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

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 regional-wise 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.

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

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 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).

Figure 1. GF contribution towards high quality economic development. Source: Authors ‘calculation.

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.

Figure 2. Overview of GF and HED in China. Source: National Bureau Statistics of China.

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 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 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 two-step 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.

Figure 3. Variables framework, Source: Authors’ calculation.

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

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:

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 CO₂ 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. In Eq. (1)(1) $${\mathrm{\text{HED}}}_{\mathrm{\text{it}}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{\text{GF}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_2{\mathrm{\text{URB}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_3{\mathrm{\text{FD}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_4{\mathrm{\text{EC}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_5{\mathrm{\text{TAX}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_6{\mathrm{\text{EDUC}}}_{\mathrm{\text{it}}}+{\mathrm{\epsilon }}_{\mathrm{\text{it}}}$$(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: (1) $${\mathrm{\text{HED}}}_{\mathrm{\text{it}}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{\text{GF}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_2{\mathrm{\text{URB}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_3{\mathrm{\text{FD}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_4{\mathrm{\text{EC}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_5{\mathrm{\text{TAX}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_6{\mathrm{\text{EDUC}}}_{\mathrm{\text{it}}}+{\mathrm{\epsilon }}_{\mathrm{\text{it}}}$$(1)

Figure 4. Empirical analysis framework.

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)(1) $${\mathrm{\text{HED}}}_{\mathrm{\text{it}}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{\text{GF}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_2{\mathrm{\text{URB}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_3{\mathrm{\text{FD}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_4{\mathrm{\text{EC}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_5{\mathrm{\text{TAX}}}_{\mathrm{\text{it}}}+{\mathrm{\beta }}_6{\mathrm{\text{EDUC}}}_{\mathrm{\text{it}}}+{\mathrm{\epsilon }}_{\mathrm{\text{it}}}$$(1) , the $\mathit{\text{HEDit}}$ represents the dependent variable, and $\mathit{\text{GFit}}$ stands as the core independent variable representing GF. The parameters to be estimated in the current study are denoted by $\beta 1$ to $6.$ The subscript $\prime i\prime$ signifies the provinces, while $\prime t\prime$ represents the time period ranging from 2006 to 2020. Finally, $\epsilon \mathit{\text{it}}$ 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.

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

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.

Table 3. Baseline regression.

Variables	M-1	M-2	M-3	M-4
GF	0.110**	0.114**	0.125***	0.128***
	(8.100)	(2.801)	(2.01)	(2.301)
URB				0.013**
				(1.130)
FD				(0.090)
				0.102
EC				(0.033)
				1.012
TAX				(0.019) ***
				2.521
EDUC				(0.42) **
				0.303
cons_	0.431***	0.761***	0.707***	(0.231) ***
	(12.105)	(4.204)	(7.107)	(2.131)
Year	Yes	No	Yes	Yes
Province	No	Yes	Yes	Yes
R^2	0.316	0.326	0.344	0.438

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.

The fourth column introduces control variables, including urbanization, fiscal decentralization, energy consumption, tax, and education, while still controlling for provinces and years. These additional variables provide a more comprehensive understanding of the factors influencing HED, further validating the robustness of the positive relationship between GF and HED. These results contribute to the existing literature by highlighting the nuanced effects of GF on economic and environmental outcomes across different contexts and regions.

The results indicate that GF indeed promotes HED. Concerning the control variables, the coefficient for urbanization is significantly positive, suggesting that urbanization level can stimulate economic growth through its impact on investment and consumption. Fiscal decentralization exhibits a positive coefficient but lacks statistical significance, implying a limited impact on HED. This aligns with the findings of Wang et al. (2023), validating the role of fiscal decentralization in promoting HED in China. Similarly, the coefficient for energy consumption is positive but not statistically significant, indicating its negligible effect on HED. This finding is supported by Yang et al. (2021), which concludes that energy consumption does not significantly influence HED in China. The coefficient for the tax level is significantly positive, implying its significance as an indicator of economic growth and its potential contribution to HED. Finally, the regression coefficient for education remains statistically significant and positive, indicating a positive contribution to HED. According to the World Bank report, education serves as a powerful instrument for long-term economic growth by promoting skills and awareness. Similarly, human capital theory posits that investing in education enhances economic efficiency and reduces poverty by improving labor productivity and earnings.

4.2. Spatial spillover analysis

In Table 4, the explanatory variables in the first column utilize the economic growth rate, while the explanatory variables in the second column employ GDP per capita. After controlling for provinces and years, the results exhibit significant positivity at the 1% level, indicating robust baseline regression outcomes. A spatial econometric model was employed to investigate the spatial spillover effect of GF on HED. The spatial weights matrix utilized is a geographic distance matrix, and the regression results are presented in column 3. The three models show that GF exerts a significantly positive impact on the HED, attaining a 5% significance level. From a spatial spillover perspective, HED negatively impacts the quantitative development of the surrounding areas, suggesting that higher HED in the local economy correlates with lower HED in adjacent regions. However, the spatial spillover effect of GF is positive, indicating that the development of local GF significantly promotes HED in surrounding areas. This implies that initiatives aimed at enhancing GF benefit the local economy and have a positive ripple effect on neighboring regions, fostering a broader landscape of HED. These outcomes are aligned with Gao et al. (2022), they depict that GF contributes to HED in China by enhancing industrial structure and the overall economic development level.

Table 4. Robustness and spatial diffusion analysis.

Variables	M-1	M-2	M-3
GF	2.071***	2.560***	0.101***
	(3.202)	(4.013)	(2.302)
URB	−0.413*	4.109**	0.104***
	(−2.305)	(0.609)	(3.590)
FD	1.061*	4.112*	2.133***
	(0.910)	(3.202)	(0.441)
EC	−0.233	0.401	0.011***
	(−0.341)	(1.501)	(2.323)
TAX	0.701	1.211*	0.321**
	(1.012)	(−3.403)	(−2.120)
EDUC	−0.617**	−7.301**	−0.041**
	(−0.601)	(−0.408)	(−0.380)
W-HED			−1.215*
			(−3.103)
W-GF			1.018**
			(3.122)
W-URB			−0.510**
			(−0.671)
W-FD			2.221***
			(0.104)
W-EC			0.224**
			(3.190)
W-TAX			−1.021
			(−0.81)
W-EDUC			−0.102
			(−0.109)
Cons	7.986	−4.90	5.461
	(2.321)	(0.652)	(3.214)
Year control	Yes	Yes	Yes
Province control	Yes	Yes	Yes
R2	0.816	0.911	0.932

4.3. Regional analysis

Group-wise heterogeneity tests were conducted to scrutinize differences among various regions in China. The regression results for the Eastern, Central, and Western regions are presented in the first, second, and third columns of Table 5, respectively. Notably, there exists substantial disparity in economic development among China’s different regions, particularly among the three major regions: Eastern, Central, and Western. Analyzing the results presented in the first column of the regression, it becomes apparent that the impact of GF on HED in the Eastern region is not statistically significant. Conversely, in the Central region, although the impact of GF on HED is statistically significant, it is accompanied by a negative regression coefficient. Turning to the third column of the regression results, a notably positive impact of GF on HED in the Western region is observed, suggesting that the influence is most pronounced in this region. This finding aligns with the observations of Li et al. (2022), their findings show a positive impact in the western areas, while a negative impact is in the central region. One reason behind the negative indication could be that the noteworthy reduction of human capital delays the progress of GF in the central regions and poses challenges to promoting HED (Li et al., 2022). Given the prevailing environmental pollution, the central region attracts better openings for the development of GF. Second, the growth of GF in the central is not like that of the eastern region (Xu & Dong, 2023). This variance twigs from the comparatively plentiful GF instruments in the eastern region, where companies primarily practice direct financing methods such as green bonds and green funds. Besides, the eastern market has become more efficient and prioritized, leading to the diminishing role of green finance in driving high-quality economic growth. On the other hand, the growth of GF in the central regions lags behind, the economic foundation is feeble, the financial instruments are comparatively unassuming, the market mechanism is faultless, and the allocation of capital and resources is incompetent. Last but not least, using access of renewable and green energy for the production process can fight against climate change and global warming (Iqbal et al., 2019).

Table 5. Regional analysis.

Variables	Eastern	Central	Western
GF	0.197	−0.603*	3.241***
	(0.909)	(−0.402)	(2.718)
URB	0.301*	−3.104**	0.013***
	(−4.018)	(−2.418)	(−0.609)
FD	0.063***	−0.014***	−0.002
	(2.602)	(−3.001)	(0.85)
EC	3.001***	−0.703	0.018
	(6.008)	(−3.109)	(0.501)
TAX	−0.015**	−0.018*	−0.091
	(−2.905)	(−0.548)	(−1.106)
EDUC	−0.111	0.112***	0.023***
	(−0.501)	(−0.546)	(1.601)
cons_	0.829***	0.905***	0.722***
	(0.123)	(4.101)	(0.311)
Year control	Yes	Yes	Yes
Province control	Yes	Yes	Yes
R2	0.481	0.585	0.504

Overall, our positive results are consistent with prior studies (Gao et al., 2024; Gao et al., 2022; Jiakui et al., 2023; Li et al., 2022; Wang & Zhi, 2016; Xu & Dong, 2023; Yang et al., 2021; Zhou et al., 2020), confirming that GF can potentially effect positive change on a broader scale. Moreover, regions with strong government policies and regulations supporting GF, as well as access to international climate finance, are likely to attract investments in environmentally friendly projects. Furthermore, regions fostering local innovation, entrepreneurship, and community participation can effectively implement green technologies and initiatives through public-private collaboration. Additionally, consumer demand for environmentally friendly products and services can contribute to economic growth. However, it is essential to recognize that the effectiveness of GF can vary significantly based on the unique context of each region, including factors such as geography, climate, politics, and local economic conditions. This nuanced understanding is crucial for tailoring GF initiatives to maximize their impact and foster sustainable economic development across diverse regional landscapes.

4.4. Non-linear analysis: quantile regression

The tests conducted above primarily utilize linear methodologies, yet we hypothesize that the relationship between GF and HED may exhibit non-linear characteristics. Consequently, a non-linear test employed a quantile regression model for empirical analysis. This study explicitly examines the 1st, 3rd, 6th, and 9th quantiles to capture various distribution segments. The quantile regression results presented in Table 6 unveil a nuanced pattern: the impact of GF on HED gradually weakens as we traverse across quantiles, accompanied by a diminishing significance level. This suggests that GF exerts a substantial positive impact on HED at lower quantiles. However, as quantiles increase, the significance of GF’s impact diminishes, becoming non-significant at higher levels. The findings underscore that while GF significantly promotes HED at lower levels of development, its impact diminishes as regions progress toward higher levels of development. This implies that the positive effect of GF on HED is contingent upon the region’s level of development. Overall, the results suggest GF's positive but diminishing impact on HED, highlighting the importance of adapting GF policies according to the region’s developmental phase to enhance their effectiveness in fostering economic and environmental sustainability. These findings are correlated with Xu and Dong (2023) covering 30 provinces in China shows that GF promotes the HED; however, regional differences were observed. This nuanced understanding is crucial for policymakers to tailor GF initiatives effectively and maximize their impact across varying developmental contexts.

Table 6. Non-linear results.

	M (1/10)	M(3/10)	M(6/10)	M(9/10)
GF	4.013***	2.330**	3.101*	1.505
	(1.105)	(1.404)	2.232)	(1.032)
URB	1.139**	−1.113*	−3.313	−0.101*
	(0.113)	(−0.332)	(−4.202)	(−2.114)
FD	1.016**	0.401**	1.811	−0.338
	(3.23)	(2.5392)	(2.41)	(−0.039)
EC	3.129***	0.256**	0.435*	0.611
	(1.401)	(1.201)	(2.131)	(0.231)
TAX	0.119*	−0.085**	1.069	0.332*
	(0.621)	(−0.63)	(0.607)	(2.621)
EDUC	−0.023**	0.109*	9.211	−0.026
	(−0.303)	(0.041)	(0.021)	(−1.331)
cons_	0.612***	0.776***	0.812***	0.905***
	(22.41)	(15.63)	(11.62)	(14.011)

5. Conclusion and policy implications

The current study scrutinized the impact of GF and HED in China, incorporating urbanization, taxation, financial decentralization, energy consumption, and education, analyzing annual data ranging from 2006 to 2020 across 31 provinces. Employing the fixed-effect model as a baseline regression, findings reveal that GF significantly contributes to HED, supported by the presence of spatial spillovers. However, upon dissecting regional heterogeneity, we observed a significant relationship only in the western region, highlighting the need for targeted interventions to bolster GF effectiveness across diverse regional landscapes. Furthermore, quantile regression unveiled a nuanced association between GF and HED. While GF showcased a substantial positive impact on HED at lower quantiles, its significance diminished as quantiles increased, suggesting a threshold effect wherein GF’s impact becomes non-significant at higher levels of development. These findings underscore the importance of tailoring GF policies to align with the developmental phase of each region to maximize their effectiveness in promoting economic and environmental sustainability. Finally, the effects of urbanization, taxation, financial decentralization, energy consumption, and education varied in the different models. This study faced some limitations while pursuing analysis. Firstly, the analysis relies on aggregated provincial-level data, which may mask intra-provincial variations and nuances. Secondly, the study’s timeframe is limited to 2006–2020, potentially overlooking longer-term trends and dynamics. Additionally, while quantile regression offers insights into non-linear relationships, it may not capture all complexities of the GF-HED connection. Besides, the study focuses solely on China, limiting its generalizability to other contexts and economies.

The insight from the current study reveals some potential policy recommendations. First, efforts such as fiscal transfers must be amplified to reduce regional differences. These ingenuities should purpose to syndicate regional fortes with GF plans. By joining regional resource advantages with GF strategies, distinguished positioning can be attained, paving the way for leapfrog growth and effectively tapering regional gaps. Second, to improve the favorable contribution of GF on HED, the central region needs to increase investment and resource allocation based on the current level of GF. The state should give robust policy provisions to the central part and upsurge capital investment and talent introduction policies to advance GF development. Third, optimizing economic race among regions is domineering to alleviate detrimental practices stemming from excessive interregional competition. Lastly, addressing the unimportant function of GF on HED observed in the central region calls for beleaguered interventions to raise GF development in these areas. Recognizing fences hindering GF effectiveness and implementing tailored plans to address these challenges are vital steps toward enhancing HED in this region.

Future research endeavors can further enrich our understanding of the GF-HED association by reconnoitering the dynamics at more granular levels, such as city-wise, provincial-wise, and region-wise analyses. It is important to analyze GF indicators on the HED and its components individually. This analysis can help reveal which indicator is most effective in fighting against climate change and global warming, while also enhancing the HED in China and other economies. By exploring additional factors such as green technology, artificial intelligence, human development, and green and clean energy, we can deepen our understanding of how to promote sustainable economic progress.

Authors’ contribution

HMS conducted the literature review, conducted the econometric analysis, explained the results, and discussed the findings; NFH edited the entire draft and supervised the work; MZ wrote the introduction, conclusions, and recommendations. HH, AMKA and NMA revised and contributed during the review process.

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

Data availability

Data used in this study is available upon request; however, we are still working on this project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by UNiSZA/2023/PSD (014) under the project no (RQ029).

Notes on contributors

Hafiz M. Sohail

Hafiz M. Sohail is Ph.D. and postdoctoral researcher at the Faculty of Business and Management, University Sultan Zainal Abidin, Malaysia, his research interests spanning various areas, including Energy Economics and Environmental Economics, Digital Economics, and Sustainable Development.

Hossam Haddad

Hossam Haddad is Ph.D. in accounting and working as an Assistant Professor at Zarqa University, Jordan. His research interests are intellectual and social capital, banking profitability management, and corporate governance.

Mirzat Ullah

Mirzat Ullah is a Ph.D. and working as researcher at Graduate School of Economics and Management (GSEM), Ural Federal University, Yekaterinburg, Russia. His research interests include investment analysis, asset pricing, portfolio & risk management, financial modeling, and cryptocurrency simulation.

Nidal Mahmoud Al-Ramahi

Nidal Mahmoud Al-Ramahi is Ph.D. and works as a President at Zarqa University, Jordan. His research interest is in accounting information systems and Quality assurance.

Nazatul Faizah Haron

Nazatul Faizah Haron is Ph.D. and serves as an Associate Professor at the Faculty of Business and Management, University Sultan Zainal Abidin in Malaysia. Her areas of research interest include Environmental Economics, Natural Resource Economics, Development Economics, and Islamic Economics.

Appendix A1.

List of provinces

Table

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