(PDF) Do financial development and institutional quality matter for ecological sustainability in the long run? Evidence from Ind

The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1477-7835.htm Do financial development and Financial development, institutional quality matter for institutions, ecology ecological sustainability in the long run? Evidence from India Ishfaq Nazir Khanday Received 3 January 2023 Revised 11 June 2023 Department of Economics, Aligarh Muslim University, Aligarh, India Accepted 15 July 2023 Inayat Ullah Wani Aligarh Muslim University, Aligarh, India, and Mohammad Tarique Department of Economics, Aligarh Muslim University, Aligarh, India Abstract Purpose – The paper assesses the moderating function of institutions in the financial development and environmental nexus covering India for the time period 1980–2019. Design/methodology/approach – Deviating from extant literature which has mostly used emissions of major greenhouse gasses as a measure of environmental quality, the present study uses a broad measure of environmental quality called ecological footprint (EFP). Financial development is measured using a robust proxy recently introduced by International Monetary Fund (IMF). This index is multifaceted and covers three broad dimensions of financial sector in terms of depth, efficiency and access of both financial institutions and markets, thus outperforming the exclusively bank-based measures used in the past literature. Further institutional quality index is generated using the data from international country risk guide. Finally, autoregressive distributed lag model is used for the empirical estimation of short-run and long-run results. Findings – The empirical estimates reveal that financial development and institutional quality are good for long-run environmental sustainability of India, whereas economic growth degrades the environment in the long- run. The results also attest to the existence of pollution heaven hypothesis in India for long run. Furthermore, regarding the moderating role of institutions, the study reveals that institutional quality complements financial development in affecting environment in the short run. While as, in the long run, they play a substitutive role whereby sound institutions cover-up the inefficiencies in financial system. Research limitations/implications – First, the paper uses the index of financial development developed by the IMF in order to quantify the level of financial development in India overtime. The index is based on three key dimensions of financial development such as the depth, efficiency and access of both financial institutions and markets. However, the index completely neglects the role of financial stability in determining financial development. Thus, future studies that are based on this IMF introduced index of financial development should incorporate the stability dimension to it. Second, this empirical study focused exclusively on India and employed aggregate EFP to measure environmental quality. Further studies can complement the content of this research by conducting similar studies to capture country-specific characteristics of other emerging economies and also scrutinize the impact on the six sub-indices of EFP. Practical implications – The results of the study reveal that the effect of financial development, and institutions on ecological footprint is sensitive to time dynamics. Moreover, the findings offer important policy implications to government and policy makers in India on how to curb the menace of environmental degradation. Originality/value – The paper addresses the gap in the literature by examining the moderating role of institutional quality in the financial development and ecological footprint nexus in India. Furthermore, the authors employ a robust proxy for both financial development and environmental quality unlike extant studies on India. Keywords Financial development, Institutions, ARDL, Cointegration, Ecological footprint Paper type Research paper 1. Introduction Management of Environmental Quality: An International Journal In the pursuit of higher economic prosperity and material well-being overexploitation of © Emerald Publishing Limited 1477-7835 natural resources in general; environmental degradation, climate change and global warming DOI 10.1108/MEQ-01-2023-0002 MEQ in particular has taken place (Dar and Asif, 2017b). The emissions of greenhouse gases (GHGs) has quadrupled post industrialization period from its pre-industrial level (Zakaria and Bibi, 2019). Environmental degradation and global warming are major contributors to the ever-shifting patterns of rainfall, steadily rising sea levels, and extreme weather events. These changes have a significant detrimental effect, not only on the health of ecosystems and the survival of biodiversity but also on the public welfare and human life (Boutabba, 2014). Further, global warming is an effect that cannot be blamed on any one nation or region. So, a global policy needs to be enacted to improve environmental quality by reducing emissions of GHGs contributing to global warming (Mohammed et al., 2019). Thus, the threat posed by global warming has prompted ecological economists to understand and investigate the relationship between GHGs emissions and the factors that contribute to them. The study is centred on India due to its rising environmental deterioration. India is one of the most polluted countries in the world, with its cities frequently ranking high on the list of the most polluted cities globally. According to a report by the World Health Organization (WHO) in 2018, 14 of the 15 most polluted cities in the world were in India. India, with the current population of around 1.4 billion, is anticipated to surpass China by mid of 2023 (United Nations Population, 2023).India is the world’s fifth-largest economy with a nominal gross domestic product of $2.94tn, and the second-fastest growing trillion-dollar economy in world possessing a vibrant and rapidly growing financial sector responsible for sustained growth rate of 6% over the last few decades (Sehrawat et al., 2015). In terms of GDP measured in purchasing power parity ($11.33tn), the South Asia giant ranks third. India is the third- largest aggregate emitter of carbon dioxide accounting for 5.9% of total global emissions in 2010 after China and the United States (Zafar et al., 2023). India is also fourth-largest GHG emitter in the world. However, India has one of the lowest worldwide per capita GHG emissions due to its massive population (Alam et al., 2011). India is also a party to various international treaties related to the protection of environment. The Indian government has made a global commitment of reducing the emissions of GHGs by over 30% by 2030 compared to 2005 levels. On the other hand, as the country’s growing population becomes more affluent, to meet rising energy demands it continues to build more coal plants and import more crude oil.). It is also the fourth-largest energy user in the world (Zafar et al., 2023). A large and a dominant portion of this energy consumption is met by using fossil fuels. India is among one of the largest consumers of fossil fuels in south Asia (Xue et al., 2021). Excessive reliance on fossil fuels no doubt has helped country in achieving higher economic growth but it is equally responsible for degradation of environmental quality (Hasnat et al., 2018; EPF, 2020). Miserable environment quality can better be understood from the deteriorating trend in India’s per capita ecological footprint (EFP) and ecological deficit. In fact, total per capita EFP surged from 0.78 in 1990 to 1.17 in 2014 while as ecological deficit for India increased from 0.33 to 0.72 (2.2 fold) for the same period (EP, 2020 GFP Network). Moreover, India is very susceptible to climate change since a substantial proportion of its populace relies on agriculture and allied activities for their livelihood. By 2050, the nation might see a $1.2tn “lost GDP” in comparison to a world without climate change. Global warming, shifting monsoon and rainfall patterns would be the factors responsible for this loss (Dar and Asif, 2017a). Thus, Policymakers are confronted with the problem of a trade-off between achieving economic development and ensuring environmental protection. To achieve sustainable economic prosperity, it becomes expedient to work out the association between environmental quality and its determining factors. This is necessary for achieving sustainable development as the capacity of humanity to coexist on earth for an extended period of time is the societal objective. According to this notion, it is more probable that natural resources will be used sustainably, effectively managed, and not squandered if the true worth of those resources is factored into the cost of utilizing them. This perception is popularly known as ecological sustainability and every economy is striving to move on this course. The existent studies have identified many social and economic factors like financial Financial development, national income, trade, foreign direct investment (FDI), financial development, development, industrialization, energy consumption, and so on, as main determinants of environmental quality (Boutabba, 2014; Dar and Asif, 2018; Imamoglu, 2018; Danish et al., 2019a,b; Rasool institutions, et al., 2020; Chowdhury et al., 2021; Uzar, 2021; Zafar et al., 2023).The present study ecology investigates the direct and the moderating role of institutional quality in the financial development and EFP nexus in the context of India. The rest of this work is structured as follows: Section 2 presents review of literature. Section 3 relates to data description and methodology used. Section 4 contains empirical analysis and findings of the study and section 5 provides the necessary diagnostic tests. Policy suggestions and recommendations are presented in concluding remarks under section 6. 2. Review of literature This section provides an overview of both theoretical and empirical research on the relationship between environmental quality and its determinants, i.e. financial development and institutions. 2.1 Theoretical underpinnings Theoretically, there are two opposing perspectives regarding the effect of financial development on the environment. School of thought lead by Komal and Abbas (2015), Gokmenoglu et al. (2015), Shahbaz et al. (2016), Majeed and Mazhar (2019), Ganda (2019), Lahiani (2020) postulate a negative relationship between financial development and environmental degradation. These scholars argue that as financial sector grows, it becomes easy for firms to finance investment in green technology, research and development (R&D), environment- friendly projects, supporting cleaner and renewable energies and so on. Financial development thus paves way for the use of more modern and energy-efficient technologies and, in return, leads to reduction in environmental degradation. Another group of scholars (Danish et al., 2018; Pata, 2018; Yang et al., 2021) argue that financial development is detrimental for environmental sustainability. Financial development by reducing/removing the various credit market imperfections like high interest rates, collateral requirements, cumbersome documentation process, and so on, actually helps households and firms in availing credit facilities. The cheap and easy availability of credit to firms and households exacerbates both the production as well as the consumption of goods and services. The increased production of goods and consumption of household durables like refrigerators, cars, and air conditioners leads to an ever increasing demand for energy and discharges of wastes into environment causing environmental pollution. Environmental governance theory (EGT) and ecological modernization theory (EMT) constitute the theoretical foundations of the relationship between institutions and environment quality. EGT theory argues that governance is the most critical factor responsible for efficient environment management (Armitage et al., 2012; Baron and Lyon, 2011). Environment is a public good, therefore protection and safety of environment is among the fundamental responsibilities of any government. Political institutions can protect the environment by enacting and executing laws related to the protection of environment and through punishing the environmental offenders. Better institutional quality in the form of well-defined property rights, impartial judicial system, rule of law, control of corruption, and so on, can limit sharp practices, restraint financing of environmentally hazardous projects and direct the flow of funds towards R&D, eco-friendly technology, green energy, and so on, which have a positive multiplier impact on environmental sustainability. Besides good MEQ institutional structures could boost eco-friendly FDI and improve host country environmental quality. EMT holds the view that both market and state/institutions are responsible for ecological transformation. However, social and institutional transformations have been and still are at the core of much current scholarship on ecological modernization. The core of all studies in the tradition of Ecological Modernization focuses on (existing and programmed) environmental reforms in social practices, institutional designs and societal and policy discourses to safeguard societies’ sustenance bases. 2.2 Empirical literature A plethora of studies have been conducted to examine the determinants of environmental quality over the last few decades. At the outset the link between environment quality and income growth was explained using the much celebrated work by (Grossman and Krueger, 1995) called in academic literature as “environmental Kuznets cure” (here after EKC). According to the EKC hypothesis, there is an inverted U-shaped link between environmental deterioration and economic growth (Grossman and Krueger, 1995). A number of studies have empirically tested the significance of EKC hypothesis; however results have remained largely mixed and inconclusive. Empirical studies by Alam et al. (2011), Boutabba (2014), Sehrawat et al. (2015), Rasool et al. (2020), Acheampong et al. (2020) and Ahmed et al. (2021) upheld EKC hypothesis in their respective investigations. However, subsequent investigations reported inconclusive and mixed findings regarding the EKC hypothesis (Dar and Asif, 2017a; Asif et al., 2022; Godil et al., 2020; Itoo and Ali, 2023). As a consequence of these contradictory and ambiguous findings, the EKC framework was criticized for failing to take into account various structural, institutional, and macroeconomic determinants that tend to have an effect on the conservation of environment. In response to the criticism labelled against EKC hypothesis, recently researchers incorporated structural, institutional, and macroeconomic variables in the empirical models to determine their significance on environmental quality by measuring environmental degradation using GHG emissions, industrial waste and water pollution (Dar and Asif, 2017b, 2019; Zakaria and Bibi, 2019; Shahbaz et al., 2020; Qayyum et al., 2021; Khan et al., 2022). The path to economic development is very complex, involving many structural changes. A significant structural change accompanying economic development is the scale and structure of financial system. Therefore, ignoring the importance of FD in determining the quality of environment will result in biased and wrong empirical results. Motivated by the Rising importance of financial system in the economic affairs recent year’s academic literature on environmental economics has seen a surge in studies documenting FD as a critical factor responsible for environmental sustainability. However, the research output of extant studies on FD-environment quality has remained largely mixed and ambiguous. Studies by scholars like Komal and Abbas (2015), Gokmenoglu et al. (2015), Shahbaz et al. (2016), Majeed and Mazhar (2019), Ganda (2019), Lahiani (2020) among others have shown that financial sector development helps in improving environmental sustainability via, investment in green technology, research and development (R&D), financing environment-friendly projects, supporting cleaner and renewable energies, and so on, together all improve quality of environment. However studies by Tamazian and Rao (2010), Zakaria and Bibi (2019), Hunjra et al. (2020), Ahmad et al. (2020), Godil et al. (2020) among others conclude that financial development causes environment degradation. Financial development by increasing the availability of credit to households and firms leads to increased production and consumption of goods and services which would lead to an ever-increasing demand for energy and discharge of more waste and emissions. This puts additional burden on environment resulting in more environmental degradation (Danish et al., 2018; Pata, 2018; Yang et al., 2021).A brief summary of studies on environment-finance nexus is encapsulated in Table 1. Time period and Environmental Financial Study country quality measured Methodology Findings development, Yasin et al. 1996–2016 Ecological Second generation Financial development deteriorates institutions, (2020) 110 counties footprint estimation techniques environmental quality. Trade openness, Institutions and Urbanization enhances ecology environment quality Majeed and 1971–2017 Ecological System GMM Financial development improves Mazhar (2019) 131 Countries footprint Fixed effect and environmental quality, whereas energy Random Effect model consumption, economic growth and FDI worsen environmental quality Ahmed et al. 1971–2014 Ecological ARDL Globalization, Economic growth, Energy (2019) Malaysia footprint consumption worsen environmental quality while as, financial development enhances environmental quality Yang et al. 1990–2016 Ecological Second generation Financial development and remittance (2021) BICS footprint estimation techniques inflows deteriorate environmental quality Technological innovation improves environmental quality Zakaria and 1984–2015 Carbon dioxide 2SLS and GLS Financial development, Trade, Energy Bibi (2019) Five South Asian emission Panel fixed effect consumption increases carbon emissions Countries technique FDI and Institutions decrease carbon including India emissions EKC hypothesis holds for selected south Asian economies Khan et al. 2002–2019 Carbon dioxide Two Step System Financial development, Economic growth (2022) 177 Countries emission GMM and Institutions lower environmental quality. Renewable energy and FDI enhance environmental quality Moderation effect of institutions exists Omoke et al. 1971–2014 Ecological Nonlinear ARDL Positive shock in financial development (2020) Nigeria footprint reduces environmental degradation Negative shock in financial development deteriorates environmental quality Dada et al. 1984–2017 Ecological ARDL bounds test Financial development, Economic growth, (2022a, b) Malaysia footprint Institutions, Trade and Natural resources all raises environmental degradation Moderation effect of institutions detected Qayyum et al. 1980–2019 Carbon dioxide ARDL bounds test Financial development, Economic growth (2021) India emissions and VECM and Urbanization all increases carbon emissions while as, Renewable energy consumption and technological innovation reduce carbon emissions Boutabba 1970–2008 Carbon dioxide ARDL bounds test EKC hypothesis valid (2014) India emissions andVECM Financial development and Energy consumption improves environmental degradation Long-run unidirectional causality running from economic growth, energy consumption and financial development to carbon emissions Rasool et al. 1971–2014 Carbon dioxide ARDL bounds test Financial development, Energy consumption (2020) India emissions and VECM and Economic growth have an adverse impact on Environmental quality EKC hypothesis valid for India Unidirectional long-run causality running from energy consumption and financial development to carbon emissions. Bi- directional causality exists between carbon emissions and economic growth Dar and Asif 1971–2013 Carbon dioxide ARDL test EKC hypothesis not valid for India (2017a, b) India emissions Hatemi-J -threshold Financial development and energy -Cointegration consumption degrade the environment. technique Insignificant long-run impact of economic growth on environmental deterioration Table 1. Synoptic view of the (continued ) related literature MEQ Time period and Environmental Study country quality measured Methodology Findings Ehigiamusoe 1990–2014 Carbon dioxide FMOLS,DOLS Financial development reduces carbon and Lean 122 countries emissions emissions in high income countries and (2019) increases the same for low and middle income economies Acheampong 1980–2015 Carbon emission Instrumental Financial market development reduces et al. (2020) 83 economies (22 intensity variable-GMM carbon intensity in case of developed and developed, 23 emerging financial economies, while as emerging, 29 exacerbates carbon emission for frontier frontier and 9 economies. In case of standalone financial standalone economies, Financial development has no economies) direct linear impact rather follows an inverted U-shaped relationship with carbon emissions Shahbaz et al. 1870–2014 Carbon dioxide Bootstrap time- Time varying effects of financial (2021) G-7 Nations emissions varying co- development on environmental quality integration and observed. Specifically an M-shaped impact of bootstrap rolling Financial development on carbon dioxide window estimation emissions in Canada, Japan and USA. techniques Inverted N-shaped impact for France, Italy and UK. In case of Germany the relationship between finance and carbon emissions followed a W-shaped impact Godil et al. 1986–2018 Ecological Quantile ARDL Financial development, Tourism and (2020) Turkey footprint Globalization all deteriorates ecological foot print U-shaped EKC hypothesis valid for economic growth and Ecological footprint Saud et al. One belt one road Ecological Westerlund panel Financial development, GDP, Energy (2020) initiative footprint, Carbon Cointegration test consumption and trade increases Ecological countries footprint Pooled mean footprint, carbon footprint and carbon dioxide Carbon dioxide group(PMG), fully emissions. Whereas, Globalization reduces all emissions modified ordinary the three measures of environment quality least square (FMOLS) and thus promotes environmental and sustainability. The results further revealed Dumitrescu and causality running from Financial Hurlin causality tests development to Ecological footprint, carbon footprint and carbon dioxide emissions Ahmed et al. 1971–2006 Ecological Symmetric ARDL and Both positive and negative shocks in financial (2021) Japan footprint Asymmetric NARDL development have detrimental impact on ecological footprint. Globalization, population density used as control variables reduce ecological footprint whereas energy consumption increases the same EKC hypothesis valid for Japan Ngoc and 1980–2016 Ecological ARDL Financial development and Economic growth Awan (2022) Singapore footprint Bayesian analysis cause environmental degradation by increasing ecological footprint. Human capital formation enhances environmental quality by reducing Ecological footprint Arogundade 1990–2017 Ecological Fixed effect Model Financial development increases whereas et al. (2022) 22 African footprint Fixed effect IV-2SLS diaspora income/remittances reduces counties Panel Quantile ecological footprint. Financial development Regression mediates negatively the impact of remittances Dumitrescu and on environment Hurlin causality test U-shaped relationship between Environment and economic growth confirmed. Natural resource use increase pollution while as urbanization decreases the same Uni-directional causality from diaspora income to ecological footprint and from financial development to ecological footprint Note(s): GMM stands for generalized method of moments; FDI is Foreign direct investment; EKC is Environmental Kuznets curve, ARDL, Autoregressive distributed lag model; VECM is Vector Error Correction Model. 2SLS is for Two stage least square, IV stands for instrumental variable, respectively Table 1. Source(s): Prepared by authors The extant literature on the relationship between FD and environment in recent times can Financial be split into two categories; those making use of panel analysis and those employing time development, series (Country specific) approach. Zakaria and Bibi (2019) employed panel data of selected south Asian economies and reported detrimental impact of financial development on institutions, environment. Ganda (2019) for Organization for Economic Cooperation and Development ecology countries and Bloach et al. (2019) for BRI economies reported similar findings. However, another strand of literature identified the positive and beneficial effects of financial development on environment quality. Panel studies by Xiong and Qi (2018) covering 30 provinces of China, (Hamdan et al., 2018), for 5 ASIAN economies and Majeed and Mazhar (2019) for a global panel of 131 countries document the beneficial effect of FD on environment. Among time series studies specific to India include; Boutabba (2014) examined the impact of financial development measured by domestic credit to private sector on carbon emissions after controlling for economic growth, energy consumption and trade using autoregressive distributed lag (ARDL) estimator for India covering 1970–2008 time period. The findings show that financial development, trade and energy consumption all produce a detrimental effect on environment. Moreover, the findings of the study also supported the existence of EKC hypothesis for India. Within ARDL frame work (Sehrawat et al., 2015) investigated the dynamic link between financial development, income growth, urbanization, trade and energy use for India. The authors found that financial development measured by domestic credit to private sector leads to a rise in carbon dioxide emissions thus reducing environmental quality. Trade exerted an insignificant impact on carbon emissions while as, urbanization and energy use were found to increase carbon emissions. Moreover, the results of the study also upheld EKC hypothesis for India. Dar and Asif (2017a) in another study investigated the dynamic impact of financial development, energy use and income growth on environmental quality using carbon dioxide emissions for India over the period 1970–2013. ARDL bounds test and Hatemi-j-threshold cointegration test were employed to check for existence of cointegration. It was observed that Energy use and financial development degrade environment quality whereas; economic growth had no significant impact on carbon emissions. The study couldn’t find any evidence supporting the EKC hypothesis in India. In synopsis, a critical review of the existing studies reveals majority of studies have used emissions of major GHGs as an indicator of environmental quality, which is a very weak proxy and captures information on one component of whole environmental damage. To overcome this limitation, the present study employed EFP as an indicator of environmental quality. EPF traces biologically productive area needed to sustain human life along with the human caused pressure on earth’s ecosystem. EPF a multidimensional measure of environmental quality, measuring the impact of human activities on six major dimensions of environment namely; “the built-upland, carbon emissions, cropland, fishing grounds, and grazing land (Ecological footprint Network (EPN) 2019).” EPF is considered as a more comprehensive and dependable indicator of environmental quality than the conventional proxies (Bagliani et al., 2008; Uddin et al., 2017). Furthermore, two studies (Zakaria and Bibi, 2019) employing panel data for 5 south Asian economies and another study by Khan et al. (2022) covering 177 economies have examined the importance of institutions in preserving environmental quality. But it is well known that any conclusion that can be drawn from these cross-country studies will only give a general idea of how the variables are related and cannot provide much information on country specific policy implications. Therefore, to the best of our knowledge no attempt has been made to study the direct and moderating role of institutions in the connection between financial development and environmental quality using EFP in the Indian context. This research is a polite attempt to cover-up this gap in the existent literature and to avoid omitted variable bias in econometric estimation. MEQ 2.3 Research gap A critical review of existing body of literature on environmental economics reveals that there are studies that try to correlate financial development and environmental quality as well as the studies relating institutions with environmental factors. But, there is no such study which has collaborated all the three variables viz. environmental quality, financial development and institutions in a single frame for a study on India. Neglecting the importance of institutions while studying the effect of financial development on environmental quality might lead to omitted variable bias and wrong policy inferences. The present study has many additional positive aspects. First, it uses an aggregate measure of institutional quality published by the International Country Risk Guide (ICRG). The index is based on “rule of law, control of corruption, voice and accountability, government effectiveness and quality of bureaucracy.” The composite variable is rescaled from 0 to 1 with higher value indicating better institutional quality and lower values indicating poorer quality. Second, the current study investigates interaction effect between financial system and institutional development. The basic intuition is that financial development and institutions cannot be expected to have an individual and mutually exclusive effect on environment. Rather, in addition to affecting environmental quality directly, the institutional quality can affect environment indirectly by affecting the distribution of financial resources in an economy, thus playing a modulating role between finance and Environment. A negative sign of the interaction term signifies that financial development and institutional quality are complementary. This implies that sound institutions complement the role of financial development in reducing environmental damage. A positive sign on the other hand suggest a substitute relationship meaning that improvements in the institutional quality eat-away some of the effects of financial development in redressing environmental damage. Third, majority of the studies have extensively used emission of some major GHGs such as the oxides of carbon, nitrogen and sulphur, industrial wastes and amount of deforestation to gauge environmental quality (Boutabba, 2014; Dar and Asif, 2017b; Zakaria and Bibi, 2019; Majeed and Mazhar, 2019; Rasool et al., 2020; Khan et al., 2022). These are all weak proxies of environmental quality as they do not capture full impact of anthropogenic activities on the environment. As a result, a more inclusive measure of environmental sustainability known as ecological footprint, compounded by the global footprint network, is used in this study. EFP compounds the human demand for ecological assets including land for food and fibre, forest-use and construction space, water resources for marine products and use of atmosphere for emission disposal. Thus, EFP accounts for damage to air, land and water resources as a consequences of human production and consumption and is used to make the assessment of environmental damage more inclusive. Finally, our study also distinguishes from other studies in the sense that we use a composite index of financial development worked out recently by IMF. This index is multifaceted and captures information on depth, efficiency, and accessibility of a variety of dimensions on financial institutions and financial markets. As a result, financial development index outperforms other indexes because it provides a more extensive and comprehensive evaluation of financial development (Svirydzenka, 2016). Previous studies have extensively relied on bank–based measures of financial development which reflect only depth of financial institutions and have completely ignored the contribution of financial markets towards environmental degradation. 3. Econometric methodology 3.1 Data description and methodology The current study makes use of yearly time-series data on India for the time period covering 1980–2019. The selection of time period is largely determined by the constraint imposed by data availability on financial development index. The data on real per capita GDP, trade, FDI and natural resources are extracted from World Development Indicators (WDI) database by Financial World Bank. The data on institutions are retrieved from the International Country Risk development, Guide. The IMF financial statistics database is used to acquire data on financial development indicators. Table 2 provides the variable description and data source. The results of institutions, descriptive statistics are presented under Table 3. ecology The primary concern of present study is to scrutinize the moderating effect of institutional quality in financial development and EFP nexus in India for the time period covering 1980–2019. To workout the role of institutions and their interaction effect, we have modified the existing models in the environmental literature to incorporate institutions. Accordingly we specify our empirical model as follows: Variable name Definition Data source Ecological footprint “Ecological Footprint adds up all the biologically Ecological Footprint (EFP) productive areas for which a population, a person or a Network (EFN) product competes. It measures the ecological assets that a given population or product requires to produce the natural resources it consumes (including plant-based food and fiber products, livestock and fish products, timber and other forest products, space for urban infrastructure) and to absorb its waste, especially carbon emissions.” It is measured as per capita of a global hectare Financial development Index of Financial development with value ranging from International Monetary (FD) 0 to 1. It is calculated using data on depth, access and Fund(IMF) efficiency of both financial institutions and financial markets. See (p. 9 Svirydzenka, 2016) Institutional quality Arithmetic mean of ICRG variables “rule of law, Control International Country (INS) of corruption, Voice and accountability, government Risk Guide(ICRG) effectiveness and quality of bureaucracy” Economic growth GDP per capita 5 GDP/midyear population. Data are in World Development (GDP) constant 2015 U.S. dollars Indicators (WDI) Foreign direct Net FDI inflows as a share of Gross domestic product World Development investment (FDI) Indicators (WDI) Trade(TRADE) Sum total of exports and imports as a percentage of GDP World Development are trade Indicators (WDI) Natural Total natural resource rent as a proportion of the gross World Development Table 2. resources(NAT) domestic product Indicators (WDI) Variable description Source(s): Prepared by authors and data source Variable lnEFP FD INS GDP FDI Trade NAT Mean 20.569 0.325 0.632 893.887 0.922 29.949 2.996 Median 20.566 0.395 0.638 750.164 0.695 25.404 2.780 Maximum 21.193 0.500 0.716 1965.540 3.620 55.793 7.104 Minimum 19.902 0.120 0.589 387.641 0.003 12.219 1.733 Std. Dev 0.373 0.126 0.029 465.685 0.895 14.659 1.104 Skewness 0.001 0.435 0.551 0.873 0.891 0.359 1.518 Kurtosis 1.958 1.496 3.248 2.610 3.268 1.652 6.104 Jarque-Bera 1.806 5.027 2.128 5.340 5.412 3.889 31.412 Probability 0.405 0.080 0.345 0.069 0.067 0.143 0.000 Table 3. Source(s): Calculated by authors Descriptive statistics MEQ LEPFt ¼ β0 þ β1 FDt þ β2 INSt þ β3 ðFDt * INSt Þ þ β4 GDPt þ β5 TRADEt þ β6 FDIt þ β7 NATt þ et (1) where LEPFt represents natural log of EFP, FDt is financial development indicator, INSt denotes institutional quality, GDPt stands for real GDP per capita, TRADEt measures trade openness, FDIt captures FDI inflows, NATt measures the stock of natural resources and et is the random error term with white noise properties. The variable (FDt* INSTt) representing the interaction effect between financial system and institutional quality capturing the mediating role of institutions in financial development and EFP nexus. A positive sign in the coefficient of interaction term in equation (1) would mean that better quality institutions reduce the marginal effect of financial development in reducing EFP. Therefore a country with best institutional set-up will see least pay-off in reducing EFP from improvements in the financial system indicating a substitutive relationship between the two. On the other hand, a negative sign in the coefficient of interaction term would mean that pro-environmental behaviour of financial development is magnified in a country where institutions are already well developed than a country with poor institutional framework, meaning financial development and institutions are complements in affecting environment sustainability. A lack of statistical significance for the coefficient of interaction term would imply that impact of financial sector development on ecological sustainability is independent of the level of institutional development. EFP, financial development and institutions constitute our main variables of interest, other variables in equation (1) are chosen as control variables. The coefficients β1, . . ., are long-run estimates. Joint significance of these coefficients would mean the existence of a long-run relationship among the underlying variables. The study uses ARDL methodology developed by Pesaran et al. (2001) to estimate the short-run and long-run relationship among the variables in the model. The ARDL method of cointegration is more desirable than those developed by Engle and Granger (1987) and Johansen (1991) which are based on the restrictive assumption that all the variables are integrated of order one. ARDL is applicable irrespective of whether the variables are integrated of order zero or one. It also maintains the un-biasedness of the long-run estimates even in the presence of some endogenous regressors. In addition to this, ARDL is suitable in case of a small sample size. Following Pesaran et al. (2001) we specify an unrestricted version of error correction variant of ARDL as given below in Equation (2): X q1 Xq2 Xq3 Xq4 ΔEPFt ¼ ∅0 þ ∅1 ΔEPFt−i þ ∅2 ΔFDt−i þ ∅3 ΔINSt−i þ ∅4 ΔðFD*INSÞt−i i¼1 i¼0 i¼0 i¼0 X q5 X q6 X q5 X q5 þ ∅5 ΔGDPt−i þ ∅6 ΔTRADEt−i þ ∅7 ΔFDIt−i þ ∅8 ΔNATt−i i¼0 i¼0 i¼0 i¼0 þ δ1 EFPt−1 þ δ2 FDt−1 þ δ3 INSt−1 þ δ4 ðFD*INSÞt−1 þ δ5 GDPt−1 þ δ6 TRADEt−1 þ δ7 FDIt−1 þ δ8 NATt−1 þ εt (2) The ARDL bounds test of co-integration empirically examines the long-run and short-run effects of variables simultaneously through a one-step approach. The coefficients ∅1 to ∅2 of the first differenced variables measure short-run effects, while parameters from δ1 to δ8 of lagged level variables represent long-run effects. The long-run association between EFP, financial development, institutions and other variables can be investigated with the help of joint F-statistic or Wald test whereby the null hypothesis of no cointegration (H0 : δ1 ¼ δ2 ¼ ¼ δ8 ¼ 0) is contested against alternate hypothesis of presence of Financial cointegration (Hα : δ1 ¼ δ2 ¼ ¼ δ8 ≠ 0) for equation (2). To confirm whether development, co-integration exists or not, we compare the computed test statistic to the upper and lower critical bounds generated by Pesaran et al. (2001). The upper critical bound is based on institutions, presumption that variables are integrated of order one, I(1), while as the lower critical bound ecology is based on the assumption that variables maintain an order of integration of zero, I(0). If the computed F-test statistic is greater than the upper critical bounds value, we get confirmation that the variables being tested have a long-term relationship. If the F-statistic is less than the lower bound value, the null hypothesis that there is no co-integration cannot be rejected. But if the computed test statistic falls between the bounds, the co-integration test can’t tell if the two things are related or not. 4. Estimation and results The ARDL bounds test of co-integration does not require prior unit root test of time series; however, the model produces inefficient and biased results in presence of series with order of integration greater than 1. In fact the F-test would give spurious results in case a variable is I(2). Thus, before applying the bounds test, we need to ensure that none of the variables is integrated of order 2. We have used Augmented Dickey-Fuller (ADF) and Phillips Peron test to determine the order of integration of the variables. The results reported in Table 4 confirm that all the variables become stationary after first differencing except GDP per capita growth which is stationary at level. Thus, our variables follow a mixed order of integration which justifies the use of ARDL estimator. Unit root tests reveal that none of our variables is I (2). The next step is to go for cointegration test. The result of F-bounds test determining the long-run relationship between the variables is reported in Table 5. Philips-Perron test ADF test Variables Level First difference Level First difference LEPF 0.792 7.653*** 0.680 7.172*** FD 1.438 4.016*** 1.462 4.130*** INS 2.744* 2.860** GDP 3.386 4.209*** 1.920 4.288*** FDI 1.499 7.553*** 1.571 7.050*** TRADE 0.929 5.327*** 0.850 5.291*** Nat.Res 2.809* 2.710* Note(s): ***, ** and * denote 1%, 5 and 10% levels of significance, respectively Table 4. Source(s): Calculated by authors Unit root tests Lower bound Upper bound Test statistic Value Significance I (0) I (1) F-statistic 13.50*** 10% 2.03 3.13 k 7 5% 2.32 3.5 2.5% 2.6 3.84 1% 2.96 4.26 Table 5. Note(s): *** denote 5% level of significance Results from ARDL Source(s): Calculated by authors bounds test MEQ Since the calculated F-statistic is greater than upper-bound and lower-bound critical values at the conventional levels of significance, we reject the null hypothesis of no co-integration among the variables and accept the alternative hypothesis that emphasizes a robust long-run relationship among EFP, financial development, institutions and other regressors over the study period of 1980–2019 in case of India. Since the bounds test is highly sensitive to the choice of lag order we used “Akaike Information Criteria” (AIC) on the basis of automatic lag selection. Since the time-series is annual, a maximum of 3 lags for the dependent as well as the independent variables is selected. Table 6 above reports the results of the short-run and long-run effects of financial development, institutions and other regressors on the EFP. The short-run results reveal that financial development increases EFP in India and causes environmental degradation. Specifically a unit increase in financial development increases EFP by 2.63%. This means that financial system does not mobilize enough of its resources in research and development, financing of environment friendly projects and green energy that act as an antidote for environmental sustainability. However, in the long run, the coefficient of financial development is negative and statistically significant. Specifically, a unit change in financial development reduces EFP by 14% in the long run. It implies that financial development promotes environmental sustainability and helps in abating environmental degradation. These results imply that the financial sector by allocating funds for investment in green energy, research and development expenditures on energy efficient technology, high-tech fuel-efficient industries and environment-friendly projects significantly improve the environmental sustainability by reducing EFP. These results are in tandem with the findings of Zhang (2011), Xiong and Qi (2018), Dar and Asif (2018), Park et al. (2018), Majeed and Mazhar (2019) and Aydin and Turan (2020) but stand against studies of Boutabba (2014), Dar and Asif (2017a), Zakaria and Bibi (2019) and Khan et al. (2022). Dependent variable: lnEFP (ecological footprint) Variable Coefficient Std. Error t-statistic Prob Short-run coefficients C 7.380*** 0.551 13.389 0.000 Δ(lnEFP(-1)) 0.857*** 0.086 9.995 0.000 Δ(FD) 2.639*** 0.452 5.835 0.000 Δ(INS) 1.350*** 0.269 5.008 0.000 Δ(FD*INS(-1)) 3.550*** 0.741 4.791 0.001 Δ(GDP) 0.0010*** 0.000 10.367 0.000 Δ(FDI) 0.041*** 0.005 6.898 0.000 Δ(TRADE) 0.007*** 0.001 6.641 0.000 D(NAT) 0.054*** 0.007 8.259 0.000 ect (1)* 0.289*** 0.022 13.298 0.000 Long-run coefficients FD 14.673** 4.943 2.968 0.013 INS 7.191*** 2.124 3.385 0.006 FD*INS 19.375** 7.546 2.568 0.026 GDP 0.00021* 0.000 2.012 0.069 FDI 0.135** 0.055 2.472 0.031 TRADE 0.0406*** 0.007 6.280 0.000 Table 6. NAT 0.219*** 0.044 5.021 0.000 Short-run and long-run Note(s): ***, **, * Correspond to 1%, 5 and 10% levels of significance, respectively ARDL results Source(s): Authors calculation Institutions also portray a different impact on environmental quality for short run and long Financial run. In the short-run institutions deteriorate environmental quality by increasing EFP. development, Specifically saying the short-run coefficient of institutional quality over EFP is 1.35. However, in the long-run institutions bear a negative and a statistically significant relationship with institutions, EFP. Economically saying a unit change in institutional quality reduces EFP by 7.19% in the ecology long run. It implies that improving institutional quality is always beneficial for environmental protection. The long-run results imply that strong institutional structures by ensuring strict compliance to environmental protection laws, by penalizing environmental offenders and by limiting sharp-practices and opportunistic behaviour of economic agents in financial markets install discipline among business and consumers enhances environmental quality. Our findings are in line with Uzar (2021) for E7 countries and Hussain and Dogan (2021) for BRICS countries but are at odds with those of Dada et al. (2022a,b), Khan et al. (2022). Institutional quality has also a moderating effect on environmental sustainability as both the short-run and long-run interaction effects are statistically significant. The short-run coefficient for interaction term is negative, implying a “complementary effect” of both financial development and institutions on EFP. It implies that in presence of high quality institutions, financial system will reduce EFP. In turn, if institutions are weak, then financial development exacerbates environmental degradation. These findings validate regulation effect hypothesis that if strict environmental regulations are in force, financial development enhances environmental quality (Zakaria and Bibi, 2019). The intuition is that in presence of high quality institutional structures, financial system will allocate credit to eco-friendly ventures. It suggests that the beneficial effects emanating from the development of financial sector on EFP needs to be complemented by sound institutions for short run. The long-run coefficient of interaction term is positive implying substitutability between financial sector development and institutions. This means that a sound institutional framework cover-ups flaws in a weak and inefficient financial sector. Strong institutions via various environmental legislations can punish environmental offenders, limit sharp practices in financial markets, may also force mandatory investment in research and development, create incentives for using eco-friendly technology and green energy (Dada and Abanikanda, 2021; Dada et al., 2022a,b). Additionally, the results demonstrate the dominant impact of long-run substitutability over the short-run complementary effect. Regarding control variables used in the study, economic growth measured by per capita income like financial development has a significant negative impact on EFP in short run but the effect turns positive in the long run. This implies that economic growth abates environmental degradation in short run, but deteriorates the same in the long run. In the long run, increased energy demands for higher economic growth in India are met with the burning of more fossil fuels and non-renewable energy sources that have a detrimental impact on environmental quality. Production and consumption wastes associated with higher economic growth also deteriorate environment in long run. The short-run findings of our model that economic growth reduces EFP corroborate with the works of Danish et al. (2019a, b) and Dada et al. (2022a, b) While our findings for long run are in tandem with the long-run results of (Ibrahim and Hanafy, 2020; Khan et al., 2021, 2022; Khan et al., 2022). FDI inflows, which measures financial openness, also exhibit different effects on EFP in India. FDI reduces EFP in short run while increases the same in the long run. The short-run results of our study justify pollution halo hypothesis that FDI inflows attract environment- related technologies, R&D in energy-efficient technology and an efficient environmental management system which benefits the host countries. These short-run findings are in congruence with the empirical submission of Solarin and Al-mulali (2018) and Zafer et al. (2019). In the long run, FDI has a detrimental impact on the ecological reserves of India. Our long-run estimates justify Pollution haven hypothesis that higher FDI inflows attract environmentally hazardous and pollutant-intensive industries to host countries due to poor MEQ infrastructure and weak environmental regulations. These industries adversely affect the environmental quality. Thus, it is critically important for India to restrict dirty FDI inflows to protect Environmental quality. The findings are similar to those of Baloch et al. (2019), Udemba (2020) and Chowdhury et al., 2021. The trade openness has a detrimental impact on environmental quality in both short run and long run for India. These results lend credence to the absence of technique and composition effects of international trade in India but attest to the existence of scale effect hypothesis in India, which postulates that international trade by increasing volumes of exports has actually put more pressure on environment via increased use of fossil fuels and discharge of industrial and human wastes. This result is in tandem with the empirical submission of Imamoglu (2018), Sabir and Gorus (2019), Kongbuamai et al. (2020) that developing countries like India possess comparative advantage in polluting industries at the beginning of transaction. Natural resources in the short-run spur environmental degradation due to inefficient usage; however, in the long run, they promote environmental sustainability. This result implies that countries abundant in natural resources have better environmental quality than natural resources deficit countries. These results are in conformity with the findings of Danish et al. (2019a, b) and Nathaniel (2021) that natural resources enhance environmental quality. The model is also dynamically stable as the coefficient ect (error- correction term) is negative and less than 1 at conventional level of significance. Dynamic stability of the model is a pre-requisite condition for the stability and robustness of the long- run term relationship. Negative sign of the ect (error-correction term) guarantees that a stable equilibrium will be re-established sequel to a series of divergence’s from the dynamics along the long-run path. The magnitude of the coefficient of the error correction term (ect) measures speed of adjustment to long-run equilibrium from an initial disequilibrium position per time period following a shock brought about by disequilibriating forces along the long-run path. The model converges to long term stable equilibrium at a speed of 28.9% per time period. 5. Diagnostic tests To check the reliability and robustness of our results, we have applied a battery of diagnostic tests of the ARDL estimate, and the same are reported in Table 7. Jarque-Bera test gives confirmation that error terms follow normal distribution. The model is also free from the problem of serial correlation in error terms. Breusch-Pagan-Godfrey test for heteroscedasticity provides confirmation that residuals of the model are homoscedastic. Furthermore, the Ramsey reset test reveals that our model is free from miss-specification bias. Furthermore, to check and confirm the stability of parameters cumulative sum (CUSUM) test and cumulative sum of squares (CUSUMQ) tests are employed. These tests take into account both the mean and variance of the residuals. As is clear from Figures 1 and 2, the parameters stability lines are within 5% critical boundaries. The parameters are stable and model could be used for making inferences. Diagnostic test Testing for p-value Decision Breusch-Godfrey Serial Correlation Test Serial Correlation 0.156 Absence of serial correlation Bruesch-Pagon-Godfrey Test Heteroscedasticity 0.409 Absence of heteroskedasticity Jarque-Bera Normality 0.553 Normally distributed CUSUM Stability Stable Table 7. CUSUMSQ Stability Stable Results of Ramsey RESET Test Model specification 0.136 No Model Misspecification diagnostic tests Source(s): Calculated by Authors 10.0 Financial 7.5 development, institutions, 5.0 ecology 2.5 0.0 –2.5 –5.0 –7.5 –10.0 30 31 32 33 34 35 36 37 38 39 40 Figure 1. CUSUM 5% Significance Stability test for ARDL estimator Source(s): Figure created by author 1.6 1.2 0.8 0.4 0.0 –0.4 30 31 32 33 34 35 36 37 38 39 40 Figure 2. CUSUM of Squares 5% Significance Stability test for linear ARDL model Source(s): Figure created by author 6. Conclusion The main purpose of this study is to scrutinize the direct and the moderating effect of institutional structures in the relationship between financial development and EFP in India for the period between 1980 and 2021. This study uses EFP to measure environmental quality in order to overcome the limitation of using weak proxies to gauge environmental degradation by earlier researchers. Further, this study employs financial development index developed recently by IMF which takes into consideration the depth, efficiency, and accessibility of both the financial institutions as well as financial markets instead of bank–based measures of financial MEQ development which reflect only depth of financial institutions. To workout both short-run and long-run relationships the study employs ARDL for estimation of model. Following the research outcomes, some important policy implications are drawn. Since financial sector development and institutional quality complement each another in reducing EFP in the short run, policies targeting simultaneous development of financial sector and institutions need to be pursued to curtail the menace of environmental pollution even in the short run. Monetary authority of the country should nudge its financial institutions to invest in green technology, R&D activities and renewable energy for the long-term sustainability of environment. As an initiative to protect environment, financial sector should keep a certain percentage of its loanable funds as “green fund.” and same should be offered as loans at lower rates of interest to firms and business ready to invest in energy-efficient and ecologically sustainable projects. Last but not least, the institutions, particularly those involved in executing environmental laws and regulations must be bolstered so that environmental offenders may be brought to justice and punished. This will ensure that, economic agents carefully comply with environmental regulations. So, there is a need for the environmental policies to be dynamic in nature. Further, the country must agree to FDI with environmental considerations. Overall policies related to economic growth and export sector call for reconsideration and must be in line with environmental criteria. This will take India on the path of sustainable development. 7. Limitations and scope for future research It is pertinent to mention here that this study contributed the existing body of knowledge on environment by examining the direct and moderating role of institutions in finance environment relation in Indian context. However, this study should be seen in light of some limitations and hence a consideration of these limitations would pave the way for future study in the area. We enumerate below some important caveats that would help future researchers contribute to the area. First, the paper uses the Index of financial development developed by the IMF in order to quantify the level of financial development in India overtime. The index is based on three key dimensions of financial development such as the depth, efficiency, and access of both financial institutions and markets. However, the index completely neglects the role of financial stability in determining financial development. Financial stability also impacts environmental sustainability (Safi et al., 2021). Thus future studies which are based on this IMF introduced index of financial development should incorporate the stability dimension to it. The resulting index would be better able to track the path of financial development in the country overtime and its impact on environmental quality. Second, this empirical study focused exclusively on India and employed aggregate EFP to measure Environmental quality. Further studies can complement and enrich the content of this research by conducting similar studies to capture country-specific characteristics of other emerging economies, and also scrutinize the impact on the six sub-indices of EFP. Lastly, although time series analysis allows for the identification of patterns and relationships that exist within the data, which can be used to make forecasts and predictions. A panel data analysis covering the states of India can provide a deeper and richer analysis of the relationship. References Acheampong, A.O., Amponsah, M. and Boateng, E. (2020), “Does financial development mitigate carbon emissions? Evidence from heterogeneous financial economies”, Energy Economics, Vol. 88, 104768. Ahmed, F., Kousar, S., Pervaiz, A. and Ramos-Requena, J.P. (2020), “Financial development, institutional quality, and environmental degradation nexus: new evidence from asymmetric ARDL co-integration approach”, Sustainability, Vol. 12 No. 18, p. 7812. Ahmed, Z., Wang, Z., Mahmood, F., Hafeez, M. and Ali, N. (2019), “Does globalization increase the Financial ecological footprint? Empirical evidence from Malaysia”, Environmental Science and Pollution Research, Vol. 26, pp. 18565-18582. development, Ahmed, Z., Zhang, B. and Cary, M. (2021), “Linking economic globalization, economic growth, institutions, financial development, and ecological footprint: evidence from symmetric and asymmetric ecology ARDL”, Ecological Indicators, Vol. 121, 107060. Alam, Jahangir, M., Begum, I.A., Buysse, J., Rahman, S. and Huylenbroeck, G.V.. (2011). “Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India", Renewable and Sustainable Energy Reviews, Vol. 15 No. 6, pp. 3243–3251. doi: 10.1016/j.rser.2011.04.029. Armitage, D., De Lo€e, R. and Plummer, R. (2012), “Environmental governance and its implications for conservation practice”, Conservation letters, Vol. 5 No. 4, pp. 245-255. Arogundade, S., Hassan, A.S. and Bila, S. (2022), “Diaspora income, financial development and ecological footprint in Africa”, International Journal of Sustainable Development and World Ecology, Vol. 29 No. 5, pp. 440-454. Asif, M., Itoo, H.H. and Dar, J.A. (2022), “On the environmental effects of development and non- development expenditure in India: evidence from an asymmetric ARDL model”, The Journal of International Trade and Economic Development, Vol. 31 No. 6, pp. 835-859. Aydin, M. and Turan, Y.E. (2020), “The influence of financial openness, trade openness, and energy intensity on ecological footprint: revisiting the environmental Kuznets curve hypothesis for BRICS countries”, Environmental Science and Pollution Research, Vol. 27 No. 34, pp. 43233-43245. Bagliani, M., Bravo, G. and Dalmazzone, S. (2008), “A Consumption-based approach to environmental Kuznets curves using the ecological footprint indicator”, Ecological Economics, Vol. 65 No. 3, pp. 650-661. Baloch, M.A., Zhang, J., Iqbal, K. and Iqbal, Z. (2019), “The effect of financial development on ecological footprint in BRI countries: evidence from panel data estimation”, Environmental Science and Pollution Research, Vol. 26, pp. 6199-6208. Baron, D.P. and Lyon, T.P. (2011), “Environmental governance”. Boutabba, M. (2014), “The Impact of Financial development, income, energy and trade on carbon emissions:evidence from the indian economy”, Economic Modelling, Vol. 40, pp. 33-41. Chowdhury, M.A., Shanto, P.A., Ahmed, A. and Rumana, R.H. (2021), “Does foreign direct investments impair the ecological footprint? New evidence from the panel quantile regression”, Environmental Science and Pollution Research, Vol. 28, pp. 14372-14385. Dada, J.T. and Abanikanda, E. (2021), “The moderating effect of institutions in foreign direct investment led growth hypothesis in Nigeria”, Economic Change and Restructuring, Vol. 55 No. 2, pp. 903-929. Dada, J.T., Adeiza, A., Ismail, N.A. and Arnaut, M. (2022a), “Financial development -ecological footprint nexus in Malaysia: the role of institutions”, Management of Environmental Quality:An International Journal, Vol. 33 No. 4, pp. 913-937. Dada, J.T., Adeiza, A., Noor, A.I. and Marina, A. (2022b), “Investigating the link between economic growth, financial development, urbanization, natural resources, human capital, trade openness and ecological footprint: evidence from Nigeria”, Journal of Bioeconomics, Vol. 24, pp. 153-179. Danish, S.S., Baloch, M.A. and Lodhi, R.N. (2018), “The nexus between energy consumption and financial development: estimating the role of globalization in Next-11 countries”, Environmental Science and Pollution Research, Vol. 25, pp. 18651-18661, doi: 10.1007/ s11356-018-2069-0. Danish, H.S., Baloch, M.A., Mehmood, N. and Zhang, J.W. (2019b), “Linking economic growth and ecological footprint through human capital and biocapacity”, Sustainable Cities and Society, Vol. 47, 101516, doi: 10.1016/j.scs.2019.101516. MEQ Danish, H.S., Baloch, M.A., Mahmood, M. and Zhang, N. (2019a), “Effect of natural resources, renewable energy and economic development on CO 2 emissions in BRICS countries”, Science of the Total Environment, Vol. 678, pp. 632-638. Dar, J.A. and Asif, M. (2017a), “Is financial development good for carbon mitigation in India? A regime shift-based cointegration analysis”, Carbon Management, Vol. 8 Nos 5-6, pp. 435-443. Dar, J.A. and Asif, M. (2017b), “On causal interactions between carbon emissions, energy consumption and economic growth: an evidence from India”, International Journal Of Ecology and Development, Vol. 32 No. 2, pp. 67-80. Dar, J.A. and Asif, M. (2018), “Does financial development improve environmental quality in Turkey? An application of endogenous structural breaks based cointegration approach”, Management of Environmental Quality:An International Journal, Vol. 29 No. 2, pp. 368-384. Dar, J.A. and Asif, M. (2019), “Do agriculture-based economies mitigate CO2 emissions? Empirical evidence from five SAARC countries”, International Journal of Energy Sector Management, Vol. 14 No. 3, pp. 638-652, doi: 10.1108/IJESM-01-2019-0011. Ehigiamusoe, K.U. and Lean, H.H. (2019), “Effects of energy consumption, economic growth, and financial development on carbon emissions: evidence from heterogeneous income groups”, Environmental Science and Pollution Research, Vol. 26 No. 22, pp. 22611-22624. Engle, R.F. and Granger, C.W. (1987), “Co-integration and error correction: representation, estimation, and testing”, Econometrica: Journal of the Econometric Society, Vol. 55 No. 2, pp. 251-276. Ganda, F. (2019), “The environmental impacts of financial development in OECD countries: a panel GMM approach”, Environmental Science and Pollution Research, Vol. 26 No. 7, pp. 6758-6772. Global Footprint, Network (2020), “Ecological footprint”, available at: https://www.footprintnetwork. org/our-work/ecologicalfootprint/ (accessed 30 June 2020). Godil, D.I., Sharif, A., Rafique, S. and Jermsittiparsert, K. (2020), “The asymmetric effect of tourism, financial development, and globalization on ecological footprint in Turkey”, Environmental Science and Pollution Research, Vol. 27, pp. 40109-40120. Gokmenoglu, K., Ozatac, N. and Eren, B.M. (2015), “Relationship between industrial production, financial development and carbon emissions: the case of Turkey”, Procedia Economics and Finance, Vol. 25, pp. 463-470. Grossman, G.M. and Krueger, A.B. (1995), “Economic growth and the environment”, The Quarterly Journal of Economics, Vol. 110 No. 2, pp. 353-377. Hamdan, R., Ab-Rahim, R. and Fah, S.S. (2018), “Financial development and environmental degradation in ASEAN-5”, International Journal of Academic Research in Business and Social Sciences, Vol. 8 No. 12, pp. 14-32. Hasnat, G.T., Kabir, M.A. and Hossain, M.A. (2018), “Major environmental issues and problems of South Asia, particularly Bangladesh”, Handbook of Environmental Materials Management, pp. 1-4. Hunjra, A.I., Tayachi, T., Chani, M.I., Verhoeven, P. and Mehmood, A. (2020), “The moderating effect of institutional quality on the financial development and environmental quality nexus”, Sustainability, Vol. 12 No. 9, p. 3805. Hussain, M. and Dogan, E. (2021), “The role of institutional quality and environment-related technologies in environmental degradation for BRICS”, Journal of Cleaner Production, Vol. 304, 127059. Ibrahiem, D.M. and Hanafy, S.A. (2020), “Dynamic linkages amongst ecological footprints, fossil fuel energy consumption and globalization: an empirical analysis”, Management of Environmental Quality: An International Journal, Vol. 31 No. 6, pp. 1549-1568. Imamoglu, H. (2018), “Is the informal economic activity a determinant of environmental quality”, Environmental Science and Pollution Research, Vol. 25, pp. 29078-29088. Itoo, Hassan, H. and Ali, N. (2023), “Analyzing the causal nexus between CO2 emissions and its Financial determinants in India: evidences from ARDL and EKC approach”, Management of Environmental Quality: An International Journal, Vol. 34 No. 1, pp. 192-213. development, Johansen, S. (1991), “Estimation and hypothesis testing of Cointegration vectors in Gaussian vector institutions, autoregressive models”, Econometrica: journal of the Econometric Society, Vol. 59 No. 6, ecology pp. 1551-1580. Khan, H., Weili, L. and Khan, I. (2021), “Environmental inovation, trade openness and quality institutions: an integrated investigation about environmental sustainability”, Environment, Development and Sustainability, Vol. 24 No. 3, pp. 1-31. Khan, H., Weili, L. and Khan, I. (2022), “Institutional quality, financial development and the influence of environmental factors on carbon emissions: evidence from a global perspective”, Environmental Science and Pollution Research, Vol. 29, pp. 13356-13368. Komal, R. and Abbas, F. (2015), “Linking financial development, economic growth and energy consumption in Pakistan”, Renewable and Sustainable Energy Reviews, Vol. 44, pp. 211-220. Kongbuamai, N., Zafar, M.W., Zaidi, S.H. and Liu, Y. (2020), “Determinants of the ecological footprint in Thailand: the influences of tourism, trade openness, and population density”, Environmental Science and Pollution Research, Vol. 27, pp. 40171-40186. Lahiani, A. (2020), “Is financial development good for the environment? An asymmetric analysis with CO2 emissions in China”, Environmental Science and Pollution Research, Vol. 27, pp. 7901-7909, doi: 10.1007/s11356-019-07467-y. Majeed, M.T. and Mazhar, M. (2019), “Financial development and ecological footprint: a global panel data analysis”, Pakistan Journal of Commerce and Social Sciences, Vol. 13 No. 2, pp. 487-514. Mohammed, A., Alotaish, P. and Khan, A. (2019), “Do government expenditure and financial development impede environmental degradation in Venezuela”, PLOS One, Vol. 14 No. 1, pp. 1-13. Nathaniel, S. (2021), “Ecological footprint and human well-being nexus: accounting for broad-based fnancial development, globalization, and natural resources in the Next-11 countries”, Future Business Journal, Vol. 28 No. 17, pp. 21766-21778. Ngoc, B.H. and Awan, A. (2022), “Does financial development reinforce ecological footprint in Singapore? Evidence from ARDL and Bayesian analysis”, Environmental Science and Pollution Research, Vol. 29, pp. 24219-24233. Omoke, P.C., Nwani, C., Effiong, E.L., Evbuomwan, O.O. and Emenekwe, C.C. (2020), “The impact of financial development on carbon, non-carbon, and total ecological footprint in Nigeria: new evidence from asymmetric dynamic analysis”, Environmental Science and Pollution Research, Vol. 27 No. 17, pp. 21628-21646. Park, Y., Meng, F. and Baloch, M.A. (2018), “The effect of ICT, financial development, growth, and trade openness on CO 2 emissions: an empirical analysis”, Environmental Science and Pollution Research, Vol. 25, pp. 30708-30719. Pata, U.K. (2018), “Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Turkey: testing EKC hypothesis with structural breaks”, Journal of Cleaner Production, Vol. 187, pp. 770-779. Pesaran, M., Shin, Y. and Smith, R. (2001), “Bounds testing approaches to the analysis of level relationships”, Applied Econometrics, Vol. 16 No. 3, pp. 289-326. Qayyum, U., Sabir, S. and Anjum, S. (2021), “Urbanization, informal economy, and ecological footprint quality in South Asia”, Environmental Science and Pollution Research, Vol. 28, pp. 67011-67021. Rasool, H., Malik, M. and Tarique, M. (2020), “The curvilinear relationship between environmental pollution and economic growth Evidence from India”, International Journal of Energy Sector Management, Vol. 14 No. 5, pp. 891-910. MEQ Sabir, S. and Gorus, M.S. (2019), “The impact of globalization on ecological footprint: empirica levidence from the South Asian countries”, Environmental Science and Pollution Research, Vol. 26, pp. 33387-33398. Safi, A., Wahab, S., Zeb, F., Amin, M. and Chen, Y. (2021), “Does financial stability and renewable energy promote sustainable environment in G-7 Countries? The role of income and international trade”, Environmental Science and Pollution Research, Vol. 28 No. 34, pp. 47628-47640. Saud, S., Chen, S. and Haseeb, A. (2020), “The role of financial development and globalization in the environment: accounting ecological footprint indicators for selected one-belt-one-road initiative countries”, Journal of Cleaner Production, Vol. 250, 119518. Sehrawat, M., Giri, A.K. and Mohapatra, G. (2015), “The impact of financial development, economic growth and energy consumption on environmental degradation: Evidence from India. Management of Environmental Quality”, An International Journal, Vol. 26 No. 5, pp. 666-682. Shahbaz, M., Destek, M.A., Dong, K. and Jiao, Z. (2021), “Time-varying impact of financial development on carbon emissions in G-7 countries: evidence from the long history”, Technological Forecasting and Social Change, Vol. 171, 120966. Shahbaz, M., Raghutla, C., Song, M., Zameer, H. and Jiao, Z. (2020), “Public-private partnerships investment in energy as new determinant of CO2 emissions: the role of technological innovations in China”, Energy Economics, Vol. 86, 104664. Shahbaz, M., Shahzad, S.J.H., Ahmad, N. and Alam, S. (2016), “Financial development and environmental quality: the way forward”, Energy Policy, Vol. 98, pp. 353-364. Solarin, S.A. and Al-mulali, U. (2018), “Influence of foreign direct investment on indicators of Environmental degradation”, Environmental Science and Pollution Research, Vol. 25 No. 25, pp. 24845-24859, doi: 10.1007/s11356-018-2562-5. Svirydzenka, K. (2016), Introducing a New Broad-Based Index of Financial Development, International Monetary Fund, IMF Working paper No.16/5. Tamazian, A. and Rao, B.B. (2010), “Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies”, Energy Economics, Vol. 32 No. 1, pp. 137-145. Uddin, G., Salahuddin, M., Alam and Gow, J. (2017), “Ecological footprint and real income: panel data evidence from the 27 highest emitting countries”, Ecological Indicators, Vol. 77, pp. 166-175. Udemba, E.N. (2020), “Ecological implication of offshored economic activities in Turkey: foreign direct investment perspective”, Environmental Science and Pollution Research, Vol. 27 No. 30, pp. 38015-38028. United Nations Population, Fund’s (UNFPA) (2023), “State of World Population Report, 2023”. Uzar, U. (2021), “The relationship between institutional quality and ecological footprint: is there a connection?”, Narural Resources Forum, Vol. 45 No. 4, pp. 380-396. Xiong, L. and Qi, S. (2018), “Financial development and carbon emissions in Chinese provinces: a spatial panel data analysis”, The Singapore Economic Review, Vol. 63 No. 02, pp. 447-464. Xue, L., Haseeb, M., Mahmood, H., Alkhateeb, T.T.Y. and Murshed, M. (2021), “Renewable energy use and ecological footprints mitigation: evidence from selected South Asian economies”, Sustainability, Vol. 13 No. 4, p. 1613. Yang, B., Jahanger, A. and Ali, M. (2021), “Remittance inflows affect the ecological footprint in BICS countries: do technological innovation and financial development matter?”, Environmental Science and Pollution Research, Vol. 28 No. 2021, pp. 23482-23500. Yasin, I., Ahmad, N. and Chaudhary, M.A. (2020), “Catechizing the environmental-impression of urbanization, financial development, and political institutions: a circumstance of ecological footprints in 110 developed and less-developed countries”, Social Indicators Research, Vol. 147, pp. 621-649. Zafar, S., Rasool, H. and Tarique, M. (2023), “Is agricultural development good for carbon mitigation in Financial India? Evidence from the asymmetric NARDL model”, Management of Environmental Quality: An International Journal, Vol. 34 No. 1, pp. 234-249. development, Zafar, M.W., Zaidi, S.A.H., Khan, N.R., Mirza, F.M., Hou, F. and Kirmani, S.A.A. (2019), “The impact of institutions, natural resources, human capital, and foreign direct investment on the ecological footprint: the ecology case of the United States”, Resources Policy, Vol. 63, 101428. Zakaria, M. and Bibi, S. (2019), “Financial development and environment in South Asia: the role of institutional quality”, Environmental Science and Pollutiion Research, Vol. 26, pp. 7926-7937. Zhang, Y.J. (2011), “The impact of financial development on carbon emissions: an empirical analysis in China”, Energy Policy, Vol. 39 No. 4, pp. 2197-2203. Further reading Akerlof, G. (1978), “The market for ‘lemons’: quality uncertainty and the market mechanism”, Uncertainty in Economics, Academic Press, pp. 235-251. Akinlo, T. and Dada, J.T. (2021), “The moderating effect of foreign direct investment on environmental degradation-poverty reduction nexus: evidence from Sub-Saharan African countries”, Environment, Development and Sustainability, Vol. 23 No. 11, pp. 15764-15784, doi: 10.1007/ s10668-021-01315-1. Aluko, A. (2020), “The foreign aid-foreign direct investment relationship in Africa: the mediating role of institutional quality and financial development”, Economic Affairs, Vol. 40 No. 1, pp. 77-84. Banerjee, A., Dolado, J. and Mestre, R. (1998), “Error-correction mechanism tests foe cointegration in a single equation framework”, Journal of Time Series Analysis, Vol. 19 No. 3, pp. 267-283. Capasso, S. (2004), “Financial markets, development and economic growth: tales of informational Asymmetries”, Journal of Economic Surveys, Vol. 18 No. 3, pp. 267-292. Cepparulo, A., Cuestas, J.C. and Intartaglia, M. (2016), “Financial development, institutions, and poverty: an empirical analysis”, Applied Economics, Vol. 49 No. 36, pp. 3611-3622. Chen, H., Hongo, D. and Ssal, M. (2020), “The asymmetric influence of financial development on economic growth in Kenya: evidence from NARDL”, Sage Open, Vol. 10 No. 1. 2158244019894071. Dada, J.T. and Ajide, F.M. (2021), “The moderating role of institutional quality in shadow economy pollution nexus in Nigeria”, Management of Environmental Quality: An International Journal, Vol. 32 No. 3, pp. 506-523, doi: 10.1108/MEQ-10-2020-0238. Haini, H. (2020), “Examining the relationship between finance, institutions and economic growth: evidence from the ASEAN economies”, Economic Change and Restructuring, Vol. 53 No. 4, pp. 519-542. Ibrahim, M.H. and Law, S.H. (2015), “Institutional quality and CO2 emission-trade relations: evidence from sub-Saharan Africa South African”, Journal of Economics, Vol. 84, p. 323. Jalilian, H. and Kirkpatrick, C. (2005), “Does financial development contribute to poverty reduction?”, The Journal Of Development Studies, Vol. 41 No. 4, pp. 636-656. Katircioglu, S. and Taspinar, N. (2017), “Testing the moderating role of financial development in an environmental Kuznets curve: empirical evidence from Turkey”, Renewable and Sustainable Energy Reviews, Vol. 68, pp. 572-586. Kubicov_a, J. (2014), “Testing greenhouse gasses in Slovakia for environmental Kuznets curve and pollution haven hypothesis”, Journal of International Studies, Vol. 7 No. 2, pp. 161-177. Law, S.H., Azman-Saini, W. and Ibrahim, M. (2013), “Institutional quality thresholds and the finance – growth nexus”, Journal of Banking and Finance, Vol. 37 No. 12, pp. 5373-5381. Law, S.H., Tan, H.B. and Azman-Saini, W.N.W. (2014), “Financial development and income inequality at different levels of institutional quality”, Emerging Markets Finance and Trade, Vol. 50 No. 1, pp. 21-33, doi: 10.2753/REE1540-496X5001S10. MEQ Leland, H. and Pyle, D. (1977), “Informational asymmetries, financial structure, and financial intermediation”, The Journal of Finance, Vol. 32 No. 2, pp. 371-387. Olaniyi, C.O. and Oladeji, S.I. (2021), “Moderating the effect of institutional quality on the finance– growth nexus: insights from West African countries”, Economic Change and Restructuring, Vol. 54 No. 1, pp. 43-74, doi: 10.1007/s10644-020-09275-8. Rasoulinezhad, E. and Saboori, B. (2018), “Panel estimation for renewable and non-renewable energy consumption, economic growth, CO2 emissions, the composite trade intensity, and financial openness of the commonwealth of independent states”, Environmental Science and Pollution Research, Vol. 25, pp. 1-17. Sharma, S.S. (2011), “Determinants of carbon dioxide emissions: empirical evidence from 69 countries”, Applied Energy, Vol. 88 No. 1, pp. 376-382, doi: 10.1016/j.apenergy.2010.07.022. Solarin, S.A., Tiwari, A.K. and Bello, M.O. (2019), “A multi-country convergence analysis of ecological footprint and its components”, Sustainable Cities and Society, Vol. 46 January, 101422, doi: 10. 1016/j.scs.2019.101422. Stiglitz, J. and Weiss, A. (1981), “Credit rationing in markets with imperfect information”, The American Economic Review, Vol. 71 No. 3, pp. 393-410. Tang, C.F. and Tan, B.W. (2015), “The impact of energy consumption, income and foreign direct investment on carbon dioxide emissions in Vietnam”, Energy, Vol. 79 C, pp. 447-454, doi: 10. 1016/j.energy.2014.11.033. Teixid_o-Figueras, J. and Duro, J.A. (2015), “The building blocks of international ecological footprint inequality: a regression-based decomposition”, Ecological Economics, Vol. 118, pp. 30-39. Ulucak, R. and Bilgili, F. (2018), “A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries”, Journal of Cleaner Production, Vol. 188, pp. 144-157, doi: 10.1016/j.jclepro.2018.03.191. Usman, M. and Hammar, N. (2021), “Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: fresh insights based on the STIRPAT model for Asia Pacific economic cooperation countries”, Environmental Science and Pollution Research, Vol. 28 No. 12, pp. 15519-15536. Usman, M., Balsalobre-Lorente, D., Jahanger, A. and Ahmad, P. (2022), “Pollution concern during globalization mode in financially resource-rich countries: do financial development, natural resources, and renewable energy consumption matter?”, Renewable Energy, Vol. 183, pp. 90-102. Corresponding author Ishfaq Nazir Khanday can be contacted at:

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