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Perils of globalization, financial development and economic growth on

environmental sustainability: Experiences from an emerging economy

Author Details

First AuthorPradeepta SethiT A Pai Management InstituteManipal - 576104Karnataka, INDIAWebsite: https://www.tapmi.edu.in/ E-mail: [email protected]

Second AuthorDebkumar ChakrabartiRamakrishna Mission VidyamandiraBelur Math, Howrah – 711202West Bengal, INDIAWebsite: http://vidyamandira.ac.in/ E-mail: [email protected]

Third Author (Corresponding Author)Sankalpa BhattacharjeeIndian Institute of Management RanchiSuchana Bhawan, Audrey House Campus Meur's Road, Ranchi – 834 008Jharkhand, INDIAWebsite: http://www.iimranchi.ac.in/ Email: [email protected]; [email protected]

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Perils of globalization, financial development and economic growth on

environmental sustainability: Experiences from an emerging economy

Abstract

The study examines the effects of globalization, financial development, economic growth,

and energy consumption on environmental sustainability in India over the period 1980–2015.

The novelty of the study is the assessment of environmental sustainability in a single

framework encompassing globalization, financial development, and growth effects. Findings

reveal that an increased level of globalization and financial development while improving

economic performance are inimical to the sustainability of the environment. In the short-run,

globalization, economic growth, and increased energy consumption are contributing directly

to environmental degradation, while banking sector development is impacting environmental

sustainability adversely through the economic growth channel. Given the severity of the

findings amidst India’s tryst with economic growth, proactive policies are warranted to

encourage adaptation of greener and cleaner technologies in environmentally sustainable

areas. This necessitates improved institutional quality encompassing stringent environmental

standards, legal systems, property rights, corruption, financial information quality, etc.

alongside the provision of incentives and subsidies to manufacturing firms undertaking

technological innovations and complying with the environmental standards.

Keywords: globalization; financial development; growth; carbon dioxide emissions; India

JEL classification: C22; F64; G10; Q43

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1. Introduction

It is now widely established that human-induced climate change poses formidable challenges

to our understanding of social and economic policy goals such as prosperity, growth, equity,

and sustainable development (Mearns & Norton, 2010). India, considered to be one of the

largest growth engines of the world, also has the dubious distinction of being one of the

world’s most vulnerable countries to climate change (INCCA, 2010; Parry, Canziani,

Palutikof, van der Linden, & Hanson, 2007). Being the world’s third-largest emitter (behind

China and US), having tripled its carbon dioxide emissions from fuel combustion alone

during 1990 and 2011, India is expected to account for 10% of global emissions by 2035

(IEA, 2013). According to the HEI (2018) report, air pollution resulted in 1.1 million

premature deaths in 2015 (which amounts to 10.6% of total number of deaths) in India. The

problem is even more compounded by the fact that about half of the Indian population is

dependent on agriculture or other climate-sensitive sectors (GOI, 2018, p. 83). Increased

recognition of India’s vulnerability to climate change is proving to be decisive for the

policymakers to strike out a balance between climate change policy and economic growth

and pursuing measures that achieve both.

The major problem in the simultaneous attainment of the dual objective is the

existence of a tradeoff between environmental pollution and economic growth. Conventional

analysis of the economic growth-environment pollution relationship revolves around the

Environmental Kuznets Curve (henceforth EKC), which posits an inverted-U relationship

between pollution and per capita income (Grossman & Krueger, 1991). It is hypothesized that

at the formative stages of development, there are obstacles to adopting pollution abatement

policies on account of high discount rates. With the growth of the economy, as the discount

rate falls, it becomes possible to implement measures to curb pollution (Di Vita, 2008).

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Empirical investigations on the presence of EKC are inconclusive with mixed results.

While there are studies that lent support to the EKC hypothesis (Ahmad et al., 2017; Bella,

Massidda, & Mattana, 2014; Kanjilal & Ghosh, 2013; Onafowora & Owoye, 2014), there are

also studies that refuted the EKC hypothesis (Ang, 2008b; Farhani & Ozturk, 2015; Jafari,

Othman, & Nor, 2012; Pal & Mitra, 2017). Moreover, studies reveal that the growth-

environment relationship, to a large extent, depends on the nature of pollutants. It has been

observed that for pollutants like sulfur dioxide, suspended particulate matters, nitrous oxides,

etc., the results for EKC hold good (Bradford David, Fender Rebecca, Shore Stephen, &

Wagner, 2005; Stern, 1998). However, for a pollutant like carbon dioxide, characterized by

the presence of both national and international externalities, the relationship is ambiguous

(Frankel, 2009).

It has been observed that human activity-induced carbon emissions act as the most

important single source of potential global warming (Schmalensee, Stoker, & Judson, 1998).

Moreover, in contrast to the advanced economies, most of the emerging economies are

experiencing an accelerating rate of carbon emissions. It, therefore, becomes extremely

important to concentrate on carbon dioxide emissions to trace out its possible policy

implications on environmental sustainability, particularly for an emerging economy like

India.

Like most transitional economies, economic growth in India has been driven by

globalization and financial development. Globalization is a concept that represents a set of

economic, political, and cultural processes that manifest in increased interdependence among

nations (Goldberg & Pavcnik, 2007; Mills, 2009). Such integration invariably raises human

demands, but in the process, harbors the potential of generating unsustainable environmental

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footprints (Hoekstra & Wiedmann, 2014). Such conflicting outcomes pose enormous

challenges to devising adequate policies for environmental sustainability.

The literature on globalization-environment interlinkage stresses on three channels,

namely, technique, composition, and scale (Frankel & Rose, 2005). While the first two

effects predict a positive impact of globalization on the environment, the scale effect, on the

contrary, predicts increased pollution owing to the expansion of the level of production. One

major problem of the composition effect is its unrivaled focus on the preference pattern,

which manifests in ignoring the production aspect of globalization. In this context, the

‘pollution haven hypothesis’ assumes prominence. It refers to the possibility of multinational

firms engaged in highly polluting activities relocating to countries with low environmental

standards.

Empirical studies analyzing the impact of globalization on environmental quality

reveal inconclusive findings. Some studies found that globalization enhances environmental

quality (Antweiler, Copeland, & Taylor, 2001; Shahbaz, Solarin, & Ozturk, 2016).

Alongside, some studies report that globalization retards environmental quality, lending

support to the pollution haven hypothesis (Cole, 2006; Fell & Maniloff, 2018; Silva & Zhu,

2009). Such counterfactuals merit further investigation to have a clearer picture of the impact

of globalization on the environment.

Financial development, on the other hand, fundamentally refers to a process of

reducing the costs of acquiring information, enforcing contracts, and making transactions

(Levine, 2005). While a well-developed financial system attracts foreign direct investment

(Ang, 2008a) and augments growth, there is ambiguity regarding the effects of financial

development on environmental quality. While some studies document that financial

development improves the quality of the environment by reducing carbon emissions (Jalil &

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Feridun, 2011; Tamazian, Chousa, & Vadlamannati, 2009), some studies also found that

financial development degrades environmental quality (Abbasi & Riaz, 2016; Boutabba,

2014; Sadorsky, 2010; Zhang, 2011).

Given the complexity and the ambiguity involved in the impact of the twin forces of

globalization and financial development on environmental quality, India has adopted a co-

benefit approach (measures that promote development objectives while also yielding co-

benefits for addressing climate change effectively) to climate policy. Recently, India has also

submitted to the Intended Nationally Determined Contribution (INDC) to the United Nations

Framework Convention on Climate Change (UNFCCC) with three qualifying goals. First,

reducing the emission intensity of its GDP by 33–35 percent by 2030 from 2005 level;

second, achieving 40 percent cumulative electric power installed capacity from non-fossil

fuel-based energy resources by 2030; and third, creating an additional carbon sink of 2.5–3

billion tons of carbon dioxide equivalent by 2030 through additional forest and tree cover

(GOI, 2015).

Considering the qualifying goals alongside the inevitability of globalization and

financial development in India’s tryst with economic growth, the paper examines the effects

of globalization, financial development, economic growth, and energy consumption on the

sustainability of the environment in India. Such analysis appears instrumental in designing

appropriate policy stance to sustain environmental standards in tune with the 2015 Paris

agreement.

Our study makes at least two important contributions to the literature. First, we

empirically examine the dynamic relationship between carbon dioxide emissions,

globalization, financial development, economic growth, energy consumption, and

urbanization in a single-country setting. Prior literature (Boutabba, 2014; Ghosh, 2010; Pal &

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Mitra, 2017), while examining the EKC hypothesis, have often overlooked the effect of

globalization or financial development or both. Given the intertwined relationship between

globalization, growth, and financial development and their ramifications on the environment,

the omission of any single factor can lead to inconsistency in findings. Hence, a combined

analysis would be better suited for policy prescriptions. To the best of our knowledge, our

study is the first single-country study to carry out such an analysis. This can help in

formulating environmental policies that can strike a balance between growth and a

sustainable environment.

Second, we contribute to the strand of literature on how financial development

influences economic growth and energy consumption to impact carbon dioxide emissions.

Existing empirical studies (Boutabba, 2014; Saud, Chen, Haseeb, & Sumayya, 2019) have

used a single indicator to examine the impact of financial development on carbon dioxide

emissions. Given the complexity of services provided by the financial system, capturing

financial development with a single indicator could lead to potential bias and mislead the

findings. We have decomposed financial development into banking sector and stock market

development indicators. This helps in assessing the direct effects of the banking sector and

stock market developments on the environment. Such an approach would also equip

policymakers to identify the nature of the relationship between carbon dioxide emissions and

the financial sector and devise concomitant climate change policies for ushering sustainable

growth.

The rest of the paper is organized as follows. Section 2 presents the data, empirical

model, and methodological framework of the study. Section 3 presents the empirical results

and analysis. Section 4 concludes with policy implications.

2. Data and methodological framework

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2.1. Data and model specification

To examine the dynamic relationship among environmental degradation, globalization,

financial development, economic growth and energy consumption, we use the following

function:

CEt=f (GI t , FDt ,Y t ,U t , EN t) (1)

where CEt is environment degradation measured by carbon dioxide emissions in metric tons

per capita; GI t represents KOF globalization index which is a composite index of social,

political and economic globalization1; FD t stands for financial development which is a

composite index of the banking sector and stock market development; Y t represents real GDP

per capita; U t is the urban population (percentage of total population); EN t is the energy

consumption per capita; and ε tis the residual term, which follows a normal distribution.

Financial development encompasses a plethora of services, which poses an enormous

challenge in capturing the effect of financial development on environmental quality using a

single indicator. To this end, we introduce separately an aggregate financial development

index, a bank-based financial development index, and a stock market-based financial

development index. Accordingly, we use the following models in our study:

Model I :CEt=f (ln GI t , ln FD t , ln Y t , ln U t . ln EN t)

Model II :CEt= f (ln GI t , ln BSt , lnY t , lnU t . ln EN t)

Model III :CEt=f ( lnGI t , ln SM t , ln Y t , ln U t . ln EN t)

1 The globalization index consists of three indices: economic, political and social. The aggregate globalization index is a weighted average of economic globalization (36%); social globalization (38%); and political globalization (26%). The indices of economic globalization capture (i) actual flows [Trade (percent of GDP); Foreign Direct Investment (percent of GDP); Portfolio Investment (percent of GDP); and Income Payments to Foreign Nationals (percent of GDP)]; and (ii) restrictions [Hidden Import Barriers, Mean Tariff Rate, Taxes on International Trade (percent of Current Revenue) and Capital Account Restrictions]. Social globalization captures (i) Data on Personal Contact; (ii) Data on Information Flows; and (iii) Data on Cultural Proximity. Political globalization captures (i) Embassies in Countries; (ii) Membership in International Organizations; and (iii) Participation in U.N. Security Council Missions and International Treaties.

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The study covers the period 1980–2015. The definition of the variables and the corresponding

data sources are provided in Table 1.

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Table 1: Definition and sources of variables

Variable Notation Measurement Data source

Environment degradation CE Carbon dioxide emissions (metric tons per capita) WDI, World Bank

Globalization index GI

KOF index

1. Economic globalization index2. Social globalization index3. Political globalization index

Dreher (2006)

Financial development

FD1. Aggregate financial development index

(i). Bank-based financial development index(ii). Stock market-based financial development index

WDI, World BankBS

2. Bank-based financial development index(i). Domestic credit to the private sector by banks (%

of GDP)(ii). Broad money (% of GDP)(iii).Money and quasi money (M2) (% of GDP)

SM

3. Stock market-based financial development index(i). The market capitalization of listed companies (%

of GDP)(ii). Stocks traded, total value (% of GDP)(iii).Stocks traded, turnover ratio (%)

Economic growth Y Real GDP per capita WDI, World Bank

Urbanization U Urban population (% of the total population) WDI, World Bank

Energy consumption

EN Energy use (kg of oil equivalent per capita) WDI, World Bank

2.2. Econometric methodology

2.2.1. ARDL bounds test cointegration

The study employs the autoregressive distributed (ARDL) bounds test proposed by Pesaran,

Shin, and Smith (2001) to examine the cointegration relationship between carbon dioxide

emissions, globalization, financial development, economic growth, and energy consumption.

The ARDL method has several advantages over other cointegration methods. First, it can be

applied irrespective of whether the underlying variables are I (0 ), I (1 ), or a combination of the

two. Second, the model takes a sufficient number of lags to capture the data generating of 29

process in general to a specific modelling framework. Third, Pesaran and Shin (1999) show

that the ordinary least squares (OLS) estimators of the short-run parameter are consistent, and

the ARDL-based estimators of the long-run coefficient are super consistent in small sample

sizes. Fourth, residual correlation is absent, which rules out the possibility of endogeneity.

The ARDL framework of Equation (1) is as follows:

∆ CEt=a0+b0 CEt−1+b1 ln GI t−1+b2 ln FD t−1+b3lnY t−1+b4lnU t−1 +b5 lnEN t−1+∑i=1

q

αi Δ lnCEt−i+∑i=1

q

βi ΔlnGI t−i+∑i=1

q

γi ΔlnFDt −i+∑i=1

q

∂i ΔlnY t−i+∑i=1

q

σ i ΔlnU t−i+∑i=1

q

θi ΔlnEN t−i+εt(2)

Here q is the lag length; Δ represents the difference operator; andε tis the white noise error

term. The first part of the equation with b i corresponds to the long-run relationship, while the

terms with summation signs represent the error correction dynamics.

There are two steps in testing the cointegration relationship between carbon dioxide

emissions, globalization, financial development, economic growth, and energy consumption.

First, we estimate Equation (2) by the OLS technique. Second, we trace the presence of

cointegration by restricting all estimated coefficients of lagged level variables equal to zero.

Therefore, the null hypothesis of no cointegration H 0 :b0=b1=b2=b3=b4=b5=0 and the

alternative hypothesis H 1: b0 ≠b1≠ b2 ≠ b3≠ b4 ≠ b5≠ 0 implies cointegration among the series.

If the computed F-statistics is less than the lower bound critical value, we do not reject the

null hypothesis of no cointegration. However, if the computed F-statistics is greater than the

upper bound critical value, we reject the null hypothesis. However, if the computed value

falls within lower and upper bound critical values, the result is inconclusive.

The long-run relationship of the selected ARDL model is estimated using the Akaike

Information Criterion (AIC) or Schwarz Information Criterion (SIC). We obtain the short-run

dynamic parameters by estimating an error correction model with the long-run estimates.

This is specified as below:

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∆ CEt=μ+∑i=1

q

αi ΔCEt−i+∑i=1

q1

β i ΔlnGI t−i+∑i=1

q2

γ i ΔlnFDt−i+∑i=1

q

μ i ΔlnY t−i+∑i=1

q

σ i ΔlnU t−i+∑i=1

q

ωi ΔlnEN t−i+ϕ ECM t−1+ε t(3)

Here α ,β , , μ , σ , ω are short-run dynamic coefficients to equilibrium, and ϕis the speed

adjustment coefficient. To ascertain the goodness of fit of the ARDL model, diagnostic and

stability tests are conducted. The diagnostic test examines serial correlation, functional form,

normality, and heteroscedasticity associated with the model. The structural stability test is

performed by employing the cumulative sum of recursive residuals (CUSUM) and the

cumulative sum of squares of recursive residuals (CUSUMSQ). The CUSUM and

CUSUMSQ statistics are updated recursively and plotted against the break-points. If the plots

of CUSUM and CUSUMSQ statistics stay within the critical bonds of 5% level of

significance, it implies that all the coefficients in the given regression are stable.

2.2.2. The VECM Granger causality test

The cointegration relationship indicates the existence but not the direction of the causal

relationship. Therefore, we conduct the Granger causality test in the vector error correction

model (VECM) to examine the causality relationship between carbon dioxide emissions,

globalization, financial development, economic growth, and energy consumption. The

VECM regresses the changes in the variables (both dependent and independent) on the

lagged deviations and in general, can be expressed by the following equation:

∆ Z t=Π Z t−1+Γ1 ∆ Z t−1+Γ 2 Δ Z t −2+…+Γ p−1 ΔZ t−p+1+e t (4)

Where, ∆ Z t=[∆ ΓY , ∆ X 1 , ∆ X 2,∆ X 3 ]'; Π=−(1m−∑i=1

p

A i); and Γ i=−(1−∑j=1

i

A j).

For ( i=1 , 2 ,…, p−1 ), Γ measures the short-run effect of the changes in Zt . Meanwhile, the

(4×4) matrix of Π = (α β ') contains both the speed of adjustment to equilibrium (α ) and the

long-run information (β) such that the term β Z t−k represents (n−1) cointegrating vector on

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the multivariate model. A test statistic is calculated by taking the sum of the squared F-

statistics of Γ i and t statistics of Π . The Granger causality is implemented by calculating the

F-statistics (Wald test) based on the null hypothesis that the set of coefficients (Γ i) on the

lagged values of independent variables are not statistically different from zero. If the null

hypothesis is accepted, then it can be concluded that the independent variables do not cause

the dependent variables. On the other hand, if Π is significant (i.e., different from zero) based

on the t-statistics, then both the independent and dependent variables have a stable

relationship in the long-run.

3. Empirical results and analysis

We start our empirical analysis by checking the stationarity properties of the variables as in

the presence of I (2 ) variables, the computed F-statistics provided by Pesaran et al. (2001)

become invalid (Ouattara, 2006). We prefer the Ng-Perron unit root test over other unit root

tests (e.g., Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), etc.) because it alleviates

the problem of severe size distribution properties when the error term has a negative moving

average root (Schwert, 2002). Ng-Perron unit root test uses GLS de-trended data, which are

based on modified SIC/AIC. Table 2 presents the unit root test results. The results show that

all the variables are non-stationary in their level data. However, the stationarity property is

found in the first difference of the variables. Overall our results report that all the variables

are integrated of order one, i.e., I (1 ). This implies that there is a possibility of a cointegrating

relationship in the VAR models.

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Table 2: Ng-Perron unit root test analysisMZa MZt MSB MPT

Variables at levelCEt -2.36681 -3.05975 0.14320 1.84511

lnGIt -3.48235 -1.11477 0.32012 2.7054lnFDt -9.43670 -2.00570 0.21254 10.3076lnBSt -2.44689 -1.10383 0.45111 7.1461lnSMt -6.11495 -1.49200 0.24399 14.6767lnYt -0.66074 -0.32375 0.48998 53.3629lnUt -3.37341 -1.14898 0.34060 24.2008

lnENt -1.54484 -0.63297 0.40973 37.3697Variables at first difference

CEt -16.3203* -2.83499 0.17371 1.58106 lnGIt -19.7076** -2.78174 0.17710 5.92300 lnFDt -18.7926** -4.80682 0.17773 5.78906 lnBSt -25.4603*** -6.75938 0.17848 6.01664 lnSMt -23.6734*** -3.44030 0.14532 3.85016 lnYt -25.9139*** -4.81627 0.17697 5.75294 lnUt -23.8000*** -3.42000 0.14300 4.03000 lnENt -22.4724** -2.72958 0.17642 6.19118

Note: ∆ denotes the first difference. *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level.

Once it is confirmed that all the variables are integrated of order I (1 ), we use the

ARDL cointegration test to examine the long-run relationship among carbon dioxide

emissions, globalization, financial development, economic growth, urbanization, and energy

consumption. This is done by applying the procedure in OLS regression in Equation (2), and

then compute the F-statistics for the joint significance of the lagged levels. Given that the

value of the F-statistics is sensitive to the number of lags imposed each time on the

differenced variables, we select the optimal order of lags of the model based on the AIC. The

calculated F-statistics, together with the critical values, are reported in Table 3. The statistics

reveal that the computed F-statistics value exceeds the upper bound critical values and is

significant at the level of 5% for all the estimated models. Therefore, we reject the null

hypothesis of no cointegration among the variables in all the three models. It implies the

existence of a cointegrating relationship between carbon dioxide emissions, globalization,

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financial development (all the three indicators), economic growth, urbanization, and energy

consumption. The long-run equilibrium relationship among the variables can be explained by

the fact that closer integration with the outside world has augmented the economic activity

and development of the financial system in India. The ensuing economic growth has

increased the demand for energy, which is met by fossil fuel, especially coal. This, again, has

resulted in environmental degradation.

Table 3: ARDL cointegration test resultsModel Calculated F statistic

Model I :CEt=f (ln GI t , ln FD t , ln Y t , ln U t . ln EN t) 5.54210 ***Model II :CEt= f ¿ 6.03495 ***

Model III :CEt= f ( lnGI t , ln SM t , ln Y t , ln U t . ln EN t) 5.97254 ***Critical Value bounds of F statistics: Intercept and no trend, 32 observations, k = 5

99% level 95% level 90% levelI (0) I (1) I (0) I (1) I (0) I (1)3.06 4.85 2.39 3.38 2.08 3.00

Note: *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level. The critical values (CV) for the lower I (0) and upper I (1) bounds are taken from Narayan (2005).

Long-run and short-run results

The long-run results are presented in Table 4. The results suggest that the coefficients

of globalization, financial development index, banking sector development, economic

growth, and energy consumption are positive and statistically significant. To get a sense of

the magnitude of the effects, a 1% increase in globalization results in an increase in carbon

dioxide emissions by 0.1808%, ceteris paribus. The corresponding numbers for models II and

III are of a similar order of magnitude. One possible explanation for this could be that

increased globalization (by increasing financial and trade openness), has attracted foreign

direct investment (FDI). Moreover, in the quest for economic growth, the Indian government

has created more favorable operating environments for investors through tax reductions or

exemptions, relaxed labour laws, and relaxations to natural environmental regulations (Rana

& Sharma, 2019). Over the past two decades, the Indian manufacturing sector, especially the

capital-intensive industries, accounted for a majority chunk of inbound FDI, and the share has

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also increased in polluting industries (Rastogi & Sawhney, 2014). Hence, we can infer that

globalization has increased carbon dioxide emissions through a displacement of dirty

industries from the developed to developing regions, which provides evidence of the

pollution haven hypothesis in India.

Table 4: Long-run coefficientsDependent variable = CEt

Variable Model I Model II Model IIICoefficient t-value Coefficient t-value Coefficient t-value

lnGIt 0.1808* 1.7302 0.1146* 1.8562 0.1843** 2.1363lnFDt 0.0951* 1.9901 ---- ---- ---- ----lnBSt ---- ---- 0.0651* 1.9851 ---- ----lnSMt ---- ---- ---- ---- 0.0190 0.2867lnYt 0.1229* 1.9014 0.1014* 1.9852 0.0385** 2.7312lnUt 0.4718 0.9314 0.2231 0.8411 0.9028 0.6214

lnENt 1.8940*** 13.6233 1.7701*** 7.0145 1.7939*** 10.1422CONS -6.5424*** -8.3625 -3.1452*** -5.0120 -7.0121*** -8.01454

Note: *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level.

The coefficients of the aggregate financial development index and the banking sector

index are positive and statistically significant, suggesting that the development of the

financial system has contributed to environmental degradation. The plausible explanation for

this can be that the development of the banks and the financial system lowers the cost of

financing and helps in increasing investments in new projects which are not necessarily

environmentally friendly. The financial system also facilitates credit access to consumers for

the purchase of high-value and carbon-intensive items like cars, air cooling systems, etc.

which enhance carbon emissions. Moreover, to remove the supply-side bottlenecks, the

Indian government has undertaken huge investments in infrastructure and core sectors (GOI,

2019), where banks provide financial assistance. There is no doubt that this process will

stimulate the economy, but on the flip side, it will also adversely impact environmental

sustainability. It, therefore, seems that the financial sector is unable to facilitate the transfer of

green technologies at the desired level of efficiency.

We find that both economic growth and energy consumption degrades the

environment. The result is quite obvious, given the fact that with higher economic growth,

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the demand for energy consumption increases. Given the fact that coal is the predominant

source of energy in India, this will adversely impact carbon emissions. Hence, India needs to

pay more attention to advanced techniques, which can boost energy efficiency levels.

Interestingly, we do not find any significant relationship between rapid urbanization affecting

carbon dioxide emissions.

The results for short-run dynamics are presented in Table 5. The coefficient of the

lagged error correction term (ECM ¿¿ t−1)¿ is negative and statistically significant at 1%

level for all the three models. The values of ECM t−1 coefficient of -1.762, -1.705, and -

1.5335 propose that any deviation from the long-run equilibrium level of carbon dioxide

emissions is corrected within six months for all the models.

The results of the short-run, which are quite similar to the long-run results imply that

globalization, financial development, economic growth, and increased energy consumption,

are contributing to environmental degradation in the short-run. The results of robustness and

diagnostics tests are presented in the lower portion of Table 5.

Table 5: Short-run elasticitiesDependent variable = CEt

Variables Model I Model II Model IIICoefficient t-value Coefficient t-value Coefficient t-value

lnGIt 0.1317** 2.5410 0.0535* 1.8014 0.2015* 1.9544 lnFDt 0.0471 1.0824 ---- ---- ---- ---- lnBSt ---- ---- 0.1170 0.8011 ---- ---- lnSMt ---- ---- ---- ---- 0.5140 0.8241 lnYt 0.0372* 1.8477 0.5943*** 3.1514 0.5115* 1.9892 lnUt 1.6952 0.4741 1.5240 1.5014 1.1156 0.8858 lnENt 1.7543*** 4.6620 2.0853** 2.0132 1.9284*** 7.9901CONS -5.01241 -8.2145 -6.0124*** -5.3621 -4.0125*** -6.9914ECMt-1 -1.7620*** -6.3661 -1.7055*** -5.2425 -1.5335*** -4.4512

Robustness Indicators2 Normal 0.9996 (0.2635) 1.2201 (0.2452) 0.5966 (0.7814)2 Serial 0.4386 (0.6213) 0.2751 (0.1756) 0.7154 (0.8854)2 ARCH 0.7763 (0.7879) 0.6093 (0.6141) 0.4457 (0.1712)2 Hetero 0.4741 (0.1451) 0.4147 (0.1214) 0.8746(0.1668)2 Reset 0.8585 (0.1661) 0.7142 (0.1101) 0.1445(0.5142)

Note: Figures in parentheses are estimated p-values. *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level. 2 Normal indicates to the Jarque-Bera statistic of the test for normal residuals, 2 Serial is the Breusch-Godfrey LM test statistic for no serial relationship, 2 ARCH is the Engle’s test statistic for no autoregressive conditional heteroskedasticity, 2 Hetero is the heteroskedasticity

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test based on the regression of squared residuals on squared fitted values, and 2 Reset is the test for functional form based Ramsey's RESET test using the square of the fitted values.

It can be concluded that all the three models pass all the diagnostics tests successfully, i.e.,

LM test for serial correlation, ARCH test, normality test of residual term, White

heteroscedasticity test, and reset test for stability of model specification. Thus, the estimated

models do not have any econometric misspecifications.

To test for structural stability of the long-run parameters, we employed the CUSUM

and CUSUMSQ test statistics proposed by Brown, Durbin, and Evans (1975) to the recursive

residuals of the models. The CUSUM and CUSUMQ statistics are updated recursively and

plotted against the break points. If the plots of CUSUM and CUSUMQ statistics stay within

the critical bounds of 5% level of significance, the null hypothesis of all coefficients in the

given regression is stable and cannot be rejected. As can be seen from Figures 1-3, the plots

are of both CUSUM and CUSUMSQ test statistics are well within the critical bounds, which

confirms that the estimated parameters are stable over the selected period. This confirms that

models seem to be steady and appropriately specified for undertaking policy decisions.

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Figure 1: Model 1

The plot of the cumulative sum of recursive residuals The plot of the cumulative sum of squares recursive residuals

Figure 2: Model 2

Figure 3: Model 3

Source: Authors’ calculation

The presence of a cointegrating relationship between carbon dioxide emissions,

globalization, financial development, economic growth, energy consumption, and

urbanization indicates one-way causality but does not reveal the direction. Consequently, the

VECM Granger causality test was employed to examine the direction of causality, both in the

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short-run and the long-run in all the three models. The results for the short-run and long-run

are reported in Table 6.

Table 6: VECM Granger causality test resultsDependent variable

Sources of CausationShort-run estimates (F- values) Long-run (t-value)

CEt lnGIt lnFDt lnYt lnUt lnENt ECM(t-1)

CEt --- 1.8014* 0.6012 2.6201* 0.5279 4.5520*** -2.0147*** lnGIt 0.0313 ---- 0.4221 1.9445* 0.1840 0.7342 -1.4101* lnFDt 0.5510 0.9190 ---- 1.8921* 0.2140 0.0714 0.0142 lnYt 1.0471 0.8011 1.8933* ---- 0.0558 2.4711** -2.5171*** lnUt 1.1529 2.0147* 0.7815 0.1556 ---- 1.0451 1.0747 lnENt 3.0174*** 2.3510** 1.1233 1.0118 0.0477 ---- -1.8969*

CEt lnGIt lnBSt lnYt lnUt lnENt ECM(t-1)

CEt --- 1.1820 0.4698 1.8511* 0.2477 2.4471** -1.9852* lnGIt 1.0399 ---- 2.0447* 1.1457 1.0557 0.2474 -1.4317 lnBSt 0.8012 0.8211 ---- 3.1416** 0.1434 1.6172 0.5434 lnYt 1.7933 0.1844 2.6597* ---- 0.0644 0.1052 0.5429 lnUt 0.8511 1.1801 1.4144* 0.5574 ---- 1.4478 0.4604 lnENt 3.0829*** 1.1397 0.1851 1.2556 0.04788 ---- 0.0787

CEt lnGIt lnSMt lnYt lnUt lnENt ECM(t-1)

CEt --- 2.5086** 1.1822 3.2592*** 0.3012 3.9449*** -2.4832** lnGIt 0.1336 ---- 1.0299 1.1880 0.9445 1.0479 0.2454 lnSMt 0.5514 0.2033 ---- 1.0585 0.3279 1.1880 0.9148 lnYt 3.6189*** 2.4810* 0.0644 ---- 0.2214 1.0778 -2.4410** lnUt 1.0024 0.8556 0.8819 0.0896 ---- 0.6998 0.8728 lnENt 3.7710*** 2.5417** 1.3828 1.9887* 0.7789 ---- -2.0114**

Note: *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level. Δ is the first difference operator. The number of appropriate lags is one according to Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan–Quinn Information Criterion (HIC)

We find that unidirectional causality running from globalization, economic growth,

and energy consumption granger cause carbon dioxide emissions in the short-run. This

implies that closer integration with the outside world, a higher degree of openness, and

economic growth have been inimical to the sustainability of the environment. Policy focus on

ensuring high growth in the short-run may not be a bad idea if, in the long-run, it has a

beneficial effect on environmental sustainability. Hence, a decision on the policy option is

contingent on the nature of the long-run relationship between carbon dioxide emissions,

economic growth, and energy consumption (Soytas & Sari, 2006). One interesting result is

the bi-directional causality between financial development and economic growth, implying of 29

financial development is impacting the environment sustainability indirectly through the

growth channel. Another important result is the bi-directional causality between carbon

dioxide emissions and energy consumption, both in the short-run and long-run. The finding is

quite straightforward and intuitive that energy consumption drives carbon dioxide emissions

because the primary source of electricity in India is the combustion of coal. Hence providing

electricity to 1.2 billion Indian population from coal-fired power plants would mean further

addition of a pollutant to the environment (Pal & Mitra, 2017). But probably the most

interesting result is that the opposite also holds. In the long-run, we report a feedback effect

on economic growth and carbon dioxide emissions. This confirms the fact that India is an

energy-dependent economy. Riding on the impressive growth and demographic dividend,

energy demand in India will increase significantly (BP, 2019) and the concomitant rise in

carbon emissions.

4. Concluding remarks and policy implications

The study examined the impact of globalization, financial development, economic growth,

and energy consumption on carbon dioxide emissions in the Indian economy over the period

1980–2015. The main results of the study provide support for a robust long-run equilibrium

relationship between the variables, indicating globalization, financial development, economic

growth, and energy consumption are positively related to carbon dioxide emissions in the

long-run. We find plausible evidence in support of the pollution haven hypothesis. Granger

causality results suggest that unidirectional causality runs from globalization, economic

growth, and energy consumption to carbon dioxide emissions in the short-run while we find a

feedback relationship between economic growth, energy consumption with carbon dioxide

emissions in the long-run.

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Our results have important implications for policymakers in India, aspiring to strike a

balance between equitable growth and environmental sustainability. We observed that

increased global integration in the form of trade and capital flows while boosting the

economy is adversely impacting the environmental sustainability in India. Future policy in

this regard should encourage only those foreign investments that rely on greener technology

in environmentally sustainable areas. This necessitates improved institutional quality

encompassing stringent environmental standards, legal systems, property rights, corruption,

financial information quality, etc. Currently, the lopsided focus on the ‘ease of doing

business’ engenders a serious threat in maintaining a proper balance between environmental

sustainability and attracting foreign investment. In this regard, India should not only engage

in proactive climate diplomacy in the global arena but should be more persuasive on

cooperation between the developed and developing nations in terms of sharing of knowledge

and advanced technologies to mitigate climate change.

On the domestic front, the positive association between financial development and

carbon emissions highlights that environmental concerns have taken a back seat while

extending finance to investment projects that have spurred the growth process. Apart from

strengthening the environmental standards, policy measures linking financial assistance with

the adaptation of greener and cleaner technologies needs to be encouraged. This requires the

provision of incentives and subsidies to manufacturing firms undertaking technological

innovations and complying with the environmental standards. Policy measures should also

focus on developing a carbon trading market that provides incentives to mitigate greenhouse

gas emissions.

From a long-term perspective, the reduction of carbon emissions depends on a two-

pronged strategy of deploying Carbon Capture and Storage (CCS) technology and the

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expansion of the usage of renewable energy sources. Concerning the CCS, it needs to be

mentioned that amidst the government’s ambitious targets, coal-fired plants still contribute

50% of India’s carbon emissions and will continue to remain critical to India’s energy

security, at least till 2050 (Singh, Rao, & Chandel, 2017). Therefore, implementation of CCS,

both at the plant and industry levels, can prove to be an effective instrument in meeting the 2-

degree Celsius limit of the 2015 Paris agreement.

With regard to renewable energy, it has been observed that renewable energy

penetration is highly cost elastic (Thambi, Bhatacharya, & Fricko, 2018). Therefore, the

widespread utilization of renewable energy sources will not be possible unless there is a

significant reduction in cost. Moreover, lack of proper technological development, the threat

of duties on imports of solar panels, and difficulties in land acquisition, etc., act as major

obstacles in the adaptability of renewable energy (Mohan & Topp, 2018). As per the

estimates of GOI (2019, pp. 123-124), attainment of environmental quality in accordance

with the Paris agreement would require around US$ 206 billion (at 2014–15 prices) between

2015–2030. Such massive funding would require, apart from budgetary and international

assistance, significant private contribution. In the global sphere, green bonds have, by far,

been the most effective instrument in this regard. By taking adequate policy measures to tap

the bond market, the government of India would be able to accumulate the resources required

to be at the fulcrum of growth, while maintaining a sustainable environment.

Acknowledgment:

We thank the four anonymous referees and the Board of Editors for the insightful comments

that have added substantial value to the work. We extend our special thanks to the editorial

assistant Ms. Sabah Cavallo who has given us comments on the preliminary draft of the

article. The usual disclaimer applies.

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