Globalization, financial development and economic growth: Perils on the environmental sustainability of an emerging economy

Journal Pre-proof Globalization, financial development and economic growth: perils on the environmental sustainability of an emerging economy Pradeepta Sethi, Debkumar Chakrabarti, Sankalpa Bhattacharjee

PII:

S0161-8938(20)30016-8

DOI:

https://doi.org/10.1016/j.jpolmod.2020.01.007

Reference:

JPO 6582

To appear in:

Journal of Policy Modeling

Received Date:

23 August 2019

Revised Date:

19 November 2019

Accepted Date:

12 January 2020

Please cite this article as: Sethi P, Chakrabarti D, Bhattacharjee S, Globalization, financial development and economic growth: perils on the environmental sustainability of an emerging economy, Journal of Policy Modeling (2020), doi: https://doi.org/10.1016/j.jpolmod.2020.01.007

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Globalization, financial development and economic growth: perils on the environmental sustainability of an emerging economy Author Details First Author Pradeepta Sethi T A Pai Management Institute Manipal – 576104 Karnataka, INDIA Website: https://www.tapmi.edu.in/ E-mail: [email protected]

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Second Author Debkumar Chakrabarti Ramakrishna Mission Vidyamandira Belur Math, Howrah – 711202 West Bengal, INDIA Website: http://vidyamandira.ac.in/, E-mail: [email protected]

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

Globalization, financial development and economic growth:

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perils on the environmental sustainability of an emerging economy

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Abstract

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The study examines the effects of globalization, financial development, economic growth, and energy consumption on environmental sustainability in India over the period 1980–2015.

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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 Page 1 of 32

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

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technological innovations and complying with the environmental standards.

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

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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

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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

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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,

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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 Page 2 of 32

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

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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

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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

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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,

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Massidda, & Mattana, 2014; Kanjilal & Ghosh, 2013; Onafowora & Owoye, 2014), there are also studies that refuted the EKC hypothesis (Ang, 2008b; Farhani & Ozturk, 2015; Jafari,

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Othman, & Nor, 2012; Pal & Mitra, 2017). Moreover, studies reveal that the growthenvironment relationship, to a large extent, depends on the nature of pollutants. It has been

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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). Page 3 of 32

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.

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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

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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

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challenges to devising adequate policies for environmental sustainability. The literature on globalization-environment interlinkage stresses on three channels,

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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

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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,

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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.

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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.

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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

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

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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,

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2014; Sadorsky, 2010; Zhang, 2011).

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

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globalization and financial development on environmental quality, India has adopted a cobenefit approach (measures that promote development objectives while also yielding co-

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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 Page 5 of 32

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

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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

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empirically examine the dynamic relationship between carbon dioxide emissions,

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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

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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

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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

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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 Page 6 of 32

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

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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

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

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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,

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

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𝐶𝐶𝐶 = 𝐶(𝐶𝐶𝐶 , 𝐶𝐶𝐶 , 𝐶𝐶 , 𝐶𝐶 , 𝐶𝐶𝐶 )

(1)

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where 𝐶𝐶𝐶 is environment degradation measured by carbon dioxide emissions in metric tons per capita; 𝐶𝐶𝐶 represents KOF globalization index which is a composite index of social, political and economic globalization1; 𝐶𝐶𝐶 stands for financial development which is a

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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

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composite index of the banking sector and stock market development; 𝐶𝐶 represents real GDP per capita; 𝐶𝐶 is the urban population (percentage of total population); 𝐶𝐶𝐶 is the energy consumption per capita; and 𝐶𝐶 is 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

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index, a bank-based financial development index, and a stock market-based financial development index. Accordingly, we use the following models in our study:

𝐶𝐶𝐶𝐶𝐶 𝐶: 𝐶𝐶𝐶 = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 )

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𝐶𝐶𝐶𝐶𝐶 𝐶𝐶: 𝐶𝐶𝐶 = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 )

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𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶: 𝐶𝐶𝐶 = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 ) The study covers the period 1980–2015. The definition of the variables and the corresponding

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data sources are provided in Table 1.

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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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

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applied irrespective of whether the underlying variables are 𝐶(0), 𝐶(1), or a combination of the two. Second, the model takes a sufficient number of lags to capture the data generating

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

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

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sizes. Fourth, residual correlation is absent, which rules out the possibility of endogeneity. The ARDL framework of Equation (1) is as follows:

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∆𝐶𝐶𝐶 = 𝐶0 + 𝐶0 𝐶𝐶𝐶−1 + 𝐶1 𝐶𝐶𝐶𝐶𝐶−1 + 𝐶2 𝐶𝐶𝐶𝐶𝐶−1 + 𝐶3 𝐶𝐶𝐶𝐶−1 + 𝐶4 𝐶𝐶𝐶𝐶−1 + 𝐶5 𝐶𝐶𝐶𝐶𝐶−1 + ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 + 𝐶 ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 + ∑𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 +

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(2)

𝐶 ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶−𝐶 + ∑𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶−𝐶 +

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∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 + 𝐶𝐶

Here q is the lag length; Δ represents the difference operator; and 𝐶𝐶 is the white noise error

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term. The first part of the equation with 𝐶𝐶 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 Page 9 of 32

cointegration by restricting all estimated coefficients of lagged level variables equal to zero. Therefore, the null hypothesis of no cointegration H0 : b0 = b1 = b2 = b3 = b4 = b5 = 0 and the alternative hypothesis H1 : 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.

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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

This is specified as below: 𝐶

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

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1 ∆𝐶𝐶𝐶 = 𝐶 + ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶−𝐶 + ∑𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 +

𝐶 2 ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 + ∑𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶−𝐶 +

(3)

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𝐶 ∑𝐶 𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶−𝐶 + ∑𝐶=1 𝐶𝐶 Δ 𝐶𝐶𝐶𝐶𝐶−𝐶 + 𝐶𝐶𝐶𝐶𝐶−1 + 𝐶𝐶

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Here 𝐶, 𝐶, , 𝐶, 𝐶, 𝐶 are short-run dynamic coefficients to equilibrium, and 𝐶 is the speed adjustment coefficient. To ascertain the goodness of fit of the ARDL model, diagnostic and

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stability tests are conducted. The diagnostic test examines serial correlation, functional form, normality, and heteroscedasticity associated with the model. The structural stability test is

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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. Page 10 of 32

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:

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(4)

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∆𝐶𝐶 = Π𝐶𝐶−1 + Γ1 ∆𝐶𝐶−1 + Γ2 Δ𝐶𝐶−2 + ⋯ + Γp−1 Δ𝐶𝐶−𝐶+1 + 𝐶𝐶

Where, ∆𝐶𝐶 = [∆ΓY, ∆𝐶1, ∆𝐶2, ∆𝐶3]′ ; Π = −(1m − ∑i=1 Ai ); and Γi = −(1 − ∑ij=1 Aj ).

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

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

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the long-run information (β) such that the term β𝐶𝐶−𝐶 represents (𝐶 − 1) cointegrating vector on the multivariate model. A test statistic is calculated by taking the sum of the

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squared F-statistics of Γi 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

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(Γi ) 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

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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

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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 Page 11 of 32

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

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relationship in the VAR models.

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

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

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

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

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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

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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

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

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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, 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 Page 12 of 32

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

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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

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

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has created more favorable operating environments for investors through tax reductions or exemptions, relaxed labour laws, and relaxations to natural environmental regulations (Rana

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& 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

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industries from the developed to developing regions, which provides evidence of the pollution haven hypothesis in India.

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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 Page 13 of 32

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

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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

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

carbon dioxide emissions.

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

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

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lagged error correction term (𝐶𝐶𝐶𝐶−1 ) is negative and statistically significant at 1% level for all the three models. The values of 𝐶𝐶𝐶𝐶−1 coefficient of -1.762, -1.705, and -1.5335

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

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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.

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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

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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

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

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

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

The presence of a cointegrating relationship between carbon dioxide emissions,

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globalization, financial development, economic growth, energy consumption, and urbanization indicates one-way causality but does not reveal the direction. Consequently, the

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VECM Granger causality test was employed to examine the direction of causality, both in the short-run and the long-run in all the three models. The results for the short-run and long-run

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are reported in Table 6.

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 Page 15 of 32

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 financial development is impacting the environment sustainability indirectly through the growth channel. Another important result is the bi-directional causality between carbon

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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

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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

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

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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,

carbon emissions.

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

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1. Concluding remarks and policy implications The study examined the impact of globalization, financial development, economic growth,

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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 Page 16 of 32

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. 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

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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

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encompassing stringent environmental standards, legal systems, property rights, corruption, financial information quality, etc. Currently, the lopsided focus on the ‘ease of doing

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

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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

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cooperation between the developed and developing nations in terms of sharing of knowledge and advanced technologies to mitigate climate change.

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On the domestic front, the positive association between financial development and carbon emissions highlights that environmental concerns have taken a back seat while

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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 Page 17 of 32

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

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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-

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degree Celsius limit of the 2015 Paris agreement.

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

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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

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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

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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

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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

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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.

Page 18 of 32

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

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article. The usual disclaimer applies.

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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 8 6 4 2 0 -2 -4

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-6 -8 2011

2012

2013 CUSUM

2014

2015

5% Significance

1.6

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1.2

0.8

0.0

-0.4 2011

2012

2013

2014

2015

5% Significance

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CUSUM of Squares

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Figure 2: Model 2 10.0 7.5 5.0

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2.5 0.0 -2.5

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-5.0 -7.5

-10.0

2007

2008

2009

2010 CUSUM

2011

2012

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5% Significance

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1.6

1.2

0.8

0.4

0.0

-0.4 2011

2012

2013

CUSUM of Squares

2014

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5% Significance

Figure 3: Model 3

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8 6 4 2 0

-4 -6 -8 2012

2013 CUSUM

2014

5% Significance

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0.8

0.0

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CUSUM of Squares

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2015

5% Significance

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Source: Authors’ calculation

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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

KOF index 1. 2. 3.

GI

2.

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

3.

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)

FD

Financial development

Economic globalization index Social globalization index Political globalization index

BS

WDI, World Bank

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 (%)

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4.

Dreher (2006)

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Globalization index

Y

Real GDP per capita

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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

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Economic growth

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SM

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Table 2: Ng-Perron unit root test analysis MZa

MZt

MSB

MPT

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Variables at level CEt -2.36681 -3.05975 0.14320 1.84511 lnGIt -3.48235 -1.11477 0.32012 2.7054 lnFDt -9.43670 -2.00570 0.21254 10.3076 lnBSt -2.44689 -1.10383 0.45111 7.1461 lnSMt -6.11495 -1.49200 0.24399 14.6767 lnYt -0.66074 -0.32375 0.48998 53.3629 lnUt -3.37341 -1.14898 0.34060 24.2008 lnENt -1.54484 -0.63297 0.40973 37.3697 Variables at first difference -16.3203* -2.83499 0.17371 1.58106  CEt -19.7076** -2.78174 0.17710 5.92300  lnGIt -18.7926** -4.80682 0.17773 5.78906  lnFDt -25.4603*** -6.75938 0.17848 6.01664  lnBSt -23.6734*** -3.44030 0.14532 3.85016  lnSMt -25.9139*** -4.81627 0.17697 5.75294  lnYt -23.8000*** -3.42000 0.14300 4.03000  lnUt -22.4724** -2.72958 0.17642 6.19118  lnENt Note: ∆ denotes the first difference. *Refer to 10% significance level. **Refer to 5% significance level. ***Refer to 1% significance level.

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Table 3: ARDL cointegration test results Model

Calculated F statistic 𝐶𝐶𝐶𝐶𝐶 𝐶: 𝐶𝐶𝐶 5.54210 *** = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 ) 𝐶𝐶𝐶𝐶𝐶 𝐶𝐶: 𝐶𝐶𝐶 6.03495 *** = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶: 𝐶𝐶𝐶 5.97254 *** = 𝐶(𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 , 𝐶𝐶𝐶𝐶 . 𝐶𝐶𝐶𝐶𝐶 ) Critical Value bounds of F statistics: Intercept and no trend, 32 observations, k = 5 99% level 95% level 90% level 𝐶(0) 𝐶(1) 𝐶(0) 𝐶(1) 𝐶(0) 𝐶(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 𝐶(0) and upper 𝐶(1) bounds are taken from Narayan (2005).

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Table 4: Long-run coefficients

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Dependent variable = CEt Variable Model I Model II Model III Coefficient t-value Coefficient t-value Coefficient t-value lnGIt 0.1808* 1.7302 0.1146* 1.8562 0.1843** 2.1363 lnFDt 0.0951* 1.9901 ------------lnBSt ------0.0651* 1.9851 ------lnSMt ------------0.0190 0.2867 lnYt 0.1229* 1.9014 0.1014* 1.9852 0.0385** 2.7312 lnUt 0.4718 0.9314 0.2231 0.8411 0.9028 0.6214 lnENt 1.8940*** 13.6233 1.7701*** 7.0145 1.7939*** 10.1422 CONS -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.

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Table 5: Short-run elasticities

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Dependent variable =  CEt Variables Model I Model II Model III Coefficient t-value Coefficient t-value Coefficient t-value 0.1317** 2.5410 0.0535* 1.8014 0.2015* 1.9544  lnGIt 0.0471 1.0824 ------------ lnFDt ------0.1170 0.8011 ------ lnBSt ------------0.5140 0.8241  lnSMt 0.0372* 1.8477 0.5943*** 3.1514 0.5115* 1.9892  lnYt 1.6952 0.4741 1.5240 1.5014 1.1156 0.8858  lnUt 1.7543*** 4.6620 2.0853** 2.0132 1.9284*** 7.9901  lnENt CONS -5.01241 -8.2145 -6.0124*** -5.3621 -4.0125*** -6.9914 ECMt-1 -1.7620*** -6.3661 -1.7055*** -5.2425 -1.5335*** -4.4512 Robustness Indicators 0.9996 (0.2635) 1.2201 (0.2452) 0.5966 (0.7814) 2 Normal 2 0.4386 (0.6213) 0.2751 (0.1756) 0.7154 (0.8854)  Serial 0.7763 (0.7879) 0.6093 (0.6141) 0.4457 (0.1712) 2 ARCH 0.4741 (0.1451) 0.4147 (0.1214) 0.8746(0.1668) 2 Hetero 0.8585 (0.1661) 0.7142 (0.1101) 0.1445(0.5142) 2 Reset 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 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.

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Table 6: VECM Granger causality test results Sources of Causation Short-run estimates (F- values) Long-run (t-value) ECM(t-1)  CEt  lnGIt  lnFDt  lnYt  lnUt  lnENt --1.8014* 0.6012 2.6201* 0.5279 4.5520*** -2.0147***  CEt 0.0313 ---0.4221 1.9445* 0.1840 0.7342 -1.4101*  lnGIt 0.5510 0.9190 ---1.8921* 0.2140 0.0714 0.0142  lnFDt 1.0471 0.8011 1.8933* ---0.0558 2.4711** -2.5171***  lnYt 1.1529 2.0147* 0.7815 0.1556 ---1.0451 1.0747  lnUt 3.0174*** 2.3510** 1.1233 1.0118 0.0477 ----1.8969*  lnENt ECM(t-1)  CEt  lnGIt  lnBSt  lnYt  lnUt  lnENt --1.1820 0.4698 1.8511* 0.2477 2.4471** -1.9852*  CEt 1.0399 ---2.0447* 1.1457 1.0557 0.2474 -1.4317  lnGIt 0.8012 0.8211 ---3.1416** 0.1434 1.6172 0.5434  lnBSt 1.7933 0.1844 2.6597* ---0.0644 0.1052 0.5429  lnYt 0.8511 1.1801 1.4144* 0.5574 ---1.4478 0.4604  lnUt 3.0829*** 1.1397 0.1851 1.2556 0.04788 ---0.0787  lnENt ECM(t-1)  CEt  lnGIt  lnSMt  lnYt  lnUt  lnENt --2.5086** 1.1822 3.2592*** 0.3012 3.9449*** -2.4832**  CEt 0.1336 ---1.0299 1.1880 0.9445 1.0479 0.2454  lnGIt 0.5514 0.2033 ---1.0585 0.3279 1.1880 0.9148  lnSMt 3.6189*** 2.4810* 0.0644 ---0.2214 1.0778 -2.4410**  lnYt 1.0024 0.8556 0.8819 0.0896 ---0.6998 0.8728  lnUt 3.7710*** 2.5417** 1.3828 1.9887* 0.7789 ----2.0114**  lnENt 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)

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