, Sri

Lanka and Myanmar’s TFP have degraded most by 42% each during this time. Most of the

countries made technological progress but degraded efficiencies in using energy for the

transportation sector. Japan and Korea attained the most efficiency gain while China and Sri Lanka

dropped the most. On average, the region lost the efficiency of energy use in the transportation

sector by 12% while gained 4% technological advancement during 1995-2013.



Figure 5.6: Share of low-carbon energy in total energy use in the Transportation sector

Table 5.10: TFP change, efficiency change, and Technological changes for transportation sector’s energy

use



Bangladesh 0.82 1.12 1.01 0.92 0.99 1.00 1.00 0.98 0.87 1.39 0.87 1.21

Brunei 0.84 1.12 0.63 0.94 1.00 1.00 1.00 1.00 1.00 1.31 1.00 1.31

Cambodia 0.83 1.12 1.03 0.93 1.00 1.00 1.00 1.00 0.88 1.21 0.88 1.07

China 1.17 1.12 0.69 1.31 0.99 1.00 0.99 0.99 0.55 1.15 0.55 0.63

India 0.93 1.12 0.84 1.04 1.00 1.00 1.00 1.00 0.82 1.12 0.82 0.92

Indonesia 0.87 1.12 0.85 0.97 1.00 1.00 1.00 0.99 0.90 1.09 0.90 0.98

Japan 0.85 1.12 0.85 0.95 1.00 1.00 1.01 1.00 1.22 1.06 1.22 1.29

Korea 0.85 1.12 0.86 0.95 1.00 1.00 1.00 0.99 1.12 1.03 1.12 1.15

Malaysia 0.83 1.12 0.86 0.93 1.00 1.00 1.00 1.00 0.94 1.00 0.94 0.94

Myanmar 1.56 1.12 0.98 1.75 0.99 1.00 1.00 0.99 0.59 0.97 0.59 0.58

Nepal 0.74 1.12 1.13 0.82 1.00 1.00 1.00 1.00 1.00 0.95 1.00 0.95

Pakistan 1.25 1.12 0.88 1.39 0.99 1.00 1.00 0.99 0.70 0.93 0.70 0.65

Philippines 0.91 1.12 0.91 1.01 0.99 1.00 1.00 0.99 1.08 0.92 1.08 1.00

Singapore 1.00 1.12 1.00 1.12 0.98 1.00 1.00 0.98 0.96 0.91 0.96 0.86

Sri Lanka 1.32 1.12 1.01 1.47 1.00 1.00 1.00 1.00 0.65 0.90 0.65 0.58

Thailand 0.97 1.12 0.87 1.09 0.98 1.00 1.00 0.98 0.96 0.89 0.96 0.85

Vietnam 0.95 1.12 0.86 1.06 1.00 1.00 1.00 1.00 0.77 0.88 0.77 0.68

Mean (StEA) 0.98 1.12 0.90 1.10 0.99 1.00 1.00 0.99 0.88 1.04 0.88 0.92



5.3.3 Policy implications

Energy usage is a critical policy tool for strengthening the low-carbon economies. Countries

should be aware of their energy use effectivity, particularly at the sectoral level. Proper distribution

and consumption of energy, energy composition, and continuous efficiency improvement

strategies must be chalked out according to the empirical analysis. Regional cooperation may help

in adoption and dissemination of state-of-arts technologies that can facilitate lifting up the

efficiency levels of the countries. Institutional arrangement for sharing common knowledge, skills,

and experiences can also help to a great extent.

The sectoral analysis is pivotal to strategize the national-levels policies. The empirical analysis

would instigate which sector requires more effort to improve technical efficiency or which sector

requires new technological adaptation. Under a regional cooperation framework, these policy

requirements can be combined, and collaborative measures can be planned accordingly with

optimal allocation of resources, and through sharing of expertise, knowledge, and technology.

5.4 Summary and Concluding remarks

The first part of this chapter analyzes how the regional energy cooperation can strengthen the

sustainable low-carbon growth by optimising the demand-management for both HCE and LCE.

Later part analyzes the issues of improving the efficiencies in energy usage. The Stochastic

Frontier Production model is used to estimate the optimal HCE and LCE demand and respective

factors for the StEA region and sub regions over the 1991-2014 period.

Estimations imply that on average, 1% increase in per capita income would result in 0.18%

decrease in HCE consumption and 0.38% increase in LCE consumption, ceteris paribus.

Population growth and energy used in the previous year have positive effects on both the demand

for HCE and LCE. Coefficients of industry share are not found significant for any of these demand

models. The price of oil has negative demand elasticity for HCE while the price of gas has the

positive elasticity. The coal price, however, is found to have no significant impact of any of these

demand models. Higher CO2 concentration of previous year also seems to have a positive impact

for both HCE and LCE demand models. Efficiency estimations reveal that the larger HCE

consumers such as China, India, Japan, Korea, and Indonesia all have lower technical efficiencies

to contain the demand-driven factors for HCE. On the contrary, small-scaled HCE consumers such


as Brunei and Singapore have high technical efficiencies for the containment of demand-driven

factors for HCE. On average, the StEA region has the average weighted efficiency of 47.5%,

indicating that it can still improve the untapped technical efficiency of containing HCE-demand

by 52.5% by adopting the best-practices within this region. The efficiency of the demand-driven

factors for LCE shows better performance from the countries. On average, the whole region has

the efficiency of 87.9% implying that there is still some room to increase the LCE-demand by

12.1%, given the existing demand-side settings.

The comprehensive end-use efficiency of energy assessment comprises both the macro as well as

the sectoral level. For macro analysis, efficiency is decomposed into three factors: conversion

efficiency, growth efficiency, and direct carbonization efficiency factor. Result depicts that most

of the countries seem to adopt better technologies to reduce conversion loss of energy gradually.

Conversion efficiency ranges 86.2% to 99.7% among all countries in StEA. Japan leads the list of

average growth efficiency of 86.3%. On average, the StEA region’s growth efficiency has also

increased from 28.8% to 29.1% over the 1995-2013 period. Such lower aggregated growth

efficiency indicates that most of the countries lag far behind from the pioneer country (i.e., Japan)

by a considerable margin in this energy intensity factor. Singapore tops with direct carbonization

efficiency of 93.2%. The whole StEA region has the mean carbonization efficiency level of 54.2%.

In aggregate, the StEA region has the overall energy use efficiency of 40.9%.

This study examines the underlying factors that can explain the energy usage efficiency in for

major energy-consumed sectors: agriculture, industry, electricity, and transportation through using

the non-parametric Data Envelopment Analysis (DEA) Malmquist model. A unique technique is

used to incorporate undesired sectoral GHG emission with the desired sectoral GDP as outputs.

We use the reciprocal value of GHG emission to make it compatible for use with the desired GDP

output. In the agriculture sector, on average, the region’s energy use efficiency in agriculture is

dropped by 19% while technology is advanced by 42% during 1995-2013. The region’s energy

use efficiency level in industry sector is decreased by 24% while technology is improved by 26%

during 1995-2013. On average, the StEA region’s energy use efficiency in electricity is improved

by 4% while the technology level is dropped by 19% during 1995-2013. The energy use efficiency

in transportation sector is dropped by 12% for the region while technology level is advanced by

4% during 1995-2013.


This chapter analyzes how the regional energy cooperation can strengthen the sustainable low-

carbon growth by optimising the demand-supply management through improving the efficiencies

in energy demand-management and usage. Regional cooperation may help in adoption and

dissemination of state-of-arts technologies that can facilitate lifting up the efficiency levels of the

countries. Institutional arrangement for sharing common knowledge, skills, and experiences can

also help to a great extent. Regional cooperation can also combine the policy requirements resulted

from the sectoral analysis. Collaborative measures can be planned accordingly with optimal

allocation of resources and through sharing of expertise, knowledge, and technology.

Next chapter will analyze the RC in sustainable green growth in agriculture sector.


Chapter 6

Sustainable Green Growth in Agriculture through Production

Efficiency: Role of regional cooperation



Agriculture is the primary sector of an economy. Relatively the low-skilled laborers of a country

are deployed in agriculture production process (Binks et al., 2014). The adequate investment and

efficiency enhancement approaches are undertaken in this sector, but at a slower pace as compared

to the other sectors of the economy (Alston et al., 2000). Besides, the growing population and

climate change phenomenon have intensified the concerns for food security among the nations

(Wheeler & von Braun, 2013). Such an urge, in turn, has compelled the producers to use the

resources of this sector, such as the land, water, and bio-capacity, at a much faster pace (Legg,

2017). Such a compulsion and subsequent exploitation of resources result in a rather inefficient

agricultural production which leads to the substantial degradation of environment and biodiversity

(Tseng et al., 2013, Robertson, & Pinstrup-Andersen, 2010, Shriar, 2002). Estimate reveals that

agricultural production and its input resources management are responsible for around one-fourth

of the total global GHG emission (IPCC, 2015). Though the crops, trees, plants, land, and water

are the sources of CO2 sink, the inefficient and unscientific management of key inputs such as

land, soil, fertilizer, energy, and manure often override the CO2 sequestration and sinking and add

significant emission into the environment. Therefore, along with ensuring the food security,

maintaining sustainability in agriculture production becomes a big challenge for the countries

(Baulcombe et al., 2009; Garnett et al., 2013).

It is evident that the geological attributes, geographical elements, and climatic factors have big

roles in agricultural production. Certain agricultural production may suit well in one location but

may not suit in a different geo-environmental setting. As a result, the countries may not always

have the self-sufficiency in all type of food. Besides, countries also may lack the capacity (i.e.,

financial, human capital, infrastructural, and institutional) in improving its technical efficiency and

technological adaptation that would be required to enhance the agricultural production base while

adopting sustainable growth policy in agriculture (Husmann et al., 2015). Technology transfer,

knowledge sharing, capacity building, and adequate investment are some pivotal factors that may

instigate the better management and higher efficiency in sustainable agricultural production for

the countries (Kiminami & Furuzawa, 2013, Darnhofer, 2015). To facilitating and fostering this

process, the countries have to work more intensely with each other. Hence, the regional agreement

and cooperation framework can play a significant role in this regard.


For reinforcing the effective green growth policy, it is important to focus on both the issues:

maximization of the production by using the given level of inputs and minimization of emission

resulted from the same level of inputs. The first factor relates to the production efficiency and the

second factor refers to the emission management efficiency. A good number of papers analyze the

role of regional cooperation in agriculture for attaining higher agricultural productivity and food

security (Khadka, 1990, Matthews, 2003, Rampa, 2012, Pant, 2014). Many of contemporary

literature highlight the impact of climate change and environmental degradation on agricultural

productions (Nelson et al., 2009, Salim & Islam, 2010, Asian Development Bank, 2013,

Intergovernmental Panel on Climate Change, 2014, Lipper et al., 2014, Kaur & Kaur, 2016).

However, the way the agricultural production (process) impacts the emission, environmental

degradation, and subsequent climatic change is rarely placed into the focus. This study focuses on

attaining the goal of sustainable green growth in agriculture through a comprehensive analysis of

the role of regional cooperation.

6.2 Areas of cooperation in targeting Low-emission green growth in agriculture

There are two main areas of regional cooperation which can channelize the efforts into low-

emission green growth outcomes in the agriculture sector of the countries as presented in Table

6.1.

Table 6.1: Areas of regional cooperation, the channel of impact, and low-emission green growth goal

Areas of cooperation Channel of impact Outcome on Low-emission

green growth

Efficient agricultural production

(Technology and knowledge sharing,

and capacity building in agricultural

production)

Higher yield Proportionate less use of

inputs, less emission, and

environmental damage

Scaling up in production

helps to lower the

production cost

Additional investment in

technology development and

adaptation

Efficient management of emission

and environmental degradation in

agriculture

(Technology and knowledge sharing,

and capacity building in agricultural

emission management)

Adoption of

environmentally

sustainable best-practiced

technologies in agriculture

Production will have lesser

emission

Source: Author’s analysis


6.2.1 Efficient agricultural production

Technology can play a crucial role in determining the agricultural productivity. Advanced

machinery, tools, and technologies for cultivation can certainly result in a higher yield of

production. In recent time, the innovation of high-yield crops through advanced applications of

agricultural biotechnology, genetic research, and satellite technology are getting momentum

(Juma, 2015). Development, transfer, and share of improved technologies, knowledge, and skills

within a regional cooperation framework, of course, will enhance the production efficiency of the

countries as it will attain more production with proportionately lesser inputs (Amanor & Chichava,

2016, Emerick et al., 2016). Less usage of inputs such as land, fertilizer, machinery, water, and

labor will, in turn, reduce environmental damage and emission. Countries can also develop and

share the new pro-green growth technologies through mutual financing and institutional support.

With improved technology, the cost of production also will be reduced over time. The saved

amount from this can be invested in further technology development.

6.2.2 Efficient management of emission and environmental degradation in agriculture

Comprehensive policies and concerted efforts from the countries can facilitate the knowledge and

technology sharing for the better environmental management in the agriculture sector. There are

few sources of emission in agriculture production process, such as soil management, animal

management, manure management, land use, energy use, and synthetic fertilizer. Not all the

countries may have the similar efficiency levels in tackling each of the sources of emission. Hence,

regional cooperation and exchange of technologies and knowledge would be some important

drivers for the countries to attaining the sustainable low-emission growth in respective agriculture

sector (Husmann et al., 2015).

6.3 Objectives and organization of this chapter

This study examines the role of regional cooperation in achieving the sustainable green agriculture

through following two areas:

i. Enhancing the agricultural productivity of the countries; and

ii. Reducing emission and environmental degradation resulted from the agricultural activities.


For a comprehensive understanding, the whole analysis has been explained in two chapters. This

chapter analyzes the role of regional cooperation in maximizing the agriculture production while

the next chapter explicates the role of regional cooperation in containing the agricultural emission

in the countries. For empirical analysis, StEA countries are considered to represent the regional

bloc.

6.4 Current status of agricultural production and emission in StEA

6.4.1 Inadequate production to meet regional agricultural demand

Other than vegetable oils and fruits, all categories of agricultural products have net import position

for the whole region, i.e., those products are imported more than exported by these StEA countries.

As depicted in Table 6.2, the gaps between the quantities of import and export of agricultural

commodities are reasonably significant which implies that with the current settings, the StEA

regional bloc is not able to meet the intra-regional demand for all agricultural commodities.

However, there are few signs of potential room for improvement as we can see that for all main

categories of products, there are few countries which have excess supply, i.e., few are net

exporters. If the productivity and efficiency can be improved further with the given level of inputs,

production of these agricultural products can be increased. Hence by using the economic scale

benefits, those net exporter countries may scale up their production to meet the regional demand.

6.4.2 Growing emission from agriculture

The average growth rate of emission from agriculture was higher for the whole region during 2000-

2010 (1.25%), but it has dropped to 0.20% in a recent time of 2010-2013. Figure 6.1 illustrates the

(2010-2013) average levels of emission along with the growth rates during this time. China and

India emit the highest among the countries with 698277 and 627349 gigagram CO2-equivalent.

Jointly, these two countries emit about 68% of total regional emission from agriculture. The

experiences of the countries show a mixed state as nine countries have increasing agricultural

emission while rest of the eight countries remain successful in attaining a declining trend in

emission. Vietnam has the highest growth rate of emission (4.66%) followed by Pakistan (3.38%).

Among the emission reducing countries, Malaysia has the highest declining rate (2.93%) followed

by Thailand (1.50%) and Japan (0.95%) during this time.


Source: Author’s calculation based on FAOSTAT (2017)

Figure 6.1: Average level and growth rate of emission from Agriculture in StEA countries (2010-2013)

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

-500000

-300000

-100000

100000

300000

500000

700000

Ban

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Bh

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Cam

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Ch

ina

Ind

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Ind

on

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Jap

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Lao

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Mal

aysi

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Mya

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ar

Nep

al

Pak

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Ph

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RO

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Sri L

anka

Thai

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Table 6.2: Net food export position of the StEA countries (2011-2013 annual average)

Countries Oilseeds Cereals Vege oils Total

meat

Fibers Cassava

equivalent

Sugar &

honey

Pulses Milk

equivalent

Beverage

& Alcohol

Fodder &

feeder

Fruits

Bangladesh -435521 -3791684 -2826450 -338 230705 -157548 -1616942 -562743 -618840 2436 -463727 -112289

Bhutan -1683 -61093 -24446 -5274 -41 0 -7802 -2667 -17169 -344 -2052 20820

Brunei -1596 -47696 -14617 -9250 -166 -1825 -10485 -550 -27309 -70934 -59510 -3593

Cambodia 4771 179984 28452 -958 -297 147151 -212462 -2069 -21460 -159603 -221862 -5001

China,

mainland

-60922549 -10185840 -20209330 -495883 -5596692 -21825801 -2508245 -16866 -8176811 400946 -1354738 232939

India 1138630 17878122 -18217769 1215417 2235436 -16579 2532795 -3365788 365439 -23249 7082327 46930

Indonesia -1920415 -11147045 43307412 -62601 -554560 -1844567 -2625863 -87459 -2312807 13913 -589499 -172173

Japan -5477835 -24470768 -1961261 -2961776 -136341 -801070 -1719966 -144148 -1657678 -1581357 -6683666 -1324421

Lao PDR 6161 187573 -286 -1 0 -19017 -33758 0 -15915 -74652 -62450 12110

Malaysia -707424 -5384258 32842680 -207463 -24418 -899466 -1556957 -91817 -1224097 549719 792956 -150769

Maldives -37 -50240 -15734 -12534 -156 -83 -10583 -1097 -37002 -19967 -455 -3751

Myanmar 48634 438385 -1032128 -6069 2042 -3922 -96443 1454994 -111401 -432954 -738 0

Nepal -137781 -534559 -424034 5651 -102848 -1743 -29060 -46717 -20552 8066 -60132 -15639

Pakistan -765209 5629789 -4369879 47897 -225102 -3116 598908 -539673 -270494 24536 -270212 447059

Philippines -186965 -3894206 1464474 -296060 15396 -324907 316615 -80499 -1334091 -5365 -1668477 2564136

Rep.of Korea -1436232 -13838660 -2045235 -937952 -270449 -1390880 -2226673 -53540 -914846 514813 -6790712 -507716

Singapore -33968 -569660 -1504956 -267779 -451 -298343 -321367 -14456 -962053 -403310 108784 -103526

Sri Lanka -937 -917718 -281028 614 394444 -17610 -587860 -197525 -598134 -25364 -16461 1010

Thailand -1995525 6018031 912359 759817 -316391 21630760 7035802 2345 -1072509 977342 -3125377 -86438

Viet Nam -1223515 2300645 -1335774 -833688 -339920 0 -603765 19 -1265273 -124786 -3595759 3265

Aggregated -74048996 -42260899 24292449 -4068229 -4689809 -5828565 -3684109 -3750255 -20293003 -430115 -16981760 842954

* Net export =Export minus import; quantity in tons



6.4.3 Emission intensity

Emission intensity measures the level of agricultural emission for one unit of agricultural

production. Broadly, it indicates how effectively the emission are managed in aggregate

agricultural production. Higher the intensity, lower will be the efficacy of emission management,

and vice versa. Figure 6.2 illustrates the average emission intensity for 2010-2013 in all the

countries. Pakistan has the highest emission intensity (6.38 kg/USD) followed by Bangladesh,

Nepal, Lao, and Myanmar. Vietnam, on the other hand, has the lowest emission intensity (0.09

kg/USD). Japan, Korea, Malaysia, and China are few other countries with lower emission

intensities.

However, we need to dig it out further to see whether this emission level is influenced by the

production level, inputs management or some other factors. We need to examine the sources of

emission in agriculture and investigate how better the countries have been dealing with those

sources.


Figure 6.2: Agricultural emission intensity of the countries (2010-2013 average)


6.5 Methodology

From a regional cooperation aspect, we need to estimate the efficiency levels of the countries in

agriculture production with their current settings. Following the efficiency levels, countries will

be able to determine the mechanism of technology sharing and other supports among them to

enhance the yield in agriculture production. Besides, by considering the regional bloc as a common

framework, we can also estimate the impact of regional cooperation on aggregated production

efficiency. Consequently, we can also estimate the untapped potential under a regional cooperation

framework.

Several non-parametric methodologies such as Data Envelopment Analysis, TFPIP, and DPIN are

frequently used in literature to estimate the agricultural productivity and efficiency (O’Donnell,

2010, Khan et al., 2015, Jahromi, 2016, Baráth & Fertő, 2017). However, the non-parametric

models often face intrinsic difficulties regarding conceptual and operational issues such as

transitivity and identity axioms of index number theory (Matthews, 2014). Inadequate set of

variables and data is another shortcoming of these models to describe the causal relationship in

detail.

This chapter uses parametric model Stochastic Frontier Analysis (SFA) which would help not

only to calculate the production efficiencies but also to show the contributions (elasticities) of the

inputs into production efficiency. In recent studies, SFA is frequently used for measuring the

production efficiency as well as environmental efficiency at farm level agricultural activities

(Lansink & Wall, 2014, Alves et al., 2015, Jiang & Sharp, 2015, Orea & Wall, 2016, Fei & Lin,

2016). However, the combined assessment of production efficiency and emission containment

efficiency is not evident. Role of any regional cooperation to balance and enhance these

efficiencies are also disregarded in literature. Therefore, this study makes a unique attempt to

address all these issues concurrently. SFA models are separately designed for production

efficiency and emission management efficiency (described in the next chapter). To determining

the combined effect of efficiencies, a Green growth index is also formulated in next chapter.

Stochastic Frontier Model is described in section 4.5.1 in details.


6.5.1 Model specification for Production efficiency

Two widely applied production functions in SFA are Cobb-Douglas and Translog functions. Each

form has its advantages and limitations. The Cobb-Douglas has been immensely used in the

agriculture economics because of its algebraic tractability while providing a reasonably good

approximation of the production function which usually suits well with agriculture production

methodology. The main limitation of this Cobb-Douglas form is its restrictive assumptions on

arbitrary (i.e., inflexible) substitutability among the inputs. Translog production function

overcomes this limitation by allowing flexible degrees of substitutability between inputs. However,

the key challenges of using translog function is the high probability of the occurrence of harmful

collinearity9 among the explanatory variables as the number of production factors increases

(Pavelescu, 2011). According to a common translog production function with n number of input

factors, the number of estimated parameters equals 𝑛.(𝑛+3)

2 (excluding the intercept term). Hence,

a model with seven input factors should have 35 estimated parameters. Hence, it would be difficult

to restrain the model from any collinearity issue. Filippini et al. (2008), however, suggests to drop

the input which has higher correlation problem with other inputs. Few authors argue that in the

case where measuring technical efficiency is the prime objective, multicollinearity problem may

be ignored to some extent as the interpretation of the coefficients remains secondary in such

circumstance (Puig-Junoy, 2001). However, this arguments may not be convincing enough to use

the translog function where the probability of collinearity remains higher. Rather, this study want

to deploy the Cobb-Douglas production function to avoid such complexities. Cobb-Douglas

function may, however, experinece the serial correlation problem for time series data and

heteroskedasticity problem for cross-section data. This study, however, uses the panel data which

would minimize these technical obstacles.

The basic model is designed as shown in equation (6.1).

99 Pavelescu (2010) refers the ‘harmful’ collinearity when the sign of at least one estimated parameter does not match

with the relevant sign of the coefficient of the correlation factors between the analyzed variable and the resultative

variable.


Maximizing

ln𝑂𝑢𝑡𝑝𝑢𝑡𝑖,𝑡 = 𝛽0 + 𝛽1ln𝐿𝑎𝑛𝑑𝑖,𝑡 + 𝛽2ln𝐿𝑎𝑏𝑜𝑟𝑖,𝑡 + 𝛽3 ln 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖,𝑡 + 𝛽4 ln 𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟𝑖,𝑡 +

𝛽5 ln 𝐸𝑛𝑒𝑟𝑔𝑦𝑖,𝑡 + 𝛽6 ln 𝐹𝐷𝐼𝑖,𝑡 + 𝛽7 𝑡𝑖𝑚𝑒 − 𝑈𝑖,𝑡 + 𝑉𝑖,𝑡 (6.1)

𝑖 = 1, 2, 3, ………… . , 𝑘 (for each country)

𝑡 = 1, 2, 3, ………… . , 𝑇 (for each year)

Here, Output refers to the aggregated agriculture output while Land refers to the total arable land

of a country, Labor and Capital denote the total labor (in number) and Capital (in USD) deployed

in agriculture. Fertilizer and Energy refer to the amount of fertilizer and energy consumed in

agriculture production. FDI denotes the cumulative FDI inflow since 2000 in the agriculture sector

of a county. FDI inflow is used as a proxy for technological progress. Variable time is incorporated

to capture the time-trend in agriculture production. Subscripts i and t represent the i-th country and

time respectively. 𝑈𝑖,𝑡 denotes the single-sided error term for the combined effects of inefficiency,

on which complete information is not available. 𝑉𝑖,𝑡 refers to the normal statistical error term which

captures the effect of inadvertently omitted variables.

For investigating the explanatory factors of production efficiency, this study uses the technical

inefficiency effects model for using the panel data as proposed by Battese and Coelli (1995).

Authors defined the technical inefficiency effect, 𝑈𝑖𝑡, as

𝑈𝑖𝑡 = 𝑧𝑖𝑡𝛿 + 𝑤𝑖𝑡

where 𝑤𝑖𝑡 is defined by the truncation of the normal distribution having zero mean and variance

of 𝜎2 such that 𝑤𝑖𝑡 ≥ −𝑧𝑖𝑡𝛿. This remains consistent with the assumption of 𝑈𝑖𝑡 being a non-

negative truncation of the (𝑧𝑖𝑡𝛿, 𝜎2 ) distribution.

Technically, the agricultural production process is largely influenced by the infrastructures

(especially at the rural areas), the capacity of the workers who are directly involved in this process,

and government’s support and facilitation in agriculture. By considering these aspects, the

production (in)efficiency is modeled as follows:

𝑈𝑖,𝑡 = 𝛿0 + 𝛿1𝑅𝐷𝑖,𝑡 + 𝛿2𝐴𝑡𝑊𝑖,𝑡 + 𝛿3𝑉𝐴𝑖,𝑡 + 𝛿4𝐸𝑋𝑃_𝐴𝑔𝑟𝑖𝑖,𝑡 + 𝛿5𝐸𝑋𝑃_𝐸𝑑𝑢𝑖,𝑡 + 𝑤𝑖𝑗,𝑡


where 𝑅𝐷𝑖,𝑡 : road density (per square km of land area of country-i at time t

𝐴𝑡𝑊𝑖,𝑡 : Access to improved water sources in rural (% of rural population) of country-i at

time t

𝑉𝐴𝑖,𝑡 : Agriculture value added per laborer (Constant 2005 US dollar) in country-i at time

t

𝐸𝑋𝑃_𝐴𝑔𝑟𝑖𝑖,𝑡 : Government expenditure in agriculture per laborer in country-i at time t

𝐸𝑋𝑃_𝐸𝑑𝑢𝑖,𝑡 : Government expenditure in education per capita in country-i at time t


using joint density functions of 𝑈𝑖,𝑡 and 𝑉𝑖,𝑡. While the parameter γ is found significant, it implies

the best-fit for the model. FRONTIER 4.1 software developed by Coelli (1996) is used to estimate

the model. Approach of the production function model is considered for the maximization of the

agricultural output.

6.5.2 Description of data

Data on aggregated food and agriculture production including livestock value of production is

measured in terms of indigenous meat (in million USD), arable land (in thousand hectors), capital

(in million USD), fertilizer (in kilogram), energy consumption in agriculture (in Tera-joule), and

FDI inflows in agriculture (in million USD) are extracted from the Food & Agriculture

Organization (FAO) of the United Nations. Labor (in thousand) data is collected from the Key

Development Indicators of the Asian Development Bank (ADB) annual reports. Data on road

density, access to improved water sources in rural, and agriculture value added per laborer are also

extracted from the World Development Indicators. Government expenditures on agriculture and

education are collected from the Asian Development Bank’s key development indicators.

Data are collected for the 2000-2013 period for all StEA countries except Bhutan, Brunei, and

Maldives for which complete set of data are not available. Along with these three countries,

Singapore is also skipped from the further analysis because of its low-scale agriculture production.

In aggregate these four countries produce only 0.024% of total agriculture output of the StEA

region, hence, skipping these countries from the analysis would not have substantial influence on

agricultural policy implication of this region.

http://www.stata.com/


6.6 Results and findings

6.6.1 Summary statistics

Table 6.3 shows the summary statistics of all the input and output factors of the countries. It states

that China remains the highest producer with an annual average agriculture production of 495.9

billion USD. India and Japan follow China with 151.2 billion USD and 64.9 billion USD

respectively. Lao PDR, Sri Lanka, and Cambodia are the smallest producers in this region. China

and India also use the largest amount of all input factors. Korea, Lao PDR, and Sri Lanka have the

least arable land areas. Malaysia, Korea, and Lao PDR have the lowest number of laborer in their

agriculture production. Due to their small-scale operations, Lao PDR, Nepal, and Cambodia

deploy the least amount of capital in agriculture production. Lao PDR, Nepal, and Cambodia also

use the least of fertilizers among the countries. Lao PDR, Sri Lanka, and Cambodia remain the

lowest energy consumers for their agriculture productions. Nepal, Lao PDR, and Sri Lanka have

the least FDI inflow in their agriculture production.

Table 6.3: Summary statistics of the input and output factors (annual average, 2000-2013)

Country/

Region

Production Land Labor Capital Fertilizer Energy FDI

billion USD million

ha

million billion

USD

million Kg peta joule billion

USD

Bangladesh 11.03 9.27 23.88 0.87 1.63 36.14 4.34

Cambodia 3.00 5.26 4.78 0.17 0.04 1.03 2.46

China 495.94 517.01 313.94 41.63 49.88 1323.50 473.98

India 151.19 180.04 219.43 25.70 21.61 460.57 94.85

Indonesia 41.79 52.78 40.69 4.22 3.60 113.96 22.93

Japan 64.90 4.70 2.75 11.96 1.32 153.90 56.51

Korea, Rep. 31.11 1.85 1.79 4.28 0.69 115.17 79.60

Lao PDR 1.25 2.06 2.08 0.10 0.01 0.14 0.55

Malaysia 12.24 7.27 1.51 2.02 1.42 17.63 31.00

Myanmar 12.19 11.75 11.74 0.31 0.09 5.37 2.46

Nepal 3.60 4.18 8.56 0.17 0.04 3.53 0.08

Pakistan 20.90 36.12 20.93 2.25 3.55 32.33 12.61

Philippines 15.04 11.74 11.58 1.37 0.70 13.95 3.35

Sri Lanka 1.99 2.50 2.35 0.34 0.29 0.31 2.38

Thailand 19.78 20.33 13.84 2.45 2.05 139.00 44.44

Viet Nam 15.52 10.07 24.32 1.26 2.12 25.23 20.84

StEA 901.69 877.47 706.62 99.15 89.03 2441.91 1015.40



6.6.2 Productivity of the inputs

The productivities of each input are examined to understand how the countries are utilizing the

input to produce the existing agricultural output. Each of the productivities is calculated as follow:

𝐼𝑛𝑝𝑢𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 𝑖′𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑖𝑛 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑗 = 𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑗

𝑈𝑠𝑎𝑔𝑒 𝑜𝑓 𝑓𝑎𝑐𝑡𝑜𝑟 𝑖 𝑖𝑛 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑗

Table 6.4 shows the relative performances of the countries in using each input for their agricultural

productions. Though it is assumed that the existing geological, environmental, and spatial factors

of the countries largely influence the performances, this ranking, however, gives a general

indication of the countries’ relative advantages or higher efficiency in using an input (or set of

inputs).

Table 6.4: Ranking in various agricultural productivities (based on 2010-2013 average)

Rank (1=Best) Land Labor Capital Fertilizer Energy FDI

1 Korea Japan Myanmar Viet Nam Sri Lanka Nepal

2 Japan Korea Nepal Myanmar Lao PDR Philippines

3 Malaysia Malaysia Cambodia Lao PDR Myanmar Myanmar

4 Bangladesh China Lao PDR Cambodia Cambodia Bangladesh

5 Philippines Thailand Bangladesh Nepal Philippines Lao PDR

6 Myanmar Myanmar Philippines Japan Nepal Indonesia

7 China Philippines Indonesia Korea Pakistan Pakistan

8 Viet Nam Indonesia China Philippines Japan Cambodia

9 Nepal Sri Lanka Pakistan Indonesia Indonesia India

10 Thailand Cambodia Korea China Viet Nam China

11 India Pakistan Viet Nam Sri Lanka China Japan

12 Indonesia Lao PDR Thailand Malaysia India Sri Lanka

13 Cambodia India Japan Thailand Malaysia Viet Nam

14 Sri Lanka Bangladesh Sri Lanka Bangladesh Bangladesh Thailand

15 Lao PDR Nepal India India Korea Malaysia

16 Pakistan Viet Nam Malaysia Pakistan Thailand Korea


From a rational perspective, if a country has a deficiency with any of the endowments or input

resources, then it is expected that the country tends to find out the way to increase its efficiency in

using that input. The scarcity of the input compels the country to re-engineer or reshuffle the

production process so that it can manage to produce more with the limited resources. Input

substitution may also help this cause for the country. For example, Korea has the least arable land


which induces it to use lowest of land per unit of agricultural output among all StEA countries.

Similarly, Japan and Korea- the countries with a dearth of labor realize the highest labor

productivities among all the countries. However, these productivity measures are calculated based

on the actually realized production function, which may or may not be the potential production

frontier showing the maximum possible output. The maximum possible output is achieved by

following the best-practice techniques of the chosen technology involving all inputs. Hence, it is

important to examine the overall measure of production efficiency of the sampled countries, which

is discussed in the following sections.

6.6.3 Estimation of the coefficients

The result of the estimation of coefficients of the production efficiency model is presented in Table

6.5. The estimations are based on 2000-2013 data for 16 StEA countries. The result implies that

land, capital, energy, and FDI have positive implications on the production outcome in this region.

For 1% increase of these input factors, ceteris paribus, would result in 0.43%, 0.40%, 0.29%, and

0.06% respective increases in agricultural production output in the StEA region.

Table 6.5: Estimation of coefficients for Production efficiency model

ln(Production) Estimation of coefficients

ln(Land) 0.429***

ln(Labor) -0.057***

ln(Capital) 0.399***

ln(Fertilizer) -0.270***

ln(Energy use) 0.285***

ln(FDI) 0.062***

time -0.011***

Inefficiency effect estimations Estimation of coefficients

ln(Road Density) -0.040*

Access to quality water in Rural -0.003

ln(Value added per worker) -0.081*

ln(Agriculture expense per labor) -0.263***

ln(Public expenditure in education per capita) 0.172**

Gamma 0.999***

Log-likelihood function -8.95

LR test 63.18

No. of observation = 224



Labor and fertilizer, on the contrary, have negative elasticities with the production outcome. It

indicates that growth rate of labor productivity, as well as the fertilizer productivity, have been at

declining rates. The elasticity is -0.06 for labor while it is -0.27 for the fertilizer. Time trend

coefficient indicates that agriculture production growth rate has been slowly declining at 1.1

percentage point during the 2000-2013 period. Estimations of all coefficients are significant at

99% confidence level. A higher value of gamma infers that the model is well-fitted for explaining

the SFA. Value of log likelihood function represents the degree of goodness of fit of the model.

According to the value, the model is well fitted.

6.6.4 Estimations of the determinants of the Production efficiency of the countries

The technical inefficiency effect model also estimates the coefficients of five determinants of the

production efficiency. Since the model estimates the coefficients for inefficiency (U), negative

signs of the estimation imply that the impacts are positive on the efficiency. The result presented

in Table 6.5 indicates that infrastructure facilities in forms of road density and quality water in

rural areas have a positive impact on production efficiency in the StEA region. 1% increase in road

density would enhance the production efficiency by 0.04%, ceteris paribus. For 1percentage point

increase in the access to quality water in rural areas, the efficiency is estimated to increase by

0.003 percentage point, ceteris paribus. The impact of road density is, however, statistically more

significant than the access to quality water in rural areas. Efficient workers in agriculture also

have a substantial impact on production efficiency. 1% increase in the agriculture value added per

worker would result in 0.08% improvement in production efficiency, ceteris paribus.

Governments’ direct expenditure to agriculture has a notable positive impact on improving

production efficiency. However, government’s expenditure on country’s education sector is found

to have rather an adverse impact on agriculture production efficiency. 1% increase in governments’

expenditure in agriculture per worker would increase the production efficiency by 0.26% while a

1% increase in government’s expenditure in education per capita would have declined the

agriculture production efficiency by 0.17%, ceteris paribus. When government’s expenditure on

education per capita increases, it may have two impacts on agriculture production efficiency.

Primarily, it would enhance the educational skills and knowledge to all including the agriculture

workers and also would encourage more research works in agriculture, which should have a


positive implication on agriculture production efficiency. On the other hand, higher expenditure

on education would also shift the skilled labor force away from the agriculture sector to the

industrial or service sectors. Such sectoral transition may have some adverse impact on agriculture

production efficiency. Resultant impact, therefore, depends on both these factors. In case of StEA,

it seems that the adverse transition impact is higher than the value-added impact on production

efficiency.

6.6.5 Estimations of Production efficiency of the countries

Agricultural production efficiency of the countries for the 2000-2013 period is presented in Table

6.6. It reveals that China has the highest production efficiency of 94.7%. Japan, Vietnam, and

Korea follow China with 91.1%, 90.6% and 85.1% of production efficiency. Thailand, on the

contrary, has the lowest efficiency of 36.5%. Cambodia, Loa PDR, and Nepal are the other least-

efficient countries with efficiency levels of 36.5%, 46.1%, and 49.8% respectively. Among the

other large-scale producers, India has the production efficiency of 65.2% and Indonesia has 58.4%.

Table 6.6: Country-wise technical efficiency in agricultural production (2000-2013)

Country Technical efficiency

Bangladesh 71.1%

Cambodia 46.1%

China 94.7%

India 65.2%

Indonesia 58.4%

Japan 91.1%

Lao PDR 49.8%

Malaysia 65.6%

Myanmar 84.1%

Nepal 52.6%

Pakistan 61.4%

Philippines 73.4%

Korea, Rep. 85.1%

Sri Lanka 75.7%

Thailand 36.5%

Vietnam 90.6%



6.6.6 Impact of Regional cooperation

Impact of regional cooperation is estimated by considering the StEA as a unitary bloc. Inputs and

output variables of all the countries are summed up to get the aggregated level of inputs and output

for the whole region. This unitary bloc is then put into the model and it’s efficiency is calculated

by the maximum likelihood estimation by comparing the entities with regard to the best-practice

performer. The estimation reveals that the overall technical efficiency of this regional bloc is

83.7%. It implies that if the countries could work under a regional cooperation bloc, on average,

its agriculture production efficiency would be 83.7%. Since most of the large agriculture

producers, such as China, Japan, and Korea have much higher efficiencies in production, hence

the overall weighted efficiency of the StEA region remains relatively higher. It also refers that the

RC bloc can work-together to further increase its production towards the untapped potential

production of 16.3% without deploying any additional resources (i.e., inputs).

For understanding the link between the enhanced production efficiency (to the optimal) and the

emission reduction potential, this study uses following calculation as shown in Table 6.7.

Table 6.7: Estimation of the link between optimal production efficiency and emission reduction

Scenario Input Output Emission

Actual scenario x y m

Optimal efficiency in production x 1.163 y m

New level of emission (with

current production level and

optimal efficiency)

0.86 x y 0.86 m


With x amount of aggregated input, the regional bloc can produce y output. Since the emission or

environmental degradation is resulted from using the inputs in agriculture production process, let's

assume that x amount of aggregated input leads towards an aggregated emission of m. Now with

further increase of efficiency by 16.3%, x amount of aggregated input will produce 116.3 y amount

of aggregated output, but the amount of aggregated emission will stay at m. Hence, for producing

the agricultural output at the current level, with this higher level of efficiency, the level of emission

may be reduced to m/1.163 or 86.0% of its current emission level. Therefore, the technical

efficiency increase in the production process may save up to 14.0% of emission from its current

emission level, considering the production level remains at the current level. The analysis is also

explained in Figure 6.3.



Figure 6.3: Estimation of the link between optimal production efficiency and emission reduction

6.6.7 Synergy Effect for potential gap reduction

Synergy effect infers that the combined action of a group of countries (i.e., regional cooperation,

here) should have more impact in bringing added benefits than the sum of individual country’s

actions. Hence, it is important to calculate the synergy effect in any RC framework to understand

the impact of the combined action.

For this purpose, gaps in potential agricultural productions (i.e., potential production minus actual

production) are calculated separately for all 16 countries. Then the model considers the whole

regional bloc (StEA) as a unitary entity and calculates the gap for that whole bloc. As the Table

6.8 indicates, the gap in the potential agricultural production of the StEA (as a single bloc) is

smaller than the sum of all 16 countries’ gaps. The calculation, thus, supports the synergy effect

phenomenon. However, synergy effect may not be always positive. The algebraic formulation

presented in Appendix 3 explains the details.

To quantify the impact of the regional cooperation, the differences between those gaps from

potential (the sum of individual country’s gaps minus the whole bloc’s gap) are measured as

percentages of the gap without cooperation. Hence, synergy effect of the regional bloc is calculated

as:

X 0.86 X

y 1.163y

Output

Input O

Country’s actual state (y, x)

Frontier


𝐼𝑚𝑝𝑎𝑐𝑡 𝑜𝑓 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐 (𝑖. 𝑒. , 𝑆𝑦𝑛𝑒𝑟𝑔𝑦 𝑒𝑓𝑓𝑒𝑐𝑡)

= (𝐺𝑎𝑝 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐 − 𝐺𝑎𝑝 𝑎𝑠 𝑎 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐)

𝐺𝑎𝑝 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐

= (𝑠𝑢𝑚 𝑜𝑓 𝑔𝑎𝑝𝑠 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 − 𝑔𝑎𝑝 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑓𝑜𝑟 𝑆𝑡𝐸𝐴)

𝑠𝑢𝑚 𝑜𝑓 𝑔𝑎𝑝𝑠 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦

Table 6.8 reveals that synergy effect, on average, is 34%. More importantly, the impact has been

increasing over the period.

Table 6.8: Synergy effect measure, i.e., Impact of Regional cooperation to close the potential gaps*

Year Sum of individual gaps Gap as a unitary bloc Impact of cooperation

2000 131.21 105.03 20.0%

2001 145.91 117.39 19.5%

2002 154.70 124.81 19.3%

2003 114.92 80.94 29.6%

2004 135.03 97.23 28.0%

2005 127.60 81.37 36.2%

2006 155.19 110.48 28.8%

2007 132.79 75.64 43.0%

2008 128.91 66.95 48.1%

2009 147.43 86.81 41.1%

2010 165.42 95.73 42.1%

2011 169.73 100.47 40.8%

2012 174.43 106.52 38.9%

2013 172.26 103.18 40.1%

Average

34.0%

* Gaps are in billion USD



From a regional cooperation perspective, policy framework based on the analysis presented in this

chapter would provide wide-ranging tools to manage the intra-regional demand-supply of foods

more efficiently while complying the measures necessary for the transition towards the low-

emission agriculture system. Obviously, under an RC framework, countries with higher efficiency

in production should produce more so that resources within the regional bloc are efficiently


managed. Results from the technical inefficiency effect model reveal that countries should highly

focus to cooperate on developing the road communication infrastructures to facilitate the

agriculture, especially in the rural areas. Countries should also work together to ensure the access

to quality water in rural areas. Mutual supports in workers capacity building for agriculture is also

pivotal. At the same time, technology transfer, knowledge sharing, and capacity building activities

should be facilitated between the high-efficient countries and the low-efficient countries so that

low-efficient countries can improve as well. Institutional settings at the regional level should be

strengthened so that it can constantly monitor the level of progress and disseminate the adequate

policy, rules, and technical support to all member countries. Creation of a common fund to finance

in agricultural green growth projects may also play an important role. Easing of trade restrictions

on agricultural production and agricultural inputs may also facilitate efficient production among

the countries (Meacham & Rafferty, 2016).

The methodologies and respective results of this study may provide good inputs in designing this

overall framework more effectively.


The growing population and climate change phenomenon have compelled the producers to the

faster exploitation of the resources in agriculture production. This chapter examines the

prospective roles of RC in attaining the sustainable green growth through enhancing countries’

production efficiencies.

This study adopts the Stochastic Frontier model to estimate the production efficiency levels of the

countries. Result reveals that land, capital, energy, and FDI have positive impacts on the

agriculture production in StEA region. Labor, and fertilizer, on the contrary, have negative impacts

on the production, Agriculture production growth rate has been slowly declining at 1.1 percentage

point during the 2000-2013 period.

The technical inefficiency effect model is used to estimate the coefficients of five determinants of

the production efficiency. The result indicates that infrastructure facilities in forms of road density

and quality water in rural areas have a positive impact on production efficiency in the StEA region.

Efficient workers in agriculture also have a substantial impact on production efficiency.

Government’s direct expenditure to agriculture has a notable positive impact on improving


production efficiency. However, government’s expenditure on country’s education sector is found

to have rather an adverse impact on agriculture production efficiency. Higher expenditure on

education would shift the skilled labor force away from the agriculture sector to the industrial or

service sectors. This adverse transition impact is higher than the value-added impact on production

efficiency in the StEA region.

China has the highest production efficiency among the countries. Japan, Vietnam, and Korea

follow China. Thailand, Cambodia, Loa PDR, and Nepal are the least-efficient countries in the

agriculture production amongst the StEA countries. The estimation reveals that the overall

technical efficiency of this regional bloc is 83.7%, implying that the RC bloc can work together to

further increase the region’s agriculture production by 16.3% towards the untapped potential

production without deploying any additional resources. Estimation of the link between optimal

production efficiency and emission reduction reveals that under the full-efficient scenario, the

technical efficiency increase in the production process may reduce 14.0% of emission from its

current emission level, considering the production level remains on the current level.

Synergy effect calculation also reveals that the StEA countries could have improved the production

closer to the potential if the countries work under a common RC bloc than work separately.

Forming an RC could have, on average, 34% added impact for the StEA countries in this regard.

More importantly, this potential synergy impact has been rising over the 2000-2013 period.

As the geological attributes, geographical elements, and climatic factors have an immense impact

on a country’s agriculture production; regional cooperation can play a big role to meet up the

respective demands of the countries. Besides, under an RC framework, countries can share and

disseminate the efficient technologies and mechanisms to enhance agricultural productivity while

managing the containment of emission in the production process. The models proposed in this

chapter would provide the evidence on how the input resources and economic settings can impact

on their efficiency levels. It is very crucial for policy design and implementation. From an RC

perspective, it would provide extensive tools to manage the intra-regional demand-supply of foods

more efficiently while confirming the low levels of emission in the regional agriculture system.

Following chapter will analyze the role of RC for the potential emission-management in

agriculture


Chapter 7

Regional Cooperation for Optimal Emission-Management in

Agriculture



For reinforcing the effective green growth policy, it is important to focus on both the issues:

maximization of the production by using the given level of inputs and minimization of emission

resulted from the same level of inputs. The previous chapter explains the first factor relating to the

production efficiency and this chapter will explicate the second factor referring to the emission

management efficiency. As mentioned earlier, many of contemporary literature highlight the

impact of climate change and environmental degradation on agricultural productions (Nelson et

al., 2009, ADB, 2013, IPCC, 2014, Lipper et al., 2014, Kaur & Kaur, 2016). However, the way

the agricultural production (process) impacts the emission, environmental degradation, and

subsequent climatic change is rarely placed into the focus.

This chapter contributes to this missing area of literature by analyzing the way agricultural

production process affects the environment through emission while focusing how the countries

under a regional cooperation framework can work together to manage the emission containment

best to attain the goal of sustainable green growth in agriculture. Specifically, this chapter

examines the countries’ current state of emission management efficiency and subsequently derives

the impact of regional cooperation in achieving the green growth in agriculture by reducing

emission and environmental degradation. For empirical analysis, 16 South-through-East Asian

(StEA) are considered.

Next section explains the methodology and model specification. Results and findings are presented

in Section 3. A composite indicator for combining the agricultural production and emission-

management efficiencies is explained in section 4. The chapter ends with the policy

recommendation and concluding remarks in section 5 and 6.

7.2 Methodology

This chapter uses parametric model Stochastic Frontier Analysis (SFA) which would help not

only to calculate the emission management efficiencies but also can estimate the contributions

(elasticities) of the inputs management into the emission. To further investigate the explanatory

factors of emission management efficiency, this study uses the technical inefficiency effects model

for using the panel data as proposed by Battese and Coelli (1995).


To have a comprehensive understanding of the sustainable green growth in agriculture, we need

to comprehend the interrelationship among the inputs, output, and emission. Primarily, inputs

(such as land, labor, capital, fertilizer, energy, and technology) are used to maximize the

agricultural production. Nevertheless, thrive for the higher production may also instigate the

deployment of more input resources which, in turn, may result in higher emission. The previous

chapter calculates the production efficiency resulted from the agricultural inputs. This chapter

calculates emission management efficiency resulted from the same set of inputs. To determining

the combined effect of efficiencies, a Green growth index is also formulated at the end.


The basic emission management (containment) model should target the minimization of emission

from the agricultural inputs and activities. The model is designed as shown in equation (7.1).

Minimizing

ln𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑖,𝑡 = 𝛽0 + 𝛽1ln𝐿𝑎𝑛𝑑𝑖,𝑡 + 𝛽2ln𝐿𝑎𝑏𝑜𝑟𝑖,𝑡 + 𝛽3 ln 𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖,𝑡 + 𝛽4 ln 𝐹𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟𝑖,𝑡 +

𝛽5 ln 𝐸𝑛𝑒𝑟𝑔𝑦𝑖,𝑡 + 𝛽6 ln 𝐹𝐷𝐼𝑖,𝑡 + 𝛽7𝑡𝑖𝑚𝑒 + 𝑈𝑖,𝑡 + 𝑉𝑖,𝑡 (7.1)

𝑖 = 1, 2, 3, ………… . , 𝑘 (for each country)

𝑡 = 1, 2, 3, ………… . , 𝑇 (for each year)

Here, Emission refers to the aggregated emissions (of all GHG including CO2, methane (CH4)

and nitrous oxide (N2O)) produced in the different agricultural activities. Land refers to the total

arable land of a country, Labor and Capital denote the total labor (in number) and Capital (in USD)

deployed in agriculture. Fertilizer and Energy refer to the amount of fertilizer and energy

consumed in agriculture production. FDI denotes the cumulative FDI inflow in agriculture since

2000. Variable time is incorporated to capture the time-trend in agricultural emission. Subscripts i

and t represent the i-th country and time respectively. 𝑈𝑖,𝑡 denotes the single-sided error term for

the combined effects of inefficiency, on which complete information is not available. 𝑉𝑖,𝑡 refers to

the normal statistical error term which captures the effect of inadvertently omitted variables.

Software FRONTIER 4.1 is used to perform the estimation with maximum likelihood method as

introduced by Coelli (1996). Cost function model is used for the minimization of emission.


7.2.2 Description of the data

Data on aggregated agriculture emission (in gigagram CO2-equivalent), arable land (in thousand

hectors), capital (in million USD), fertilizer (in kilogram), energy consumption in agriculture (in

Tera-joule), and FDI inflows in agriculture (in million USD) are extracted from the Food &

Agriculture Organization (FAO) of the United Nations. Labor (in thousand) data is collected from

the Key Development Indicators of the Asian Development Bank (ADB) annual reports. Data on

road density, access to improved water sources in rural, and agriculture value added per laborer

are also extracted from the WDI. Government expenditures on agriculture and education are

collected from ADB.

Data are collected for the 2000-2013 period for all StEA countries except Bhutan, Brunei, and

Maldives for which complete set of data are not available. Along with these three countries,

Singapore is also skipped from the further analysis because of its low-scale agriculture emission.

In aggregate these four countries emit only 0.03% of total agricultural emission of the region,

hence, skipping these countries from the analysis would not have substantial influence on

agricultural policy implication of this region.


7.3.1 Summary statistics

Table 7.1 shows the summary statistics of all the emission input and output factors of the countries.

It states that China remains the highest emitter in the agriculture sector with an annual average

agriculture emission of 661.76 megatons (1 megaton = 1000 gigagrams) of CO2-equivalent. India

and Indonesia follow China with 591.83 megatons and 146.27 megatons CO2-equivalent

respectively. Sri Lanka, Lao PDR, and Korea are the least emitting countries for agriculture. As

mentioned in the previous chapter, China and India also use the largest amount of all input factors.

Korea, Lao PDR, and Sri Lanka have the least arable land areas. Malaysia, Korea, and Lao PDR

have the lowest number of laborer in their agriculture production. Due to their small-scale

operations, Lao PDR, Nepal, and Cambodia deploy the least amount of capital in agriculture

production. Lao PDR, Nepal, and Cambodia also use the least of fertilizers among the countries.

Lao PDR, Sri Lanka, and Cambodia remain the lowest energy consumers for their agriculture

productions. Nepal, Lao PDR, and Sri Lanka have the least FDI inflow in their agriculture

production.

http://www.stata.com/


Table 7.1: Summary statistics of the input and output factors (annual average, 2000-2013)

Country/

Region

Emission Land Labor Capital Fertilizer Energy FDI

megatons

CO2-

equivalent

million

ha

million billion

USD

million Kg peta joule billion

USD

Bangladesh 70.37 9.27 23.88 0.87 1.63 36.14 4.34

Cambodia 17.35 5.26 4.78 0.17 0.04 1.03 2.46

China 661.76 517.01 313.94 41.63 49.88 1323.50 473.98

India 591.83 180.04 219.43 25.70 21.61 460.57 94.85

Indonesia 146.27 52.78 40.69 4.22 3.60 113.96 22.93

Japan 22.30 4.70 2.75 11.96 1.32 153.90 56.51

Korea, Rep. 12.77 1.85 1.79 4.28 0.69 115.17 79.60

Lao PDR 6.71 2.06 2.08 0.10 0.01 0.14 0.55

Malaysia 13.96 7.27 1.51 2.02 1.42 17.63 31.00

Myanmar 58.46 11.75 11.74 0.31 0.09 5.37 2.46

Nepal 19.56 4.18 8.56 0.17 0.04 3.53 0.08

Pakistan 121.74 36.12 20.93 2.25 3.55 32.33 12.61

Philippines 49.30 11.74 11.58 1.37 0.70 13.95 3.35

Sri Lanka 5.60 2.50 2.35 0.34 0.29 0.31 2.38

Thailand 62.74 20.33 13.84 2.45 2.05 139.00 44.44

Viet Nam 59.80 10.07 24.32 1.26 2.12 25.23 20.84

StEA 1921.12 877.47 706.62 99.15 89.03 2441.91 1015.40


7.3.2 Estimation of the coefficients

The result of the estimation of coefficients of the emission management efficiency model is

presented in Table 7.2. It implies that land, labor, fertilizer, and energy have positive elasticities

with agricultural emission in the StEA region; i.e., more use of land, labor, fertilizer, or energy

will increase the emission, ceteris paribus. For 1% increase of these input factors, ceteris paribus,

would result in 0.47%, 0.23%, 0.09%, and 0.21% respective increases in the agricultural emission

in the StEA region. Conversely, more capital or FDI in agriculture (which proxies better

technology) will contain the growth in emission, ceteris paribus. It is quite rational as more

contribution of capital, machinery, and state-of-arts technology will lessen the reliance on other

inputs like land, labor, fertilizers, and energy which are more prone to emission generation. Time

trend reveals that emission has been increasing at 4.4%, on average, ceteris paribus. The model

also fits reasonably well with a gamma of 61.1%. Value of log likelihood function represents the

degree of goodness of fit of the model. According to the value, the model is well fitted.


Table 7.2: Estimation of coefficients for Emission management efficiency model

ln(Emission) Estimation of coefficients

ln(Land) 0.466***

ln(Labor) 0.228***

ln(Capital) -0.117***

ln(Fertilizer) 0.094***

ln(Energy use) 0.208***

ln(FDI) -0.031***

time 0.044***

Inefficiency effect estimations Estimation of coefficients

ln(Road Density) -0.051**

Access to quality water in Rural 0.004**

ln(Value added per worker) 0.231***

ln(Agriculture expense per labor) -0.084***

ln(Public expenditure in education per capita) -0.213***

Gamma 0.611***

Log likelihood function 46.803

LR test 83.840

Total observation: 224


7.3.3 Estimations of the determinants of the emission management efficiency

The technical inefficiency effect model also estimates the coefficients of five determinants of the

emission management efficiency. The result presented in Table 7.2 indicates that infrastructure

facilities in forms of road density have a positive impact on emission-management efficiency in

the StEA region. 1% increase in road density would enhance the efficiency by 0.05%, ceteris

paribus. Governments’ direct expenditure on agriculture as well as to education sector also have

substantial positive effects on improving this emission-management efficiency. 1% increase in

governments’ expenditure in agriculture per worker would increase the efficiency by 0.08% while

a 1% increase in governments’ expenditure in education per capita would have increased it by

0.21%, ceteris paribus. This phenomenon reveals that governments of the StEA region have been

acknowledging the sustainability issues relating to the agriculture and subsequently, encouraging

the public expenditure in enhancing the emission-management efficiencies of the countries.


On the contrary, a higher share of access to quality water is found to have negative elasticity with

the emission-management efficiency in the StEA region. It may be due to the incitement of

utilizing the rather easily available water to enhance more production. Higher production would

demand the more proportionate use of the inputs, which, in turn, would result in higher emission.

Agriculture value added per worker also have a substantial adverse impact on emission-

management efficiency. It indicates that productivity of the workers is channelizing primarily to

enhance the production, but not to contain the emission. Countries have to focus on these issues

while balancing the efforts towards higher production and lower emission in agriculture.

7.3.4 Estimations of the Emission-management efficiency of the countries

The emission-management efficiency of the countries for the 2000-2013 period is shown in Table

7.3. It depicts that China has the highest emission-management efficiency of 70.4%. Thailand,

Malaysia, and Sri Lanka follow China with 60.3%, 58.7% and 54.3% efficiency. Among other big

producers and emitters, India and Indonesia have an emission-management efficiency of 42.8%

and 53.3% respectively. Myanmar, Lao, and Cambodia are the three least-efficient countries with

efficiency levels of 30.5%, 32.2%, and 34.0% respectively.

Table 7.3: Country-wise technical efficiency in Emission Management (2000-2013)

Country Technical efficiency

Bangladesh 40.7%

Cambodia 34.0%

China 70.4%

India 42.8%

Indonesia 53.3%

Japan 49.4%

Lao PDR 32.2%

Malaysia 58.7%

Myanmar 30.5%

Nepal 42.7%

Pakistan 38.6%

Philippines 40.9%

Korea, Rep. 51.5%

Sri Lanka 54.3%

Thailand 60.3%

Vietnam 42.2%



7.3.5 Performance in managing the sources of emission

Six sources of agricultural emission are analyzed: Soil management, animal management, manure

management, land use, energy use, and synthetic fertilizer use in agriculture. For more specific

analysis, we use the following formulas of emission intensity:

• 𝑆𝑜𝑖𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑠𝑜𝑖𝑙 𝑚𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡

𝐴𝑟𝑎𝑏𝑙𝑒 𝑙𝑎𝑛𝑑

• 𝐴𝑛𝑖𝑚𝑎𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑎𝑛𝑖𝑚𝑎𝑙 𝑚𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡

𝑇𝑜𝑡𝑎𝑙 𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛

• 𝑀𝑎𝑛𝑢𝑟𝑒 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑚𝑎𝑛𝑢𝑟𝑒 𝑚𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡

𝑇𝑜𝑡𝑎𝑙 𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛

• 𝐿𝑎𝑛𝑑 𝑢𝑠𝑒 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑙𝑎𝑛𝑑 𝑢𝑠𝑒

𝐴𝑟𝑎𝑏𝑙𝑒 𝑙𝑎𝑛𝑑

• 𝐸𝑛𝑒𝑟𝑔𝑦 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑒𝑛𝑒𝑟𝑔𝑦 𝑢𝑠𝑒

𝐸𝑛𝑒𝑟𝑔𝑦 𝑢𝑠𝑎𝑔𝑒 𝑖𝑛 𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒

• 𝑆𝑦𝑛𝑡ℎ𝑒𝑡𝑖𝑐 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑠𝑦𝑛𝑡ℎ𝑒𝑡𝑖𝑐 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 𝑢𝑠𝑒𝑡

𝑆𝑦𝑛𝑡ℎ𝑒𝑡𝑖𝑐 𝑓𝑒𝑟𝑡𝑖𝑙𝑖𝑧𝑒𝑟 𝑢𝑠𝑎𝑔𝑒 𝑖𝑛 𝑎𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒

Table 7.4: Ranking in source-based emission intensity management (2010-2013 average)

Rank Soil

emission

intensity

Animal

emission

intensity

Manure

emission

intensity

Land use

emission

intensity

Energy

emission

intensity

Synthetic

fertilizer

emission

intensity

1 Cambodia Japan Japan Korea Myanmar Japan

2 China Malaysia Korea Japan Indonesia Malaysia

3 Lao Korea Malaysia China Vietnam Cambodia

4 Philippines China Sri Lanka Vietnam Sri Lanka China

5 Thailand Thailand China India Nepal Korea

6 Sri Lanka Indonesia Indonesia Philippines Cambodia Lao

7 Myanmar Philippines Thailand Thailand Thailand Sri Lanka

8 India Sri Lanka India Pakistan Japan Thailand

9 Nepal Vietnam Bangladesh Bangladesh Malaysia India

10 Pakistan Myanmar Philippines Nepal Bangladesh Bangladesh

11 Indonesia Cambodia Nepal Sri Lanka Philippines Nepal

12 Malaysia India Pakistan Cambodia Lao Philippines

13 Vietnam Bangladesh Myanmar Myanmar Korea Indonesia

14 Japan Lao Vietnam Lao Pakistan Pakistan

15 Korea Nepal Cambodia Malaysia China Myanmar

16 Bangladesh Pakistan Lao Indonesia India Vietnam

* Green font represents the top-5 efficient countries



Based on the calculation, the performances of the countries are ranked and presented in Table 7.4.

Ranking 1 shows the best performing country while ranking 16 represents the worst. Levels of

emission intensities are also portrayed in Figure 7.1. Evidently, the lower the emission intensity,

the better the country manage the source. For instances, Cambodia is the best-performing country

in managing emission from the soil management. Japan performs as the best in animal

management, manure management, and synthetic fertilizer usage. Korea is found the best-ranked

in managing the land use, while Myanmar is found the best for managing energy-related emission

in agriculture.


Source: Author’s calculation based on FAOSTAT (2017) and using the MS-Excel mapping feature

Figure 7.1: Emission intensities of different sources in countries, (2010-2013 average)


7.3.6 Impact of Regional cooperation

In measuring the impact of regional cooperation on the aggregated emission-management

efficiency, the similar approach as described in the previous chapter is adopted. It considers

the StEA as a unitary bloc. Inputs and output variables of all the countries are summed up to

get the aggregated level of inputs and output for the whole region. This unitary bloc is then put

into the model and it’s efficiency is calculated by the maximum likelihood estimation by

comparing the entities with regard to the best-practice performer. The estimation reveals that

the overall technical efficiency of this regional bloc for emission containment is 52.6%. It

implies that if the countries could work under a regional cooperation bloc, on average, its

agriculture emission-management efficiency would be 52.6%. It also refers that if the RC bloc

can work together, with the given set of inputs, the region can further contain (i.e., decline) the

agricultural emission by 47.4% from the current level.

For understanding the link between the potential emission-management efficiency and the

potential level of production, this study uses following calculation as shown in Table 7.5.

Table 7.5: Estimation of the link between optimal emission-management efficiency and potential

production level

Scenario Input Output Emission

Actual scenario x y m

Optimal efficiency in emission-

management x y 0.526m

New level of production (with

current emission level and optimal

efficiency)

1.90 x 1.90y m


With x amount of aggregated input, the regional bloc can produce y output and m level of

emission. Now for the optimal efficiency in emission-containment, with the given set of inputs,

emission can further be reduced by 47.4% from the current level. Therefore, the optimal

emission level with the given input (x) and given production level (y) would be (1-0.474)m or

0.526m.

Now, assuming that the StEA region adopts the optimal emission-management efficiency

practices. If at that optimal efficiency level, it wants to remain on the current emission level

(m), it would, in turn, allow the aggregated input level to (1/0.526) x, i.e., 1.90x. Since x level

of input produces y level of output, the new level of input (i.e., 1.90x) would produce 1.90y


level of output. Therefore, the optimal technical efficiency in the emissionen-managemt

process may further enhance the production level by 90% from its current level, considering it

allows the emission remains at the current level. The analysis is also explained in Figure 7.2.


Figure 7.2: Estimation of the link between optimal production efficiency and emission reduction

7.3.7 Synergy Effect for potential gap reduction

Synergy effect infers that the combined action of a group of countries (i.e., regional

cooperation, here) should have more impact in bringing added benefits than the sum of

individual country’s actions. Hence, it is important to calculate the synergy effect in any RC

framework to understand the impact of the combined action.

For this purpose, gaps in potential emission reduction, i.e., actual emission minus optimal

(reduced) emission are calculated separately for all 16 countries. Then the model considers the

whole regional bloc (StEA) as a unitary entity and calculates the gap for that whole bloc. As

the Table 7.6 indicates, the gap in the potential emission reduction of the StEA (as a single

bloc) is smaller than the sum of all 16 countries’ gaps. The calculation, thus, supports the

synergy effect phenomenon.

To quantify the impact of the regional cooperation, the differences between those gaps from

potential (the sum of individual country’s gaps minus the whole bloc’s gap) are measured as

percentages of the gap without cooperation. Hence, synergy effect of the regional bloc is

calculated as:

X 1.90 X

m

0.526 m

Emission

Input O

Country’s actual state (m, x)

Frontier


𝐼𝑚𝑝𝑎𝑐𝑡 𝑜𝑓 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐 (𝑖. 𝑒. , 𝑆𝑦𝑛𝑒𝑟𝑔𝑦 𝑒𝑓𝑓𝑒𝑐𝑡)

= (𝐺𝑎𝑝 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐 − 𝐺𝑎𝑝 𝑎𝑠 𝑎 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐)

𝐺𝑎𝑝 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑏𝑙𝑜𝑐

= (𝑠𝑢𝑚 𝑜𝑓 𝑔𝑎𝑝𝑠 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 − 𝑔𝑎𝑝 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑓𝑜𝑟 𝑆𝑡𝐸𝐴)

𝑠𝑢𝑚 𝑜𝑓 𝑔𝑎𝑝𝑠 𝑓𝑟𝑜𝑚 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑐𝑜𝑢𝑛𝑡𝑟𝑦

Table 7.6 reveals that synergy effect, on average, is 2.63%.

Table 7.6: Synergy effect measure, to close the potential gaps in emission*

Year Sum of individual countries’ Gap Gap as a unitary bloc Synergy effect

1 968387 948397 2.1%

2 957174 935426 2.3%

3 918954 885113 3.7%

4 927660 910911 1.8%

5 937411 912845 2.6%

6 945930 936279 1.0%

7 933553 918447 1.6%

8 940615 921909 2.0%

9 924321 896399 3.0%

10 908267 868028 4.4%

11 877396 837508 4.5%

12 833397 817264 1.9%

13 780142 758197 2.8%

14 764181 740653 3.1%

Average 2.6%

* Gaps in giga grams CO2-equivalent


7.4 Green Growth Index in Agriculture: A Composite indicator

For an effective green growth policy in agriculture, a country should simultaneously focus on

growing its production as well as lowering the production-related emissions. It becomes

plausible through attaining higher efficiency both in the production process as well as emission

management. In reality, some countries may have higher productive efficiency owing to the

better use of inputs to maximize the production while some countries may have high efficiency

in managing the emission. Hence to have an idea about the resultant efficiency towards

attaining the green growth policy, a combined index needs to be prepared. Saltelli (2007)

mentions that a composite indicator is not only easier for the general interpretation but also

more useful in evaluating the performance rather than following the trends of several separate


indicators. According to the author, a composite indicator (or combined index) always help to

initiate the discussion and to motivate the common’s interest.

Though the formulation of a composite indicator seems to follow the mathematical or

computational models, the essence and justification for it depend on the intended purpose, peer

acceptance, and the craftsmanship of the modeler (Rosen, 1991). OECD (2008) handbook

identifies two key steps, multivariate analysis, and normalization, as essential steps for the

aggregation. Multivariate analysis requires to investigate the structure of the dataset and assess

its suitability to combine. Since this study attempts to combine two factors, both are in

efficiency terms, and for the same set of 16 countries, it is, therefore, plausible to combine.

Normalization is also attained since both efficiencies are estimated based on the best-practices

as the benchmark. Yet, the key discussion for an aggregation technique remains towards the

arbitrary weighting process (Sharpe, 2004). This study reveals that both the agriculture

production efficiency and the emission-management efficiency are equally significant for

attaining the sustainable green growth in agriculture. It also shows that there exist strong

linkages between the optimal efficiency of production with emission reduction and the optimal

emission-management efficiency with production rise. Hence, to concurrently emphasizing on

countries’ production and emission-management efficiency, equal weightages are considered

for aggregation technique.

Next question comes, whether the additive (linear aggregation) or multiplicative (geometric

aggregation) technique is suitable for this study. In fact, a country with lower scores would

prefer an additive aggregation technique over the multiplicative. However, it also means, a

multiplicative aggregation would set greater incentives to address the limiting factors more

intensely for the low-score country as it would give it a better chance of improving its position

in the ranking (Munda & Nardo, 2005). To implanting more emphasize in improving the

countries’ performances, the multiplicative aggregation techniques is considered.

Green Growth Index in Agriculture (GGIA), is thus proposed as follows:

𝑮𝑮𝑰𝑨 = √𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒄𝒚 × 𝑬𝒎𝒊𝒔𝒔𝒊𝒐𝒏 𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒄𝒚

Based on 2000-2013 average efficiencies, GGIA is calculated which is presented in Table 7.7.

China, Japan, and Korea have the highest GGIA with 82%, 67%, and 66%. Cambodia, Lao

PDR, and Thailand, on the contrary, have the lowest of GGIAs with 40%, 40%, and 47%. In

aggregate, the StEA region has the GGIA of 66%.


Figure 7.7: Countries overall performance regarding production efficiency, emission

management efficiency, and GGIA

Country Production

efficiency

Emission-management

efficiency

GGIA

Bangladesh 71% 41% 54%

Cambodia 46% 34% 40%

China 95% 70% 82%

India 65% 43% 53%

Indonesia 58% 53% 56%

Japan 91% 49% 67%

Lao PDR 50% 32% 40%

Malaysia 66% 59% 62%

Myanmar 84% 31% 51%

Nepal 53% 43% 47%

Pakistan 61% 39% 49%

Philippines 73% 41% 55%

Korea, Rep. 85% 52% 66%

Sri Lanka 76% 54% 64%

Thailand 37% 60% 47%

Vietnam 91% 42% 62%

StEA 84% 53% 66%



The analysis presented in this study has several policy implications in strengthening the

sustainable green growth in the agriculture sector of the StEA countries. From measuring the

production and emission management efficiencies, countries can have a comprehensive idea

about the respective strengths, and challenges which can help them improve their efficiency.

Adopting SFA models will also provide the evidence on how the input resources and economic

settings can impact on their efficiency levels.

From a regional cooperation perspective, policy framework based on the analysis presented in

this chapter would provide wide-ranging tools to manage the agricultural emission more

efficiently while complying the measures necessary for the transition towards the low-emission

agriculture system. At the same time, technology transfer, knowledge sharing, and capacity

building activities should be facilitated between the high-efficient countries and the low-

efficient countries so that low-efficient countries can improve as well. Institutional settings at


the regional level should be strengthened so that it can constantly monitor the level of progress

and disseminate the adequate policy, rules, and technical support to all member countries.

Creation of a common fund to finance in agricultural green growth projects may also play an

important role.


This chapter examines the countries’ current state of emission management efficiency and

subsequently derives the impact of regional cooperation in achieving the green growth in

agriculture by reducing emission and environmental degradation.

SF analysis for emission-management shows that land, labor, fertilizer, and energy have

positive elasticities with agricultural emission in the StEA region. In contrast, capital or FDI

in agriculture have the negative elasticities with agricultural emission. Time trend reveals that

emission has been increasing at 4.4%, on average, ceteris paribus.

The technical inefficiency effect model is also adopted to estimate the determinants of the

emission management efficiency. Results indicate that infrastructure facilities in forms of road

density have a positive impact on emission-management efficiency in the StEA region.

Governments’ direct expenditure on agriculture as well as to education sector also have

substantial positive effects on improving this emission-management efficiency. On the

contrary, a higher share of access to quality water and Agriculture value added per worker are

found to have negative elasticity with the emission-management efficiency in the StEA region.

Result also reveals that China has the highest emission-management efficiency of 70.4% while

Myanmar has the lowest at 30.5%. The estimation reveals that the overall technical efficiency

of this regional bloc for emission containment is 52.6%. It also refers that if the RC bloc can

work together, with the given set of inputs, the region can further contain (i.e., decline) the

agricultural emission by 47.4% from the current level.

The calculation also shows that regional cooperation has positive synergy effect in emission-

management for the StEA countries.

To have an idea about the resultant efficiency towards attaining the green growth policy, a

combined index needs to be prepared from the production efficiency and emission-

management efficiency of each country. In this regard, the multiplicative (geometric

aggregation) technique is used for generating the Green Growth Index in Agriculture (GGIA).

Calculation shows that China, Japan, and Korea have the highest GGIA with 82%, 67%, and


66%. Cambodia, Lao PDR, and Thailand, on the contrary, have the lowest of GGIAs with 40%,

40%, and 47%. In aggregate, the StEA region has the GGIA of 66%.

The analysis of production efficiency (presented in the previous chapter) and emission-

management efficiency (presented in this chapter) would provide a comprehensive insight for

the respective countries to understand how the input resources and economic settings can

impact their efficiency levels in attaining sustainable green growth in agriculture. It is very

crucial for policy design and subsequent implementation. From an RC perspective, it would

provide extensive tools to manage the intra-regional demand-supply of foods more efficiently

while confirming the low levels of emission in the regional agriculture system.

Next chapter will discuss on the role of regional cooperation for sustainable natural resources

management.


Chapter 8

Regional cooperation for Sustainable Natural Resources

Management



Country’s wealth is a function not only of physical capital and human capital, but also natural

capital, the depletion or degradation of which will affect the sustainability of its current level

of the economy (Asafu-Adjaye, 2004). Natural resource usage, in general, is a function of

demand and supply of the resources. Increasing population, fast economic growth, and

widening social inequities have wielded mounting pressures on natural resources of the

countries. The demand-side shows an ever-growing trend as a result of the countries’ continual

efforts to achieve higher growth and improved living standards. On the contrary, the supply

side remains more or less unpredictable. In case the supply fails to match the rising demand,

or due to the prolong inefficient use of resources, the result could be a scarcity of natural

resources, which brings with it the risk of reaching a point of no return regarding environmental

degradation in the course of time. It would not only result in a depleted ecosystem but also act

as a source of threats of over-exploration attempts for new resource stocks that would damage

the environment’s long-term sustainability. The impact of these complex value chains is not

confined only within the national boundary; rather, a significant portion of this affects beyond

the geographical boundaries and ultimately becomes a global concern.

Increasing concerns over the complex dimensions of natural resource dynamics have induced

the paradigm transition of global policies focusing more on the sustainable and resilient

environment in recent times. Hence, efficient and effective natural resources management has

become utmost crucial for ensuring sustained development and a better future for a region. A

country or a region may have an abundance of natural resources, however, may still face the

challenge of keeping a balance between environmental sustainability and economic

development (Anbumozhi & Bauer, 2010). A concerted effort under regional cooperation is of

much significance in this regard. It is expected that such regional ties and cooperation

framework would help the countries to complement each other in attaining sustainable

extraction and productive usage of natural resources. It can be achieved through mutual sharing

of endowments, technologies, and relevant knowledge and experiences among the countries.

This chapter is an attempt to empirically investigate this role of regional cooperation in

strengthening a sustainable natural resources management in a selected region.

For a comprehensive study of this issues, a two-stage analysis is required. At first stage, it is

crucial to comprehend the role of fundamental factors that prompt the consumption of natural

resources in an economy. Understandably, there are few demand-driven factors which follow

the natural course of actions such as population growth and gradual uplift in living standards.


Increase in these factors would require more consumption of resources. There are also other

factors which would help to reduce the consumption level such as adoption of advanced

technology or any restricting government policy that curbs the use of natural resources.

However, along with analyzing these demand-driven factors for resource consumption, it is

also crucial to investigate how well the resources are used. Estimating the productivity trends

would be an effective tool for this second stage in the analysis. Classical growth theories mainly

deal with two sets of productivities: labour productivity and capital productivity. Productivities

of other transitional inputs such as raw materials and energy are often unheeded and afterward

mingled under the residual term, commonly known as Multi-Factor Productivity (MFP).

Nonetheless, packaging multifarious impacts of several factors into a single factor may easily

understate or delude the policymakers about the significance of any particular factor (e.g.,

natural resources). Considering the faster rate of growing demand for resources under this

integrated globalized system, resource productivity (RP) is, therefore, becoming one of the

significant priority policy schemas for all the countries. Hence, an intensive understanding of

the underlying factors’ effect on resource productivity would help to deliver higher value from

the natural resources and would also boost the international competitiveness of the countries.

Through this comprehensive analysis, countries would be able to figure out their respective

areas of strengths and challenges in sustainable natural resource management. From a regional

cooperation’s point of view, such analysis would help to frame a strenuous collective effort to

support each other for the greater mutual benefits for the whole region.

Therefore, the specific objectives of this chapter are set as below:

• To analyze how the resource consumptions are influenced by the demand-side factors

of the economy; i.e., GDP, population, per capita income (living standard of the

people), and technological readiness.

• To investigate the trends in sustainable productivities10 of resource usage and the role

of technical efficiency, technological changes, and input substitutability of resources

in it.

For empirical analysis, we have chosen 20 South-through-East Asian (StEA) countries.

10 Conventional productivity refers to the GDP resulted from a unit of input (such as, labor, capital, or energy). In

this case, sustainable productivity of resources comprises GDP per emission as the output. The rationale of

sustainable productivity implies that the productivity will be higher if the rate of growth of GDP is greater than

the rate of growth of emission.


8.2 Current states of Natural resources management in StEA countries

StEA region has the fastest extraction of natural resources among the regions. It has almost a

six-fold increase in natural resource extraction since 1980 as depicted in Table 8.1. Role of two

emerging economies, China, and India, remains the key in this regard. StEA region extracted

24.5% of global resources in 1980, which is increased to 54.1% in 2017. Recent trend shows

an average annual growth rate in the extraction of 3.7% in this region, much faster than the

global average of 2.5% p.a.

Table 8.1: Regional Extraction of natural resource materials (in Gigatons)

Regions 1980 1990 2000 2010 2017 Growth

(2010-2017)

Africa 2.51 3.05 3.80 5.55 6.22 1.7%

Asia and the Pacific 9.52 15.22 23.13 41.22 51.87 3.7%

Europe 11.03 11.82 10.44 11.64 12.37 0.9%

Latin America and the Caribbean 2.78 3.54 5.11 6.81 7.63 1.7%

North America 6.58 7.76 8.97 7.69 7.34 -0.6%

Sub-Saharan Africa (M49) 1.91 2.25 2.71 3.89 4.49 2.2%

West Asia 1.24 1.13 1.60 2.41 2.93 3.1%

World 33.65 42.52 53.06 75.31 88.36 2.5%

StEA 8.26 13.31 20.71 38.00 47.85 3.7%

Source: UNEP (2018)

Table 8.2: Regional Consumption of natural resource materials (in Giga Tons)

Regions 1980 1990 2000 2010 2017 Growth

(2010-2017)

Africa 2.24 2.79 3.42 5.18 5.99 2.2%

Asia and the Pacific 9.87 15.54 23.83 42.44 53.74 3.8%

Europe 11.82 12.55 10.94 11.85 12.44 0.7%

Latin America and the Caribbean 2.56 3.22 4.63 6.09 6.82 1.7%

North America 6.56 7.82 9.36 7.89 7.09 -1.4%

Sub-Saharan Africa (M49) 1.77 2.09 2.45 3.61 4.17 2.2%

West Asia 0.47 0.57 0.85 1.70 2.10 3.4%

World 33.53 42.48 53.03 75.15 88.18 2.5%

StEA 8.79 13.98 21.92 40.14 51.15 3.9%

Source: UNEP (2018)

Like the resource extraction, StEA region is also the fastest region in natural resource consumption.

Table 8.2 reveals that its consumption increased from 8.79 Gigatons in 1980 to 51.15 Gigatons in 2017.

Global share of resource consumption increased from 26% to 58% during this period. Recent trend


shows an average annual growth rate in the extraction of 3.9% in this region, much faster than

the global average of 2.5% p.a.

Table 8.3: Regional per capita income and resource consumption trend

Regions

Per Capita income (2005 constant

USD)

Per capita resource consumption

(in tons)

2000 2010 2013 2000 2010 2013

Africa 1083 1393 1411 4.18 4.94 5.04

Asia and the Pacific 2593 3578 3928 6.76 10.77 11.83

Europe 17227 19825 20169 12.6 13.2 13.3

Latin America and

the Caribbean 4661 5612 5973 8.86 10.26 10.4

North America 39921 42900 44506 29.56 22.75 21.22

West Asia 6522 7923 8542 8.94 12.94 12.18

World 6716 7633 7917 8.63 10.8 11.29

StEA 3199 4596 5180 6.49 10.66 11.76

Source: UNEP (2018)

Table 8.3 refers to the region’s resource consumption trend with increasing per capita income

(i.e., higher living standards). North America and Europe have the highest per capita income

among the regions. On average, the StEA region, in spite of its faster growth rate over the

decades, has the per capita income of USD 5180 in 2013, still well-below as compared to the

world average of USD 7917. North America and Europe also have the highest per capita

resource consumption among the regions. On average, the StEA region, with its faster growth

rate over the decades, has the per capita consumption of 11.76 tons in 2013, just above the

world average of 11.29 tons.

Source: UNEP (2018)

Figure 8.1: Growth rates in per capita income and resource consumption, by regions (2000-2013)

-3.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0%

Africa

Asia and the Pacific

Europe

Latin America & the Caribbean

North America

West Asia

World

StEA

Growth (per capita resource consumption) Growth (per capita income)


Figure 8.1 illustrates the growth rates of per capita income and resource consumption of the

regions during the 2000-2013 period. Interestingly, the developed regions, like North America

and Europe have the lowest growth in resource consumption. Contrariwise, the StEA and Asia-

pacific region have the highest rate of resource consumption growth. It seems that there are

hints of convergence in per capita resource consumption among the regions.

Table 8.4 shows the annual average volume of natural resource extraction as well as

consumption in the StEA countries over the 2010-2017 period. China and India, in aggregate,

share 82.7% of the region’s natural resource extraction. Among the others, Indonesia and

Vietnam extract 5.5% and 2.2% of regional natural resources. China and India also consume

the most, 81.8%, in aggregate. Indonesia and Japan, among the others, consume more with the

regional shares of 4.2% and 2.7% respectively.

Table 8.4: Country-wise extraction and consumption of NR* (2010-2017 average)

Extraction of NR Consumption of NR

Annual average

(in MT)

Share

(in %)

Growth Annual average

(in MT)

Share

(in %)

Growth

Bangladesh 362.78 0.84 2.6% 397.16 0.86 2.9%

Bhutan 9.45 0.02 5.0% 7.58 0.02 5.6%

Brunei 19.72 0.05 -1.7% 8.85 0.02 6.1%

Cambodia 67.92 0.16 -1.5% 74.19 0.16 -0.4%

China 29327.08 67.90 4.7% 30939.54 67.31 4.9%

India 6373.69 14.76 3.4% 6698.42 14.57 4.0%

Indonesia 2357.97 5.46 1.5% 1932.02 4.20 1.1%

Japan 573.24 1.33 -2.8% 1217.31 2.65 -1.4%

Korea, Rep. 245.03 0.57 -8.9% 632.18 1.38 -3.8%

Lao PDR 66.32 0.15 8.3% 65.50 0.14 8.2%

Malaysia 493.77 1.14 -0.6% 490.06 1.07 0.0%

Maldives 1.43 0.00 4.1% 2.54 0.01 4.8%

Myanmar 179.93 0.42 1.4% 172.93 0.38 1.5%

Nepal 94.98 0.22 2.7% 101.66 0.22 2.9%

Pakistan 754.27 1.75 3.8% 782.73 1.70 4.6%

Philippines 405.85 0.94 2.5% 395.83 0.86 1.2%

Singapore 33.75 0.08 -2.3% 175.19 0.38 3.4%

Sri Lanka 80.63 0.19 4.2% 97.34 0.21 5.5%

Thailand 778.20 1.80 3.6% 809.50 1.76 4.0%

Viet Nam 966.21 2.24 -0.7% 963.67 2.10 -0.5%

StEA (aggregated) 43192.23 100.00 3.7% 45964.20 100.00 3.9%

Source: UNEP (2018)

* Natural resources (NR) and its components are defined in section 8.5


Most of the fastest-growing economies, such as Lao, Bhutan, Sri Lanka, and the Maldives

along with the economic power China, and India have the faster rates of extraction. Conversely,

seven countries have a negative rate of extraction. The developed economies like Korea, Japan,

and Singapore have more negative rates of resource extraction during the referred period.

Similarly, Lao, Brunei, Bhutan, Sri Lanka, and China are the top-5 fastest resource consuming

countries in this region. Only four countries, on the other hand, experience decline rate in their

respective resource consumptions: Korea, Japan, Vietnam, and Cambodia.

Figure 8.2 illustrates the regional aggregated demand (consumption) and supply (extraction) of

natural resources material over the 1990-2017 period. It shows that the self-sufficiency in

natural resource has been declining throughout this period as the gap between the consumption

and extraction of resource materials has been widening.

Source: Author’s calculation based on UNEP (2018)

Figure 8.2: Demand and supply of natural resource materials in StEA region (1990-2017)

Table 8.5 shows the rates of extraction and consumption of natural resources in different time periods.

The rate for both extraction and consumption was highest during 2000-2010 period, which has

substantially slowed down in recent time. It is largely related to the economic growth rates of the

countries.

Table 8.5: Extraction and consumption rates in different time periods

Column1 1990-2000 2000-2010 2010-2017

Resource Extraction 5.55% 8.35% 3.70%

Resource Consumption 5.69% 8.31% 3.92%

Source: Author’s calculation based on UNEP (2018)

0.00

10.00

20.00

30.00

40.00

50.00

60.00

1990 1995 2000 2005 2010 2015

Consumption Extraction


8.3 Areas of Cooperation for NRM

Countries under a regional cooperation framework can set their priority agendas based on the

common assessment of the region’s natural resources along with the key cross-boundary

challenges. ASEAN sets an example by adopting 11 priority areas which comprise the

transboundary issues, environmental education, environmentally sound technologies, quality

living standards, sustainable use of coastal, marine, and biodiversity, forest management, and

climate change adaptation and mitigations techniques (Kalirajan et al. 2015).

Institutional and regulatory settings may help to formulate the appropriate policies for each

country that would align the common goals for the region. Mutual sharing of information,

knowledge, expertise, finance, and other resources would be crucial in this regard.

To framing these cooperation areas, it is pivotal to make some economic analysis which would

provide adequate indications about each countries’ strengths and challenges so that the

effective policies can be adopted regarding what is needed and how best the countries can

collectively act to achieve the targets. This chapter would primarily focus on that economic

analysis which could instigate the framing of effective regional cooperation for sustainable

NRM.

8.4 Methodology and Data

Two policy objectives set for this chapter require separate methodologies to investigate. Both

are discussed here in details.

8.4.1 Methodology for analyzing the determinants of resource consumption (demand-side)

It is evident that natural resources, like labors and physical capitals, are crucial endowment and

embedded tool for economic growth. Notably, the emerging economies have to depend on these

resources for sustaining their growth intensely. Growing needs from the increasing population

in meeting up higher living standards largely influences the economy’s demand for natural

resources. However, as mentioned earlier, the extraction of resources should be optimized in a

way that makes it sustainable not only regarding ensuring adequate reserves of resources for

the future but also providing a low-emission growth in the economy. Adopting prudent policy-

mix, thus, need to be justified through analyzing the interrelations and dynamics among these

targets. It is also pivotal to track the changes of these components over a specified period. Kaya


identity is a well-recognized tool extensively used for such analysis, especially in

environmental policy analysis (O'Mahony, 2013; Tavakoli, 2017). In general, it is a

decomposition technique used to analyze the contribution of key drivers to the factor of interest.

Usually, it quantifies the absolute or relative changes of the underlying factors, such as the

scale of economic, technological, and demographic changes, on a specific environmental factor

or policy variable (Kaya, 1989).

For this chapter, the factor of interest is the natural resources, for which we use the following

identity equation:

𝑅 = 𝑃 ∗𝐺𝐷𝑃

𝑃∗

𝑅

𝐺𝐷𝑃 (8.1)

where R: natural resources consumption in an economy

P: Population size

GDP: Real GDP of the economy

The first-term P is used to indicate the demand for resources come due to the increasing number

of population. The second term, GDP per capita, refers to the affluence factor. The third term,

resource intensity to GDP, signposts the level of technological advancement. Lower the value,

higher will be the technological readiness and advancement.

To investigating the impact of the previous year’s emission on the reference year’s demand for

resources, the GHG emission term is incorporated in following extended equation:

𝑅 = 𝑃 ∗𝐺𝐷𝑃

𝑃∗

𝑀−1

𝐺𝐷𝑃∗

𝑅

𝑀−1 (8.2)

Here, 𝑀−1 : GHG emission level in time (t-1), considering t as the reference time.

Now the term 𝑀−1

𝐺𝐷𝑃 refers to the emission intensity to GDP. Lower the value, higher will be the

technological readiness and advancement in managing emission from economic activities.

Final term, 𝑅

𝑀−1, on the other hand, measures the implication of resource consumption on

emission. Since both the factors- resource consumption and emission need to be constrained

for efficient policy settings, the expected trend of this factor is rather ambiguous.

Any change in the four parameters mentioned in the right side of the equation would ultimately

lead to the change in resource consumption of the economy. With the adoption of mathematical

approximation, we can assume that the rate of change in resource consumption will be


approximately equivalent to the sum of the rate of changes of all those terms in the

corresponding period.

Let’s take the logarithm form of the equation:

𝑙𝑛𝑅 = 𝑙𝑛𝑃 + 𝑙𝑛 (𝐺𝐷𝑃

𝑃) + 𝑙𝑛 (

𝑀−1

𝐺𝐷𝑃) + 𝑙𝑛 (

𝑅

𝑀−1) (8.3)

To measure the changes, preferably in the annual average change rate of each term, we can

translate the above equation into the following equation:

𝑑(𝑙𝑛𝑅) = 𝑑(𝑃) + 𝑑 (𝑙𝑛𝐺𝐷𝑃

𝑃) + 𝑑 (𝑙𝑛

𝑀−1

𝐺𝐷𝑃) + 𝑑 (𝑙𝑛

𝑅

𝑀−1

) (8.4)

8.4.2 Methodology for measuring resource use productivity and its determinants

Productivity is one of the primary drivers that sustain the long-lasting economic growth. In

simple words, improved productivity refers to the higher economic value added from a unit of

particular input or multi-factor inputs. Resource productivity, in conventional economic terms,

can be defined as the ratio of economic value outcome (output) and resources consumed

(input). However, in this study, we would extend this definition and, hence, incorporate the

sustainability factor to design the model. Environmental degradation, measured in the form of

emission, are considered in this regard.

By considering the availability of data, this study uses Data Envelope Analysis (DEA), a non-

parametric approach, to decompose the change in RP. According to the classical growth

models, the improvement of RP in the long-run should be primarily influenced by two factors:

the technological progress, and how effectively the chosen technology is applied. The former

involves innovating resource-efficient products and equipment. The latter is termed as

technical efficiency in literature, which indicates the extent towards applying the best-practice

techniques of the given technology by a country. Besides these two factors, RP growth is also

influenced by the input substitutability of natural resources (with capital and labour) during its

usage in the production process. Scale efficiency is another factor which could influence the

role of natural resources.

The main advantage of DEA in the current context is that the efficiency calculated from DEA

helps to make relative comparison among the countries with respect to the most-efficient

performance in the sample, i.e., the actual frontier (Fare et al., 1994). Decomposition of RP


through DEA is different from the conventional Solow (1956) approach of decomposing the

output growth. Solow model decomposes the output growth into inputs growth and total factor

productivity growth with the critical assumption that all firms (or economic agents) are fully

technically efficient. Conversely, DEA approach assumes that not all economic agents are

technically full efficient, which is more realistic and hence, this study prompts to use the

approach.

8.4.2.1 DEA and the decomposition framework

DEA designs the linear programming problems for multiple outputs and multiple inputs model

with a given set of technologies to measure the relative efficiencies among the peer decision-

making units (DMUs).

Malmquist productivity indexes are computed to apply the DEA techniques for the

decomposition of changes in Total Factor Productivity (TFP) into technical efficiency change

and technological change (Fare et al., 1994).

Detailed theoretical background on the decomposition technique is presented here.

The Malmquist index of RP change

To estimating the output-based Malmquist index, let’s assume that at period 𝑡 = 1,2, … , 𝑇,

production technology, 𝑆𝑡, is available for the transformation of inputs, 𝑋𝑡 ≡ 𝐾𝑡, 𝐿𝑡, 𝑅𝑡 ∈ ℝ+𝑁

into the output, 𝑌𝑡 ∈ ℝ+𝑀. Here 𝐾𝑡, 𝐿𝑡 and 𝑅𝑡 refer to capital, labour, and natural resource

respectively, and 𝑌𝑡 represents the output at time t.

Hence, technology can be expressed as

𝑆𝑡 = {(𝐾𝑡, 𝐿𝑡 , 𝑅𝑡, 𝑌𝑡): (𝐾𝑡, 𝐿𝑡, 𝑅𝑡) can produce 𝑌𝑡}

It is assumed that the set of 𝑆𝑡 satisfies certain conditions that enables to define the output

distance functions which can be stated as follows:

𝐷0𝑡(𝐾𝑡, 𝐿𝑡 , 𝑅𝑡, 𝑌𝑡) = inf{𝜃: (𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡 𝜃⁄ ) ∈ 𝑆𝑡} (8.5)

Distance function (8.5) measures the maximum feasible increase in the observed output at time

t, 𝑌𝑡, given the input vector (𝐾𝑡, 𝐿𝑡 , 𝑅𝑡), and technology, 𝑆𝑡. It is important to note that

𝐷0𝑡(𝐾𝑡, 𝐿𝑡 , 𝑅𝑡, 𝑌𝑡) ≤ 1 always holds, and 𝐷0

𝑡(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡) = 1 if and only if the observation

(𝐾𝑡, 𝐿𝑡, 𝑅𝑡 , 𝑌𝑡) lies on the boundary or frontier of the technology, 𝑆𝑡. In such a case, production


is considered to be fully technically efficient. Figure represents an illustration in which scalar

input is used to produce scalar output.

In Figure 8.3, at time t, for given inputs (𝑋𝑡) and technology (𝑆𝑡), the observed output is 𝑌𝑡,

represented by Oa. The distance function shows that the maximum feasible production, given

inputs (𝑋𝑡), is at 𝑌𝑡 𝜃∗⁄ , represented by Ob. Farrell (1957) measures the output technical

efficiency, which calculates how far an observation is from the frontier of technology. Hence

the technical efficiency at time t for (𝐾𝑡, 𝐿𝑡, 𝑅𝑡 , 𝑌𝑡) is 𝑂𝑎 𝑂𝑏⁄ . Similarly, at time τ, with the

input-output set of (𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏), the technical efficiency is is 𝑂𝑑 𝑂𝑓⁄ .

Figure 8.3: Illustration of the Output distance functions

Again, the impact of the technological change would result in different frontiers of production.

For defining the Malmquist index, we need to estimate the output distance functions concerning

different time periods. Two factors could be used to explain this inter-frontier movement.

i. Next period’s inputs and output considering the current period’s technology level; we

can express it as 𝑂𝑑 𝑂𝑒⁄

ii. Current inputs and output can be expressed as follows by considering the next period’s

technology level, presented as 𝑂𝑎 𝑂𝑐⁄

Expressing in distance function,

𝐷0𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) = inf{𝜃: (𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏 𝜃⁄ ) ∈ 𝑆𝑡} (8.6)

𝑌

0

𝑌𝑡 = 𝑎

(𝑋𝜏, 𝑌𝜏)

𝑓

𝑆𝜏

𝑆𝑡 𝑌𝜏 = 𝑑

𝑒, 𝑐

(𝑋𝑡, 𝑌𝑡)

𝑋 ∈ {𝐿,𝐾, 𝑅} 𝑋𝑡 𝑋𝜏

𝑌𝑡 𝜃∗⁄ = 𝑏


This distance function (8.6) calculates the maximum proportional change in outputs required

to make (𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) feasible in relation to the technology at time period t. For instance,

production (𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) occurs outside the set of feasible production in period 𝑡, implying

that technical progress has occurred. The value of distance function, 𝐷0𝑡(𝐾𝜏 , 𝐿𝜏, 𝑅𝜏, 𝑌𝜏),

evaluating (𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) relative to technology in period 𝑡 is 0𝑑 0𝑒⁄ , which is greater than

one11.

The output distance functions are homogeneous of degree +1 in outputs, 𝐷0𝜏(𝐾𝑡, 𝐿𝑡 , 𝑅𝑡, 𝛼𝑌𝑡) =

𝛼𝐷0𝜏(𝐾𝑡, 𝐿𝑡 , 𝑅𝑡, 𝑌𝑡) where 𝛼 is a positive scalar.

Using the production technology in period 𝑡 as a reference, the output-based index for RP

change between time periods 𝑡 and 𝜏 can be defined as

𝑅𝑃𝐶𝐻 =𝑌𝜏 𝑅𝜏⁄

𝑌𝑡 𝑅𝑡⁄=

𝐷0𝜏(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)

𝐷0𝑡(𝐾𝑡,𝐿𝑡,𝑅𝑡,𝑌𝑡)

×𝐷0

𝑡(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)


× {(𝑌𝜏 𝑅𝜏⁄ ) 𝐷0

𝑡(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)⁄

(𝑌𝑡 𝑅𝑡⁄ ) 𝐷0𝑡(𝐾𝑡,𝐿𝑡,𝑅𝑡,𝑌𝑡)⁄

}

=𝐷0

𝜏(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)


×𝐷0



×𝐷0

𝑡(𝐾𝑡,𝐿𝑡,𝑅𝑡,𝑌𝑡)×𝑅𝑡

𝐷0𝑡(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)×𝑅𝜏

(8.7)

According to equation (8.5), 𝑌𝑡 𝐷0𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏)⁄ [= 1 𝐷0

𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏)⁄ ]12 represents the

maximum potential output when the production technology is 𝑆𝑡 and the input bundle is

(𝐾𝑡, 𝐿𝑡, 𝑅𝑡). Thus, the third term in equation (8.7), (𝐷0𝑡(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡) × 𝐸𝑡) (𝐷0

𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) × 𝑅𝜏)⁄ [=

(1 𝐷0𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏)⁄ 𝑅𝜏⁄ ) (1 𝐷0

𝑡(𝐾𝑡 , 𝐿𝑡 , 𝑅𝑡 , 𝑌𝑡)⁄ 𝑅𝑡⁄ )⁄ ], measures the change in maximum potential energy

productivity during the two periods by using the technology 𝑆𝑡 as a reference technology.

To further decompose it, following Färe & Lovell (1988), we assume that the production

technology is constant-return-to-scale (CRS), implying that the output distance function is

homogeneous to degree -1 in inputs: 𝐷0𝜏(𝛽𝐾𝑡, 𝛽𝐿𝑡, 𝛽𝑅𝑡, 𝑌𝑡) = 𝛽−1𝐷0

𝜏(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡) where 𝛽 is

a positive scalar. Thus, the following identity holds:

(𝐷0𝑡(𝐾𝑡, 𝐿𝑡, 𝑅𝑡 , 𝑌𝑡) × 𝑅𝑡) (𝐷0

𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) × 𝑅𝜏)⁄ = 𝐷0𝑡(𝑘𝑡, 𝑙𝑡, 1, 𝑦𝑡) 𝐷0

𝑡(𝑘𝜏, 𝑙𝜏, 1, 𝑦𝜏)⁄ , where

𝑘𝑡 = 𝐾𝑡 𝑅𝑡⁄ , 𝑙𝑡 = 𝐿𝑡 𝑅𝑡⁄ and 𝑦𝑡 = 𝑌𝑡 𝑅𝑡⁄ respectively denote capital-natural resource ratio,

labour-natural resource ration and output-natural resource ratio. Therefore, equation (8.7) can

be rewritten as

𝑅𝑃𝐶𝐻 =𝐷0

𝜏(𝐾𝜏,𝐿𝜏,𝑅𝜏,𝑌𝜏)


×𝐷0



×𝐷0

𝑡(𝑘𝑡,𝑙𝑡,1,𝑦𝑡)

𝐷0𝑡(𝑘𝜏,𝑙𝜏,1,𝑦𝜏)

≡ 𝐸𝐹𝐹𝐶𝐻 × 𝑇𝐸𝐶𝐻(𝜏) × 𝑃𝑅𝑃𝐶𝐻(𝑡) (8.8)

11 Similarly, the value of the distance function, 𝐷0

𝜏(𝐾𝑡 , 𝐿𝑡 , 𝐸𝑡 , 𝑌𝑡), is 0𝑎 0𝑐⁄ , which is less than one. 12 This equation holds as output distance function is homogenous of degree +1 in outputs.


Equation (8.8) suggests that RP change is the product of three components: technical efficiency

change, (𝐸𝐹𝐹𝐶𝐻); technological change measured by using the inputs and outputs in period 𝜏,

𝑇𝐸𝐶𝐻(𝜏); and change in the maximum potential resource productivity, 𝑃𝑅𝑃𝐶𝐻𝑡, which is

measured by using time period 𝑡 technology as a reference.

Using the production technology in period 𝜏 instead of the technology in time period 𝑡 as a

reference, similar to (8.8), equation (8.9) can be obtained. For instance, RP change between

time periods 𝑡 and 𝜏 by using production technology in period 𝜏 as a reference, will be

𝑅𝑃𝐶𝐻 = 𝐸𝐹𝐹𝐶𝐻 × 𝑇𝐸𝐶𝐻(𝑡) × 𝑃𝑅𝑃𝐶𝐻(𝜏) (8.9)

As suggested by Färe (1994), to avoid the ambiguity of choosing one of the decompositions

in equations (8.8) and (8.9), the geometric mean of the two decompositions can be used.

Therefore, RP change is given by

𝑅𝑃𝐶𝐻 = 𝐸𝐹𝐹𝐶𝐻 × [𝑇𝐸𝐶𝐻(𝜏) ∗ 𝑇𝐸𝐶𝐻(𝑡)]1 2⁄ × [𝑃𝑅𝑃𝐶𝐻(𝑡) ∗ 𝑃𝑅𝑃𝐶𝐻(𝜏)]1 2⁄

≡ 𝐸𝐹𝐹𝐶𝐻 × 𝑇𝐸𝐶𝐻 × 𝑃𝑅𝑃𝐶𝐻 (8.10)

The RP change is decomposed using equation (8.10) in this chapter. It shows that the RP change

can be decomposed into three components. The first component, 𝐸𝐹𝐹𝐶𝐻, measures technical

efficiency change (i.e., change in in how far observed production is far from maximum

potential production) between the two periods. Values greater than one (𝐸𝐹𝐹𝐶𝐻 > 1) imply

improvements in technical efficiency. The second term, 𝑇𝐸𝐶𝐻, measures technological

change, which is the shift in the technology or production frontier between the two periods.

The third component, 𝑃𝑅𝑃𝐶𝐻, measures the effects on RP change from the changes in capital-

resource ratio and labour-resource ratio between the two periods, and is calculated as a residual

using equation (8.10) as we have values for 𝑅𝑃𝐶𝐻, 𝐸𝐹𝐹𝐶𝐻 and 𝑇𝐸𝐶𝐻.

It is important to note that the product of the first two components in equation (8.10) is the

Malmquist total factor productivity (TFP) change index (i.e., 𝑇𝐹𝑃𝐶𝐻 = 𝐸𝐹𝐹𝐶𝐻 × 𝑇𝐸𝐶𝐻),

which is widely studied in the literature on productivity and efficiency analysis.

There are some different methods (i.e., Balk 1993) that can be used to calculate the output

distance functions that are used in constructing the Malmquist TFP index. The most popular

method has been the data envelopment analysis (DEA), which is used in this chapter.

To calculate the TFP change index, 𝑇𝐹𝑃𝐶𝐻, of country i between two time periods 𝑡 and 𝜏, it

is necessary to solve four different linear-programming problems under the assumption of a


CRS technology13: 𝐷0𝑡(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡), 𝐷0

𝜏(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏), 𝐷0𝑡(𝐾𝜏, 𝐿𝜏, 𝑅𝜏 , 𝑌𝜏) and

𝐷0𝜏(𝐾𝑡, 𝐿𝑡 , 𝑅, 𝑌𝑡)14. It is assumed that each observation of inputs and outputs in data set is

strictly positive, and the number of observations remain constant over all the years. Moreover,

it is assumed that there are 𝑖 = 1,2, 3, … , 𝐼 countries which use 𝑛 = 1,2,3, … , 𝑁 inputs 𝑋𝑛𝑡𝑖 to

produce 𝑚 = 1,2,3, … ,𝑀 outputs 𝑌𝑚𝑡𝑖 at each time period 𝑡 = 1,2,3, , 𝑇. Following Färe et al.

(1994), the reference (or frontier) technology in period 𝑡 is assumed as

𝑆𝑡 = {(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡): 𝑌𝑚𝑡 ≤ ∑ 𝑧𝑖,𝑡𝐼𝑖=1 𝑌𝑚𝑡

𝑖 𝑚 = 1,2,3, ,𝑀; (8.11)

∑ 𝑧𝑖,𝑡𝐼𝑖=1 𝑋𝑛𝑡

𝑖 ≤ 𝑋𝑛𝑡 𝑛 = 1,2,3, , 𝑁;

𝑧𝑖,𝑡 ≥ 0 𝑖 = 1,2,3, … , 𝐼}

which exhibits CRS and strong disposability of inputs and outputs; where 𝑋 ∈ {𝐿, 𝐾, 𝑅}, 𝑌𝑚𝑡

and 𝑋𝑛𝑡 are respectively any given output 𝑚 and input 𝑛 in the data set and 𝑧𝑖,𝑡 is an intensity

variable representing at what intensity a particular activity (in our case, each country is an

activity) may be employed in production.

Based on the fact that the output distance function (i.e., 0𝑎 0𝑏⁄ ) is reciprocal to the output-

based Farrell measure of technical efficiencies (i.e., 0𝑏 0𝑎⁄ ), the linear-programming problem

for computing 𝐷0𝑡(𝐾𝑡, 𝐿𝑡 , 𝐸𝑡, 𝑌𝑡) for each 𝑖′ = 1,2,3, … , 𝐼 is as follows:

The objective function is defined as to maximize the technical efficiencies (Farrel measure):

(𝐷0𝑡 (𝐾𝑖′ ,𝑡, 𝐿𝑖′ ,𝑡, 𝑅𝑖′ ,𝑡, 𝑌𝑖′,𝑡

) )−1

= max𝜑𝑖′ ,𝑧𝑖,𝑡 𝜑𝑖′ , (8.12)

which is subject to the technology constraints in period 𝑡:

𝜑𝑖′ 𝑌𝑚𝑡𝑖′ ≤ ∑ 𝑧𝑖,𝑡

𝐼𝑖=1 𝑌𝑚𝑡

𝑖 𝑚 = 1,… ,𝑀


𝑖 ≤ 𝑋𝑛𝑡𝑖′ 𝑛 = 1,… ,𝑁

𝑧𝑖,𝑡 ≥ 0 𝑖 = 1,… , 𝐼

where 1 ≤ 𝜑𝑖′ < ∞ is the Farrel measure of technical efficiencies in period 𝑡, and 𝜑𝑖′ − 1 is

the proportional increase in outputs that could be achieved by the 𝑖′-th country, with input

quantities held constant. From Figure 8.3, 𝜑𝑖′ = (0𝑏 0𝑎⁄ ) = (1 𝜃𝑖′⁄ ), where 𝜃𝑖′is the output-

13 This assumption ensures that resulting TFP change measures satisfy the fundamental property that if all inputs

are multiplied by the (positive) scaler 𝛿 and all outputs are multiplied by the (non-negative) scaler 𝛼, then the

resulting TFP change index will equal 𝛼 𝛿⁄ . 14 To make the full decomposition, including the scale-change component, needs calculation of an additional two

programming problems: 𝐷0𝑣𝑡 (𝐾𝑡 , 𝐿𝑡 , 𝑅𝑡 , 𝑌𝑡) and 𝐷0𝑣

𝜏 (𝐾𝜏 , 𝐿𝜏 , 𝑅𝜏, 𝑌𝜏).


oriented technical efficiency score that varies between zero and one (and reported by DEAP

program). The computation of 𝐷0𝜏(𝐾𝜏, 𝐿𝜏, 𝑅𝜏, 𝑌𝜏) is exactly like equation (8.11), where 𝜏 is

replaced for 𝑡.

Two remaining linear programming problems are based on the mixed period problems. The

linear-programming problem for computing 𝐷0𝑡(𝐾𝜏, 𝐿𝜏 , 𝑅𝜏, 𝑌𝜏) is computed for observation 𝑖′

as follows:

(𝐷0𝑡(𝐾𝑖′,𝜏, 𝐿𝑖′,𝜏, 𝑅𝑖′,𝜏, 𝑌𝑖′,𝜏) )

−1= max𝜑𝑖′,𝑧𝑖,𝑡

𝜑𝑖′ (8.13)

subject to

𝜑𝑖′𝑌𝑚𝜏𝑖′ ≤ ∑ 𝑧𝑖,𝑡

𝐼𝑖=1 𝑌𝑚𝑡

𝑖 𝑚 = 1,… ,𝑀


𝑖 ≤ 𝑋𝑛𝜏𝑖′ 𝑛 = 1,… ,𝑁

𝑧𝑖,𝑡 ≥ 0 𝑖 = 1,… , 𝐼

This linear-programming of equation (8.13) involves observations from both periods 𝑡 and 𝜏.

The reference technology relative to which (𝐾𝑖′,𝜏, 𝐿𝑖′,𝜏 , 𝑅𝑖′,𝜏, 𝑌𝑖′,𝜏) is evaluated is constructed

from observations 𝑡. In equation (8.12), 𝐷0𝑡(𝐾𝑖′,𝑡, 𝐿𝑖′,𝑡, 𝑅𝑖′,𝑡, 𝑌𝑖′,𝑡) ≤ 1 because of

(𝐾𝑖′,𝑡, 𝐿𝑖′,𝑡, 𝑅𝑖′,𝑡, 𝑌𝑖′,𝑡) ∈ 𝑆𝑡, however, 𝐷0𝑡(𝐾𝑖′,𝜏, 𝐿𝑖′,𝜏, 𝑅𝑖′,𝜏, 𝑌𝑖′,𝜏) may take values greater than

1 since (𝐾𝑖′,𝜏, 𝐿𝑖′,𝜏, 𝑅𝑖′,𝜏, 𝑌𝑖′,𝜏) need not belong to 𝑆𝑡. The computation of 𝐷0𝜏(𝐾𝑡, 𝐿𝑡, 𝑅𝑡, 𝑌𝑡) is

like equation (8.13), but the 𝑡 and 𝜏 superscripts are transposed. Both equations (8.12) and

(8.13) exhibit CRS and strong disposability of inputs and outputs15. It is important to note that

the 𝜑𝑖′s and 𝑧𝑖,𝑡s are likely to take different values in the above four linear-programming

methods for each period. Moreover, the above four linear programming methods must be

solved for each country. If there are 𝑇 time periods, then (3𝑇 − 2) linear programming must

be solved for each country in the sample.

8.4.2.2 Models specification

The abovementioned approach of decomposing the RP is used for three different models to

capture different aspects of sustainable natural resource management.

15 Following Afriat (1972), some studies relax the assumption of CRS as allowing non-increasing returns to scale

by adding following restriction: ∑ 𝑧𝑘,𝑡𝐾𝑘=1 ≤ 1.


Model 1: RP in economic growth

The model is designed to measure how the natural resources are used to increase the GDP of

each country. Hence, real GDP is taken as the observed output against the input endowments

of labor, capital, and natural resources. Output-oriented DEA measure technique is used to

estimate how much the GDP can be proportionally extended with the given input levels.

Therefore, linear programming problems, in this case, set the objective to maximize GDP with

the given conditions and constraints.

Model 2: Resource use and limiting GHG emission

Model 2 is designed to examine the role of natural resource consumption in generating

environmental emission within the country. GHG emission is taken as the observed output

against the input endowments of labor, capital, and natural resources. Unlike model 1, an input-

oriented DEA measure technique is used to estimate how for a given level of output (emission,

in this case), the inputs can be minimized. The outcome of the model will, therefore, investigate

how the increased RP (as for the same level of output, the input is reduced) for limiting

emission is influenced by the technological progress, technical efficiency, and input

substitutability of natural resources.

Model 3: RP in a combined effect of economic growth and GHG emission

To capturing the combined objective of higher economic growth and lower emission resulted

from the consumption of natural resources, model 3 is designed. Real GDP per emission is

taken as the observed output against the input endowments. Output-oriented DEA measure

technique is, therefore, used to estimate how much the GDP per emission can be proportionally

extended with the given input levels. Linear programming problems, in this case, set the

objective to maximize the output with the given conditions and constraints.

8.5 Data sources

For resources consumption data, this study uses Domestic material consumption data from

UNEP’s Environment Live database (https://environmentlive.unep.org/downloader).

Domestic material consumption (DMC) refers to the aggregated amount of materials

(comprising biomass, fossil fuels, metal ores, non-metallic minerals and other industrial

materials) used by an economy. It is calculated as the volume of raw materials extracted

domestically, plus all physical imports and minus all physical exports of these resources. It

comprises both the intermediate and final consumption until unconstrained to the environment.

https://environmentlive.unep.org/downloader


Population data are extracted from UNPD’s World Population Prospects

(http://esa.un.org/unpd/wpp/). GDP data, measured in 2010 constant USD, are collected from

World Banks’s World Development Indicators database. Emission data are extracted from the

CAIT - WRI’s Climate Data Explorer (http://cait.wri.org/).

Data are collected for all 20 StEA countries over the 2000-2015 period.

8.6 Results and Findings

8.6.1 Demand-side analysis

Figure 8.4 illustrates the contribution of the determinants of resource consumption in the StEA

countries measured through Kaya identity over the 2000-2015 period. It illustrates that

emerging economies have the higher annual average growth rate of resource consumption as

compared to the developed economies. Countries like Lao PDR, Vietnam, and Cambodia have

the highest consumption rate at 11.1%, 6.9%, and 6.9% respectively. China and India also

consume at the average rate of 6.8% and 3.9% respectively. Conversely, Singapore, Japan, and

Korea have the contraction in resource consumption with the rate of -2.2%, -1.9%, and -1.7%

respectively. In aggregate, StEA region experiences average annual resource consumption

growth of 5.2% during this period.


Figure 8.4: Components of Kaya identity for natural resource consumption (2000-2015)

-10.0%

-5.0%

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5.0%

10.0%

15.0%

20.0%

Ban

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Bh

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Bru

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Cam

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dia

Ch

ina

Ind

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Ind

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Jap

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Ko

rea,

Re

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Lao

PD

R

Mal

aysi

a

Mal

div

es

Mya

nm

ar

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Pak

ista

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Ph

ilip

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Sin

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Sri L

anka

Thai

lan

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StEA

Change in P Change in GDP/P Change in M/GDP Change in R/M growth rate (Resource consumption)

http://esa.un.org/unpd/wpp/

http://cait.wri.org/


The result implies that population growth has a positive but little contribution to resource

consumption growth in the StEA countries. It has a relatively higher contribution for Maldives,

Singapore, and Bhutan with growth rates of 2.7%, 2.3%, and 2.1% respectively. Contrariwise,

it has least contribution for Japan, Korea, and China with growth rates of 0.0%, 0.4%, and 0.6%

respectively. On average, population growth rate contributes 1.0% towards the resource

consumption for the whole StEA region.

Affluence, i.e., GDP per capita, has a higher positive contribution to resource consumption

growth for all countries but Brunei. It has a relatively higher contribution to the faster-growing

countries like China, India, and Vietnam with 8.6%, 5.8%, and 5.2% respectively. The

contribution is highest in Myanmar with 9.0%. Brunei, Japan, and Pakistan, on the other hand,

have the least contribution to resource consumption from this affluence factor. Since the rate

of economic growth of an emerging economy is higher, so as its economic activities, it is

expected that the resource consumption rate would also be higher. The result presented in Table

8.6 supports this intuition. In aggregate, StEA region experiences average annual growth of per

capita income at 3.7% during the 2000-2015 period.

Emission intensity shows declining trends for all the countries except Vietnam, Maldives, and

Brunei. It is a good sign since the declining values indicate countries’ improvement in

technological readiness and progress in managing emission from economic activities. Besides,

it suggests that the resource consumption trends help in a way through which the growth rate

of emission becomes lesser than the growth rate of GDP for most of the countries. Myanmar,

Malaysia, and Bhutan have the highest contribution in this regard. Big economies like China,

Japan, Korea, and India also have the moderate contribution of this emission intensity factor to

shape their resource consumptions. On average, the growth rate of emission intensity in the

StEA region is 0.17% during the 2000-2015 period.

Expected trend of the resource intensity of emission, as mentioned earlier, is ambiguous. The

result also supports the statement as 12 countries experience increasing trends while rest of the

countries experience the declining trends of this factor. Noticeably, the fastest growing

countries like China, India, Vietnam, and Indonesia all have negative average growth of

resource per emission factor. It indicates that in these countries resource consumption rate is

lesser than the rate of growth in emission. In aggregate, the factor grows at an annual average

rate of 0.26% in the whole StEA region over the 2000-2015 period.

Above analysis explains the absolute growth rate of the factors that contribute to the resource

consumption of the countries. However, to understand the relative impacts of the factors, it is


pivotal to measure the sensitivities of resource consumption towards these factors, i.e., how

much change in resource consumption would result from a 1% change in each determinant

factor? Table 8.6 presents the elasticity values to answer this question. We can have both

horizontal as well as vertical analysis from it.

Horizontal analysis helps comparative investigations regarding the sensitivities of all four

factors within a country. For instance, in China, affluence factor is mostly sensitive to increase

resource consumption. 1% increase in GDP per capita would increase the resource

consumption by 1.41%. Conversely, resource per emission factor is most sensitive in reducing

the resource consumption. 1% increase in this factor would result in 0.31% decrease in China’s

resource consumption. Based on this analysis, countries can design their domestic plan on

resource demand.

Table 8.6: Elasticities of the determinants of resource consumption

Country Population GDP per capita Emission per GDP Resource per emission

Bangladesh 0.42 1.34 -0.99 0.23

Bhutan 0.62 1.52 -2.14 1.00

Brunei 0.42 -0.18 0.06 0.70

Cambodia 0.24 0.84 -1.00 0.92

China 0.08 1.28 -0.31 -0.06

India 0.37 1.41 -0.60 -0.18

Indonesia 0.42 1.23 -0.44 -0.22

Japan -0.01 -0.39 0.03 1.37

Korea, Rep. -0.26 -2.04 0.67 2.63

Lao PDR 0.13 0.51 -0.35 0.71

Malaysia 1.24 1.88 -5.12 3.00

Maldives 0.47 0.69 0.11 -0.28

Myanmar 0.25 2.66 -2.66 0.74

Nepal 0.35 0.75 -1.78 1.69

Pakistan 0.64 0.61 -0.41 0.16

Philippines 0.88 1.61 -2.73 1.24

Singapore -1.05 -1.27 1.65 1.67

Sri Lanka 0.17 1.24 -0.93 0.52

Thailand 0.15 0.90 -0.36 0.31

Vietnam 0.15 0.74 0.23 -0.12

StEA 0.20 0.71 0.03 0.05 Source: Author’s calculation

Vertical analysis, on the other hand, helps to analyze the comparative sensitivities of each

factor among the countries. As the Table 8.6 indicates, Malaysia has the highest sensitivity of

population growth on resource consumption. 1% increase in population in Malaysia would lead


to 1.24% increase in domestic resource consumption. Elasticity for GDP per capita is highest

for Myanmar. 1% increase in per capita income would lead to 2.66% increase in resource

consumption here. The adverse sensitivity of emission intensity is highest in Malaysia. 1%

increase in emission intensity would decrease the resource consumption by 5.12%. Malaysia

also has the highest sensitivity of resource per emission on its resource consumption. 1%

increase in this factor would result in 3.0% increase in resource consumption.

Results also reveal that in aggregate, 1% increase in population, per capita income, emission

intensity, and resource per emission would lead to increase the resource consumption in the

StEA region by 0.20%, 0.71%, 0.03%, and 0.05% respectively.

8.6.2 Productivity analysis

Model 1: RP in economic growth

Table 8.7 presents the decomposition of RP in economic growth in the StEA countries over the

2000-2014 period. As the result depicts, Singapore, Korea, Bhutan, Malaysia, and the

Philippines have experienced the highest improvement in RP during this time. Over this period,

these five countries attained 192%, 120%, 72%, 60%, and 52% increase in their respective RP.

Contrariwise, Lao, Brunei, and Vietnam have experienced declining RP of 45%, 29%, and 11%

respectively during the 2000-2014 period.

Table 8.7 also refers that all the countries’ RP changes are mostly dominated by the efficiency

changes in producing output. Though the technological progress is not vividly evident in most

of the countries, the role of input substitutability of natural resources is found relatively

significant. Pakistan, Philippines, and Indonesia have the highest progress in technical

efficiency (in producing output) with 166%, 96%, and 83% respectively over the 2000-2014

period. Conversely, Lao and Brunei have declined technical efficiency with 7%, and 2%

respectively during this period. Japan, Sri Lanka, and Korea have highest improvement in

technological factor in producing output with 28%, 22%, and 17% respectively. Cambodia,

Lao, and Vietnam have devalued their respective technological level by 49% each from the

reference year 2000. Substitutability of capital and labor with natural resources factor has

experienced the highest increase in Singapore, Nepal, and Cambodia with 178%, 77%, and

68% respectively over the reference period. Conversely, Pakistan and Sri Lanka have negative

improvement in this substitutability factor with a decline of 18%, and 2% respectively during

this period.


Table 8.7: Decomposition of RP in economic growth

Country RPCH EFFCH TECHCH PRPCH

Bangladesh 1.40 1.45 0.60 1.60

Bhutan 1.72 1.41 1.05 1.17

Brunei 0.71 0.98 0.68 1.07

Cambodia 1.08 1.28 0.51 1.68

China 1.39 1.57 0.64 1.39

India 1.51 1.69 0.61 1.47

Indonesia 1.36 1.83 0.60 1.23

Japan 1.43 1.00 1.28 1.12

Lao PDR 0.55 0.93 0.51 1.17

Malaysia 1.60 1.75 0.8 1.14

Maldives 1.17 1.15 0.86 1.18

Nepal 1.05 1.14 0.52 1.77

Pakistan 1.11 2.66 0.51 0.82

Philippines 1.52 1.96 0.63 1.22

Rep. Korea 2.2 1.32 1.17 1.42

Singapore 2.92 1.17 0.90 2.78

Sri Lanka 1.24 1.03 1.22 0.98

Thailand 1.03 1.42 0.71 1.01

Vietnam 0.89 1.46 0.51 1.21

* Calculation of Myanmar is skipped due to some missing data


Model 2: Resource use and limiting GHG emission

Table 8.8 presents the decomposition of RP regarding limiting the GHG emission in the StEA

countries over the 2000-2014 period. As the result depicts, Korea, Singapore, Japan, Maldives,

and Indonesia have experienced the highest improvement in using least natural resources for a

specified level of emission. Over this period, these five countries attained improvement in RP

(in limiting emission) by 68%, 56%, 42%, 25%, and 23% respectively. Contrariwise, Lao,

Nepal, Cambodia, Malaysia, and Bhutan have experienced declining RP (in limiting emission)

by 66%, 59%, 58%, 49%, and 39% respectively during the 2000-2014 period.

Table 8.8 also refers that all the countries’ RP in limiting emission is mostly affected by the

input substitutability of natural resources with capital and labor. Though the technological

progress in using lesser resources for given level of emission is not evident in any country, the

role of technical efficiency seems to be influential for most of the countries.


Table 8.8: Decomposition of RP in resource use and limiting emission


Bangladesh 0.91 1.10 0.57 1.46

Bhutan 0.61 0.83 0.73 1.00

Brunei 0.71 1.00 0.69 1.03

Cambodia 0.42 0.76 0.39 1.42

China 1.04 1.32 0.61 1.30

India 1.11 1.41 0.58 1.35

Indonesia 1.23 1.68 0.61 1.20

Japan 1.42 1.94 0.73 1.00

Lao PDR 0.34 0.65 0.47 1.12

Malaysia 0.51 0.64 0.76 1.04

Maldives 1.25 0.61 0.73 2.78

Nepal 0.41 0.60 0.45 1.51

Pakistan 0.94 2.68 0.42 0.84

Philippines 0.70 0.95 0.63 1.16

Rep. Korea 1.68 2.30 0.73 1.00

Singapore 1.56 0.81 0.80 2.42

Sri Lanka 0.74 1.00 0.73 1.01

Thailand 0.86 1.17 0.73 1.00

Vietnam 1.10 2.18 0.44 1.14



Pakistan, Korea, Vietnam, Japan, and Indonesia have the highest progress in technical

efficiency (in using lesser resources for given emission level) with 168%, 130%, 118%, 94%

and 68% respectively over the 2000-2014 period. Conversely, Nepal, Maldives, Malaysia, Lao,

and Cambodia have a maximum decline in technical efficiency with 40%, 39%, 36%, 35%%,

and 24% respectively during this period. None of the countries’ technological improvement is

evident. Singapore, Malaysia, Korea, and Japan experienced the least decline in technological

factor with 20%, 17.1%, 17%, 16.8%, and 16.7% respectively. Contrariwise, Cambodia,

Pakistan, Vietnam, Nepal, and Lao experienced the most devaluation in their respective

technological level (in adopting the lesser use of natural resources for a given level of emission)

during the 2000-2014 period. Substitutability of capital and labor with natural resources factor

has experienced the highest increase in Maldives, Singapore, and Nepal 178%, 142%, and 51%

respectively over the reference period. Conversely, Pakistan has negative improvement in this

substitutability factor with a decline of 16%. For some countries like Japan, Korea, and

Thailand, this factor has no overall change during this period.


Model 3: RP in a combined effect of economic growth and GHG emission

Table 8.9 presents the decomposition of RP in a combined effect of economic growth and GHG

emission. As it depicts, Malaysia, Singapore, Korea, Philippines, and Nepal have experienced

the highest improvement in this combined RP factor during the 20001-2014 period. Over this

period, these five countries attained 161%, 159%, 66%, 66%, and 52% increase in their

respective RP. Contrariwise, Vietnam, Lao, Maldives, China, and Bhutan have experienced

highest declines in this RP by 69%, 67, 58%, 50%, and 39% respectively during the 2000-2014

period.

Table 8.9: Decomposition of RP in economic growth and limiting emission


Bangladesh 0.98 2.19 0.23 1.91

Bhutan 0.61 1.46 0.42 1.00

Brunei 0.63 1.91 0.33 1.01

Cambodia 0.97 2.45 0.23 1.69

China 0.50 1.18 0.31 1.35

India 0.77 1.64 0.23 2.00

Indonesia 0.72 2.03 0.23 1.52

Japan 1.31 2.47 0.42 1.26

Lao PDR 0.33 1.21 0.23 1.17

Malaysia 2.62 5.17 0.42 1.21

Maldives 0.42 1.00 0.31 1.34

Nepal 1.52 3.56 0.23 1.83

Pakistan 0.74 3.85 0.23 0.82

Philippines 1.66 4.04 0.23 1.75

Rep. Korea 1.66 2.70 0.42 1.47

Singapore 2.59 2.98 0.38 2.31

Sri Lanka 0.98 2.33 0.42 1.00

Thailand 0.70 2.81 0.23 1.06

Vietnam 0.31 1.08 0.23 1.21



Table 8.9 also refers that this RP is mostly dominated by the efficiency changes for the

combined action of producing more output while constraining the emission due to resource

consumption. Technological progress in attaining the combined goals, like the previous

models, is not in most evident in any country. However, the role of input substitutability of

natural resources is found relatively significant. Malaysia, Philippines, Pakistan, Nepal, and


Singapore have the highest progress in technical efficiency (in maintaining higher output-

emission ratio) with 417%, 304%, 284%, 255%, and 198% respectively over the 2000-2014

period. Conversely, Maldives, Vietnam, China, Lao, and Bhutan have the least progress in

technical efficiency with 0.1%, 8%, 18%, 21% and 46% respectively during this period. None

of the countries’ technological improvement in attaining the combined goals is evident. Sri

Lanka, Malaysia, Korea, Japan, and Bhutan experienced the least decline in technological

factor with 58% each. Contrariwise, Vietnam, Lao, India, Indonesia, and Bangladesh

experienced the most devaluation in their respective technological level (in increasing output

while adopting the lesser use of natural resources for a given level of emission) during the

2000-2014 period. Substitutability of capital and labor with natural resources factor has

experienced the highest increase in Singapore, India, Bangladesh, Nepal, and the Philippines

with 130%, 99%, 91%, 83% and 75% respectively over the reference period. Conversely,

Pakistan and Sri Lanka have negative improvement in this substitutability factor with a decline

of 18%, and 0.01% respectively during this period.

8.7 Policy issues

Planning for a sustainable NRM requires a specialized approach from the conventional

economic planning. Since every small neighborhood has its unique environmental

characteristics and diversified demand from the locals, the native communities would better

understand the challenges, prospects and the way forward. Hence a holistic bottom-up

approach, therefore, should be the most effective for NRM planning (Kalirajan et al., 2015).

This chapter provides in-depth economic analysis of the natural resource extraction and

consumption trends through investigating the respective determinants in all StEA countries.

Furthermore, it examines the sources of productivity of natural resources used for economic

growth, environmental degradation in the form of emission, and a combined goal of sustainable

natural resource management in these countries. Following the outcome of this analysis,

countries would be able to figure out their respective areas of strengths and limitations, and can

set their national resource management policies accordingly.

On a broader scale, countries can agree on a regional cooperation framework to overcome not

only the domestic challenges but also the transboundary issues on natural resource

exploitations. Natural resource management has, indeed, a much broader scope which requires

all levels of stakeholders to involve for its success. Proper synchronization between domestic

and regional NRM policies is pivotal. Strong institutional setting (both at domestic and regional


level), prudent legal and regulatory reforms, financial innovation, technological diffusion, and

funding in technology adaptation and capacity building are few of the channels for regional

cooperation in this regard. The analysis, based on the countries’ current states and ongoing

trends as presented in this chapter, may play an important role to design these issues. For

instance, some of the countries are technologically advanced which can help the less

technologically-developed countries to face the sustainable NRM challenges. Some countries

are more efficient to manage the issues than others. Mutual sharing, in this process, could have

a substantial aggregated benefit for the whole region.

8.8 Concluding Remarks

In an NRM context, the learning environment is imperative, which should be conducive for all

stakeholders to scrutinize the efficacy of their respective role and policies in the process of

sustainable natural resources management. With this continuous self-assessment, the strategies

may be polished and updated to improve the system further. Consequently, a new set of action

plans may be framed and pooled with those stakeholders again.

Following chapter will analyze the role of RC for promoting intraregional trade of the

environmental goods.


Chapter 9

Regional cooperation for Sustainable Green Growth:

Role of intraregional trade on Low-Carbon Goods



Trade is identified as an engine for sustained economic growth and massive poverty alleviation,

especially in the Asian regions (Kalirajan & Anbumozhi, 2014). However, too much focus on

conventional trade and economic growth exert immense pressure on the unsustainable

resources extraction and subsequent environmental degradation. Global awareness of these

issues has instigated the countries towards a paradigm policy shift for attaining the sustainable

Low-Carbon Green Growth (LCGG) in recent time. Such a compulsion should turn the

countries more towards the sustainable production and consumption in the forms of Low-

Carbon Goods (LCG) or Environment-friendly Goods (EG). In this study, LCG and EG are

interchangeably used and referred to as LCG. OECD (2005) defines LCG as “the goods and

services to measure, prevent, limit, minimize, or correct environmental damage to water, air,

and soil, as well as problems related to waste, noise, and ecosystems.” In the WTO

negotiations, coverage of such goods is, however, constricted to those goods and services

which would result in positive environmental impacts. In other words, LCG is defined as those

goods and services whose end-result refers to the abatement of pollution (Cosbey et al. 2010).

UNCTAD (2004) defines such environmentally preferable products as the products which

cause “significantly less environmental harm at some stage of their life cycle” as compared to

the substitute goods and services which would be used for similar purpose.

LCG industry is at its early stage of development, and the analysts have emphasized on ‘low-

carbon industrial revolution’ to make a successful transition towards the low-carbon

economies (Stern, 2012; Pearson & Foxon, 2012; Leggewie & Messner, 2012). Literatures

advocate on several propositions that would ensure the low-carbon transition economically and

environmentally desirable; such as financing the long-term technological advancements,

institutional settings, adoption of best-practices, productivity gains, and disseminating

economic welfare. Since the degree of adoption of those LCG industries varies among the

countries, concerted regional cooperation should help to accelerate this process. To match the

internal as well as the external demand and supply of these goods, intraregional trade, therefore,

should play a pivotal role.

Despite substantial critiques against the extent of liberalizing the multilateral trade, most of the

literature portray the multilateral trade as one of the key engines for sustained development

(Wacziarg & Welch, 2003, Kalirajan, 2007, Gandolfo, 2014, Broda et al., 2017). Economic

theories imply that trade facilitation would increase the consumer surplus (in importing

countries) and producer surplus (in exporting countries). It would also promote the efficient


use of resources in a most productive way, increase factor productivities, and stimulate the

specialization of products and competitiveness (Leamer & Levinsohn, 1995, Davis et al., 1997,

Harrigan & Zakrajsek, 2000). Hence, along with meeting the demand-supply gaps of the

countries, intraregional trade of LCG can provide adequate supports through abovementioned

channels that would accelerate the implementation of the sustainable green growth in the

region. However, to understand how the regional cooperation would instigate intraregional

trade, it is imperative to measure how efficiently the countries can perform and facilitate their

trade with others. From a regional cooperation’s point of view, it is also essential to scrutinize

all possible determinants which could influence the performances of the countries. Such an

analysis would be crucial to understanding the strengths and deficiencies of the countries for

the intraregional trade of LCG, which could subsequently help to devise the strategies for

boosting up the trade volume under a regional cooperation framework.

This chapter attempts to estimate the intraregional export performances of 20 South-through-

East Asian (StEA) countries. From the analysis, it aims to estimate the countries’ untapped

intraregional trade potential for the LCG products. It also aims to comprehensively investigate

the implications of various determinants of trade in these countries. Such an analysis would

provide substantial indications on the strengths and weaknesses of the countries in boosting the

intraregional LCG trade. Subsequently, it would instigate to formulating policy guidelines on

how the experiences, knowledge, or expertise of the top-performing countries can be shared or

diffused to the others under a regional cooperation framework.

9.2 Statistics of the LCG trade in StEA countries

9.2.1 Intraregional trade (aggregated)

Table 9.1 shows the intraregional aggregated LCG export and import volume of the StEA

countries. It reveals that Japan, China, and Korea are the three largest exporters in this region

with 330.5, 290.2, and 109.7 billion USD of intraregional export over the 2006-2016 period.

These three countries jointly share about 77% of total regional export. Singapore and Thailand

also have moderate shares of 4.9%, and 4.7% respectively. Rest of the countries have the shares

of 2% or below. China is the largest importers with 258.6 billion USD which constitutes 27.3%

of total intraregional import. Japan, Korea, Thailand, and Singapore are the other larger

importing countries. Export to import ratio implies that Japan, Korea, and China are net


exporters, i.e., have more intraregional export than import. Rest of the countries are

predominantly net importers.

Table 9.1: Aggregated intraregional export and import of the StEA countries (2006-2016)

Country Export (in

million USD)

Import (in

million USD)

Export to

import ratio

Export

share

Import

share

Bangladesh 1898 6695 0.28 0.20% 0.71%

Bhutan - 65 0.00 0.00% 0.01%

Brunei 56 726 0.08 0.01% 0.08%

Cambodia 73 821 0.09 0.01% 0.09%

China 290235 258528 1.12 30.66% 27.31%

India 15014 45032 0.33 1.59% 4.76%

Indonesia 19722 33303 0.59 2.08% 3.52%

Japan 330487 173248 1.91 34.91% 18.30%

Korea, Rep. 109689 126582 0.87 11.59% 13.37%

Lao PDR - 908 0.00 0.00% 0.10%

Malaysia 57089 49023 1.16 6.03% 5.18%

Maldives - 541 0.00 0.00% 0.06%

Myanmar 169 1981 0.09 0.02% 0.21%

Nepal 228 1024 0.22 0.02% 0.11%

Pakistan 194 13608 0.01 0.02% 1.44%

Philippines 18543 20109 0.92 1.96% 2.12%

Singapore 46629 79612 0.59 4.93% 8.41%

Sri Lanka 165 2446 0.07 0.02% 0.26%

Thailand 44195 91934 0.48 4.67% 9.71%

Vietnam 12228 40430 0.30 1.29% 4.27% Source: World Integrated Trade Solution (2017)

9.2.2 Intraregional trade (category-wise)

WTO-153 list of environmental goods is classified into 12 different groups (Kalirajan, 2016).

For the sake of purpose-based understanding, this study combines those twelve groups of LCG

into four broad categories: Proactive Management products (PMGT), Pollution and clean-up

products (POLL), Technology-based products (TECH), and Monitoring-based product

(MONIT). Table 9.2 refers to the product categorization of the LCGs.

Table 9.2: Product categorization of the LCGs

Category Groups

Proactive management products:

LCGs which are required for the

precautionary measures for any pollution

or environmental degradation.

1. Solid & hazardous waste recycling (SHR)

2. Heat & Energy Management (HEM)

3. Natural risk management (NRM)

4. Natural resources protection (NRP)


Pollution and clean-up products:

LCGs which are required to revive or

hinder the environment that has already

been polluted or damaged.

1. Air (AIR)

2. Soil & Water (SOILWAT)

3. Noise & vibration abatement (NVA)

4. Wastewater management (WWM)

Technology-based products:

Products and technologies that are needed

to strengthen the green economy and to

provide added support for the transition.

1. Environmentally preferred products (EPP)

2. Renewable energy (REN)

3. Resource efficient technology (RET)

Monitoring-based products:

Products that help to provide tools for

assessing the situation and taking actions

against environmental degradation.

1. Monitoring (MONITOR)

Source: Author


Figure 9.1: LCG export and import product composition, by countries (2006-2016)

Figure 9.1 illustrates the intraregional LCG export and import products’ composition in the

StEA countries over the 2006-2016 period. Only Brunei has the bulk of its intraregional LCG

export shares in PMGT products. Nine countries have their major shares of LCG export in the

POLL category. The list includes Japan, Korea, India, Indonesia, Thailand, Vietnam, Nepal,

Pakistan, and Sri Lanka. Seven countries, such as China, Malaysia, Singapore, Philippines,

0% 50% 100%

Bangladesh

Brueni

Cambodia

China

India

Indonesia

Japan

Korea, Rep.

Malaysia

Myanmar

Nepal

Pakistan

Philippines

Singapore

Sri Lanka

Thailand

Vietnam

Export composition

PMGT POLL TECH MONIT

0% 50% 100%

Bangladesh

Bhutan

Brueni

Cambodia

China

India

Indonesia

Japan

Korea, Rep.

Lao PDR

Malaysia

Maldives

Myanmar

Nepal

Pakistan

Philippines

Singapore

Sri Lanka

Thailand

VietnamImport composition



Myanmar, Bangladesh, and Cambodia, have their major shares in TECH products. None of the

countries, however, has a major share for MONIT products. For import composition, 12

countries have their major shares in POLL products import while eight countries have their

major shares in TECH import.

In aggregate, the whole StEA region has its highest intraregional trade share in TECH products

(37%), followed by the POLL products (36%), as shown in Figure 9.2. MGT products comprise

16%, while MONIT products share around 11% of total intraregional LCG export.


Figure 9.2: Shares of product categories in aggregated Intraregional LCG trade in StEA region

9.3 Methodology

Facilitating the regional cooperation through trade necessitates understanding the dynamics of

the intraregional trade. More specifically, identifying the determinants, and constraints; and

measuring the export efficiency levels of the trading countries are so vital from the trade policy

perspective. This chapter uses the Stochastic Frontier Gravity model to measure the potential

level of exports for each country, where export efficiency is defined as the ratio of the actual

level of exports to the potential level of exports.

The details of Stochastic Frontier Gravity Model is earlier explained in Chapter 4 (section

4.5.1 and 4.5.2).

PMGT16%

POLL36%

TECH37%

MONIT11%




The similar approach for intraregional trade model as explained in Chapter 4 (section 4.5.2) is

adopted for this chapter.

This chapter uses the following SF Gravity model:

ln𝑋𝑖𝑗,𝑡 = 𝛽0 + 𝛽1ln𝐺𝐷𝑃𝑋𝑡 + 𝛽1ln𝐺𝐷𝑃𝑀𝑡 + 𝛽3 ln 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖𝑗 + 𝛽4 𝑇𝑎𝑟𝑖𝑓𝑓𝑗𝑖,𝑡

+ 𝛽5 𝑅𝑎𝑡𝑖𝑜𝑖𝑗,𝑡 + 𝛽6 𝑅𝑇𝐴𝑖𝑗,𝑡 − 𝑈𝑖𝑗,𝑡 + 𝑉𝑖𝑗,𝑡

where, 𝑋𝑖𝑗,𝑡 : Value of export from country-i to country-j at time-t

𝐺𝐷𝑃𝑋𝑡 : Gross Domestic Product of the exporting country at time-t

𝐺𝐷𝑃𝑀𝑡 : Gross Domestic Product of the importing country at time-t

𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖𝑗: Distance between country-i and country-j

𝑇𝑎𝑟𝑖𝑓𝑓𝑗𝑖,𝑡 : Tariff imposed by the importing country (country-j) on country-i at time-t

𝑅𝑎𝑡𝑖𝑜𝑖𝑗,𝑡 : Cross exchange rate ratio is measured as the exchange rate of the importing

country divided by the exchange rate of the exporting country

𝑅𝑇𝐴𝑖𝑗,𝑡 : Dummy variables for Regional Trade Agreement between country-i and

country-j at time-t. RTA=1, if there is any trade agreement between the

countries at time t; else equals to zero.

𝑈𝑖𝑗,𝑡 : Single-sided error term for the combined effects of the ‘behind the border’

constraints on which full information is not available

𝑉𝑖𝑗,𝑡 : Normal statistical error term, captures the effect of inadvertently omitted

variables, and implicit beyond the border constraints.

The technical inefficiency effect model is also adopted with the following extension:

𝑈𝑖𝑗,𝑡 = 𝛿0 + 𝛿1𝐼𝑁𝑆𝑇𝑖𝑗,𝑡 + 𝛿2𝐼𝑁𝐹𝑅𝑖𝑗,𝑡 + 𝛿3𝑀𝐾𝑇𝐸𝐹𝐹𝑖𝑗,𝑡 + 𝛿4𝑇𝐸𝐶𝐻𝑖𝑗,𝑡 + 𝑤𝑖𝑗,𝑡

where 𝐼𝑁𝑆𝑇𝑖𝑗,𝑡 denotes the average of the strengths of institutions in exporting and importing

countries at time t

𝐼𝑁𝐹𝑅𝑖𝑗,𝑡 denotes the average of the quality of infrastructures in exporting and importing

countries at time t


𝑀𝐾𝑇𝐸𝐹𝐹𝑖𝑗,𝑡 denotes the average of level of goods market eefficiency in exporting and

importing countries at time t

𝑇𝐸𝐶𝐻𝑖𝑗,𝑡 denotes the average of the state of technological adoption and readiness in exporting

and importing countries at time t


using joint density functions of 𝑈𝑖𝑗,𝑡 and 𝑉𝑖𝑗,𝑡. While the parameter γ is found significant, it

implies that behind the border determinants are important factors for the model.

The model is applied to intraregional exports of the LCG products in the StEA countries.

FRONTIER 4.1 software developed by Coelli (1996) is used to estimate the model.

9.3.2 Description of Data

All trade and tariff data are extracted from World Integrated Trade Solution (WITS), a trade

database provided by the World Bank. The WTO-153 list is followed to extract the LCG trade

data. In case of the tariff, the weighted applied tariff rate is considered. Data are collected for

all 20 countries of StEA region over 2006-2016. GDP at constant 2010 and exchange rate data

are collected from World Development Indicators of World Bank. The distance between the

capital cities is extracted from Bertoli et al. (2016). Regional Trade Agreement (RTA) data

are collected from Asia Regional Integration Center dataset. In determining the trade agreement

between countries, only the agreements signed & in-effect is considered. The earlier agreement

is considered in case there is more than one agreement. Free trade agreement (FTA),

preferential trade agreement (PTA), Comprehensive Economic or Trade cooperation all are

considered. For any bilateral trade, the effective year of a trade agreement between those

countries is followed to place the respective value of the dummy variable.

The data for the institutional strengths, quality of infrastructure, goods market efficiency, and

technological adoption and readiness are extracted from the Global Competitiveness Index

(GCI) 2006-2016, prepared by the World Economic Forum in an annual basis. In most cases,

indicators derived from the survey are presented on a 1–7 scale, with 7 being the most desirable

outcome. Units are omitted for the sake of readability for these indicators.

https://halshs.archives-ouvertes.fr/halshs-01288748/document

https://aric.adb.org/fta-country



Stochastic Frontier Gravity model is applied both for LCG categories as well as at aggregated

exports for each country.

9.4.1 Estimations of intraregional export efficiency for each LCG category

Since the countries may have varied specializations or comparative advantages in producing

and exporting different LCG products, it would be worthy to examine the export performances

of each country for each of the four product categories. It would, therefore, indicate the

countries’ strengths and weakness for different categories of LCG export. Estimated export

efficiencies of the countries for each category are presented in Table 9.3.

Table 9.3: Export efficiency of the countries, by category

Exporting country PMGT POLL TECH MONIT

Bangladesh 57.6% 60.5% 50.6% 60.4%

Brunei 54.4% 58.5% 43.2% 55.9%

Cambodia 47.8% n/a 66.2% 47.3%

China 42.8% 73.6% 76.3% 74.7%

India 60.8% 67.2% 64.2% 70.7%

Indonesia 50.2% 54.4% 62.9% 59.5%

Japan 68.7% 53.5% 51.7% 59.8%

Korea 66.3% 71.1% 80.7% 80.8%

Malaysia 68.3% 47.2% 66.0% 65.4%

Myanmar 22.7% 10.0% 64.5% n/a

Nepal 56.2% 57.1% 29.8% n/a

Pakistan 25.1% 57.8% 48.9% 56.1%

Philippines 62.2% 63.3% 44.1% 64.0%

Singapore 67.2% 67.0% 68.3% 70.0%

Sri Lanka 47.6% 46.5% 59.5% 62.2%

Thailand 64.5% 69.7% 71.1% 69.8%

Vietnam 63.7% 67.7% 59.7% 48.7%

StEA 61.5% 63.8% 66.6% 66.5% n/ a: Not available due to lack of sufficient observation or misfit with the model


According to the results, intraregional export efficiency in PMGT products in the StEA region

is 61.5%. Japan, Malaysia, and Singapore are the most efficient among the countries in the

intraregional export of PMGT. Myanmar, Pakistan, and China are among the least efficient

countries.


For POLL category products, China, Korea, and Thailand are the top-most efficient countries

while Myanmar, Sri Lanka, and Malaysia are estimated as the least efficient countries. The

StEA region, in aggregate, has 63.8% export efficiency in intraregional POLL exports.

As mentioned earlier, TECH products have the highest share in export among all four

categories. Overall regional export efficiency in TECH products is 66.6% which remains the

highest among all categories. All big exporters such as Korea, China, and Thailand are among

the most efficient countries in this category. Contrariwise, Nepal, Brunei, and the Philippines

are the least efficient among the countries.

Korea, China, and India have the most efficiencies in intraregional MONIT products export.

Cambodia, Vietnam, and Brunei, on the other hand, have the least efficiencies. In aggregate,

the regional export efficiency for MONIT products is estimated at 66.5%.

9.4.2 Estimations of intraregional export efficiency for aggregated LCG

Intraregional export efficiencies of each country are also estimated for aggregated LCG

products. Table 9.4 provides the summarized representation of the countries’ intraregional

export performances. Korea is the most efficient in aggregated intraregional export among the

countries with 78% weighted efficiency. Korea is followed by Malaysia (73%) and Singapore

(71%). Conversely, Myanmar, Vietnam, and Brunei are estimated with the least efficiencies of

43%, 48%, and 49% respectively. Among the two top exporters, China has the efficiency of

66%, while Japan has been in a bit lower-side with 58%. The prime reason for Japan’s lower

efficiency is due to its declining trend in intraregional LCG export in this StEA region

throughout the 2006-2016 period. Calculation reveals that Japan’s intraregional LCG export

has been dropping from 32.9 billion USD in 2006 to 22.3 billion USD in 2016, i.e., at an annual

average rate of 3.8%. Conversely, the intraregional export of LCG in China and Korea has been

increased by 5.7% and 7.0% p.a. respectively during this period. Based on these country-level

estimations, the weighted intraregional export efficiency for StEA for the LCG is calculated as

65.2%, revealing the fact that there is still around 35% untapped intraregional export potential

exists in this StEA region.


Table 9.4: Intraregional export efficiencies of each country (aggregated LCG)

Export Countries

Ban

glad

esh

Bru

nei

Cam

bo

dia

Ch

ina

Ind

ia

Ind

on

esia

Jap

an

Ko

rea,

Rep

.

Mal

aysi

a

Mya

nm

ar

Nep

al

Pak

ista

n

Ph

ilip

pin

e

Sin

gap

ore

Sri L

anka

Thai

lan

d

Vie

tnam

Imp

ort

co

un

trie

s

Bangladesh 0.01 0.38 0.57 0.72 0.11 0.08 0.58 0.48 0.01 0.00 0.52 0.08 0.64 0.41 0.70 0.29

Bhutan 0.69 0.03 0.41 0.14 0.03 0.06 0.07 0.85 0.00 0.02 0.03 0.00

Brunei 0.03 0.00 0.09 0.01 0.42 0.12 0.44 0.66 0.00 0.02 0.01 0.00 0.20 0.00 0.08 0.05

Cambodia 0.18 0.05 0.05 0.04 0.56 0.13 0.02 0.10 0.05 0.02 0.08 0.61 0.08 0.52

China 0.61 0.54 0.68 0.83 0.37 0.55 0.79 0.75 0.44 0.66 0.69 0.39 0.84 0.71 0.69 0.23

India 0.38 0.62 0.14 0.67 0.27 0.06 0.66 0.44 0.01 0.64 0.08 0.33 0.50 0.64 0.64 0.42

Indonesia 0.50 0.52 0.04 0.27 0.62 0.15 0.64 0.24 0.06 0.00 0.51 0.19 0.66 0.47 0.48 0.04

Japan 0.09 0.28 0.53 0.59 0.44 0.49 0.79 0.73 0.25 0.44 0.71 0.81 0.28 0.61 0.76 0.56

Korea, Rep. 0.07 0.04 0.25 0.89 0.64 0.50 0.74 0.80 0.05 0.85 0.64 0.36 0.69 0.73 0.40 0.70

Lao PDR 0.01 0.64 0.05 0.30 0.05 0.14 0.07 0.02 0.02 0.79 0.19 0.33

Malaysia 0.18 0.43 0.44 0.65 0.69 0.12 0.28 0.80 0.04 0.25 0.37 0.56 0.73 0.40 0.83 0.39

Maldives 0.11 0.18 0.00 0.31 0.05 0.61 0.13 0.54 0.59 0.00 0.09 0.72 0.54 0.40 0.16

Myanmar 0.00 0.00 0.33 0.33 0.07 0.06 0.48 0.19 0.01 0.00 0.42 0.07 0.74 0.53

Nepal 0.65 0.17 0.23 0.76 0.34 0.06 0.60 0.09 0.00 0.01 0.09 0.14 0.01 0.07 0.63

Pakistan 0.63 0.43 0.14 0.52 0.11 0.43 0.12 0.64 0.45 0.58 0.00 0.42 0.23 0.22 0.50 0.34

Philippines 0.02 0.69 0.12 0.26 0.29 0.16 0.15 0.71 0.70 0.00 0.66 0.67 0.76 0.21 0.30 0.28

Singapore 0.63 0.47 0.42 0.84 0.80 0.73 0.68 0.83 0.73 0.02 0.62 0.70 0.58 0.65 0.78 0.57

Sri Lanka 0.01 0.21 0.23 0.20 0.70 0.10 0.05 0.65 0.36 0.00 0.66 0.06 0.31 0.09 0.04

Thailand 0.73 0.49 0.44 0.64 0.80 0.68 0.80 0.79 0.89 0.78 0.31 0.72 0.80 0.84 0.54 0.31

Vietnam 0.50 0.42 0.47 0.83 0.53 0.79 0.46 0.84 0.72 0.05 0.08 0.71 0.69 0.71 0.28 0.74 Aggregated export efficiency 0.51 0.48 0.55 0.66 0.69 0.58 0.58 0.78 0.73 0.43 0.64 0.61 0.65 0.71 0.60 0.70 0.48



9.4.3 Determinants of the export performances

9.4.3.1 Core components of trade

Impact of the core components on bilateral trade performances can be estimated by analyzing the

coefficients of the explanatory variables used in the SF gravity model. Six explanatory variables

are used in the models: GDP of exporting country (Supply side capability), GDP of importing

country (Demand-side factor), distance between the countries, tariff rate, effective exchange rate

ratio, and the presence of any trade agreement between the exporting and importing countries.

Values of the coefficients indicate the degree of sensitivity (i.e., extent of effect) of the components

on the countries’ export performances. Table 9.5 presents the estimation of these coefficients for

each country.

Higher values of gamma imply that all the inefficiency models suit better. Values of log likelihood

function represent the degree of goodness of fit of the models. According to the values, all country-

specific models are well fitted.

Table 9.5: Coefficients of explanatory variables (core components)

Exporting

country lnGDPX lnGDPM lnDist Tariff Ration

RTA

dummy Gamma

Log

likelihood

Total

observation

Bangladesh 0.980 0.588*** 0.561 -0.017* -0.012* 1.951*** 0.99 -247.75 130

Brunei 1.796 0.473** -1.958*** -0.026* 2.792*** -0.952 0.87 -189.22 92

Cambodia 5.954*** -0.209*** -0.624 0.022 0.000 0.406 0.82 -241.13 110

China 0.759*** 0.667*** -0.268** -0.007* -0.100*** 0.661*** 0.97 -129.16 147

India 0.899*** 0.158*** 0.149** 0.035 0.012*** 0.153*** 0.99 -188.28 149

Indonesia 0.925*** 0.941*** -2.849*** -0.027*** -0.0003*** 0.488** 0.99 -196.10 146

Japan -1.772*** 1.072*** -0.753*** -0.002* 0.008** 0.331 1.00 -165.90 147

Korea -0.251** 1.087*** -0.256* 0.016 0.001 1.230*** 0.81 -211.12 147

Malaysia 0.987*** 0.834*** -1.254*** 0.022* -0.142 0.814*** 0.98 -182.18 147

Myanmar 1.426*** 0.434** -0.677 -0.024* 0.004*** -0.126 1.00 -190.79 91

Nepal 2.728*** -0.488*** -2.854*** -0.016* 0.002 5.834*** 1.00 -201.79 90

Pakistan 1.136*** -0.195* -2.109*** 0.057** 0.002 1.274*** 0.98 -231.07 128

Philippines 1.581*** 0.735*** -2.416*** 0.057*** 0.017*** 0.435*** 1.00 -195.08 139

Singapore 0.394 0.510*** -0.410*** 0.023 0.758** 0.740** 1.00 -178.73 145

Sri Lanka 1.554*** 0.420*** -0.467 0.120*** 0.016*** 1.729*** 0.99 -231.60 126

Thailand 0.828* 0.554*** -1.031*** -0.021* -0.029*** 0.390** 0.95 -180.88 147

Vietnam 4.657*** 0.760*** -2.288*** -0.082*** -0.0001*** -

0.330*** 1.00 -205.63 146




i. Supply-side capability

Results imply that supply-side capability (presented by the GDP of exporting country), in general,

has a positive impact on export growth. Estimation reveals that small exporters like Cambodia,

Vietnam, and Nepal have the highest supply-side elasticities of export, i.e., 1% increase in supply-

side capability would result in highest growth in LCG intraregional export for these countries. As

shown in Table, elasticity for Cambodia, Vietnam, and Nepal are 5.95, 4.66 and 2.73 respectively.

China and India have moderate elasticities of 0.76 and 0.90 respectively. Conversely, the other big

exporting countries like Japan and Korea have the negative elasticities with -1.77 and -0.25

respectively. This phenomenon can be explained by the convergence theorem. Smaller economies

are expected to grow faster, and under a regional framework, they are expected to increase their

export-base at much faster paces than the developed economies. Another reason behind this

phenomenon may be due to the composition of economies. Smaller economies are mainly labor-

intensive which induces them to have a relatively higher share of primary, manufacturing, and

industrial sector in their economic composition as compared to the developed economies.

Developed countries like Japan is now shifting away from their manufacturing and industry

sectors; rather it is investing and establishing manufacturing firms in the developing countries that

could meet up its demand. Data also reveals that intraregional LCG export of Japan has been

gradually reducing even if its GDP keeps growing.

ii. Demand-side factor

Demand-side factor, represented by the GDP of importing country, is found to have a positive

impact on export for most of the countries except Nepal, Pakistan, and Cambodia. It points towards

a strong indication that the intraregional export of this region is mainly demand-driven. This factor

is most elastic for the big exporters like Korea, Japan, and Indonesia with the elasticities of 1.09,

1.07, and 0.94 respectively. China, India, and Singapore have this demand-factor elasticity of 0.67,

0.16, and 0.51 respectively.

iii. Distance between trading countries

Other than India, all countries have a negative impact on distance in their bilateral exports as

expected. In general, the relatively small exporters such as Nepal, Pakistan, Philippines, Vietnam,


and Indonesia are found to be mostly affected by this factor. The negative elasticity is higher than

2.00 for these countries. Big exporters like China, Singapore, Korea, and Japan seem to be

comparatively less affected due to the distance. The positive elasticities of India may be resulted

due to its special focus or concentration of exporting to some far-distant countries rather than the

closer neighbors. For instance, only 17% of India’s total LCG export goes to its neighboring

SAARC countries while 16% goes to the far-distant Japan and Korea.

iv. Tariff rate

While considering only the significant estimations, the impact of tariff rate is found negative for

nine countries while it is found positive for four countries. Vietnam and Indonesia are mostly

affected due to tariff rate. Ceteris paribus, 1% increase in tariff rate would reduce the exports of

these countries by 8.2%, and 2.7% respectively. Positive coefficients of tariff for four countries

could be due to the special nature of the export goods (e.g., highly tariff-insensitive or necessary

goods). It could also result if the importing countries have a high reliance on the specific exporting

countries either because of a specific trade or regional agreements or any special market

arrangements that would setback the tariff issue with less priority.

v. Effective exchange rate ratio

Impact of exchange rate ratio seems to be minimal and very insignificant for most of the countries.

It reveals that exchange rate has too little role to play in intraregional trade of LCG in StEA region.

Brunei and Singapore have the higher impact of the factors among the countries with positive

coefficients. For some of the big exporters like Japan, Korea, and India (though statistically not

significant), the coefficient is positive while it is negative for China and Indonesia.

vi. Regional Trade agreement

An RTA would improve the higher trade between the trading countries, ceteris paribus. As the

figure illustrates, RTA factor is highly influential for Nepal, Bangladesh, and Sri Lanka. It also

has a sizable impact on the big exporting countries like Korea, China, Singapore, and Malaysia.


9.4.3.2 Trade environment factors

Four important factors which could usually influence the environment of the trade are incorporated

in the technical inefficiency effect model. The following analysis in this section focuses both on

the current states of these factors in each country and the impact of these factors on respective

export performances of the countries.

Table 9.6: Average rating scales of the trade environment factors (2006-2016)

Country Institutional

strength

Infrastructure

quality

Goods Market

efficiency

Technological

readiness

Bangladesh 3.06 2.35 3.98 2.56

Brunei 3.02 1.97 3.76 2.45

Cambodia 3.47 2.93 4.15 2.86

China 4.15 4.40 4.37 3.42

India 4.10 3.58 4.33 3.14

Indonesia 3.95 3.61 4.50 3.31

Japan 5.18 5.98 5.12 5.36

Korea, Rep. 4.17 5.71 4.79 5.46

Malaysia 4.94 5.25 5.14 4.36

Myanmar 3.02 1.97 3.76 2.45

Nepal 3.20 1.97 3.77 2.49

Pakistan 3.37 2.86 3.99 2.82

Philippines 3.49 3.09 4.12 3.44

Singapore 6.07 6.40 5.70 5.84

Sri Lanka 3.97 3.79 4.43 3.19

Thailand 3.94 4.64 4.60 3.72

Vietnam 3.70 3.35 4.17 3.23

Source: Global Competitiveness Index (2017)

a) Institutional strengths

Surveys cover both the public and private institutions. Sub-factors consist of property rights, ethics

and corruption, efficiency in public sectors performances, regulations, policymaking, business cost

and security, private sector’s accountability, and investors’ protection. As Table 9.6 reveals, all

big traders (exporters as well as importers) such as Singapore, Japan, China, Korea, and Malaysia

have the strongest institutional settings to support their business and export. Conversely, countries


like Bangladesh, Nepal, Pakistan, and Cambodia have the weakest institutional settings and hence,

require much institutional improvement to facilitate a higher trade for them.

Figure 9.3 illustrates the estimation of coefficients of institutional strengths incorporated in the

export efficiency model. Only the coefficients with at least 10% significance level are plotted. It

indicates that institution has a positive impact on export performance for most of the countries.

Notably, the impact is higher for the small exporting countries. Nepal, Bangladesh, Brunei, and

Sri Lanka have the highest elasticities, revealing that LCG export of these countries is largely

influenced by the institutional factor.


Figure 9.3: Estimation of coefficients of institutional strengths

China and Japan seem to have no significant impact on respective institutional factors while the

impact is lower in some other large exporters such as India, Korea, and Singapore. The impact is

found negative only for Pakistan. It is because Pakistan’s institutional strength experienced a

declining trend over the 2006-2016 period, as shown in Figure 9.4.

-20.00 -10.00 0.00 10.00 20.00 30.00 40.00

Bangladesh

Brunei

Cambodia

India

Indonesia

Korea, Rep.

Malaysia

Myanmar

Nepal

Pakistan

Philippines

Singapore

Sri Lanka

Thailand

Vietnam



Figure 9.4: Trend of Pakistan’s Institutional Strengths (2006-2016)

b) Infrastructure quality

Surveys cover both the transport infrastructure, and electricity and telephony infrastructures. The

quality of the road, port, and air transport infrastructures are mostly surveyed. Quality of electricity

supply, telephone, and mobile phone subscriptions are also analyzed. Table 9.6 indicates that

Singapore, Japan, Korea, Malaysia, and Thailand have the strongest quality infrastructure to

support their domestic businesses and facilitate both their exports as well as imports. Contrariwise,

Nepal, Bangladesh, Pakistan, Cambodia, and the Philippines have the weakest quality of

infrastructures which deter to attain optimal trade in these countries.

Figure 9.5 illustrates the estimation of coefficients of infrastructure quality. It reveals that the

quality of infrastructure has a substantial positive impact on countries’ export. For all countries

(with at least 10% level of significance), infrastructure quality seems to have a profound effect on

their respective exports. Exports from Sri Lanka, Pakistan, and Singapore are largely influenced

by this infrastructure factor as compared to the other countries. Exports from all big exporters such

as China, Japan, and Korea are also positively influenced by infrastructure, but to a lower extent.

2.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016



Figure 9.5: Estimation of coefficients of infrastructure quality

c) Goods market efficiency

Goods market efficiency is measured by analyzing several sub-factors such as domestic market

structure and competitions among the producers, foreign competitions, and quality of demand

conditions from the consumer-end. Table 9.6 indicates that Singapore, Malaysia, Japan, Korea,

and Thailand have the most efficient goods markets while Nepal, Bangladesh, Pakistan,

Philippines, and Cambodia have the least efficient market systems and mechanisms.

Market efficiency seems to have a profound impact on the export performance of the countries.

All 15 countries (having the coefficients with at least 10% significance level) are found positive

inclination of market efficiency on their exports. Nepal, Sri Lanka, and Malaysia are most sensitive

to this factor as shown in Figure 9.6. China, Japan, Korea, and Indonesia-all big exporters are also

influenced by this market efficiency, but at a lower extent.

0.00 10.00 20.00 30.00 40.00 50.00

Cambodia

China

India

Japan

Korea, Rep.

Malaysia

Myanmar

Pakistan

Philippines

Singapore

Sri Lanka

Vietnam



Figure 9.6: Estimation of coefficients of Good market efficiency

d) Technological readiness

The factor refers to the technological adoption, availability of latest technologies, technology

transfer through FDI, and use of ICT in business. Higher the technological readiness, higher will

be the production capability of the country. It should increase the potentiality to extend the export.

It would also aid to decline the reliance on import. Table 9.6 reveals that Singapore, Korea, Japan,

Malaysia, and Thailand are the most advanced StEA countries for technological adoption and

readiness. Contrariwise, all South Asian countries along with Cambodia lag behind in

technological readiness.

Technological readiness and adoption is crucial to scale-up the production base of an economy

and subsequent growth in its export. However, the estimation results are found rather ambiguous.

Estimations of only seven countries are found statistically significant, out of which four have the

positive influence while three have the negative. Technological factor seems to have a positive

impact on the export performance for Indonesia, Pakistan, Vietnam, and Thailand. Philippines,

Myanmar, and Sri Lanka have negative elasticities. Notably, all the larger exporters have no

significant impact of technological readiness for their export growth, as shown in Figure 9.7.

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00

Bangladesh

Brunei

China

India

Indonesia

Japan

Korea, Rep.

Malaysia

Myanmar

Nepal

Pakistan

Philippines

Sri Lanka

Thailand

Vietnam



Figure 9.7: Estimation of coefficients of Technological readiness and adoption

9.4.3.3 Distributional efficiency factors for export

The distributional effect of endowments in trade (i.e., labor, capital, and other resources) among

the countries are discussed both in classical as well as in contemporary literature (Bernard and

Jensen, 2004, Krugman, 2000, Yeaple, 2005, Matsuyama, 2007, Eaton & Kortum, 2012, Bernard

et al. 2012). Literature also explains the distributional effects of trade policy or trade liberalization

(Galle et al., 2017, Grossman et al., 2017, Baccini et al., 2017). However, the distributional

efficiency has not been highlighted in the existing trade literature. Since the bilateral trade

efficiency depends mostly on the country-specific factors of the exporting and importing

economies, it is important to consider the spatial distribution of trade. A country may have high

efficiency in exporting to one particular country in a region but may have a very little share of its

export there. For having real higher export efficiency, the country should improve its export shares

in those countries for which it has higher efficiency. Hence, the distributional efficiency should

play a pivotal role in intraregional trade analysis.

For an efficient distributional point of view, country’s intraregional exports to other member

countries in the region should be consistent with two factors:

• Trade (export) efficiencies, and

• Intraregional import-demand shares.

-6.00 -4.00 -2.00 0.00 2.00 4.00

Indonesia

Myanmar

Pakistan

Philippines

Sri Lanka

Thailand

Vietnam


Let's consider the first term (i.e., trade efficiency) as Distributional efficiency factor 1 (DEF1) and

the second term (i.e., intraregional import-demand shares) as Distributional efficiency factor 2

(DEF2).

a) Trade efficiency based distribution

The concept is already discussed in earlier sections. In case of export, the correlation between the

shares of export to different countries and respective bilateral export efficiencies is measured for

each country. Higher correlation value (i.e., positive, and close to one) would imply that export is

distributed more competently. Figure 9.8 shows DEF1 values of export for all countries.

It reveals that all the countries have positive DEF1 values, i.e., their exports are well-distributed

according to the respective export efficiencies. India, Cambodia, Japan, Singapore, and Thailand

have a higher correlation between the intraregional export share and export efficiency. In other

words, their intraregional exports within StEA region are distributed more efficiently. Conversely,

the exports by Brunei, Nepal, Pakistan, Vietnam, and Bangladesh are least competently distributed

within the region.


Figure 9.8: Distributional efficiency measures for the export efficiency

b) Demand-based distribution of export

The demand-based distributional factor is used to investigate how efficiently a country manage its

intraregional export shares in different countries in line with the intraregional import demand of

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

DEF1 Export efficiency


those countries. The correlation between the export shares and respective import-demand is

measured. While calculating the intraregional import-demand shares, the import of the reference

country is not included, i.e., when it is calculated for Bangladesh, the import shares of other

countries are calculated by based on the aggregated import demands of all countries excluding

Bangladesh. Higher correlation value (i.e., positive, and close to one) would imply that export of

the reference country is distributed more competently to support the intraregional demand. The

results are shown in Figure 9.9. According to the results, larger exporting countries have better

demand-based distribution. Japan, China, Korea, Singapore, and Thailand are the top-5 demand-

based distribution efficient countries. Contrariwise, Pakistan, Nepal, Brunei, Sri Lanka, and

Bangladesh are the least efficient countries from this distributional perspective. Presence of four

South Asian countries in the bottom-5 list is due to the countries’ bulk concentration on India for

their export while not considering the fair distribution based on the intraregional import-demand

shares of the countries.


Figure 9.9: Distributional efficiency measures (DEF2) for demand-based distribution

9.4.4 Summary performances and implications of the various trade determinants

Table 9.7 maps the summary performances and implications of 12 trade determinants under three

groups: core determinants, trade environmental factors, and distributional factors for each

exporting countries.

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

DEF2 Export efficiency


It is to note that, higher or lower values of the core determinant factors do not directly indicate the

performances of the countries, rather they represent how sensitive the country is towards the

respective factors. Higher sensitivity (or elasticity), measured by the coefficients of those

variables, may or may not be always desirable. For example, higher (negative) elasticity of

distance implies that if the distance is reduced, the export will be increased more for the country,

i.e., the country is highly sensitive to distance factor. But, we cannot conclude whether this is good

or bad for the country. In one hand, we may argue that the country is more efficient to export in

close neighboring countries which could lead towards high export concentration only to those

neighbors. We can also assume that the exporting country may not be able to reduce its

transportation cost or reduce the trade distance (by using an alternative route or through increased

connectivity). On the flip-side, such high sensitivity of distance may be advantageous for the

country in case of a regional cooperation initiative that aims to establish more transportation routes

and connectivity among the countries. In the same way, we can also consider the lower sensitivity

of these core determinants as the characteristics of better resilience. For instance, lower sensitivity

to tariff rate implies that even the tariff rate does not reduce or even increase, the country’s export

may not be highly affected.

The implications of the trade environment factors are, however, straight-forward. Better the

quality and strengths of these factors, country is expected to have better trade facilitation and

environment. Hence, these coefficient values for the four factors should be higher for higher export

facilitation.

Two distributional factors have different approaches. While efficiency-based factor put more

emphasis on efficiency, demand-based factor emphasizes on equality in export distribution. From

a regional cooperation’s point of views, both the factors are significant for higher trade potentiality

in the region.

Weighted efficiency column is included to roughly assume which factors could have more

influence on the export efficiency of the countries. One key implication of using different colors

for top-5 or bottom-5 performers is that it would provide the guidelines of how the experiences,

knowledge, or expertise of the top-performing countries can be shared or diffused to the others

under a regional cooperation framework.


Table 9.7: Summary performances and implications of the various trade determinants of the exporting countries

Exporting

countries

Core Components Trade Environment factors Distributional

factors

Weighted

Efficiency GD

PX

GD

PM

Dis

tan

ce

Tari

ff

Exch

an

ge

rate

rati

o

Tra

de

Agre

emen

t

Institution Infrastructure

Market

efficiency Technology

DEF1 DEF2

stat

e

sensi

tivit

y

stat

e

sensi

tivit

y

stat

e

sensi

tivit

y

stat

e

sensi

tivit

y

Bangladesh 0.59 -0.02 -0.01 1.95 3.06 14.30 2.35 3.98 15.65 2.56 0.35 0.32 0.51

Brunei 0.47 -1.96 -0.03 2.79 3.02 10.26 1.97 3.76 9.50 2.45 0.18 0.03 0.48

Cambodia 5.95 3.47 2.47 2.93 4.66 4.15 2.86 0.76 0.81 0.55

China 0.76 0.67 -0.27 -0.01 -0.10 0.66 4.15 4.40 2.46 4.37 2.80 3.42 0.49 0.94 0.66

India 0.90 0.16 0.01 4.10 5.33 3.58 6.64 4.33 9.86 3.14 0.79 0.82 0.69

Indonesia 0.93 0.94 -2.85 -0.03 0.00 0.49 3.95 1.46 3.61 4.50 2.94 3.31 1.84 0.56 0.53 0.58

Japan -1.77 1.07 -0.75 0.00 0.01 5.18 5.98 1.98 5.12 2.25 5.36 0.63 0.98 0.58

Korea, Rep. -0.25 1.09 1.23 4.17 3.33 5.71 7.75 4.79 9.55 5.46 0.38 0.93 0.78

Malaysia 0.99 0.83 -1.25 0.02 0.81 4.94 1.33 5.25 13.72 5.14 17.91 4.36 0.59 0.76 0.73

Myanmar 1.43 0.43 -0.02 0.00 3.02 4.90 1.97 4.40 3.76 8.44 2.45 -3.39 0.60 0.83 0.43

Nepal 2.73 -0.49 2.85 -0.02 5.83 3.20 29.37 1.97 3.77 30.35 2.49 0.26 -0.02 0.64

Pakistan 1.14 -0.20 -2.11 0.06 1.27 3.37 -10.89 2.86 22.34 3.99 10.82 2.82 1.52 0.29 -0.16 0.61

Philippines 1.58 0.74 -2.42 0.06 0.02 0.43 3.49 4.33 3.09 9.23 4.12 9.60 3.44 -5.20 0.60 0.77 0.65

Singapore 0.51 -0.41 0.76 0.74 6.07 1.46 6.40 15.89 5.70 5.84 0.60 0.86 0.71

Sri Lanka 1.55 0.42 -0.47 0.12 0.02 1.73 3.97 6.98 3.79 42.15 4.43 20.96 3.19 -2.75 0.42 0.16 0.60

Thailand 0.83 0.55 -1.03 -0.02 -0.03 0.39 3.94 6.06 4.64 4.60 5.18 3.72 0.79 0.60 0.85 0.70

Vietnam 4.66 0.76 -2.29 -0.08 0.00 -0.33 3.70 3.39 3.35 2.10 4.17 6.82 3.23 1.35 0.31 0.72 0.48

Note: For trade environment factors: Green: top-5 countries with higher trade facilitation environment, Yellow: bottom-5 countries, Grey: for the rest.

For distributional factor & Weighted efficiency: Green: top-5 countries with higher efficiency, Yellow: bottom-5 countries, Grey: for the rest.



The comprehensive analysis of the intra-regional LCG trade in the StEA countries presented

in this study is expected to have proficient policy implications in this region. By gauging the

export efficiencies, countries can have a broad idea about their respective strengths, and the

areas of challenges for LCG trade. From the regional cooperation perspective, the efficiency

analysis of intra-regional LCG exports presented in this chapter would provide significant

intuitions to the policymakers of this region. Estimating the determinants of export efficiencies

would help the countries to take necessary measures to improve the relevant trade facilitations.

The empirical results indicate that the StEA countries appear to have a relatively more

comparative advantage in the production and exports of Technology-based products (TECH)

and Monitoring-based products (MONIT) that facilitate green growth. However, still, about

35% of export potential in both these categories can be achieved with the existing factor

endowments, and by eliminating the ‘behind the border’ constraints in the exporting countries.

Nevertheless, regional cooperation can increase the phase of achieving the export potential.

For example, concerning the export efficiency of Proactive Management Products (PMGT),

Japan ranks number one; China ranks number one in Pollution and clean-up products (POLL),

while in the case of TECH and MONIT, the first rank goes to Korea. These three leaders can

assist the lagging countries in the region to improve their export efficiencies of LCG through

establishing and strengthening production network and the process of technology transfer.

On a broader scale, the regional institutional framework can be established to make the

necessary reforms. The measures of efficiency can be considered as the yardstick to help the

countries setting an optimal target based on its resource, endowments, infrastructures, and other

trade facilities. From the regional aspect, such measures would also determine the areas of an

effective cooperation framework to scale-up the trade through plausible trade creation,

technology transfer, and capacity building of the countries. Higher efficiency in exports, in

turn, ensures the higher welfare for the consumers as well as the producers in this region. It

would also widen up the scope of intensifying the production and usage of low-carbon goods

and services for the countries, which is so crucial in the strengthening of green growth within

the region.


Chapter 10

Policy Framework, Monitoring, Summary and Conclusion



This study has so far used the economic models to analyze the implications of RC for

strengthening the LCGG strategies. Empirically, the models are adopted for estimating the

performances of the countries and subsequently, for envisaging the potential role of an RC

framework to improve those performances. Relevant policy issues have also been discussed in

each chapter. This chapter will confer on the RC framework, i.e., the outline to suggest how

the policies should be framed, diffused, and implemented under a common RC setting.

Specifically, the prospective outlines for a policy framework, institutional framework, and

operational framework will be discussed. As part of the framework, the role of monitoring and

reviewing the performances will also be discussed. Finally, the chapter (and the thesis) will

end up with the overall summary, contributions and concluding remarks.

10.2 Basic RC Framework

Undoubtedly, structural reforms in respective development actors (such as government, the

private sector, NGOs, and multilateral development partners) of all member countries are

pivotal for an effective RC framework for implementing the LCGG strategies. However, the

true synergistic outcome would be expected when all the resources, endowments, expertise,

skills, and incentives are better-embedded and aligned through the effective policy-mix and

partnership strategies. The effectiveness of an RC framework thus primarily depends on the

successful interconnectedness of all these elements. In a basic RC framework, there should be

an external cooperation block, an internal cooperation block recipient bloc, and flows of

resources. Figure 10.1 illustrates a basic RC framework for LCGG.

It should follow the concept of ‘360-degree partnership’ which connects the external

cooperation block with the recipient country’s government, the private sector, civil society,

academia, and other development agents including the common people. Regional and local

governments within the country should play a principal role in linking the domestic

stakeholders.


Source: Author

Figure 10.1: Basic RC framework model

10.2.1 Policy Framework

Prudent policy measures under an RC framework for LCGG must be inclusive, yet, prioritizing

the national ownership and interests. Collective policy actions should focus on concrete

outcomes that are mutually beneficial on the win-win basis. Some of the key features of a

prudent policy framework for RC for LCGGs are stated in below:

• Policy agenda should be balanced towards the two key dimensions of LCGG (i.e.,

economic growth and advancement towards low-emission). Synchronization among

these policies adopted for different sectors would remain the key to a successful policy

framework.

• Policies should be specific (for individual sectors or areas), flexible, and transparent. It

should also have high-standard accountability process and follow-up mechanisms.

• Policy framework should outline the specific timetable for action plans from both the

domestic and external stakeholders. National action plans under an RC framework

Regional or

Sub-Regional

Blocs

Multilateral

cooperation

- Development

institutions

- Pool of donors

Government

NGOs, Civil

society,

Researchers

Private Sector LCGG Action

plans &

Programs

External cooperation (Donor-side)

Internal cooperation (Recipient-side)

Policy

Coherence

Monitoring

Flow of resources


should also have a comprehensive monitoring plan. It would ensure that all the

stakeholders remain engaged in constant reporting and learning process that would

further strengthen the action plans.

• LCGG-related policies and key programs must have evaluability. Through evaluating

the performance of the programs and measuring the impact of the policies on attaining

the goals, an RC framework may share the lessons learned across the countries and the

sectors.

• Policy framework should enhance the coordination impact among the various working

groups under an RC framework through frequent measurement or reports.

• The Policy framework must acknowledge the reforms as a continual process. It should

be open to incorporate any update based on the empirical evidence, evolving needs, and

new challenges for the countries and the region, as a whole.

10.2.2 Institutional Framework

Effective implementation of the LCGG strategies would require a notable paradigm shift and

re-engineering of the RC institutional framework. To meet up the challenges of multi-

dimensional and interconnected goals for a diverse range of actors at all level, an RC should

not only involve in financial support but also should signify “the ‘managing’ development

relationships for results” (ECOSOC, 2016). Figure 10.2 illustrates three key interlinked areas

to re-design the RC institutional framework.

Source: Author

Figure 10.2: Institutional Multilateral Cooperation Framework for the 2030 Agenda

InstitutionalFramework

Resource Mobilization-

Better incentives

Resource Management-

Better integration and

coherence

Resource Allocation-

Better alignment


Effective institutional reforms, in this regard, should address the relevant context (e.g., donors,

recipients, and policy agenda) and follow the demand-driven approaches. Besides, the global

and regional policy groups working on the institutional reforms may also extend their expertise

by sharing knowledge and experience in this process.

10.2.3 Operational Framework

The operational framework for RC for sustainable development must ensure the inclusive

participation and mutual sharing of all related stakeholders. Figure 10.3 demonstrates the

chronological phases of actions required to frame the operational structure for attaining the

LCGG.

Source: Kalirajan et al. (2015)

Figure 10.3: Chronological phases of actions for an operational framework

Step are discussed here:

• Since the LCGG concept is still a new one, at the earliest stage, the building up of the

public awareness and cognizance of the respective stakeholders is essential.

• It will thus provide the fundamental basis for further development of action plans

through formal and informal education along with subsequent technological

specialization in new emerging issues in different sectors.


• Alongside, the LCGG agenda should be integrated into national decision-making, and

respective policies should be harmonized at local, national, regional, and global level.

Both horizontal (breaking down the silos) as well as vertical (localizing the LCGG

agenda) policy coherence should be ensured. Multiple stakeholders should be engaged

in this process of planning. As the harmonization of policies (among the sectors,

countries, or regions) may face difficulties due to different stages of development of

the sectors or countries, the concerted effort through RC is significant at this stage.

• The RC framework can provide support in the form of the capacity building during the

process of adaptation of operational framework with the course of actions.

• Enforcement of the adopted action plan is the following stage in this framework which

demands direct financial, and technical support under an RC framework, especially for

the low-income and developing countries.

One of the key aspects of the operational framework for the LCGG is the connectedness of

various interlinked working streams and development working groups. The process needs to

identify each stream and working group along with their division of labor in different member

countries. Accordingly, all of these working units should be facilitated with valued information,

finance, and other logistics under a planned RC framework. They also need to be empowered

with proper legislation and capacity. The framework should also establish a tracking

mechanism to follow the progress of each unit and programs.

10.3 Monitoring process

Monitoring is such a vital phase in any RC framework as it ensures the quality management of

resources as well as the policy performances through continuous evaluation. The first chapter

of this thesis describes the monitoring phase as an embedded part of the Comprehensive RC

framework model. It mentions that the proposed system is dynamic as the policy outcomes

would result in constant changes in the level of LCGG attainment of the countries. Such

changes need to be monitored and reviewed on a regular basis. The monitoring phase should

consider the outcomes from both the initial strategic phase as well as the cooperation phase.

Based on the analysis at this phase, a new set of policy agenda and outcomes might result over

time which would compel to revise the strategies in both the initial strategic and cooperation

phases accordingly.


Basically, the monitoring should focus on two broad activities as follows:

i. Monitoring the resources (asset) condition

It indicates the monitoring of changes in the state of resources (such as capital, labor,

natural resources, and technologies) along with portraying the trends of their condition

using a set of indicators and benchmarks.

ii. Monitoring the performance of the policy and programs

It is also pivotal to examine the consequences of the adopted policy and programs to

strengthen the LCGG under an RC framework. Along with reviewing the effectiveness

of the action plans, the role and nexus of the people, institutions, methodologies, and

policies for that outcome of the program should also be evaluated.

Indicators are essential for proper monitoring and assessment of the situation. Since there are

two broad areas of monitoring, the indicators may also be grouped accordingly-

1) Indicators for the status of resources

2) Indicators to assess the programs’ performance

10.4 Summary of the thesis

Following the enduring changes in the climate system, along with it’s universal and irreversible


policy towards the ‘low-carbon green growth’ perspective is extolled in recent time. Since

environmental pollution and CO2 emission has become a cross-boundary issue, actions taken

by one nation have affected the development path of others, especially for its neighboring

countries. Under such a scenario, countries should strategize the comprehensive regional or

sub-regional cooperation frameworks so that they can mutually overcome the impacts of this

environmental degradation and emission-related issues.

This thesis aims to analyze these challenging areas of RC frameworks for LCGG and to find

out the ways to overcome these challenges. The basic focus is to develop a Comprehensive RC

Framework model that would help to understand the significance and implication of several

inter-related issues involving in the entire RC process. The framework consists of three

different phases in RC: initial strategic phase, cooperation phase, and monitoring and review

phase. The thesis develops a comprehensive LCGG policy framework that would be based on


the economic analysis for an RC bloc. The current states and challenges of each country in

respective areas are investigated. Subsequently, it outlines how best an RC can help to

strengthen the LCGG activities in the member countries. Both theoretical and empirical

analyses are conducted in this regard.

Historically, countries show lesser intent to cooperate with each other in tackling climate

change and controlling emissions. One of the main reasons behind such intent is the mounting

challenges perceived in those lengthy stretching negotiation process. Socio-political interests

of the vested groups become the dominant factors while the standard economic analysis and

suitable approaches in figuring out the mutual issues are often overlooked in this negotiation.

This study attempts to provide a plausible solution to this as it develops Geo-Environmental

Importance index by which a country can quantify the potential risk and benefit resulted from

the emission or environmental degradation of all negotiating countries. At the regional level,

this index could also help to differentiate the countries’ role as geoenvironmental risk

disseminators or assimilators within the regional bloc. Once the background information on

the emission and its impacts are precisely available, it would be easier for all countries to

negotiate and make a better decision. Model is empirically tested for a proposed grand regional

bloc comprising 20 Asian countries: 10 ASEAN, seven SAARC, China, Japan, and Korea.

Results reveal that eight countries are identified as predominantly geoenvironmental risk

assimilator, one risk neutral while rest of the countries are identified as predominantly risk

disseminator. The top-5 geoenvironmental risk disseminating countries are China, India,

Singapore, Japan, and Indonesia. Conversely, Bhutan, Nepal, Laos, Cambodia, and Myanmar

are the top-5 countries most susceptible to the regional geoenvironmental risk.

Chapter 3 designs a comprehensive theoretical framework supported by the economics

principles which is necessary to understand the implications of regional cooperation for low-

carbon green growth (LCGG). Starting with the supply of resources from a country, it uses a

two-goods, two-input model to explain the dynamics of economic transition towards

environmental goods and services. Then it analyzes the implications of adopting LCGG

strategy with a particular focus on share and accumulation of resources under an RC

arrangement. Implications of technological advancement under an RC arrangement are

expounded. The chapter mathematically formulates the maximum possible technological

advancement, i.e., Technology Frontier. Next, it determines the impact of technological

advancement on production efficiency. To making an in-depth analysis, the chapter introduces

the diffusion of resources phenomenon to model how the available resources are diffused or


distributed most efficiently. Finally, the chapter concludes with developing the model for

explaining the optimal utilization of resources to ensure maximum welfare from the LCGG

policies under an RC arrangement.

Following chapters empirically examine the role of regional energy cooperation for attaining

LCGG. It deals with the prudent demand-supply management of energy, which is very crucial

not only for ensuring the energy security of the countries but also strengthening their transitions

towards the sustainable low-carbon system. This study empirically explains the existing

demand-supply gaps of energy in 20 South-though-East Asian (StEA) countries and analyzes

how the gaps can be minimized most efficiently while making the transition towards the low-

carbon system. The analysis reveals that self-sufficiency of the region in meeting aggregated

energy demand is 76.5% in 2014. Brunei, Indonesia, Malaysia, Myanmar, and Viet Nam are

net producers of energy, i.e., self-sufficiency over 100%, while the rests are net users. On

average, the self-sufficiency of high-carbon enegy (HCE) sources is 74.6% while low-cabon

energy (LCE) sources full self-sufficiency of 115.7%. Korea possesses the greenest energy mix

having 85% of its total energy production as LCE. Japan and Philippines to follow with 46%

and 37% respectively. Top three energy producers- China, India, and Indonesia, in contrast,

possesses only 6.4%, 4.6%, and 4.0% LCE in their production mix. Stochastic Frontier Gravity

model extended with the determinants of efficiency model is used for both primary energy and

REG trade. The result implies that for most of the countries, intraregional export of primary

energy, as well as REG, are positively influenced by GDP of the exporting and importing

countries. Tariff and distance adversely affect the exports while the implication of cross

exchange ratio seems minimal in both cases. RTA is also found to have a notable positive

impact on intraregional trade. Results show that China and Malaysia are the most-efficient in

this intraregional primary energy exports, while Bangladesh and Myanmar remain the least-

efficient. China and Japan are the most-efficient in REG exports, while Myanmar remains the

least-efficient. On average, the whole region has the weighted export efficiency of 58.4% in

intraregional primary energy trade and 65.4% in REG trade. The determinants of efficiency

model reveal that institutional quality, better infrastructure, goods market efficiency, and

technological readiness have reasonable impacts to enhance the countries’ intraregional energy

trade efficiencies.

This following chapter analyses two channels of REC which can instigate the sustainable low-

carbon energy system under an RC framework in the StEA countries. First part investigates

how efficiently countries are managing their energy demand towards attaining the LCGG


objectives. The later part examines the macro and sectoral level efficiencies of the countries in

energy usage. The Stochastic Frontier Production model is used to estimate the optimal energy

consumption based on the demand-driven factors such as income, population, industry’s

contribution, energy prices, previous energy usage, and emission level. Model is tested for

South-through-East Asian (StEA) regions over the 1991-2014 period. Results indicate that

higher the income, countries are preferring to shift away from the HCE consumption towards

the LCE consumption. Population growth and energy used in the previous year have positive

effects on both the demand for HCE and LCE. Coefficients of industry share are not found

significant for any of these demand models. The price of oil has negative demand elasticity for

HCE. The higher price of gas, conversely, is found to have a positive drive for HCE demand.

The coal price, however, is found to have no significant impact of any of these demand models.

Higher CO2 concentration of previous year also seems to have a positive impact for both HCE

and LCE demand models. Positive effect on HCE demand indicates that some of the countries

still heavily relying on fossil-fuel based energy system in this region. Results imply that the

larger HCE consumers such as China, India, Japan, Korea, and Indonesia all have lower

technical efficiencies to contain the demand-driven factors for HCE. On the contrary, small-

scaled HCE consumers such as Brunei and Singapore have high technical efficiencies for the

containment of demand-driven factors for HCE. On average, the StEA region has the average

weighted efficiency of 47.5%, indicating that it can still improve the untapped technical

efficiency of containing HCE-demand by 52.5% by adopting the best-practices within this

region. Other than Indonesia, all top LCE consumers have the higher level of efficiency in

boosting the LCE-demand factors further. On average, the whole region has the efficiency of

87.9%.

This study examines the underlying factors which can explain the energy usage efficiency for

major energy-consumed sectors: industry, transportation, electricity, and agriculture. A non-

parametric Data Envelopment Analysis (DEA) Malmquist model is used for this analysis. On

average, the region’s energy use efficiency in agriculture is declined by 19% while technology

level is advanced by 40% during 1995-2013. Energy use efficiency in industry sector is

dropped by 25% while technology level is improved by 25% during this time. For electricity

sector, energy use efficiency is improved by 2% while the technology level is dropped by 19%.

Energy use efficiency in the transportation sector is declined by 14% while technology is level

improved by 3% during 1995-2013.


Due to the continuous urge in meeting the food security for the growing population, the

resource exploitation in agriculture sector is quite evident. Hence, both the production growth

and the sustainability become key policy dilemma for this agricultural economics. In the next

two chapters, this study examines the prospective roles of RC in attaining the sustainable green

growth in the agricultural production process. Stochastic Frontier models are separately

designed for production efficiency and emission management efficiency. For measuring the

combined effect of efficiencies, a Green growth index is also formulated. The result implies

that all the direct inputs- land, capital, energy, FDI have positive impacts on agriculture

production. Labor and fertilizer, on the contrary, have negative elasticities with the production

outcome. It indicates that growth rate of labor productivity, as well as the fertilizer productivity,

have been at declining rates. Extended inefficiency effect model is also deployed which reveals

that infrastructure facilities in forms of road density and quality water in rural areas have a

positive impact on production efficiency in the StEA region. Efficient workers in agriculture

also have a substantial positive impact on production efficiency. However, government’s

expenditure on country’s education sector is found to have rather an adverse impact on

agriculture production efficiency. China has the highest production efficiency with 94.7%

while Cambodia remains the least-efficient in production with 36.5% efficiency level. It also

estimates that if the countries could work under a regional cooperation bloc, on average, it can

add the untapped potential production of 16.3% without deploying any additional resources.

The study also calculates the synergy effect of the RC framework to understand the impact of

the combined action. Empirical evidence reveals that it has positive synergy effect of 34%, on

average.

Analysis for emission management reveals that more use of land, labor, fertilizer, or energy

will increase the emission while capital or FDI will reduce the emission. It also indicates that

infrastructure facilities in forms of road density have a positive impact on emission-

management efficiency in the StEA region. Governments’ direct expenditure on agriculture as

well as to education sector also have substantial positive effects on improving this emission-

management efficiency. China has the highest emission-management efficiency with 70.4%

while Myanmar remains the least-efficient with 30.5%. If the countries could work under an

RC bloc, on average, its agriculture emission management efficiency would be 52.6%. Synergy

effect analysis reveals that shows that forming the regional bloc can have 2.6% added impact

in reducing the gap for potential emission level in 2013.


Concerted efforts under regional cooperation may instigate the optimal extraction and

productive usage of natural resources for the long-term sustainability and growth in the

countries. Following chapter (Chapter 8) uses a two-stage analysis for an in-depth

understanding of the factors affecting the sustainable natural resources management in 20

countries in South-through-East Asia (StEA), the region having the fastest rate of growth in

resource extraction and consumption in the world. The first stage uses the extended Kaya

identity approach to decompose the underlying factors for resource consumption. The result

implies that population growth has a positive but little contribution to resource consumption

growth in the StEA countries. GDP per capita has a higher positive contribution to resource

consumption growth for all countries. Emission intensity also plays a substantial role to decline

the resource consumption while the impact of the resource intensity of emission remains

ambiguous. Results also reveal that in aggregate, 1% increase in population, per capita income,

emission intensity, and resource per emission would lead to increase the resource consumption

in the StEA region by 0.20%, 0.71%, 0.03%, and 0.05% respectively. The second stage analysis

uses the non-parametric Data Envelopment Analysis (DEA) method to examine the role of

technological change, efficiency change, and input substitutability changes in resource

productivity (RP) estimation. Three different models are used in calculating resources’

productivity for economic growth, limiting emission, and attaining the combined goals. Results

show that Singapore, Korea, Malaysia, and the Philippines have the highest improvement in

RP while Laos, Bhutan, and Vietnam experienced the declining RP according to all three

models. RP changes for economic growth and combined goals are mostly influenced by the

efficiency changes while input substitutability factor dominates to the RP changes for limiting

emission. Japan, Korea, Singapore, and Malaysia remain the top-performing economies in

technological advancement in all three models. Pakistan, Philippines, Indonesia, Singapore,

and Korea remain the most efficient to use the given technologies.

Implications of low-carbon production and consumption are becoming imperative with the

emergence of the ‘sustainable development’ concept. Towards matching the internal as well as

the external demand and supply of the Low-carbon goods (LCG), intra-regional trade can play

a pivotal role. Chapter 9 examines the intra-regional trade potential derived from estimating

the export performances of 20 South-through-East Asian (StEA) countries. It also investigates

the implications of various determinants of trade performances in these countries. Stochastic

Frontier Gravity model estimates that untapped intra-regional export potential of the LCG for

the StEA region is 65.2%. Korea has the highest intra-regional export efficiency while


Myanmar has the lowest. The study also investigates the implications of various determinants

of trade in the countries under three broad categories: core determinants, trade environmental

factors, and distributional efficiency factors. Among the core components, GDP of both

exporting and importing countries, and trade agreement factor have found to have positive

influences on trade. Distance (between the exporting and importing countries) and tariff rate

are found to have an adverse impact on trade while the role of exchange rate seems

inconclusive, in general. Trade environment factors, such as institutional strength, quality

infrastructure, and market efficiencies are found to have profound influences on export

efficiency. Distributional factors, conversely, seem to have a reasonable influence on intra-

regional export efficiency. Analysis on the summary performances and implications of the

various trade determinants would provide the substantial guidelines on how the experiences,

knowledge, or expertise of the top-performing countries can be shared or diffused to the others

under a regional cooperation framework.

10.5 Contributions and Concluding remarks

This study is expected to add noteworthy contribution in the contemporary literature on the

dynamics of regional cooperation, especially in the field of sustainable green growth. It is a

unique attempt to comprehensively analyze the implications of an RC with substantial

economic and quantitative analysis. In one hand, the thesis suggests some new economic

models supported by relevant economic rationales and theories; on the other hand, it adopts the

contemporary models into some new areas to explicate the role of RC in those economic

sectors. Following marks some specific contribution of each chapter:

• As an effective and plausible policy solution to the ever-challenging negotiation process

of the multilateral environmental issues, this thesis develops a quantitative tool (i.e.,

Geo-environmental importance index) that can measure the probable risks through

quantifying the geoenvironmental impact of each country on others within a region.

Once the countries can quantify their respective risks, they are in a better state to settle

down with an efficient outcome from that early phase negotiation. From an optimistic

point of view, this index, therefore, can also be used as a crucial quantifying tool in

intense discussion and negotiation on global climate change issues: for instances,

shared responsibility, contribution, and allocation of climate finance.


• Chapter three develops some new theoretical frameworks to explain various aspects of

an RC for LCGG. It uses a two-goods, two-inputs model to explain the dynamics of

economic transition towards environmental goods and services. Then it analyzes the

implications of adopting LCGG strategy with a particular focus on share and

accumulation of resources under an RC arrangement. Next, it determines the impact of

technological advancement on production efficiency. The chapter mathematically

formulates the maximum possible technological advancement, i.e., Technology

Frontier. To making an in-depth analysis of resource sharing and diffusions of

resources, the chapter also outlines the model in explaining the optimal utilization of

resources to ensure maximum welfare from the LCGG policies under an RC

arrangement.

• Chapter four applies Stochastic Frontier Gravity model for the Primary energy trade.

So far the exploration refers, it is an original attempt in this area of energy trade.

Adoption of the inefficiency effect model and using across the border factors into the

model are some unique attempts made in that chapter.

• Application of the Stochastic Frontier analysis in analyzing the role of demand-driven

factors in the energy consumption is also a new-fangled strategy in energy economics.

Adopting the model for both HCE and LCE with two opposite optimization techniques

also seems to be a new inclusion in such a group of literature.

• A new energy use efficiency formula has been suggested in chapter five. Incorporating

the emission factor into that formula is a new idea that would signify the energy use

efficiency of the countries from a sustainable growth aspect.

• The implication of RC in agriculture is quite common, yet, the application of Stochastic

Frontier is relatively newer. Quantification of the impact of RC and calculating the

synergy effects are the innovative attempts in this regard. Linking the production

efficiency with the emission efficiency is also an original approach adopted in this

thesis.

• A new methodology is used in chapter eight to decompose the resource productivity

changes into efficiency change, technological change, and the change in substitutability

of capital and labor with natural resources factor. Adopting the Kaya identity for

explaining the resource consumption determinants is also a fresh approach.


• Distributional factors of the intraregional export efficiency adopted in chapter nine is a

new idea to explain the comprehensive feature of export efficiency to signify the

equitable aspect of an RC.

This comprehensive economic analysis throughout this thesis would provide adequate

indications about each countries’ strengths and challenges for different economic sectors. Such

an analysis would prompt the policymakers for adopting the effective policies regarding what

is needed and how best the countries can collectively act to achieve the targets through mutual

sharing of information, knowledge, expertise, finance, and other resources. A low-carbon

world remains the ultimate goal, yet, the transition remains one of the biggest of challenges. A

planned and concerted effort with proper economic analysis would be the key in this regard.

This thesis could be considered as a momentous step-forward to that.


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Appendix 1

Explanation of GEI index (Proposition 2)

Proposition-2: Country with proximity should matter in RC for LCGG

Let’s consider country-j as the reference country.

GEI of country-i on country-j


𝐴𝑖×

𝑚𝑖

𝑚𝑗×

𝑔 (𝑚𝑖)


GEI of country-k on country-j

𝐺𝐸𝐼𝑘,𝑗= 𝐴𝑗

𝐴𝑘×

𝑚𝑘

𝑚𝑗×

𝑔 (𝑚𝑘)

𝑔 (𝑚𝑗)× 𝜃𝑘,𝑗

Therefore, 𝐺𝐸𝐼𝑘,𝑗

𝐺𝐸𝐼𝑖,𝑗=

𝐴𝑖

𝐴𝑘×

𝑚𝑘

𝑚𝑖×

𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖)×

𝜃𝑘,𝑗

𝜃𝑖,𝑗 (𝐴1.1)

Let's consider following cases as examples.

Case-1: country-i and country-k with same CO2 Emission and 𝜃, but different Area

Conditions are: 𝑚𝑖 = 𝑚𝑘 and 𝜃𝑖,𝑗= 𝜃𝑘,𝑗.

Therefore, from equation (A1.1) we can derive as:

𝐺𝐸𝐼𝑘,𝑗


𝐴𝑖

𝐴𝑘×

𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖) (𝐴1.2)

Practically, the range of growth factor 𝑔 (𝑚𝑖) or 𝑔 (𝑚𝑘) swivels between 0.9 to 1.1;

i.e., from 10% reduction to 10% increase in growth rate, on average. This would imply

the probable range of 𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖) as (

1−0.1

1+0.1) to (

1+0.1

1−0.1) 𝑖. 𝑒. 0.82 to 1.22.

For simplification, we can assume 𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖)≈ 1.

Hence, equation (A1.2) implies,



𝐴𝑖

𝐴𝑘 (𝐴1.3)

Again, 𝜃𝑖,𝑗 = 𝑎𝑟𝑐𝑖

𝑑𝑖𝑠𝑡𝑖,𝑗

𝜃𝑘,𝑗 = 𝑎𝑟𝑐𝑘

𝑑𝑖𝑠𝑡𝑘,𝑗


Figure A1.1: Illustration of the example

Hence, considering 𝜃𝑖,𝑗= 𝜃𝑘,𝑗, we have

𝑎𝑟𝑐𝑖

𝑑𝑖𝑠𝑡𝑖,𝑗=

𝑎𝑟𝑐𝑘


Since 𝑑𝑖𝑠𝑡𝑖,𝑗 < 𝑑𝑖𝑠𝑡𝑘,𝑗 is assumed, therefore,

𝑎𝑟𝑐𝑖 < 𝑎𝑟𝑐𝑘

Typically this implies that the area of country-k is bigger than country-i.

Now, if the area of country-k is larger than country-i, from equation (A1.3) we get

𝐺𝐸𝐼𝑖,𝑗 > 𝐺𝐸𝐼𝑘,𝑗

So, the closer the country, ceteris paribus, more importance it has than the far-distant

country.

Case-2: Country-i and country-k with same area & CO2 Emission but different θ

Conditions of 𝐴𝑖 = 𝐴𝑘, 𝑚𝑖 = 𝑚𝑘 and 𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖)≈ 1 will take the equation as:



𝜃𝑘,𝑗

𝜃𝑖,𝑗 (𝐴1.4)

Reference

Country-j

Partner

Country –i

𝜃𝑖,𝑗


Reference

Country-j

Partner

Country –k

𝜃𝑘,𝑗


arci

arck


Again,

𝜃𝑖,𝑗 = 𝑎𝑟𝑐𝑖


𝜃𝑘,𝑗 = 𝑎𝑟𝑐𝑘


For similar area, we can assume that, arci= arck

So, we have,

𝜃𝑘,𝑗 = 𝑑𝑖𝑠𝑡𝑖,𝑗

𝑑𝑖𝑠𝑡𝑘,𝑗 × 𝜃𝑖,𝑗

Therefore, if country-k is assumed far from country-i, then

𝑑𝑖𝑠𝑡𝑘,𝑗 > 𝑑𝑖𝑠𝑡𝑖,𝑗

If 𝜃𝑘,𝑗 < 𝜃𝑖,𝑗, then from equation (A1.4) we have,

𝐺𝐸𝐼𝑖,𝑗 > 𝐺𝐸𝐼𝑘,𝑗

So, the closer the country, ceteris paribus, is of more importance than the far-distant

countries.

Case-3: Country-i and country-k with same area and θ, but with different CO2 Emission

Conditions of 𝐴𝑖 = 𝐴𝑘, 𝜃𝑖,𝑗= 𝜃𝑘,𝑗 and 𝑔 (𝑚𝑘)

𝑔 (𝑚𝑖)≈ 1 will take the equation (A1.1) as



𝑚𝑘

𝑚𝑖

Therefore, with such equal proximity, higher the level of CO2 emission, the greater importance

of that country will be for the reference country.


Appendix 2

Table: A2.1 List of the Renewable Energy Goods from the APEC 54 List

HS

code

Description

840290 Steam or other vapor generating boilers; super-heated water boilers

840490 Parts of auxiliary plant for boilers, condensers for steam, vapor power unit

840690 Parts for steam and other vapor turbines

841182 Gas turbines, except turbo-jets and turbo-propellers, of a power exceeding 5,000

kW.

841199 Parts of gas turbines (841182).

841290 Engine and motor parts, nesoi (Wind turbine blades and hubs).

841919 Instantaneous or storage water heaters, non-electric other than instant water

heaters.

841990 Parts of machinery, plant or laboratory equipment involving temperature

change, nesoi

850164 AC generators (alternator), with an output exceeding 750 kVA.

850231 Other electric generating sets: Wind-powered

850239 Electric generating sets and rotary converters: other.

850300 Parts suitable for use solely or principally with the machines of heading 8501 or

8502.

850490 Parts for electrical transformers, static converters, and inductors

854140 Photosensitive semiconductor devices, including photovoltaic cells.

901380 Optical devices, appliances and instruments

901390 Parts and accessories for optical devices, appliances and instruments


Appendix 3

Explanation of the Synergy Effect

Let’s assume a two-country model example. Country A produces 𝑦1with the input 𝑥1, while

country B produces 𝑦2with the input 𝑥2. Regional output, therefore, consists of the sum of

countries’ productions, i.e.,( 𝑦1 + 𝑦2) with the aggregated input of ( 𝑥1 + 𝑥2). Table A3.1

illustrates the gross efficiency (i.e., output/input), relative efficiency (from the frontier) of each

country as well as the region.

Country/

region Output Input

Gross

efficiency

Relative efficiency

(considering country

A is more efficient

and on the frontier)

i.e., 𝑦1

𝑥1>

𝑦2

𝑥2

Untapped efficiency

= (1-Relative efficiency)

A 𝑦1 𝑥1 𝑦1

𝑥1 1 0

B 𝑦2 𝑥2 𝑦2

𝑥2

𝑦2𝑥2

⁄𝑦1

𝑥1⁄

(1 −

𝑦2𝑥2

⁄𝑦1

𝑥1⁄

)

Region 𝑦1 + 𝑦2 𝑥1 + 𝑥2 𝑦1 + 𝑦2

𝑥1 + 𝑥2

(𝑦1 + 𝑦2)(𝑥1 + 𝑥2)

⁄

𝑦1𝑥1

⁄

(1 −

(𝑦1 + 𝑦2)(𝑥1 + 𝑥2)

⁄

𝑦1𝑥1

⁄)

Hence, the gap between potential production and actual production is calculated as follow:

Country A: 0

Country B: 𝑦2 (1 −𝑦2

𝑥2⁄

𝑦1𝑥1

⁄)

For Region: (𝑦1 + 𝑦2) (1 −

(𝑦1+𝑦

2)

(𝑥1+𝑥2)⁄

𝑦1

𝑥1⁄

)


Sum of the gaps for two countries, GC = 0 + 𝑦2 (1 −

𝑦2𝑥1

𝑦1𝑥2) =

𝑥2𝑦1𝑦2−𝑥1𝑦22

𝑥2𝑦1

=𝑦2(𝑥2𝑦1−𝑥1𝑦2)

𝑥2𝑦1

Gap for the region (unitary bloc) GR = (𝑦1 + 𝑦2) (𝑥1𝑦1+𝑥2𝑦1−𝑥1𝑦1−𝑥1𝑦2

𝑥1𝑦1+𝑥2𝑦1)

= 𝑥2𝑦1

2 − 𝑥1𝑦22 + 𝑥2𝑦1𝑦2 − 𝑥1𝑦1𝑦2

𝑥1𝑦1 + 𝑥2𝑦1

= (𝑥2𝑦1−𝑥1𝑦2)(𝑦1+𝑦2)

𝑥1𝑦1+𝑥2𝑦1

Now, Synergy effect will be positive if GR < Gc, i.e., 𝐺𝐶

𝐺𝑅> 1

From the above equations, 𝐺𝐶

𝐺𝑅=

𝑦2(𝑥2𝑦1−𝑥1𝑦2)

𝑥2𝑦1(𝑥2𝑦1−𝑥1𝑦2)(𝑦1+𝑦2)

𝑥1𝑦1+𝑥2𝑦1

= 𝑦2 (𝑥1𝑦1+𝑥2𝑦1)

𝑥2𝑦1(𝑦1+𝑦2)

= 𝑦2 (𝑥1 +𝑥2)

𝑥2 (𝑦1 +𝑦2)

=(1 +

𝑥1

𝑥2)

(1 +𝑦1

𝑦2)

Now, if 𝑥1

𝑥2>

𝑦1

𝑦2 then

𝐺𝐶

𝐺𝑅> 1 and hence the Synergy effect is positive.

Otherwise,

If 𝑥1

𝑥2=

𝑦1

𝑦2 then

𝐺𝐶

𝐺𝑅= 1 and hence the Synergy effect is zero.

If 𝑥1

𝑥2<

𝑦1

𝑦2 then

𝐺𝐶

𝐺𝑅< 1 and hence the Synergy effect is negative.

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