Optimizing sustainable high-quality economic development through Green Finance with robust spatial estimation

Sohail, Hafiz M. et al. Article Optimizing sustainable high-quality economic development through Green Finance with robust spatial estimation Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Sohail, Hafiz M. et al. (2024) : Optimizing sustainable high-quality economic development through Green Finance with robust spatial estimation, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-14, https://doi.org/10.1080/23322039.2024.2363466 This Version is available at: https://hdl.handle.net/10419/321509 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Optimizing sustainable high-quality economic development through Green Finance with robust spatial estimation Hafiz M. Sohail, Hossam Haddad, Mirzat Ullah, Nidal Mahmoud Al-Ramahi, Nazatul Faizah Haron & Ayman Mansour Khalaf Alkhazaleh To cite this article: Hafiz M. Sohail, Hossam Haddad, Mirzat Ullah, Nidal Mahmoud Al-Ramahi, Nazatul Faizah Haron & Ayman Mansour Khalaf Alkhazaleh (2024) Optimizing sustainable highquality economic development through Green Finance with robust spatial estimation, Cogent Economics & Finance, 12:1, 2363466, DOI: 10.1080/23322039.2024.2363466 To link to this article: https://doi.org/10.1080/23322039.2024.2363466 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 01 Jul 2024. Submit your article to this journal Article views: 1744 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Optimizing sustainable high-quality economic development through Green Finance with robust spatial estimation Hafiz M. Sohail a , Hossam Haddad b,c , Mirzat Ullah d,e , Nidal Mahmoud Al-Ramahi b , Nazatul Faizah Haron a and Ayman Mansour Khalaf Alkhazaleh f a Faculty of Business and Management, Universit Sultan Zainal Abidin, Terengganu, Malaysia; b Accounting Department and Business Faculty, Zarqa University, Zarqa, Jordan; c University of Business and Technology, Jeddah, Saudi Arabia; d Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia; e School of Management Sciences, Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology, Topi, Swabi, Pakistan; f Faculty of Business, Middle East University, Amman, Jordan ABSTRACT Green finance (GF) holds significant potential in fostering high-quality economic development (HED), enhancing societal affluence consistency, and alleviating poverty by promoting sustainable development, innovation, and resilience. This study addresses environmental challenges and the promotion of sustainable economic growth through the pivotal tools of GF. We employ spatial spillover, quantile regression, and regionalwise models to derive four key findings. Firstly, baseline regression analysis reveals a noteworthy positive association between GF and HED, indicating that the adoption and utilization of green financial mechanisms significantly advance economic development while maintaining ecological sustainability. Secondly, using a spatial econometric model, this study identifies the presence of a spillover effect, showing that the positive impact of GF on HED extends beyond individual provinces and contributes to overall economic development on a broader geographical scale. Thirdly, the analysis of regional heterogeneity demonstrates that the correlation between GF and HED varies across different regions of China. Notably, a significant association between GF and HED is observed in the western region, highlighting the importance of considering regional disparities in the implementation and effectiveness of green financial policies. Lastly, through quantile regression analysis, this study uncovers non-linear relationships between GF and HED, emphasizing that the impact of green financial strategies on economic development varies across different quantiles of the economic development distribution. This study provides several practical policy implications for financial institutions and policymakers. IMPACT STATEMENT The study examines the role of Green Finance (GF) in promoting High-quality Economic Development (HED) across 31 provinces of China from 2006 to 2020. We used spatial spillover, quantile regression, and regional models to analyze the relationship between GF and HED. The baseline regression shows a positive relationship, with spillover effects indicating GF’s impact extends beyond individual provinces. Regional analysis reveals a significant GF-HED relationship, particularly in western China, representing regional variation in policy effectiveness. Quantile regression confirms a non-linear GF-HED association. This study advocates for financial transfers to address regional disparities. Diagnosing fences hindering GF effectiveness and implementing tailored strategies to overcome these challenges are critical steps toward inspiring HED. This study contributed to the existing literature by addressing a previous relationship, including non-linear, spatial spillover effects and regional analysis. It offers fresh insights into environmental practices and aims to attain sustainable development goals through the promotion of sustainable and green finance. Abbreviations: EC: energy consumption; ED: economic development; EDUC: education; EG: economic growth; FD: financial decentralization; FIs: financial institutions; GDP: gross domestic product; GF: green finance; HED: high-quality economic development; PCA: principal component analysis; TFP: total factor productivity; URB: urbanization ARTICLE HISTORY Received 3 March 2024 Revised 27 May 2024 Accepted 29 May 2024 KEYWORDS Green finance; sustainable economic development; regional heterogeneity; spatial effect; non-linear relationship; China JEL CLASSIFICATION Q56; Q01; R11; C14; O53 REVIEWING EDITOR Goodness Aye, University of Agriculture, Makurdi Benue State, Benue, Nigeria SUBJECTS Development Economics; Finance; Environmental Economics; CONTACT Nazatul Faizah Haron [email protected] Faculty of Business and Management, Universit Sultan Zainal Abidin, Terengganu, 21300, Malaysia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2363466 https://doi.org/10.1080/23322039.2024.2363466 1. Introduction In the nascent era of China’s reform and opening-up policy, the country embarked on a monumental journey of economic transformation, marked by significant adjustments in its developmental priorities and industrial landscape. This transformative phase witnessed a strategic shift towards prioritizing the light industry and optimizing the overall industrial structure. As a result of these reforms, China experienced unprecedented economic growth (EG), propelling it to the rank of the world’s second-largest economy by 2010. However, this remarkable EG faced challenges, particularly concerning environmental degradation from pursuing rapid EG. Numerous studies, such as Li et al. (2016) and Liu et al. (2018), have highlighted the adverse environmental impact of China’s EG trajectory. Notably, the extensive use of traditional energy, which constitutes a significant portion of China’s energy consumption, has led to a surge in pollution emissions, posing threats to both human health and ecological stability. In response to these pressing environmental concerns, Chinese authorities have shifted from the traditional pursuit of rapid EG to a more comprehensive approach termed High-Quality Economic Development (HED), also known as sustainable EG. This shift, as discussed by Gu et al. (2021) reflects a broader recognition of balancing EG with environmental sustainability. Conventional economic frameworks, such as the Solow growth and endogenous growth models, often emphasize technological advancements as the primary driver of eco-friendly EG, measured primarily through total factor productivity (TFP) growth (Gemmell, 1995). However, we noted, that the effective resource management and environmental preservation have broader societal benefits that these metrics cannot fully capture. Addressing this challenge requires a paradigm shift towards a more holistic approach to economic development, exemplified by the Green Productivity (GP) concept. Figure 1 illustrates the pivotal role of GP in fostering sustainable economic and ecological development, garnering domestic and international recognition (Akomea-Frimpong et al., 2022). At the intersection of environmental preservation and financial innovation lies the domain of Green Finance (GF). Cowan (1999) describes GF as the capital investment in green economic development, and Labatt and Figure 1. GF contribution towards high quality economic development. Source: Authors ‘calculation. 2 H. M. SOHAIL ET AL. White (2002) describe it as a potential tool for the green economy. Scholtens (2006) highlights it as a financial institution’s social accountability, pointing to the balance of economic development with the ecological environment. Notably, GF strongly emphasizes environmental protection and resource efficiency, distinguishing it from traditional finance (Gao et al., 2024). The growing prominence of GF is evidenced by significant investments in environmental projects, such as clean energy initiatives (Zhang et al., 2019). China’s commitment to GF is further underscored by its burgeoning green bond market, which reached a total issuance of US$15.7 billion in 2021, positioning China as the world’s second-largest bond market after the United States (Reuters, 2021). Additionally, green loans in China soared to US$1.8 trillion by the end of 2020 Huaxia, reflecting a substantial increase in financing for environmentally sustainable projects. Furthermore, the emergence of carbon finance as a significant component of China’s environmental finance landscape has contributed to establishing a robust carbon trading market (Zhou & Li, 2019). This trend underscores China’s growing recognition of the importance of carbon mitigation efforts in achieving HED. Figure 2 depicts China’s GF and HED trends from 2006 to 2020. The GF emblem is due to government-led creativities in green infrastructure and clean and green energy consumption. Oscillations in GF during other epochs may stem from economic changes or policy variations. HED must mix EG with the eco-environment, where GF has a potential function. The rising trend in HED, especially from 2014 to 2020, replicates China’s focus on technological progression and education, with mountains corresponding to strategic investments in research and development. Peaks during intervals like 2008-2009, 2011, and 2017 may result from global financial disasters or policy adjustments affecting China’s economy. Besides, the above-mentioned goals are also related to the goals of the United Nations, which are recognized as sustainable development goals. In 2015, the UN and member countries agreed to reduce poverty, sanctuary the environment, and promote EG (United Nations, 2015). Consequently, GF is crucial for promoting EG while safeguarding the preservation of the environment. It encompasses various financial instruments and investments designed to contribute to environmental initiatives, decrease environmental harm, and encourage HED reported by Chowdhury et al. (2013) and Lindenberg. Gabr and Elbannan (2023) emphasize financial system restructuring to discourse environmental risks and encourage sustainability. They highlight the significance of green bonds and loans. Green bonds increase funds for environmental projects like renewable energy and clean transportation. Green loans finance environmentally beneficial projects, often with specific outcome conditions. The banking sector boosts GF through products like green securities, investments, insurance, and infrastructure bonds (Li et al., 2023; Park & Kim, 2020). Akomea-Frimpong et al. (2022) highlight multiple factors, such as environmental and climate change legislation, interest rates, and the focus on social inclusion, shape these products. Financial institutions progressively incorporate environmental factors into their lending and investment choices, acknowledging the financial hazards linked to climate change and environmental deterioration and the rising demand from consumers and investors for sustainable financial products. In recognition of the significance of GF and its function in encouraging economic sustainability, this study aims to contribute in the following ways. Although some studies analyze the impact of GF on HED, they produce mixed results (Gao et al., 2022; Li et al., 2022). Firstly, this study analyzes whether GF can contribute to China’s HED both with and without control variables. To the authors’knowledge, none of the studies have yet evaluated the spatial spillover effects within the same relationship; thus, the second aim of this research lies here. Thirdly, only Li et al. (2022) scrutinize the regional heterogeneity to identify regions Figure 2. Overview of GF and HED in China. Source: National Bureau Statistics of China. COGENT ECONOMICS & FINANCE 3 that require more attention for improvement, which is also the purpose of this paper. Fourthly, quantile regression assesses the non-linear relationship, which almost all existing studies overlook in their analysis. The remainder of the paper proceeds as follows: After the introductory section, section two emphasizes the theoretical and empirical studies related to the association between GF and HED. Section three outlines the data and methodology used in this study, while section four elaborates on the empirical results. Finally, section five provides the conclusion, incorporating policy implications, and outlines potential avenues for future research. 2. Literature review HED relates to the efficiency of input-output production and the TFP function in promoting economic progress (Hong et al., 2022). In other words, it includes economic plans, organizational agendas, and operational dynamics designed to lecture assorted societal requirements and show multidimensional landscapes. This theoretical outline highlights principles of reasonable growth, ecological stewardship, and individual advancement (Jin, 2018). HED comprise heightened and reliable economic growth, an efficient economic agenda, and inclusive engagement on a global scale (Feng et al., 2021; Lin & Kim, 2022). Key features of this developmental pattern include efficiency, equity, environmental realization, and sustainability, alongside encouraging collaboration across various sectors (Imran & Jijian, 2023; Xiao et al., 2023). The notion of GF originated in the Western discourse and later garnered extensive attention from Chinese researchers. Cowan (1999) defines GF as a form of capital financing aimed at promoting green economic development, serving as a bridge between the green economy and the financial sector. Similarly, Labatt and White (2002) argue that the primary objective of GF is to support a green economy, with the development of green financial tools facilitating sustainable economic growth. Scholtens (2006) elaborate GF represents the social responsibility of financial institutions (FIs), emphasizing the need for ongoing financial innovation in the developmental process to address potential ecological and social risks, ultimately seeking to achieve a balance between economic development and the ecological environment. In 2016, the G20 GF Inclusive Report was published, defining GF as an investment and financing activity capable of generating environmental benefits and promoting sustainable development. However, research on GF in China started relatively late and largely focused on the current state of affairs and development prospects for GF. In July 2007, the "Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks" jointly issued by the People’s Bank of China, the Banking Regulatory Commission, and the State Environmental Protection Administration introduced the concept of GF for the first time. Ye (2008) believes that China’s GF strategy has achieved phased success, effectively curbing the uncontrolled expansion of enterprises characterized by high energy consumption, pollution, and resource utilization through investment and financing interventions. Several scholars have investigated the role of GF in promoting HED using various econometric techniques and considering different regions. For instance, Yang et al. (2021) examined the impact of fintech and GF on HED from 2007 to 2019 across 30 provinces in China. Using a two-step generalized method of moments, they found that GF as a whole contributes positively to three key aspects: the ecological environment, economic efficiency, and economic structure. Furthermore, they claimed that fintech enhances these positive contributions, particularly in the case of the ecological environment and economic structure. Confirming the positive impact of GF, Li et al. (2022) conducted a study on a panel of 30 provinces in China from 2008 to 2017. Their research indicated that GF significantly promotes HED, reduces environmental pollution, and curtails energy consumption. In the eastern region, GF has a positive impact on HED as well, while the western and central regions do not seem to be impacted by GF. Using data from the 30 provinces of China spanning from 2011 to 2021, Han et al. (2023) demonstrated that GF may improve HED. Similarly, Liu et al. (2021) employing a two-way fixed-effect model, concluded that GF promotes HED across the 30 provinces of China from 2009 to 2019, with technological innovation and industrial structure playing intermediate roles. In a study by Gao et al. (2022), data from 30 provinces in China spanning from 2010 to 2019 were used to model the relationship between GF, environmental pollution, and HED. Using auto-spatial regressive and spatial error models, the researchers concluded that GF contributes to HED in China by enhancing industrial structure and the overall economic development level. Another study conducted by Xu and Dong (2023) covering 30 provinces in China from 2009 to 2019 evaluated the impact of GF on 4 H. M. SOHAIL ET AL. HED and its transmission way utilizing the intermediatory model. Using the family of econometrics approaches, findings indicated although regional differences were observed. Ouyang et al. (2023) applied the Hamilton optimization theory and Difference in Difference model to data from Chinese provinces between 2014 and 2018. Their findings supported the idea that GF policies can help standardize the quality of economic growth while reducing the growth rate. Using non-parametric data from 2011 to 2021 and the directional distance function, Jiakui et al. (2023) confirmed that GF, green technology innovation, and financial development promote HED in 30 provinces of China. A recent study by Zhao et al. (2024) Scrutinizes the impact of GF and green technology innovation on green growth in the 31 provinces in China over the period from 2004 to 2018. Findings indicate the positive contribution of both GF and GTI on green growth. This effect was more robust in the Middle Eastern region than the Western region. Similarly, Qing et al. (2024) utilized data from 12 provinces of China from 2000 to 2019 to explore the function of renewable energy and GF in attaining carbon neutrality. FMOLS approach reveals that China can go smoothly regarding carbon neutrality, thus enhancing the environmental quality. Another study by Afzal et al. (2024) focus on the 27 European countries from 2013 to 2022. Using the twostep generalized method of moment indicated that GF in green environmental initiatives could pose environmental risks due to increased liquidity and credit risks. Upon conducting an in-depth review of the related literature, we have found that (Akomea-Frimpong et al., 2022; Li et al., 2022) have assessed the regional impact of GF on HED but have neglected to consider non-linear effects. Therefore, our study is pioneering in analyzing both the non-linear and regional impact of GF on HED in Chinese provinces. 3. Method, model and materials 3.1. Materials The current study aims to evaluate the impact of GF on HED alongside other potential contributing factors across the 31 provinces of China (see Table 1). The data utilized in this study ranged from 2006 to 2020 and was meticulously sourced from databases such as EPS, the China Statistical Yearbook, the China Insurance Yearbook, and the China Fiscal Yearbook. In this context, HED, serving as the dependent variable, encompasses the multifaceted dimensions of high-quality economic development. While many previous studies have relied on the growth rate of total factor productivity as an indicator of HED (Gao et al., 2022; Jiakui et al., 2023), we have opted for a more nuanced approach. Specifically, we have chosen to assess sub-factors, including the productivity of capital, labor, and land, to provide a more comprehensive understanding of HED dynamics (refer to Figure 3). This deliberate selection enables a granular examination of the individual components contributing to overall economic development quality, thereby enriching the analytical framework of this study. 3.2. Variables selection 3.2.1. Explanatory and control variables Green finance (GF) serves as the primary explanatory variable in this study, constructed from various sub-factors aimed at gauging its multifaceted impact. These sub-factors include: Table 1. Variables and definition. Variables Description HED High-quality economic development proxied by total factor productivity GF Green finance is a type of funding that supports environmental sustainability URB Urbanization consists of the ratio of the URB population to the total population FD Fiscal decentralization is the process of transferring fiscal and decision-making powers from the central government to local or regional governments within a country EC Energy consumption of the last year TAX Tax is a financial or economic payment imposed by the government on individuals, companies and institutions to finance public spending and provide government services EDUC Education is the average of schooling years COGENT ECONOMICS & FINANCE 5 Green Securities: A-share of market value high energy consumption industry/The total value of A-share (Li et al., 2022). Green Credit: Assessed by the ratio of interest expenses in energy-intensive industries to the total industrial interest expenditure, reflecting the extent to which financial institutions allocate credit to environmentally sustainable initiatives. Green Investment: Quantified by the ratio of investment in environmental pollution control to GDP, indicating the magnitude of investments channeled towards environmental conservation efforts. Green Insurance: Evaluated based on agricultural insurance income and the industry’s total output value, offering insights into the insurance sector’s role in mitigating environmental risks. Carbon Finance: Measured by the ratio of CO 2 emissions to GDP, indicating the economy’s carbon footprint and the degree of carbon mitigation measures in place. Additionally, this study incorporates various economic and social indicators as control variables to account for potential confounding factors. These control variables include: Urbanization: Reflecting the level of urban development and its potential influence on economic dynamics (Han et al., 2023). Fiscal Decentralization: Assessed to understand the impact of fiscal policies and decentralization on economic development (Song et al., 2018). Energy Consumption: Considered as a crucial factor influencing economic growth and environmental sustainability (Yang & Wang, 2021). Tax Level: Quantified to analyze the impact of taxation policies on economic activities and development (Wang et al., 2023). Education: Measured to account for the influence of human capital development on economic growth and development (Lee & Lee, 2022). Prior to estimation and indexing, Principal Component Analysis (PCA) was employed to create indices for both GF and HED, following the approach outlined by Zeqiraj et al. (2022). This method enables the synthesis of multiple variables into composite indices, capturing the underlying dimensions of GF and HED. Each variable within these indices was normalized to facilitate meaningful comparisons and interpretations, enhancing the analytical robustness of this study. 3.3. Regression model specification To assess the impact of Green Finance (GF) on high-quality economic development (HED) alongside other contributing factors, we employed a fixed-effect model to evaluate the real-time nexus among the variables under consideration. This study’s empirical analysis builds on previous research, including the works of Liu et al. (2023) and Zhao et al. (2023), which serve as benchmarks for our regression analysis method. Before proceeding with the primary analysis, we conducted several pre-estimation analyses, such as regional heterogeneity tests. Based on the results of these pre-estimation analyses, we selected our main estimation model. Additionally, we applied spatial spillover analysis as a robustness check to further confirm our findings and explore the relationship dynamics more deeply. For a clear understanding, we presented the estimation application hierarchy graphically in Figure 4.InEq. (1) of this study, we delineate the econometric nexus among the dependent and independent variables, establishing a comprehensive framework for our analysis. The fixed-effect model is specified as follows: Figure 3. Variables framework, Source: Authors’calculation. 6 H. M. SOHAIL ET AL. HEDit ¼b0þb1GFit þb2URBit þb3FDit þb4ECit þb5TAXit þb6EDUCit þeit (1) By integrating these methods and specifications, our study aims to provide robust and insightful conclusions regarding the role of GF in promoting sustainable economic development. In Eq. (1), the HEDit represents the dependent variable, and GFit stands as the core independent variable representing GF. The parameters to be estimated in the current study are denoted by b1to6:The subscript 0i0signifies the provinces, while 0t0represents the time period ranging from 2006 to 2020. Finally, eit refers to the error term inherent in this analysis, capturing unobservable factors influencing the dependent variable. 3.4. Pre-Estimation and summary statistics The parameters used in the analysis are summarized in Table 2. The mean value of HED is 1.421, and the standard deviation is 2.213, indicating the typical deviation. GF has the highest deviation in the given year, with an average value of 71.184 and a standard deviation of 1.039. While urbanization has an average value of 5.008, and the standard deviation is 0.533. Similarly, financial decentralization has average and standard deviation values of 1.187 and 1.379, respectively. Energy consumption and Tax level have average values of 3.818 and 49.353, respectively. Moreover, the skewness and kurtosis values indicate moderate skewness and normal distribution. The Jarque-Bera test confirms the normality of the data, with p-values greater than 0.05%, allowing the analysis to proceed to the next step. 4. Empirical findings 4.1. Summary of analysis parameters and baseline regression results Table 3 presents the baseline regression results for this research study. The first three columns include only the explanatory variables without any control variables. In the first column, only the year is controlled, while in the second column, only the provinces are controlled. In the third column, both the year and provinces are controlled. The regression results reveal a positive coefficient for Green Finance (GF) at a significant level of 10%, suggesting its contribution to high-quality economic development (HED). These results align with the findings of Zhao et al. (2024), who examined the impact of GF and green technology innovation (GTI) on green growth across 31 provinces in China, confirming the positive contribution of both GF and GTI to green growth. Similarly, Qing et al. (2024) investigated the role of renewable energy and GF in achieving carbon neutrality. Using the Fully Modified Ordinary Least Squares (FMOLS) approach, they concluded that China is on a viable path toward carbon neutrality, thereby enhancing environmental quality. Another study by Afzal et al. (2024), focusing on 27 European countries, indicated that GF in green environmental initiatives could pose environmental risks due to increased liquidity and credit risks. Figure 4. Empirical analysis framework. Table 2. Summary statistics. HED GF URB FD EC Tax EDUC Mean 1.421 71.184 5.008 1.187 3.818 49.353 8.184 Mean 1.421 71.184 5.008 1.187 3.818 49.353 8.184 Median 1.536 70.805 5.185 0.61 3.831 47.509 7.805 Maximum 5.095 75.193 5.625 3.53 4.11 58.091 5.113 Minimum −5.75 70.297 3.851 −0.665 3.611 44.276 6.297 Std. Dev. 2.213 1.039 0.533 1.379 0.146 3.832 1.029 Skewness −0.95 2.544 −0.745 0.191 0.517 0.778 2.504 Kurtosis 2.767 3.481 2.276 1.45 2.438 2.491 3.411 Jarque-Bera 8.743 87.709 3.544 3.288 1.789 3.462 9.704 COGENT ECONOMICS & FINANCE 7 Yang, Y., Su, X., & Yao, S. (2021). Nexus between green finance, fintech, and high-quality economic development: Empirical evidence from China. Resources Policy,74, 102445. https://doi.org/10.1016/j.resourpol.2021.102445 Yang, F., & Wang, C. (2021). Clean energy, financial development, and economic growth: Evidence from spatial spillover effects and quasi-natural experiments. 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List of provinces No National Western Central Eastern 1 Anhui Chongqing Anhui Beijing 2 Beijing Gansu Heilongjiang Fujian 3 Chongqing Guangxi Henan Guangdong 4 Fujian Guizhou Hubei Hebei 5 Gansu Inner Mongolia Hunan Jiangsu 6 Guangdong Ningxia Jiangxi Liaoning 7 Guangxi Qinghai Jilin Shandong 8 Guizhou Shaanxi Shanxi Shanghai 9 Hainan Sichuan Tianjin 10 Hebei Tibet Zhejiang 11 Heilongjiang Xinjiang 12 Henan Yunnan 13 Hubei 14 Hunan 15 Inner Mongolia 16 Jiangsu 17 Jiangxi 18 Jilin 19 Liaoning 20 Ningxia 21 Qinghai 22 Shaanxi 23 Shandong 24 Shanghai 25 Shanxi 26 Sichuan 27 Tianjin 28 Tibet 29 Xinjiang 30 Yunnan 31 Zhejiang 14 H. M. SOHAIL ET AL.