The Dynamic Nexus Between Financial Development, Renewable Energy and Carbon Emission: Role of Globalization and Institutional Quality Across BRI Countries

3 This empirical study examines the endogenous relationship between carbon emission (CO 2 ), financial development, 4 renewable energy, globalization, and institutional quality in 64 belt and road initiative countries (BRI) using a two-5 step system generalized method of moments (GMM) approach with panel data over the period 2003 to 2018. 6 Furthermore, this study used (Dumitrescu & Hurlin, 2012) causality test to estimate the variables ’ causal relationship. 7 The results indicate that financial development significantly increases CO 2 emissions and causes environmental 8 degradation in BRI countries. However, renewable energy and globalization mitigate CO 2 emission and improve the 9 quality of the environment. Institutional quality was found to be positive in correlation with CO 2 emission and 10 indicates bad governance, corruption, weak bureaucracy, and improper implementation of environmental laws cause 11 environmental degradation. Further, the study also reports a bidirectional relationship of financial development, 12 renewable energy, and institutional quality with CO 2 emission and a unidirectional causality running from 13 globalization to CO 2 emission in BRI countries. This study offers insight to policymakers to restructure the financial 14 system, energy consumption pattern, and global integration and to improve institutions’ quality for a sustainable 15 environment and the economy at the national and regional levels. 16


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Global warming and climate change are the most serious global threats the world is facing today. Globalization

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and economic growth are the essential factors that raise carbon emissions (CO2) and greenhouse gases (GHGs). For 20 the past few decades, global warming and climate change have been subjects of discussion and concern among 21 research scholars, experts, and governments. As an estimation, carbon emission (CO2) contributes 60% of greenhouse

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In practical terms, while making an environmental strategy that can reduce carbon emission, it is essential to

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The first proposition entails financial development, which is a source of financing for various projects and also 40 interconnects with economic growth (GDP) and environmental quality (Khan et al., 2017). The financial sector lends 41 to private creditors through institutions and economic activities takes place (Khan, 2001). However, it is still factors as control variables. As per our best knowledge, this will be the first study incorporating institutional quality 96 as an explanatory variable with financial development, renewable energy, and globalization with respect to carbon 97 emission in the same framework. Second, previous studies have had mixed results regarding explanatory variables on 98 environmental quality (CO2). To address these issues, we will provide a comprehensive guideline for policymakers, 99 institutions, and other stakeholders to formulate new policies, techniques, and laws to cut down carbon emission.

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Third, we used a dynamic system, "Generalized Method of Moment" (GMM), a panel technique for reliable findings.

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Unlike other econometric techniques, it addresses heterogeneity and endogeneity by using Hansen/Sargan test and

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providing additional information about autocorrelation AR (1) and AR (2). Further, we also used panel Granger

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The road map of the paper is as follows: the second section provides a literature review of past studies, third 106 presents data sources, fourth presents empirical results, and fifth provides discussion and conclusion.

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Since the day the EKC concept was proposed by Grossman and Krueger (1995), several studies have examined 112 the nexus between FD and CO2 emission. Financial institutions finance investors and the household sector so economic 113 activities take place (Khan, 2001

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Findings also confirm that globalization causes environmental degradation. Doytch and Uctum's (2016) findings also 160 indicate that globalization increases carbon emission.

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Institutional quality is the most ignored factor in the context of environmental quality. Institutional quality plays

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Our study sample comprises upper middle, middle-or lower-income countries of East Asia, South Asia, Central Asia,

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Middle East, North Africa, South Africa, and Europe (details in Appendix A Table 8). The sample of the study is 183 based on secondary data obtained from "world development indicators" (WDI) and the "international country risk 184 guide" (ICRG) database provided in Appendix-B, Table 9.

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The study's empirical framework is based on carbon emission (CO2) as a dependent variable, while financial

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This study employed the most advanced econometric approach to test cross-sectional dependence (CD) between 218 the variables as conventional econometric approaches cannot address this issue. Therefore, we adopted Pesaran's 219 (2004) cross-sectional dependence test which is crucial before a unit root test. We used following equations for cross-220 sectional dependence and Lagrange multiplier (LM): Further, to confirm the stationarity of the variables, we employed second generation IPS (i.e. CADF) and CIPS (i.e.

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CIPS) unit root test proposed by Pesaran (2007) where the subscripts "t" and "i"

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globalization, institutional quality, gross domestic product, and socio-economic conditions, respectively.

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Results of correlations indicate no multicollinearity issue as the coefficient value of all dependent variables is less 314 than 0.85. Besides, we employed the VIF test which confirmed that our sample of the study has no multicollinearity 315 issues as the value of the VIF test is lower than the standard limit of 5.  Table 4. Results indicate that null hypothesis of no-CD is rejected and alternative hypothesis for the 322 existence of CD is accepted. In the presence of CD and heterogeneity, we also employed the second generation IPS 323 (i.e., CADF) and CIPS unit root test to check the stationarity of the variables.   Note: Standard errors in parentheses at 1%, 5%, and 10% levels are *** p < 0.01, ** p < 0.05, * p < 0.1, respectively. Table 6 presents the results of static and dynamic model of the study. However, we preferred a two-step system 332 GMM (dynamic model) over a static model as it is consistent for output and the instrument's validity (tests for 333 autocorrelation and over-identification). Similarly, specification tests confirm that two-step system GMM is 334 appropriate for study as statistic results show that AR (1) = -2.110 is significant with p-value 0.0349, p < 0.05, 5%, 335 whereas AR (2) = -0.729 is insignificant with p-value 0.466, p > 0.05, 5%. This indicates that the serial correlation 336 test is unable to reject the null hypothesis of no second order autocorrelation. However, the null hypothesis of no first-337 order autocorrelation is rejected, which confirms that results are not affected by second-order autocorrelation.

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Similarly, the Hansen test value = 42.88 was insignificant with p-value 0.169, p > 0.05, 5%, implying that the null 339 hypothesis fails to reject the instrumental validity and supports it. We also observed over the year the time effect of 340 explanatory variables on carbon emission Appendix-D

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Based on the two-step system GMM results (column 5), FD is statistically significant at the 1% level with 343 optimistic influence on CO2 emission, which suggests that a 1% increase in FD can increase the CO2 emission by

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Finally, we also reported the findings of our control variables of studies that included GDP and SEC. The

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reported results in Table 6 indicate that GDP has an adverse effect on carbon emissions as a 1% change in GDP leads 385 to 0.03% reduction in emission in BRI countries. This suggests that due to an increase in economic activities, demand