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204 National Energy Map for India: Technology Vision 2030

apparent to achieve these goals over thenext 30 years.

6.3.1 Power generationtechnologies

6.3.1.1 Nuclear

K eeping the long-term time frame of 50years or more, nuclear-based power genera-tion would emerge as the clear winner interms of sustainable and energy-efficientpower generation. T he three-phaseprogramme proposed by the Department of Atomic Energy, using fast breeder reactorsin the second phase and subsequently tho-rium-based reactors for power generation inthe third phase, is well conceived.

6.3.1.2 Integrated gasificationcombined cycle

Because of the technical barrier to the adop-tion of Indian high-ash coals, it is recom-mended that commercial-scale coal-basedIGCC demonstration projects be set up onindigenous and imported coal. T his will fa-cilitate familiarization with technology andcost reduction of IGCC-based power plants.

With increased refining capacity, refinery

residue such as vacuum residue and petro-leum coke will be available on large scale. I tis recommended that refinery-residue-basedIGCC power generation plants also be setup. International experience in this technol-ogy is already available. Handling refiningresidue is comparatively easier than han-dling high-ash coal for gasification for pro-

duction of ‘syn’ (synthetic) gas and use ingas turbines for power generation. T he gov-ernment should adopt this technology assoon as possible.

6.3.1.3 Advanced gas turbines

Adoption of aero derivative advanced gasturbines like H-frame for power generationshould be aggressively promoted. In the fu-ture, it is possible that natural gas reserveswill increase especially due to the efforts of the Government of India in deep-sea explo-ration and due to the viability of extractingnatural gas from gas hydrates. T herefore,aggressive adoption of advanced gas tur-bines will also help in enhancing the efficien-cies of IGCC plants. It will also be useful if the Government of I ndia can adopt aresearch programme on advanced gasturbines in national research institutions orlaboratories like National Aeronautics L tdand H industan Aeronautics Ltd.

6.3.1.4 Supercritical/ultra-supercritical boiler

Supercritical steam properties require ‘oncethrough’ or ‘Benson’ boilers, which are dif-ferent from the drum-type boilers used for

power generation based on sub-critical con-ditions of steam. Since coal-based powergeneration will continue to play a criticalrole in the next 30–50 years, it becomes es-sential to adopt well-proven technologieslike super-critical and ultra-supercriticalboilers in the immediate future, that is, inthe Eleventh F ive Year Plan, instead of using


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Key observations and recommendations 205

sub-critical technology. T he Benson boilerwas first designed in 1924, 1 and ever sincethese boilers are being designed and oper-ated at higher steam properties (for example,pressures of 300 bar and temperaturesgreater than 600 oC). I t is strongly recom-mended that India adopt this technologyimmediately. Experience worldwide hasshown that Benson boilers become cost ef-fective if the unit size is around 1000 M W(megawatts) or more.

6.3.2 Transmission anddistribution loss

I t is also possible to reduce technical T &D(transmission and distribution) losses to8%–12% as against 16%–19% in the coun-try. T he technologies for these would be toadopt very high voltage AC (alternating cur-rent) transmission and HVDC (high voltagedirect current) transmission. Distributionlosses can be reduced by adoption of anenergy-efficient transformer, which useshigh-grade steel in the transformer core.

6.3.3 End-use technologies

T he adoption of energy-efficient technolo-gies in the end-use energy-consuming sec-tors can have a major impact on the finalenergy demand, primarily in transport andresidential sectors. In addition, there is apossibility of technical loss reduction in thetransmission and distribution of power.

6.3.3.1 Industrial sector

Although commercial energy consumptionis the highest in the industrial sector, majorenergy-intensive industries are already mov-ing towards energy-efficient technologies.

T he cement and the iron and steel sectorsare already adopting state-of-the-art tech-nologies, barring a few old plants. However,there are sectors where the energy-efficienttechnologies can be penetrated at a fasterrate, especially for technologies which arewell proven; for example, cogeneration inthe industrial sector and use of waste heat inthe industrial processes. T hese technologiesare well known and can be promoted by theBureau of Energy Efficiency by proper dis-semination of information.

6.3.3.2 Residential sector

Electricity consumption in the residentialsector will increase at the rate of 8.8%,which is primarily due to increased utiliza-tion and the policies of the Government of India to provide electricity to all. Promotionof energy-efficient lighting like CFL and en-ergy-efficient white goods like refrigeratorsand air-conditioners can achieve a reductionof about 23% in 2030. Although thesetechnologies are well known, the Govern-ment of India needs a policy to promotethese technologies.

11111 Siemens Power Generation (1995)


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206 National Energy Map for India: Technology Vision 2030

6.3.3.3 Transport sector

T he transport sector requires planning toincorporate integrated transport systems inurban areas so that public transport systemsare easily accessible to the public at large.Apart from the shift to a public transportsystem and the use of rail, energy-efficient

automobile technologies, which are continu-ously improving in the OECD (Organizationfor Economic Co-operation and Develop-ment) countries, should be adopted. Thetechnological features are wide ranging(from fuel injection improvements to effi-cient combustion and efficient control dueto electronic governance).


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Description of energy sector models

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11111 A1.1 Introduction

Energy models can be developed using thebottom-up approach or the top-down ap-proach. T he bottom-up energy models aredeveloped from engineering data applied tospecific technologies whereas the top-downenergy models are based on statistical analy-sis of past data. Both can be useful in under-standing the effects of policy on energymarkets. However, the bottom-up modelsoften neglect certain costs that reduce re-turns on investment below what is predicted,resulting in unrealistic estimates of what willoccur if energy markets are shocked. On theother hand, the top-down models are basedon the technology and institutions existingat the time their data applies to, and hencemay underestimate the ability of markets toadapt.

Within these two approaches, energymodels can be categorized into four broadcategories: (i) optimization models, (ii)simulation models (bottom-up), (iii) energysector equilibrium models, and (iv) input–output models (top-down). The characteris-tic features of these models are summarizedbelow.

A1.2 Energy optimization models

T hese technology-oriented models mini-mize the total costs of the energy system, in-cluding all end-use sectors, over a 40–50year horizon. T he costs include investmentand operation costs of all sectors based on adetailed representation of factor costs. T herecent versions of these models allow de-mand to respond to prices. A link has alsobeen established between aggregate macro-economic demand and energy demand. Pro-

jections of future development are oftenimplemented with a model generator via theoptimization algorithms based on linear pro-gramming. Given below are some examplesof these models.

A1.2.1 Model for Energy Supply Systems Analysis and GeneralEnvironment

M ESSAGE (M odel for Energy SupplySystems Analysis and General Environment)is generally used for the optimization of en-ergy supply systems. H owever, other systems

A PPEN D I X 1


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208 Appendix 1

supplying specified demands of goods,which have to be processed before deliveryto the final consumer, could be optimized.M ESSAGE is an instrument for medium- tolong-term dynamic planning of the opera-tion and expansion of energy systems. T heobjectives include resource extraction analy-sis, estimation of import/export of energy,energy conversion analysis, energy transportand distribution analysis, final energy utili-zation by consumer analysis, recommenda-

tions for environmental protection policyand investment policy, and analysis of op-portunity costs (shadow prices and marginalcosts).

A1.2.2 Asia–Pacific IntegratedModel

AIM (Asia–Pacific Integrated M odel) is atechnology selection framework for analysisof country-level policies related to GH G(greenhouse gas) emissions mitigation andlocal air pollution control. I t can also assistin energy policy analysis. I t simulates flowsof energy and materials in an economy, fromsupply of primary energy and materials,through conversion and supply of secondaryenergy materials, to satisfaction of end-useservices. AIM /ENDUSE models these flowsof energy and materials through detailedrepresentation of technologies. Selection of technologies takes place in a linear optimiza-tion framework where system cost is mini-mized under several constraints likesatisfaction of service demands, availabilityof energy and material supplies, and so on.Various scenarios including policy counter-measures can be analysed in AIM /ENDUSE.

A1.2.3 Energy Flow OptimizationModels

EFOM -ENV (Energy Flow OptimizationM odels) are national dynamic optimizationmodels (employing linear programming),representing the energy producing and con-suming sectors in each state/province. T heyoptimize the development of these sectorsunder given fuel import prices and useful

energy demand over a pre-defined time hori-zon. T he development of national energysystems can be subject to energy and envi-ronment constraints like availability of fuel,penetration rates of certain technologies,emission standards, and emission ceilings.

T he model databases contain a wide range of conversion and end-use technologies such asconventional technologies, renewable energytechnologies, efficient fossil fuel burningtechnologies, combined heat and power

technologies, and energy conservation tech-nologies in the demand sectors. T he mainobjective of EFOM -ENV is energy andenvironment policy analysis and planning,particularly cost-effectiveness analysis of energy policy options for reducing pollutantemissions.

A1.2.4 Modular Energy System Analysis and Planning software

M ESAP (M odular Energy System Analysisand Planning software) is a modularenergy planning package developed with thespecific needs of developing countries inmind. I t is designed as a flexible planningpackage providing energy analysts and plan-ners with tools to perform complex energy


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Appendix 1 209

scenarios. It is based on a hierarchical sys-tem of interconnected sub-models at the in-ternational, regional, and national levels.Simulation is carried out for the interna-tional energy markets, national energy bal-ances, and technical subsystems for finalenergy consumption, energy transformation,and production. Technological developmentand diffusion of new technologies as well asGHG emissions of the energy sector aretaken into account.

A1.3.2 Model of Power systemplanning and Comprehensive

Assessment

T his model is composed of two parts. T hefirst part compares and assesses comprehen-sively the different development options of the power system (decision-making analy-sis). I t uses the AHP (Analytic HierarchyProcess) for comparing and ranking differ-ent development options. T he second part of M OPCA (M odel Of Power system planningand Comprehensive Assessment) is a simu-lation model of the power producing optionsfor power system development planningwith constraints regarding financing, re-sources, and environment. In the first part,the most important aspects of the power sys-tem such as energy independence, economicaspects, system reliability, environmentaland ecological impacts as well as social im-pacts are taken into account. In the secondpart, macro-economic analysis, power sys-tem analysis, and environmental burdensanalysis are carried out for the purpose of power system development planning. T hetwo parts of M OPCA can be used separately.

T he model is designed to serve small coun-

analysis. It consists of basic techniques forenergy planning, a set of tested energy mod-ules, and data management and processingsoftware. At the heart of M ESAP is a net-work-oriented database. Its objective is toassist in energy and environmental policyanalysis and planning.

A1.3 Simulation models

T hese models involve a detailed representa-tion of energy demand and supply technolo-gies, which include end-use, conversion, andproduction technologies. Demand and tech-nology developments are driven by exog-enous scenario assumptions often linked totechnology vintage models and econometricforecasts. T he demand sectors are generallydisaggregated for industrial sub-sectors andprocesses, residential and service categories,transport modes, and so on. T his allows de-

velopment trends to be projected throughtechnology development scenarios. Qualityof the expert estimations is the decisive fac-tor to ensure the quality of the simulation.

T he main areas of application for simulationmodels are research questions concerningtechnologically oriented measures, where ahigh level of detailed knowledge is necessary,and where macro-economic interaction andprice are less important. Some of the simula-tion models include the following.

A1.3.1 Prospective Outlook onLong-term Energy Systems

POL ES (Prospective Outlook on Long-termEnergy Systems) is a simulation model pro-viding long-term energy supply and demand


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210 Appendix 1

tries, especially developing countries, andregions of large countries.

A1.3.3 Brundtland Scenario model

BRUS (Brundtland Scenario) is a long-termsimulation model for the energy demandand supply system. Being a technical–eco-nomic model, it allows for the calculation of demand-driven scenarios for the total na-

tional energy system. T he main purpose of the model is to analyse cost-effective strate-gies for the reduction of CO 2 (carbondioxide). Simultaneously, potential minimi-zations of exhaustible resources areanalysed. T he model is total (in contrast topartial or marginal models), making it pos-sible to introduce significant changes, for ex-ample, on the demand side, and even thenget reliable results for the total energy sys-tem. It is subdivided into different sectors of

energy demand and supply, which are inte-grated to provide useful and comprehensivetool.

A1.3.4 Long-range Energy Alternatives Planning

L EAP (L ong-range Energy AlternativesPlanning) is an energy planning model thatcovers energy demand, transformation, and

supply. I t uses a simulation approach to rep-resent the current energy situation for agiven area and to develop forecasts for thefuture under certain assumptions. L EAP is acomputer-based accounting and simulationtool designed to assist policy-makers inevaluating energy policies and developingsound, sustainable energy plans. L EAP canbe used to project the energy supply and

demand situation in order to glimpse futurepatterns, identify potential problems, andassess the likely impacts of energy policies. Itcan assist in examining a wide variety of projects, programmes, technologies, andother energy initiatives, and in arriving atstrategies that best address environmentaland energy problems.

A1.3.5 Multinational Integrated

Demand And Supply

M IDAS (M ultinational I ntegrated DemandAnd Supply) is a large-scale energy systemplanning and forecasting model. I t performsdynamic simulation of the energy system,which is represented by combining engi-neering process analysis and econometricformulations. T he model is used for scenarioanalysis and forecasting. MIDAS covers thewhole energy system and ensures, on an an-nual basis, the consistent and simultaneousprojection of energy demand, supply, pric-ing, and costing, so that the system is in bothquantity- and price-dependent balance. Themodel output is a time-series of detailedEUROSTAT energy balance sheets, lists of costs and prices by sector and fuel, and a setof capacity expansion plans including emis-sion data.

A1.4 Energy sector equilibriummodels

T hese models are conceptually similar to theeconomic equilibrium models that representdecision-making processes of producers andconsumers. T hey typically simulate marketsfor factors of production (such as labour,


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Appendix 1 211

capital, and energy), products, and foreignexchange, with equations that specify supplyand demand behaviour. T hey offer a closedtheoretical approach of obtaining marketequilibrium that can increasingly be ad-

justed for imperfect market conditions. T heonly difference is that the non-energy mar-kets are not represented here. T hese modelsrequire the energy demand as an exogenousinput, which is typically based on other eco-nomic and demographic forecasts. T hese

models include the following.

A1.4.1 GEM-E3

T he GEM -E3 model is an applied generalequilibrium model, simultaneously repre-senting world regions or European coun-tries, linked through endogenous bilateraltrade flows and environmental flows. GEM -E3 aims at covering the interactions between

the economy, the energy system, and the en-vironment. It is built in a modular wayaround its central C GE (computable generalequilibrium) core. It supports defining sev-eral alternative regimes and closure ruleswithout having to re-specify or re-calibratethe model. Although global, the model ex-hibits a sufficient degree of disaggregationconcerning sectors, structural features of en-ergy/environment, and policy-oriented in-struments (for example, taxation). T he

model formulates production technologiesin an endogenous manner allowing for price-driven derivation of all intermediate con-sumption, and the services from capital andlabour. I n the electricity sector, the choice of production factors can be based on the ex-plicit modelling of technologies. For the de-mand side, the model formulates consumerbehaviour, and distinguishes between

durable (equipment) and consumable goodsand services.

A1.4.2 PRIMES

T he PRIM ES model, used by the EU (Euro-pean U nion) environmental agencies, is de-signed only for measuring sectoral effectsand not economy-wide effects. PRIM ES, apartial equilibrium model, is primarily de-

signed to show the effect of policy changeson energy markets. It can calculate the directcost implications of reduced energy use, butnot the economy-wide impact on GDP(gross domestic product), employment, andinvestment.

A1.4.3 Energy and PowerEvaluation Program

ENPEP (Energy and Power EvaluationProgram) is a set of microcomputer-based energy planning tools that aredesigned to provide an integrated analysiscapability. ENPEP begins with a macro-economic analysis, develops an energydemand forecast based on this analysis,carries out an integrated demand/supplyanalysis for the entire energy system, evalu-ates the electric system component of the energy system in detail, and determines

the impacts of alternative configurations.Also, it explicitly considers the impacts thepower system have on the rest of the energysystem and on the economy as a whole.ENPEP is mainly employed for energypolicy analysis, energy tariff development,energy project investment analysis, electricsystem expansion planning, and environ-mental policy analysis.


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212 Appendix 1

A1.4.4 Canadian IntegratedModelling System

CIM S (Canadian Integrated M odelling Sys-tem) is a nearly full equilibrium system,which tracks the flow of energy in the entireeconomic system beginning with productionprocesses through to the eventual end-useby individual technologies. I t may incorpo-rate demand-dependent energy supply costs,price-driven demand feedbacks, second or-der macro-economic effects, and energytrade. CIM S is ideal for modelling policiesintended to affect energy efficiency, GHGemissions, and air quality.

A1.4.5 National Energy ModellingSystem

NEM S (N ational Energy Modelling System)

is a computer-based, energy economy mod-elling system of the US energy markets forthe medium-term period through 2020. D e-signed and implemented by the US Depart-ment of Energy, it represents domesticenergy markets by explicitly representing theeconomic decision-making involved in theproduction, conversion, and consumption of energy products. NEM S provides a consis-tent framework for representing the complexinteractions of the US energy system and its

response to a wide variety of alternative as-sumptions and policies or policy initiatives.As an annual model, it can also highlight theimpacts of transitions to new energyprogrammes and policies.

A1.4.6 Input–output models

Input–output models are based on the timeseries of the macro-economic interactionmatrices with their input–output tables, en-ergy balances, and labour market statistics.Activities are explained with respect tosectoral development, energy carrier con-sumption, and emission development. T hissegment includes the following.

A1.4.6.1 Energy ScenarioGenerator

T he main purpose of the ESG (E nergy Sce-nario Generator) model is to generate con-sistent scenarios of economic development,which simultaneously determine energy de-mand and supply as well as the major envi-ronmental impacts. T he feedback betweeneconomic development, energy demand, andenergy supply is fully integrated into themodel, that is, an energy technology model islinked with a macro-economic model. T hemodel aims at coordination of macro-eco-nomic energy and environmental policies atthe national level. As inputs, this model re-quires data such as (i) base year energy bal-ances, (ii) base year economic data, (iii) baseyear input–output table, (iv) time series of major economic data (consumption, trade,

investment), (v) data on disaggregated capi-tal stocks, (vi) capital market data (interestrates, inflation), (vii) population data, and(viii) energy technology data like efficiency(actual and expected future), disaggregatedinvestments (actual and expected), emissiondata (plus reduction potential), labour in-put, and technology lifetime.


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Appendix 1 213

A1.4.6.2 MEGEVE-E3ME

T his is a general energy–environment–economy model, developed for Europe, ca-pable of addressing issues that linkdevelopments and policies in the areas of energy, environment, and economy. T hemain purpose of the model is to provide aframework for evaluating different policies,particularly those aimed at achieving sus-tainable energy use over the long term.M EGEVE-E3ME uses a neo-K eynesianeconometric input–output model in a gen-eral equilibrium framework. I t provides de-tailed results for the economy, the energysector, and environmental emissions. Basicinput data are input–output tables; nationalaccounts; investment data; energy balances,energy prices, and taxes; electricity stationdata; and emissions into air.

A1.4.6.3 MICRO-MELODIE

M EL OD IE is a French macro-economicmodel with a detailed technological descrip-tion of the energy sector, especially in theelectricity sector. T he model also computespolluting emissions such as NO x, SO 2, andCO 2. T he economy, energy, and environ-ment are then described in a single frame-work, but for each topic, a specificmethodology has been developed.M EL ODIE is adapted to measure any en-ergy policy modifying the cost structure of electricity supply. Input/output tables at cur-rent and constant prices, economic accountsof the institutional sectors, and technologi-cal and economic data on the electricity sec-tor including fuel cycle, internationaleconomic data energy balances in physicaland monetary units, and environmental data(polluting emissions) are the main inputs re-quired for this model.


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Sectoral reference energy system(RES)

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22222 A PPEN D I X 2

Figure A2.1 Reference energy system for the agriculture sector


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216 Appendix 2

Figure A2.2 Reference energy system for the transport sector


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Appendix 2 217

Figure A2.3 Reference energy system for the residential sector


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218 Appendix 2

Figure A2.4 Reference energy system for the industry sector


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Appendix 2 219

Figure A2.5 Reference energy system for the electricity sector


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Socio-economic drivers of energy demand

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33333 A3.1 Methodology for estimatingincome-wise household distribution

T he overtime proportion of households ineach expenditure class depends on factorssuch as rate of growth of population, share of urban population to rural population,household size, and rate of growth of GDP.

Given these parameters, distribution of households in various expenditure classes isgenerated using a lognormal distribution for

M PCE (monthly per capita consumptionexpenditure) data for rural and urban avail-able from NSSO (N ational Sample SurveyOrganization) for ‘consumer expenditurerounds; 1993/94 and 1999/2000’.

T he lognormal distribution of M PC E hasprobability density function

22 2/)x(lne2x

1),;x(f σµ−−πσ

=σµ

where, x is the household consumption ex-penditure for x > 0, where µ and σ are themean and standard deviation of the M PCE’slogarithm. T he expected value is

E(X ) = e µ+σ2/2 (A-3.1)

and the variance is

var (X ) = (eσ2

− 1)e2µ+σ2

T he cumulative probability of populationbelow an expenditure level is given by

(ln (L) − µ)/σ (A-3.2)

where, L is the consumption expenditurelevel.

In order to forecast the probability of population in an expenditure class, the two

unknowns – µ and σ – for the above twoequations need to be estimated over the fore-cast period.

σ has been assumed to follow the pasttrend of decline during 1993/94 to 1999/2000 for rural and urban areas. µ, mean ex-penditure, has been determined by incomeas per the K eynesian consumption theory.

T herefore, increase in GDP implies an in-crease in expenditure thereby implying arightward shift in the lognormal curve. Pri-

vate final consumption expenditure has beenused for consumption expenditure. T here-fore, forecast of growth rate of private finalconsumption expenditure determines thegrowth rate of M PCE. T he growth rate of private final consumption expenditure hasbeen forecasted using the following equa-tion.

A PPEN D I X 3


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222 Appendix 3

PFCE = 115 235 + 0.54 (Y) (A-3.3)(-11.79) (20.83)(Adjusted R 2= 0.953)

where,PFCE = private final consumption ex-

penditureand Y = GDP

Coefficient of GDP is the M PC (marginalpropensity to consume). In other words, an

M PC of 0.54 implies that one rupee increasein income leads to an increase of 0.54 rupeein consumption.

MPCE =PFCE/P

where,H = population

In I ndia, the per capita income increasedfrom 5823 rupees in 1981 to 12 281 rupees

in 2001. Correspondingly, the per capita ex-penditure increased from 5044 rupees to8441 rupees during the same time period.

T his increase in per capita expenditure wasat the annual rate of 2.48% during 1981–2001, when the per capita income growthrate was 3.64%. T he same is expected to in-crease at the rate of 4.8%, 6.0%, and 7.8%with the per capita income growing at therate of 5.5%, 6.7%, and 8.5% at a GDP

growth rate of 6.7%, 8%, and 10% respec-tively during 2001–36. T he NSS (National Sample Survey) data

of per capita calorie intake by M PCE classeshas been used to find out the monetary cutoff corresponding to minimum calorie re-quirement norm. T he national-level officialpoverty line corresponds to a basket of goodsand services, which satisfies the calorie normof per capita daily requirement of 2400 kcal(kilocalories) in rural areas. Accordingly,

people below an M PCE of 525 rupees inrural areas and 575 rupees in urban areashave been considered to be below povertyline (Table A3.1).

For simplifying the analysis, these expen-diture classes have been categorized into sixexpenditure groups namely BPL (belowpoverty line), L (low), L M (lower middle),M (middle), U M (upper middle), and H(high) in rural and urban areas. M PCE lessthan or equal to 525–615 rupees is consid-

ered to be under the BPL group. For the ur-ban low-income group, the figure is 575–665 rupees (Table A3.1).

Based on the probabilities computed forrural and urban population under variousGDP growth rate scenarios (Tables A3.2–A3.7), the number of households in ruraland urban areas is estimated for six expendi-ture classes.


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Appendix 3 223

Table A3.1 Monthly per capita expenditure and calories intake

Rural Urban

Monthly per Calorie intake Monthly per Calorie intake

capita expenditure (in Rs) (kcal) capita expenditure (kcal)

0–225 1383 0–300 1398

225–255 1609 300–350 1654

255–300 1733 350–425 1729

300–340 1868 425–500 1912

340–380 1957 500–575 1968

F380–420 2054 575–665 2091

420–470 2173 665–775 2187470–525 2289 775–915 2297

525–615 2403 915–1120 2467

615–775 2581 1120–1500 2536

775–950 2735 1500–1925 2736

Above 950 3178 Above 1925 2938

Source NSO (2000)

Table A3.2 Probability of households (rural) 6.7% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.016 0.005 0.001 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.014 0.004 0.001 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.031 0.014 0.004 0.001 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.038 0.019 0.006 0.001 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.046 0.026 0.010 0.002 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.052 0.032 0.014 0.004 0.001 0.000 0.000

420–470 0.084 0.086 0.084 0.070 0.049 0.024 0.007 0.001 0.000 0.000

470–525 0.082 0.087 0.087 0.079 0.061 0.034 0.012 0.002 0.000 0.000

525–615 0.106 0.120 0.124 0.126 0.108 0.073 0.031 0.008 0.001 0.000

615–775 0.122 0.148 0.159 0.185 0.189 0.155 0.090 0.030 0.005 0.000775–950 0.070 0.092 0.102 0.138 0.167 0.172 0.131 0.062 0.015 0.002

950–1200 0.044 0.061 0.072 0.109 0.154 0.196 0.198 0.134 0.051 0.009

1200–1500 0.019 0.028 0.033 0.057 0.094 0.148 0.196 0.186 0.105 0.029

1500–2000 0.008 0.013 0.015 0.030 0.057 0.109 0.191 0.260 0.230 0.111

2000–2800 0.001 0.002 0.003 0.008 0.018 0.044 0.104 0.213 0.311 0.271

> 2800 0.001 0.001 0.001 0.001 0.003 0.009 0.032 0.103 0.282 0.578

MPCE – monthly per capita expenditure; GDP – gross domestic product


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224 Appendix 3

Table A3.4 Probability of households (rural) 8% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.012 0.002 0.000 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.010 0.002 0.000 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.025 0.006 0.001 0.000 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.031 0.010 0.002 0.000 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.038 0.014 0.003 0.000 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.046 0.020 0.005 0.001 0.000 0.000 0.000

420–470 0.084 0.086 0.084 0.063 0.031 0.009 0.001 0.000 0.000 0.000

470–525 0.082 0.087 0.087 0.073 0.043 0.015 0.003 0.000 0.000 0.000

525–615 0.106 0.120 0.124 0.121 0.084 0.037 0.009 0.001 0.000 0.000

615–775 0.122 0.148 0.159 0.188 0.166 0.097 0.032 0.005 0.000 0.000

775–950 0.070 0.092 0.102 0.148 0.171 0.135 0.064 0.016 0.001 0.000

950–1200 0.044 0.061 0.072 0.124 0.183 0.195 0.133 0.048 0.009 0.001

1200–1500 0.019 0.028 0.033 0.069 0.130 0.188 0.181 0.100 0.026 0.002

1500–2000 0.008 0.013 0.015 0.039 0.094 0.181 0.252 0.218 0.099 0.020

2000–2800 0.001 0.002 0.003 0.011 0.036 0.100 0.212 0.304 0.250 0.101

> 2800 0.001 0.001 0.001 0.002 0.008 0.032 0.112 0.308 0.615 0.876


Table A3.3 Probability of households (urban) 6.7% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.012 0.003 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.011 0.003 0.001 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.028 0.011 0.002 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.038 0.019 0.005 0.001 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.049 0.026 0.010 0.001 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.066 0.043 0.018 0.004 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.088 0.065 0.034 0.010 0.001 0.000 0.000

775–915 0.097 0.109 0.111 0.111 0.094 0.059 0.022 0.004 0.000 0.000

915–1120 0.106 0.125 0.132 0.145 0.142 0.110 0.055 0.013 0.001 0.0001120–1500 0.117 0.146 0.158 0.195 0.223 0.220 0.158 0.064 0.011 0.000

1500–1925 0.064 0.081 0.089 0.122 0.164 0.203 0.203 0.132 0.040 0.004

1925–2400 0.033 0.043 0.047 0.069 0.100 0.148 0.193 0.181 0.093 0.018

2400–3200 0.021 0.026 0.030 0.045 0.071 0.120 0.199 0.268 0.228 0.090

3200–4000 0.007 0.008 0.009 0.013 0.023 0.044 0.089 0.167 0.225 0.160

> 4000 0.004 0.005 0.005 0.008 0.013 0.026 0.065 0.170 0.402 0.728



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Appendix 3 225

Table A3.5 Probability of households (urban) 8% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.009 0.001 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.009 0.002 0.000 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.023 0.006 0.001 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.033 0.010 0.002 0.000 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.042 0.017 0.003 0.000 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.059 0.028 0.008 0.001 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.080 0.045 0.015 0.003 0.000 0.000 0.000

775–915 0.097 0.109 0.111 0.104 0.071 0.030 0.006 0.001 0.000 0.000

915–1120 0.106 0.125 0.132 0.142 0.119 0.065 0.019 0.002 0.000 0.0001120–1500 0.117 0.146 0.158 0.200 0.213 0.164 0.075 0.016 0.001 0.000

1500–1925 0.064 0.081 0.089 0.134 0.181 0.191 0.131 0.047 0.007 0.000

1925–2400 0.033 0.043 0.047 0.080 0.130 0.174 0.168 0.092 0.022 0.002

2400–3200 0.021 0.026 0.030 0.053 0.107 0.183 0.243 0.207 0.088 0.014

3200–4000 0.007 0.008 0.009 0.021 0.041 0.088 0.158 0.201 0.143 0.042

> 4000 0.004 0.005 0.005 0.011 0.029 0.076 0.196 0.434 0.739 0.942


Table A3.6 Probability of households (rural) 10% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.009 0.001 0.000 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.008 0.001 0.000 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.020 0.002 0.000 0.000 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.027 0.005 0.000 0.000 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.034 0.008 0.001 0.000 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.040 0.012 0.002 0.000 0.000 0.000 0.000

420–470 0.084 0.086 0.084 0.057 0.019 0.002 0.000 0.000 0.000 0.000

470–525 0.082 0.087 0.087 0.069 0.029 0.005 0.001 0.000 0.000 0.000

525–615 0.106 0.120 0.124 0.116 0.061 0.015 0.001 0.000 0.000 0.000615–775 0.122 0.148 0.159 0.187 0.136 0.047 0.006 0.000 0.000 0.000

775–950 0.070 0.092 0.102 0.155 0.159 0.084 0.018 0.001 0.000 0.000

950–1200 0.044 0.061 0.072 0.135 0.195 0.152 0.052 0.006 0.000 0.000

1200–1500 0.019 0.028 0.033 0.079 0.160 0.187 0.101 0.020 0.001 0.000

1500–2000 0.008 0.013 0.015 0.047 0.133 0.237 0.216 0.079 0.009 0.000

2000–2800 0.001 0.002 0.003 0.015 0.062 0.180 0.294 0.215 0.055 0.004

> 2800 0.001 0.001 0.001 0.002 0.017 0.088 0.311 0.679 0.935 0.996



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226 Appendix 3

the performance of the Indian economy dur-

ing the Eighth and the Ninth Plan periods.During these plan periods many of the com-monly held beliefs regarding the potentiali-ties and constraints that govern the opera-tion of the economic system have been ques-tioned and highlighted.

T here are three major experiences fromthe previous plan periods, as highlighted inthe Tenth F ive Year Plan that lay down theguidelines for setting the growth targets forthe future.

Firstly, the growth rate of the Indianeconomy is no longer constrained by theavailability of savings or investible resources.

T he clearest evidence for this is given by thepersistent difference between the externalcapital inflows and the CAD (current ac-count deficit) that has existed through muchof the 1990s. CAD represents the excess of total investment in the country over domes-

A3.2 Rationale for choice of 8%gross domestic product growth rate

T he Tenth Five Year Plan covering the period2002–07 prepared by the Planning Commis-sion, GoI (Government of India) aims atachieving an average growth rate of realGDP of 8% per annum over the period2002–07. T he 8% average growth rate targetset for the Tenth Plan appears quite optimis-tic when compared with the short-term

GDP growth rate forecasts of other organi-zations. However, the rationale behind tar-geting 8% GDP growth rate is doubling theper capita incomes over the next decade witha more equitable regional distribution. Thiswould bring about substantial improvementin the welfare of the entire population.

Furthermore, the Tenth F ive Year Planhas been prepared against the backdrop of

Table A3.7 Probability of households (urban) 10% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.007 0.000 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.008 0.001 0.000 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.019 0.003 0.000 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.028 0.006 0.001 0.000 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.037 0.010 0.001 0.000 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.054 0.017 0.002 0.000 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.074 0.031 0.005 0.000 0.000 0.000 0.000

775–915 0.097 0.109 0.111 0.099 0.052 0.012 0.001 0.000 0.000 0.000

915–1120 0.106 0.125 0.132 0.139 0.094 0.032 0.004 0.000 0.000 0.0001120–1500 0.117 0.146 0.158 0.204 0.194 0.100 0.021 0.001 0.000 0.000

1500–1925 0.064 0.081 0.089 0.143 0.188 0.149 0.053 0.006 0.000 0.000

1925–2400 0.033 0.043 0.047 0.088 0.151 0.170 0.095 0.018 0.001 0.000

2400–3200 0.021 0.026 0.030 0.064 0.141 0.224 0.201 0.073 0.007 0.000

3200–4000 0.007 0.008 0.009 0.022 0.062 0.137 0.190 0.119 0.024 0.001

> 4000 0.004 0.005 0.005 0.014 0.050 0.167 0.435 0.783 0.968 0.999



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Appendix 3 227

tic savings while external capital flows repre-sent the inflow of potential savings fromabroad. T he excess of external capital in-flows over CAD is therefore an indication of the failure of investment demand to absorbforeign savings. T hus, it can be stated thatthe availability of investible resources wasnot the primary constraint to growth and in-vestment in I ndia.

Secondly, the growth rate of an economyis not wholly determined by the level of in-

vestment activity. T he Tenth Five Year Planhighlights the fact that the rate of real invest-ment as a percentage of GDP was higherduring the Ninth F ive Year Plan as comparedto the previous plan period. T he Ninth F ive

Year Plan recorded a real investment rate of 26.3% of GDP as compared to 24.9% dur-ing the Eighth F ive Year Plan. H owever, theeconomy registered an average annual GDPgrowth rate of 6.7% per annum as against5.3% during the Ninth Plan. T his is ex-

plained by the fact that the investment ratewhen measured in nominal terms has de-clined from 24.8% in the Eighth Plan to24.3% in the Ninth Plan period. Also, thenominal investment rate has been at or be-low the private savings rate. T he Ninth Planperiod was characterized by a decline in thelevels of capacity utilization thereby explain-ing a decline in the investment rate in nomi-nal terms.

T hirdly, the growth of the agriculture sec-

tor is a key determinant of the overall eco-nomic growth rate. Although the share of agriculture in aggregate GDP has declinedto 26.9% of GDP reducing the sensitivity of GDP growth rate to fluctuations in agricul-

tural performance, the agricultural incomesplay an important role in determining thedemand for non-agricultural commodities.

T herefore, growth of the agriculture sectoris a determinant of future growth rate.

T he imperatives for achieving an 8% realGDP growth rate given in the Tenth Five

Year Plan document of the Planning Com-mission are as follows.(a) T he Planning Commission envisages

that the investment rate be accelerated

from 24.4% in 2001/02 to 32.6% in2006/07 for achieving a target GDPgrowth rate of 8%. T his targeted in-vestment rate differs from the invest-ment rate projected by other organiza-tions such as IEG (I nstitute of Eco-nomic Growth).

In order to finance a gross capitalformation (investment) of this magni-tude, the Tenth Five Year Plan targets adomestic savings rate of 29.8% of

GDP and foreign savings rate of 2.8%. T hat is, the domestic savings 1 ratewould have to rise by 6 percentagepoints from the current levels over the

Tenth Five Year Plan period. Of this6% increase in the domestic savingsrate, 2.11% is expected to be in the pri-vate sector and the rest in the publicsector. In this context, it is mentionedthat the savings rate in the domestichousehold sector is expected to decline

during the Tenth Plan period. T his isbecause, on the one hand, rapid in-crease in personal disposal incomes (asa result of rise in G DP) would raise thesavings rate and on the other, fiscal

11111 Domestic savings comprise domestic public and domestic private savings. Domestic private savings are further sub-divided into household savings and savings by the private corporate sector.


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228 Appendix 3

policy would necessitate significantstepping up of tax/GDP ratio. T hisstepping up would tend to reduce thehousehold savings. Furthermore, thesavings rate of the private corporatesector is determined mainly by itsshare in GDP, its profit rate, and capi-tal intensity. I t is envisaged that in the

Tenth F ive Year Plan, the savings rateof this sector will rise sharply with im-proved capacity utilization thereby im-

proving both its profitability and GDPshare. T he Tenth Five Year Plan re-quires the government sector to reduceits dissavings by nearly 2 percentagepoints in order to meet the aggregatedomestic savings target.

(b) T he Tenth Five Year Plan projects theexports to grow at the rate of 12.4%and invisibles to perform strongly. T hiswould further raise the real GDPgrowth rate by creating demand abroad

for domestic goods and services.(c) T he Tenth Five Year Plan further rec-

ognizes four priority sectors that arecritical for generating high rate of eco-nomic growth. These sectors are agri-culture, construction, transport, andother services. Public investment inthe agriculture sector would be in-creased significantly to reduce the sen-sitivity of this sector to weather-relatedfluctuations. T he construction sector is

considered as a potential sector forgrowth given that the land-related sug-gestions mentioned in the plan areimplemented judiciously. A fastergrowth in other transport can beachieved if required policy changespermitting greater involvement of theprivate sector are implemented. T his,coupled with high growth rates in the

information, communication, and en-tertainment sectors would lead to ac-celeration in growth of other services.

T hus, the 8% growth rate is consideredfeasible in the Tenth Plan period since thescope for realizing improvements in effi-ciency is very large both in the public andprivate sector assuming that the policy im-peratives discussed above materialize.

For these aforementioned reasons, TERI

has adopted the GDP growth rate of 8% forenergy demand projections for the TenthFive Year Plan period consistent with theplans of the GoI. Based on the assumptionthat the 8% growth rate can be sustained fora period extending beyond the Tenth F ive

Year Plan period, T ERI has projected GDPto grow at an average annual rate of 8% perannum through the entire modelling period(2001–36).

A3.3 Methodology for GDP projec-tions under 6.7% GDP growth rate

In a separate exercise to project GDP growthfor India, T ERI has modified the model de-veloped by Goldman Sachs (2003) for long-term GDP projections in Brazil, Russia, In-dia, and China popularly referred to asBRICs countries. For this purpose, we haveused the growth accounting framework used

by Goldman Sachs, which was first devel-oped by Solow in 1956. According to thisframework, growth in output can be brokendown into the following components.(a) Growth in output due to measured

growth in labour input(b) Growth in output due to measured

growth in capital input(c) Technological progress


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Appendix 3 229

A3.3.1 Assumptions

A3.3.1.1 Production function

T he main point of departure from theBRICs study16 is in our choice of a labouraugmenting Cobb Douglas production func-tion. The production function exhibits con-stant returns to scale, and the technologicalprogress is of the type that increases the effi-

ciency of the abundant factor of production,which in the case of India is labour. L abour-augmenting technical change can be mani-fested in the adoption of technologies that leadto the production of more labour-intensivegoods or in technologies that increase the effi-ciency of the labour input (Acemoglu 2002).

We believe that this specification is morerelevant than the Goldman Sachs specifica-tion, to the direction that Indian growth ismost likely to take.

Our production function is specified as Y= (AL ) 1-α K αwhere A= labour-augmenting technologyL = labour inputK = capitalα = share of capital in income

A3.3.1.2 Convergence

One of the factors driving growth in the

model is the rate of growth of T FP (totalfactor productivity). T he difference betweenthe per capita income of the US and the percapita income of India determines the po-tential for technological ‘catch up’. T he rateof convergence would depend on the initialincome of the developing country. Underthese conditions, technological progresscould be expected to be faster in developing

countries such as India than in the US. Ashigher T FPG (total factor productivitygrowth) rates and diminishing returns tocapital lead to higher output growth rates,the potential for catch up decreases and thedeveloping country converges towards thesteady-state growth rate of technologicalprogress in the US.

Unconditional convergence would implythat the steady-state balanced growth pathsfor the developing and developed country

coincide (I slam 1998). T his, however, neednot be the case when conditional conver-gence is assumed. I n this case, any one ormore of the parameters defining steady statecan differ among countries. This model as-sumes the convergence of T FP growth ratesin steady state. T he economies can, however,differ in terms of steady-state growth rates of population, savings rates, educational attain-ment, depreciation, and T FP levels (Jones1997). T he growth rates that a country can

achieve in the steady state would depend oncountry-specific factors such as techniquechoice, geography, and institutional struc-tures that affect saving and investment rates,physical and social infrastructure, educationlevels, and quality of governance. T his wouldimply that given the same initial levels of percapita income, a country with an underde-veloped infrastructure or lower levels of edu-cational attainment would converge atslower rates than a country with more

favourable conditions.We use the same specification for the evo-

lution of T FPG as in the BRICs paper. T hegrowth rate of T FP in the developing coun-try is given by the following relation.

L og (A t/At-1) = (long-run T FPG for theUS) − βL og [(per capita GDP DC )/(per capitaGDP US)]


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230 Appendix 3

where A t = T FP level at time tAt-1 = T FP level at time t-1βL og [(per capita GDP DC )/(per capita

GD P US)] = conditional convergence rate forthe developing country

T his specification assumes that the T FPGrates along the path to steady state in thedeveloping country are higher than thesteady state T FP growth rate in the US . T hehigher the rate of convergence ( β), and the

larger the difference in per capita incomes,the higher the rate of T FP growth in the de-veloping country relative to the long-run

T FPG rate in the US. Conventional esti-mates of the rates of convergence in percapita income for developed countries totheir own steady states are about 2%(M ankiew, Romer, and Weil 1992). Wewould expect conditional convergence ratesin developing countries to be lower as a con-sequence of retarding institutional factors.

T he BRI Cs paper has assigned a conver-gence rate of about 1.5% to developingcountries. It mentions that calculations forlong-term projections of GDP use lower ini-tial convergence rates for Brazil and India.

T hese increase to 1.5% through the periodfor which the projections are made. We haveassumed the conditional β convergence ratesfor I ndia to be equal to 1.3% throughout ouranalysis. We expect convergence rates forIndia to be lower than those used for China

and Russia in the BRICs paper, because of higher illiteracy levels, infrastructuralbottlenecks, and social constraints that af-fect the participation of women in theworkforce and the large proportion of thepopulation employed in subsistence or unor-ganized activities in both the agriculturaland urban sectors.

We have used long-term T FP growth ratesfor the US to be consistent with those used

by the CBO (C ongressional Budget Office)of the Government of the US. T he CBO(2002) uses a T FP growth rate of 1.3% fortheir long-term GDP projections. T his wasrevised upwards from 1% average annualgrowth (CBO 1997). T he CBO also usesmore conservative long-run US TFPG(1.1%) rates for alternative projections.

T he results from our analysis are verysensitive to the assumptions made regardingthe convergence rates and long-run TFPG

rates. Changing long-term U S T FPG ratesfrom 1.3% to a more pessimistic 1.1% wouldchange our results appreciably.

A3.3.1.3 Capital stock

T he growth of net capital stock in the modelfollows the equation given below.

K t = K t-1(1 − δ ) + sY t-1

where K t = net capital stock at time tK t-1 = net capital stock at time t-1s = investment rate

Y t-1 = GDP at time t-1

From this specification we find that sY t-1gives us the gross capital formation in timet-1. T his forms a part of the net capital stockthat is available for use in the following year.

To calculate the net capital stock for the ini-

tial year in our analysis, we have used data onnet capital stock and gross capital formationfrom the National Accounts Statistics pub-lished by the CSO (C entral Statistical Orga-nization), GoI. To calculate the subsequentcapital series we assume that the investmentrate in India will remain at 24% throughoutthe period for which projections are made.We also assume that the depreciation ratewill remain at 5% for the entire period. T his


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Appendix 3 231

assumption is again a very stringent one andthe rates of capital attrition (a combinationof deterioration and technological obsoles-cence) can be expected to change. I n the In-dian case we could expect rates of capital at-trition to rise in the long term as capital in-tensity and ICORs (incremental capital–output ratio) decline due to relocation of capital from investment in infrastructure toinvestment in production. Capital attritiondue to technological obsolescence could also

be expected to rise with changing marketstructures favouring the increase in competi-tion and a corresponding increase in theshare of R&D (research and development)expenditures in total investment.

T he share of capital in income, as com-puted from data on operating surplus andnet national product from the National Ac-counts Statistics, is approximately 60%. Thisis on the higher side and we would expectthis share to decline in the long run with the

adoption of more labour-augmenting tech-nologies, increased employment, and regula-tion in the unorganized sector. T he long-runshare of capital in income could be taken as1/3, which is the share of capital in the in-come of the US and several other developedcountries. For the purpose of our analysis wekeep the share of capital in GDP as 3/5.

A3.3.1.4 Demographics

We have used population figures given by thePFI (Population Foundation of India). Wehave projected population till 2030 assum-ing growth rates to decrease from currentlevels of 2003 to about 0.9% in 2030. Wehave assumed a constant rate of increase of 1.07% in the Indian work force. Assuming ashift in the age structure of the population infavour of people above the age of 65, and a

decline in population growth, a constant rateof increase allows for future increases in therate of participation of women in the workforce. T his is an expected consequence of in-creased expenditures on social infrastruc-ture and increased life expectancies.

A3.3.1.5 Other assumptions

As in the BRICs paper, we assume that the GoI

continues to pursue liberal economic and so-cial policy with emphasis on the gradual with-drawal of government intervention in industryand trade, and increasing government expen-ditures on health and education.

We have used the estimates of the US percapita GDP calculated in the BRICs paperto arrive at T FP growth rate figures for In-dia. We have deflated these values to 1993/94rupee values. We have taken 2003 as the ini-tial year in our analysis, and have used avail-

able data on GDP and capital stock valued at1993/94 prices. L abour force, work force,and population figures are in millions andthe values for the initial year are obtainedfrom the PF I.

We have computed long-term GDPgrowth rates for I ndia for the base modelwith investment rates at 24%, depreciationat 5%, convergence rate at 1.3%, and long-term US T FPG at 1.3%.

A3.3.2 Results

T he results from the simulations in our basemodel indicate that the long-term averageannual GDP growth rate for India is 6.7%per annum for the modelling time frame2001–36. T ERI considers it be the low-growth scenario relative to the 8% GDPgrowth rate adopted in the baseline scenario.


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232 Appendix 3

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Region-wise hydrocarbon reservesat the end of 2005

AAAAA


Figure A4.1 Distribution of proved reserves of

hydrocarbons in 1985, 1995, 2005


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236 Appendix 4

Figure A4.2 Production of crude oil indifferent regions (million barrels daily)


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Appendix 4 237

Figure A4.3 Reserves-to-production ratio andreserves (in percentage) for crude oil

Figure A4.4 Proved reserves of gas at theend of 2005 (trillion cubic metres)


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238 Appendix 4

Figure A4.5 Distribution of provedreserves in 1985, 1995, and 2005

Figure A4.6 Production of gas in different regions (billion cubic metres)


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Appendix 4 239

Figure A4.7 Reserves-to-production ratioand reserves (in percentage) for gas


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Sankey diagrams

AAAAA



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Figure A5.1 Sankey diagram for the business-as-usual scenario (2001)


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Figure A5.2 Sankey diagram for the business-as-usual scenario (2031)


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Figure A5.3 Sankey diagram for low-growth scenario (2031)


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Figure A5.4 Sankey diagram for the high-growth scenario (2031)


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Figure A5.5 Sankey diagram for high energy efficiency scenario (2031)


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Figure A5.6 Sankey diagram for high nuclear capacity scenario (2031)


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Figure A5.7 Sankey diagram for renewable energy scenario (2031)


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250 Appendix 6

Table A6.2 Energy balance in the business-as-usual scenario in 2021 (all figures are in Mtoe)

Oil and

natural Petroleum Hydro (large Nuclear Renewable Total

Supply–demand Coal gas products and small) energy energy power Total

Supply 466 132 405 30 13 1 1046

Conversions

Power generation 76 59 — 178

Conversion losses and

auxiliary consumption

Power generation 159 51 210

Oil refining 28 28

Transmission and distribution 40 40

Consumption

Agriculture — — 10 11 22

Industry 231 23 96 58 407

Transport — — 226 5 231

Residential — — 37 48 85

Commercial — — 7 16 23

End-use consumption 231 23 377 138 768

Notes

— Nil or negl igible.Figures may not add up to the total due to rounding off.Energy supply from hydro and nuclear options are considered equal to the amount of electricity generated.Energy consumption in industry includes energy use for process heating, captive power generation, and feedstock.

Table A6.3 Energy balance in the business-as-usual scenario in 2031 (all figures are in Mtoe)

Oil and



Supply 1176 136 757 40 13 1 2123

Conversions


Conversion losses and



Oil refining 50 50

Transmission and distribution 68 68Consumption


Industry 513 31 190 114 848

Transport — — 452 9 461




Notes



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Appendix 6 251

Table A6.4 Energy balance in the hybrid scenario in 2011 (all figures are in Mtoe)

Oil and



Supply 215 49 189 18 4 3 478

Conversions


Conversions losses and



Oil refining 12 12


Consumption


Industry 98 24 48 21 191

Transport — — 89 2 3 93




Notes



Oil and



Supply 329 129 299 31 24 11 823

Conversions





Oil refining 22 22


Consumption

Agriculture 0 0 9 10 18

Industry 214 30 79 47 370

Transport 0 10 144 9 7 171

Residential 0 0 37 39 76

Commercial 0 0 8 12 20


Notes



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252 Appendix 6


Oil and



Supply 767 136 484 41 42 33 1503

Conversions





Oil refining 41 41

T&D 51 51

Consumption


Industry 471 37 148 86 743

Transport — 25 231 28 17 302




Notes



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