A Study on the Impact of Green Finance on the High-Quality Economic Development of Beijing–Tianjin–Hebei Region

Abstract

China’s economy has shifted to a new stage of high-quality development, which means that economic development is no longer simply pursuing the increase of quantity, but a balance of quality and quantity. High-quality economic development (HQED) has become essential for China to achieve sustainable economic and social advancement. This paper explores the influence of green finance on HQED, along with the mediating roles of green innovation and industrial structure upgrading. A fixed-effects model is employed to assess the relationship between green finance and HQED and conduct an empirical test in the Beijing–Tianjin–Hebei (BTH) region of China. The findings reveal that green finance significantly contributes to the HQED of the BTH region, and green innovation and industrial structure upgrading have intermediary effects in this process. Based on these insights, this paper proposes several strategies to improve HQED in China, including the development of a green financial system, the promotion of green innovation, and the acceleration of industrial structure optimization and upgrading in the BTH region.

1. Introduction

As economies and technologies progress rapidly, environmental challenges, including climate change and pollution, have intensified. Addressing the delicate balance between economic growth and ecological sustainability has emerged as a pivotal concern worldwide. In response, the Chinese government has advocated for “high-quality development” as a strategic approach to harmonize economic advancement with ecological preservation. Viewed through the lens of the new development paradigm, HQED is a mode characterized by innovative, coordinated, green, open, and inclusive development, emphasizing that economic development should strike a balance between speed and quality.

Green development represents a key indicator of China’s transition to a new development stage [1]. The innovation in financial instruments and the launch of eco-friendly financial products have a positive significance for environmental sustainability and green development. Green finance offers strategic solutions for mitigating climate risks and enhancing environmental quality [2]. Aiming to improve the ecological environment, green finance actively supports the growth of green industries and promotes the technological upgrading and green transformation of traditional industries, thereby reducing the environmental impact of social production activities. Consequently, green finance aligns with the dual objectives of achieving economic gains and environmental preservation.

The genesis of green finance traces back to 1974 with the establishment of the world’s first policy-based environmental bank, specializing in financing projects for environmental protection and social and ecological business. After the introduction of the Equator Principle, green finance began to capture the attention of governments worldwide, leading to its rapid global expansion. Numerous developed countries have formed a relatively complete system of green finance. Governments and central banks of many countries have formulated relevant laws and policies and made a lot of efforts in the related areas of green finance. According to the latest research report of The City UK and BNP Paribas, in the past 10 years, the proportion of green finance in the global capital market has increased from 0.1% in 2012 to 4% in 2021, from USD 5.2 billion to USD 540.6 billion, an increase of more than 100 times [3]. Bloomberg has projected that global green assets are expected to exceed USD 53 trillion by 2025. China, recognizing the critical importance of green finance, has implemented numerous incentive policies and established green finance reform and innovation pilot zones to bolster its development. According to the data of the China Banking and Insurance Regulatory Commission, as of the end of September 2021, the green credit balance at 21 major banking institutions in China reached about USD 1.96 trillion. China has thus developed a multifaceted green financial system, featuring a wide array of financial instruments and a multi-level financial market, offering diverse financing options and risk management solutions for energy conservation and environmental protection initiatives. Consequently, the financial sector’s ability to serve green and low-carbon economic development has been constantly improving.

The BTH Urban Agglomeration emerges as a pivotal synergistic development zone, shaped by its geographical location and cultural heritage. This conglomerate, comprising Beijing, Tianjin, and cities from Hebei province, represents the capital’s economic sphere and a crucial component of China’s spatial economic layout. According to recent data, the combined economic output of Beijing, Tianjin, and Hebei exceeded USD 1.39 trillion in 2022, signifying a further enhancement in the region’s economic prowess. However, there are still problems such as high inequality within urban agglomerations [4], large differences in development stages and positioning, and insufficient integration of the industrial chain and innovation chain [5]. Therefore, the ability of inter-regional coordinated development and HQED of the region has become a hot issue.

In this paper, we examine the mechanism and impacts of green finance on HQED within the BTH region through regression analysis. Furthermore, we examine the intermediary role played by industrial structure upgrading and green innovation in this process. This research aims to offer theoretical insights for advancing HQED in the BTH region through the application of green finance.

This paper makes three significant contributions. First, although green finance has received unprecedented attention and relevant financial products have been applied to cope with environmental changes and achieve carbon emission reduction, the precise role and mechanisms of green finance in HQED still need further study. Notably, existing research predominantly focuses on the national level, with limited studies examining regional dynamics. Therefore, we focus our research perspective on the BTH region. This investigation seeks to serve as a foundational reference for scholars interested in examining the interplay between green finance and HQED, both within the BTH region and in analogous contexts. Second, many studies have demonstrated the relationship between green finance and HQED. However, the factors influencing the relationship between them have yet to be studied. This paper explores the specific influence mechanism between the two economic variables and examines the intermediary role of industrial structure upgrading and green innovation in the relationship. Third, employing the entropy method to establish the HQED index system is a common practice. Nonetheless, this research adopts a more nuanced approach by selecting indicators specifically relevant to the BTH region, across five dimensions: innovative, coordinated, green, open, and inclusive development. We develop an evaluative framework to measure the HQED of 13 cities in the BTH region.

The rest of the paper is organized as follows: Section 2 provides a brief overview of the relevant literature, analyzes the theoretical effects and mechanisms of green finance’s influence on development quality, from which the research hypotheses are derived, and details the empirical model, including data collection methods and the construction of indicators. Section 3 presents the empirical results and robustness tests. Section 4 explains the results and compare them with previous studies. Section 5 summarizes the conclusions and offers actionable recommendations.

2. Materials and Methods

2.1. Literature Review

2.1.1. Green Finance

Salazar put forward green finance earlier. He considered that green finance was a financial innovation driven by environmental protection, and it acted as a conduit between finance and environmental industries [6]. Following the introduction of green finance, extensive research has been conducted in this field. Scholtens highlighted green finance’s critical role in addressing environmental pollution and climate change, asserting it as a vital tool for mitigating these issues [7]. Through the provision of optimized financial services and products tailored for environmental enterprises, green finance facilitates the sustainable development of the economy, society, and environment.

There are numerous research findings describing the role of green finance. Financial institutions provide research and innovation funds to enterprises through green credit, motivating them to adopt green, low-carbon, and eco-friendly development trajectories while enhancing their capacity for innovation [8]. In addition to providing financial backing for enterprises’ green investment, green finance stimulates the growth of green enterprises [9]. Green finance brings working capital to green enterprises by generating corresponding scale, structure, technology, and other effects [10]. Green finance improves energy and environmental performance and promotes green innovation in regions with underdeveloped credit and capital markets [11]. Fintech plays a critical role in expediting the green finance process and elevating the quality of environmental protection [12]. The green innovation of state-owned high-polluting enterprises is more affected by green credit than other enterprises [13]. Green finance significantly enhances green total factor productivity by promoting green technology transformation [14].

A lot of research has been conducted on the measurement of green finance. The proportion of green credit in total bank loans serves as a metric for assessing the development and efficiency of green finance [15]. Utilizing the entropy method, a comprehensive indicator system was constructed from three dimensions of scale, efficiency, and structure, to evaluate the level of green finance. The research concluded that green financial reform in Northwest China should be adapted to the local industrial structure in the future to achieve green and sustainable economic development [16]. By conducting weight analysis through data enveloping analysis, a low-carbon finance index was built to evaluate the level of low-carbon finance in developing and less-developed nations [17]. The principal component analysis method was used to calculate the green development of China’s provinces, and the results showed that the green finance development in China continued to rise [18]. Guo constructed a regional green finance index and found through empirical analysis that there was a two-way promoting effect between green finance and a low-carbon economy [19].

2.1.2. HQED

Economic growth typically denotes an increase in output, often measured by the per capita growth rates of Gross National Product (GNP) or Gross Domestic Product (GDP). Conversely, economic development encompasses not only enhanced output but also significant changes in the technical and institutional frameworks that underpin the production and distribution of goods, including structural transformations and advancements in income equity. Economic development represents the genuine progress in welfare for a country or region, assessed on a per capita basis. This concept extends beyond mere wealth accumulation to encompass qualitative improvements such as enhanced living standards and social unity, evolution of economic and social structures, elevation of social quality of life, and augmentation of input–output efficiency.

In addition to the speed of economic growth, HQED encompasses considerations beyond the economic growth rate; it should also consider factors such as risk control, sustainable development of the environment, and opportunity distribution [20]. HQED depends more on quality and efficiency than on quantity and speed [21]. Quantitative indicators, such as GDP, are no longer the only focus of the government but are gradually shifting from quantity to quality. Policymaking increasingly revolves around creating a resource-efficient and eco-friendly society [22]. HQED is a high integration of five development concepts and a new development concept with efficiency and quality as its value orientation [23]. Therefore, the HQED means that the focus of development is no longer simply quantitative expansion but a trend to achieve through quality, which is the unity of quantitative expansion and quality improvement.

For the measurement of HQED, some scholars advocate for using a singular indicator, with economic total factor productivity frequently cited as a proxy for HQED [24]. However, due to the impact of conceptual errors and measurement methods, there are obvious limitations in the feasibility and rationality of measuring the development quality with total factor productivity [25]. Additionally, other researchers propose using the value-added rate to assess regional development quality [26]. However, this method is affected by the upper threshold and the different levels. Different levels below or above the threshold have different response effects [27]. Consequently, these singular indicators are fraught with numerous limitations.

In recent years, a growing consensus among scholars suggests that a single indicator is insufficient to encapsulate HQED. Thus, there has been a shift towards developing an integrated indicator system for a more holistic assessment of HQED. Jiang et al. built an index system of HQED based on the five development values [28]. In terms of the four links of production, distribution, circulation, and consumption and their externalities, an HQED evaluation system was constructed, and the study showed that the HQED in China needs to be improved [29]. Drawing from the “Five Development Concepts”, 20 indicators were selected to measure the level of economic development [30]. Tian et al. selected 27 indexes to evaluate the HQED of the BTH region from three aspects: target, standard, and index level [31]. An evaluation index system was formed to evaluate the level of economic development, derived from the four development concepts of economic development, social progress, ecological civilization, and innovation-driven [32].

2.1.3. The Relationship between Green Finance and HQED

A considerable amount of literature has been published on the relationship between green finance and HQED. Many scholars agree that green finance can improve HQED, while some other scholars believe that there are more complex relationships between green finance and HQED. Green finance plays a pivotal role in spurring science and technological innovation, augmenting investments in environmental protection, and elevating the caliber of regional economic development [33]. Green finance is a crucial factor in determining regional environmental quality. With the improvement of financial development levels, the corresponding level of environmental quality also increases [34]. Green finance supports the development of green industry by promoting green development awareness, concepts, and innovative financial service models, thereby promoting the HQED of the regional economy [35]. In the process of green finance development, green finance guides industrial transformation and upgrading through enterprises and countries, promotes coordinated development of the environment and economy, and achieves HQED [36]. Green finance also contributes to HQED by enhancing carbon productivity [37]. However, some scholars argue that green finance is not just a simple promotion for HQED [38]. In the long run, the scale of green finance development and the efficiency of green financial resource allocation might negatively affect macroeconomic stability and impede growth [39]. Pan et al. came to the conclusion that there was a U-shaped relationship between green finance and HQED in the Yangtze River Economic Belt [40]. With the improvement of green finance, the HQED showed a trend of first declining and then rising. Based on the spatial Durbin model, Zhu et al. discovered that green finance’s impact on local green economic efficiency is U-shaped, whereas its effect on the green economic efficiency of adjacent areas is inverted U-shaped, demonstrating a single-threshold effect in enhancing green economic efficiency [41].

From the point of view of the research object, some studies focused on the national level, and some focused on the region. The first objective is to focus on the national level [42]. Based on data from India, Sreenu et al. found that green finance promoted HQED by affecting finance structure, financial effectiveness, and environmental quality protection [43]. Using data from 30 provinces, municipalities, and autonomous districts in China from 2011 to 2019, a spatial econometric analysis was conducted by constructing the weight matrix of geographical proximity and the weight matrix of economic distance, and the result showed that green finance had a significant positive effect on HQED [44]. The second objective is to focus on the regional level [45]. Taking ethnic populated areas as the research object, Feng et al. pointed out that the development of green finance was a key node of economic growth and would have a comprehensive and multi-level impact on the economic development of ethnic areas [46]. Taking Huzhou of Zhejiang Province as an example, a PVAR model was built to find that green credit input directly promoted the development of green industry and regional economic growth [47]. Additionally, Fu et al. analyzed the importance of developing green finance in the real economy of the Guangdong–Hong Kong–Macao Greater Bay Area and explored the mechanism for green finance to promote the economic development of the Greater Bay Area [48].

Upon reviewing the existing literature on green finance and HQED, we found that scholars have given different conclusions about the effect of green finance on HQED, and the relationship between the two may be different in different economic regions. Moreover, prior research has predominantly concentrated on the national level, or specific areas such as the Yangtze River Economic Belt, the Guangdong–Hong Kong–Macao Greater Bay Area, etc., with limited attention given to the BTH region. To address this gap, this paper focuses on 13 municipal indicators within the BTH region to explore the relationship between green finance and HQED in the BTH region. In terms of indicator selection, this paper adopts a comprehensive approach, tailoring an evaluation system of HQED specifically for the BTH region to ensure local relevance.

2.2. Theoretical Analysis and Hypothesis

Contrary to traditional finance, green finance is more intimately connected with resources and environmental considerations, playing a pivotal role in fostering HQED [33]. Firstly, the enhancement of green finance policies has led to an expansion in the scope of green financial products, such as green bonds and green funds. This expansion allows relevant green enterprises to access diversified financing channels, thereby boosting their innovation capabilities and contributing to the HQED of society at large [35]. Secondly, enterprises benefiting from green finance can swiftly elevate their technological standards by adopting advanced foreign technologies, enhancing the international competitiveness of their products, and fostering greater regional openness. Thirdly, green finance directs social capital towards green industries, curtailing investments in polluting activities, promoting carbon neutrality, and facilitating the upgrading of industrial structures [49]. Consequently, green finance is instrumental in accelerating reforms on both the supply and demand sides, enabling the equitable distribution of the benefits of green development, and steering the economy towards a trajectory of higher quality growth [37]. Accordingly, we propose the following hypothesis:

Hypothesis 1.

The advancement of green finance significantly positively affects HQED, impacting five dimensions of HQED—innovative development, coordinated development, green development, open development, and inclusive development—favorably.

Green finance and green innovation exhibit a significant positive autocorrelation [50]. Green innovation not only contributes to economic growth but also provides essential support for achieving HQED. Green finance enhances the capabilities of green innovation by broadening financing channels, directing capital flows, and minimizing transaction costs. Firstly, green financial policies have simplified the process for green innovation enterprises to secure external funding. The advent of green bonds, green funds, and other green financial products has augmented funding sources for these enterprises, enabling further investment in research and technological development, thereby significantly benefiting green innovation. Secondly, banks and other financial institutions take social responsibility into account when making resource and money allocations and implement differentiated credit policies to promote investment in green innovation and help the heavy-pollution and high-energy-consuming enterprises to take measures in enterprise transformation. In this process, the potential of green innovation is stimulated and more green technology achievements are produced. Thirdly, financial institutions carry out green development ratings for enterprises, helping to make information more transparent and reduce the information transaction costs of enterprises and providing investment institutions with more reference information and investment directions, which greatly improves investment efficiency. Green innovation helps to create new products and services and includes advanced production technology and energy-saving technology in the process from production and sales to the recycling and degradation of products, which could achieve multiple development goals such as economic growth, environmental protection, social development, and ecological balance, and then promote HQED. Accordingly, the following hypothesis is proposed:

Hypothesis 2.

Green finance promotes HQED by promoting green innovation.

The influence of green finance on the adjustment of industrial structure is primarily manifested through its promotion of financial and industrial integration, facilitating the growth and development of green enterprises. Simultaneously, it elevates the financing costs for polluting enterprises, effectively encouraging a shift towards more sustainable business practices. The upgrading of the industrial structure, fostered by such financial incentives, significantly contributes to HQED.

Firstly, green finance directs capital towards industries that are resource-efficient and environmentally friendly while imposing restrictions on those that are energy-intensive and highly polluting. Such financial guidance fosters the growth of sustainable industries and imposes constraints on the latter, facilitating a shift towards a more sustainable economic model. This dynamic encourages the adjustment and enhancement of the industrial structure, as well as the optimization of resource allocation across society. Ultimately, this leads to the rationalization and modernization of the industrial landscape, a shift that aligns with the principles of HQED. Secondly, green finance facilitates industry integration. With the concept of green development, industries with high energy consumption, such as electricity, steel, cement, and petrochemical, are compelled to curtail production, enhance production technologies, and explore new industrial avenues to decrease energy consumption. This transition encourages the reallocation of capital, labor, land, and other resources towards the tertiary sector, fostering industrial restructuring. Consequently, such shifts significantly contribute to the improvement of HQED. Thirdly, the issuance of relevant policies has catalyzed a profound acknowledgment among enterprises and financial institutions regarding the significance of green development. Through collective societal endeavors, such recognition is set to optimize the industrial structure and achieve the objectives of HQED. In light of this, we propose the following hypothesis:

Hypothesis 3.

Green finance fosters HQED through the upgrading of the industrial structure.

2.3. Variable Selection

2.3.1. Calculation of HQED Index

Drawing on prior research [51], this study constructs an evaluation system of HQED in the BTH region. We select measurement indicators from five dimensions: innovative development, coordinated development, green development, open development, and inclusive development, in terms of the “five development concepts” of China. The index system is shown in

Table 1. The evaluation indicator of the HQED index.

Indicator System	Primary Indicator	Secondary Indicator	Variables Used to Express the Secondary Indicators
HQED	Innovative development	Innovation input	R&D expenditure equivalent to regional GDP
			R&D personnel full-time equivalent
		Innovation output	Number of patents granted per 10,000 population
	Coordinated development	Coordination between urban and rural areas	Disposable income gap between urban and rural areas per capita
			Urbanization rate
		Harmony between material civilization and spiritual civilization	Public library book collection per 100 people
	Green development	Energy conservation	Energy consumption per unit of GDP
		Air quality	The proportion of air quality above grade 2 throughout the year
		Ecological construction	Green coverage of built-up areas
	Open development	Utilization of foreign capital	The proportion of actual utilization of foreign capital to GDP
		Trade development	The proportion of total foreign trade imports and exports to GDP
		Personnel exchange	The proportion of inbound tourist arrivals in total tourists
	Inclusive development	Sharing of basic public services	Number of beds in health facilities per capita
		Infrastructure sharing	Public transport per capita
		Educational equity	Per capita expenditure on education

.

We use the entropy method to allocate weights to each indicator, utilizing it to assess the dispersion of indicators and thereby determine their respective weights. There are 5 dimensions (innovative, coordinated, green, open, and inclusive development), and each dimension has 3 indicators. Based on m years, k provinces, and n indicators, the calculation steps are shown in Equations (1)–(6). Initially, we standardized the original indicators of HQED to mitigate the impact of outliers. The normalization method is shown as follows:

Positive indicators:

$$x_{\mathrm{i}\mathrm{j}}=\frac{a_{\mathrm{i}\mathrm{j}}-{min}_{\mathrm{j}}}{{max}_{\mathrm{j}}-{min}_{\mathrm{j}}}$$

(1)

Negative indicators:

$$x_{\mathrm{i}\mathrm{j}}=\frac{{max}_{\mathrm{j}}-a_{\mathrm{i}\mathrm{j}}}{{max}_{\mathrm{j}}-{min}_{\mathrm{j}}}$$

(2)

where x_ij is the normalized value of dimension i of indicator j; a_ij is the actual value of dimension i of indicator j; and min_j and max_j are the minimum value and maximum value of dimension i of indicator j, respectively. The normalized indicators x_ij have a value between 0 and 1, where 0 is the lowest score and 1 is the highest.

The entropy method is applied to calculate the weights of the HQED indicators. First, we calculate the value of X_ij, which represents the normalized value x_ij as a percentage of the sum of the j indicator.

$$X_{\mathrm{i}\mathrm{j}}=\frac{x_{\mathrm{i}\mathrm{j}}}{\sum x_{\mathrm{i}\mathrm{j}}}$$

(3)

Then, the information entropy is calculated by the formula below:

$$e_{\mathrm{j}}=-\frac{1}{\ln (m\times k)}\sum X_{\mathrm{i}\mathrm{j}}\ln X_{\mathrm{i}\mathrm{j}}$$

(4)

The entropy redundancy of the j indicator can be obtained as

$$g_{\mathrm{j}}=1-e_{\mathrm{j}}$$

(5)

Lastly, the weight of indicator j can be calculated with the below formula:

$$w_j=\frac{g_{\mathrm{j}}}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{g}_{\mathrm{j}}}$$

(6)

2.3.2. Variable Selection and Data Sources

Dependent Variable: The HQED index (Quality): This index is calculated according to the comprehensive HQED index system outlined in Section 2.3.1.

Independent Variable: green finance (GF). Based on Liu et al. [52], we establish a green financial development evaluation system from the aspects of green credit, green securities, green insurance, and green investment. The entropy method is applied to evaluate the green finance index in the BTH regions.

Mediating Variables: Our study incorporates two mediating variables: green innovation (GI) and industrial structure upgrading (IS). Green innovation (GI) refers to the behavior of providing new products and processes through technological innovation with the core pursuit of realizing green development. The facilitation of green innovation by firms plays a crucial role in minimizing natural resource consumption and mitigating environmental degradation. Based on Wang et al. [53], the number of green patent grants is used to express green innovation. Industrial structure upgrading (IS) is a dynamic process that changes from traditional industries to more advanced and innovative sectors. In this paper, the industrial structural upgrade (IS) is calculated by the ratio of the added value of the tertiary sector to that of the secondary sector.

Control Variables: Informed by prior studies, our analysis includes the following control variables: the level of economic development (GDP), the level of opening up (FDI), environmental regulation (ER), and urbanization rate (UR). GDP influences regional openness, innovation, and income levels, thus impacting the region’s HQED. This paper uses per capita GDP to characterize the level of regional economic development. The level of opening up (FDI) affects regional economic growth and fosters opportunities for innovative development, contributing to overall economic progress. This paper measures regional openness through the ratio of actual foreign investment to GDP. Environmental regulation (ER) involves governmental efforts to limit corporate activities via administrative orders and policies to diminish environmental pollution and foster sustainable development. Based on the method of Zhang et al. [54], we adopted the frequency ratio of environmental terms in government work reports as a proxy for environmental regulation. Urbanization rate (UR) signifies the transition towards urban living, which supports the formation of new development paradigms, encourages shared prosperity, and facilitates HQED. This paper selects the proportion of urban population to total population to characterize the urbanization rate.

2.3.3. Data Sources

This study focuses on 13 cities within China’s BTH region, covering a data span from 2010 to 2020. The data related to economic development and green finance were primarily sourced from China’s Urban Statistical Yearbook, China’s Industrial Statistical Yearbook, Beijing Municipal Bureau of Statistics, Tianjin Bureau of Statistics, and Hebei Provincial Bureau of Statistics. The data on air quality were obtained from the China National Environmental Monitoring Centre. The data on green innovation were sourced from the Chinese Research Data Services Platform (CNRDS).

2.4. Empirical Model

2.4.1. Regression Model

To verify the influence of green finance on the HQED in the BTH region, this paper takes the HQED index of BTH as the explained variable and the green finance development index as the core explanatory variable and constructs the following econometric model:

$${Quality}_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(7)

where Quality_it denotes the HQED index of region i in year t, GF_it represents the GF development index of region i in year t, Control_it is the control variables, and ε_it is the random error term.

Specifically, to verify the impact of green finance on each of the five dimensions of HQED, regression analysis is conducted with the five indicators of innovative development (I), coordinated development (Coord), green development (G), open development (O), and inclusive development (Incl) as the dependent variables, respectively.

The formulas are as follows:

$$I_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(8)

$${Coord}_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(9)

$$G_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(10)

$$O_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(11)

$${Incl}_{it}={\alpha }_0+{\alpha }_1{GF}_{it}+{\alpha }_i{Control}_{it}+{\epsilon }_{it}$$

(12)

2.4.2. Analysis of Influence Mechanism

To verify the mediating role of green innovation in the process of green finance-promoting HQED, the following mediating effect models are constructed:

$${GI}_{it}={\beta }_0+{\beta }_1{GF}_{it}+{\beta }_i{Control}_{it}+{\epsilon }_{it}$$

(13)

$${Quality}_{it}={\gamma }_0+{\gamma }_1{GF}_{it}+{\gamma }_2{GI}_{it}+{\gamma }_i{Control}_{it}+{\epsilon }_{it}$$

(14)

where GI_it denotes the level of green innovation of region i in year t.

To verify the mediating role of industrial structure upgrading in the process of green finance-promoting HQED, the following mediating effect models are constructed:

$${IS}_{it}={\beta }_0+{\beta }_1{GF}_{it}+{\beta }_i{Control}_{it}+{\lambda }_i+{\mu }_{it}+{\epsilon }_{it}$$

(15)

$${Quality}_{it}={\gamma }_0+{\gamma }_1{GF}_{it}+{\gamma }_2{IS}_{it}+{\gamma }_i{Control}_{it}+{\lambda }_i+{\mu }_{it}+{\epsilon }_{it}$$

(16)

where IS_it indicates the industrial structure of region i in year t.

3. Results

3.1. Estimation Results of the HQED Index

Utilizing the entropy method, the HQED indices of 13 cities are derived (See Figure 1). In general, these indices exhibit a year-on-year ascending trend, as shown in Figure 1. Furthermore, the results reveal that the indices for Beijing and Tianjin surpass those of cities within Hebei Province, indicating marked disparities in HQED indices across Beijing, Tianjin, and Hebei Province. These differences are particularly pronounced in areas of innovative, coordinated, and open development.

3.2. Diagnostic Tests

3.2.1. Correlation Analysis

The outcomes of the correlation analysis, as presented in

Table 2. Results of correlation analysis.

	Quality	I	Coord	G	O	Incl	GF	GDP	FDI	ER	UR
GF	0.885 ***	0.865 ***	0.897 ***	0.829 ***	0.702 ***	0.815 ***	1 ***
GDP	0.83 ***	0.858 ***	0.871 ***	0.779 ***	0.562 ***	0.764 ***	0.391 ***	1 ***
FDI	0.565 ***	0.375 ***	0.504 ***	0.489 **	0.78 ***	0.368 ***	0.547 ***	0.381 ***	1 ***
ER	−0.1 **	−0.087 *	−0.11 **	−0.063 **	−0.1 **	−0.092 *	−0.058 *	−0.125	−0.141 *	1 ***
UR	0.91 ***	0.85 ***	0.947 ***	0.68 **	0.771 ***	0.774 ***	0.63 ***	0.845 ***	0.635 ***	0.125	1 ***

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

, reveal that the key explanatory variables and control variables are significantly correlated with the dependent variables. This finding underscores the appropriateness of the explanatory variables chosen for this study. Moreover, besides the relationship between economic development level and urbanization rate, the correlation coefficients between other variables are all below 0.7. This indicates that the absence of significant multicollinearity issues among the explanatory variables.

3.2.2. Autocorrelation Tests

To determine the presence of autocorrelation, the Wooldridge test for autocorrelation in panel data was conducted. The results indicate an absence of autocorrelation issues (

Table 3. Wooldridge test for autocorrelation in panel data.

Equation	F(1, 12)	p
(7)	4.088	0.2109
(8)	7.067	0.1105
(9)	10.045	0.1000
(10)	9.968	0.1035
(11)	6.166	0.1288
(12)	8.546	0.1128

).

3.2.3. Heteroscedasticity Tests

To assess the presence of heteroscedasticity, the White test was performed, with the findings presented in

Table 4. Results of White test.

Equation	X2	p
(7)	39.198	0.073
(8)	37.679	0.218
(9)	35.332	0.255
(10)	24.622	0.369
(11)	41.396	0.060
(12)	20.424	0.432

. All p-values exceed 0.05, suggesting that the null hypothesis of homoscedasticity cannot be rejected, indicating the absence of heteroscedasticity in the data.

3.3. Panel Regression Results

3.3.1. Descriptive Statistics

The descriptive statistical results of the variables are presented in

Table 5. Results of descriptive statistics.

Variables	Sample Size	Minimum	Maximum	Mean	Standard Deviation	Median
Quality	143	0.070	0.716	0.194	0.159	0.134
I	143	0.015	0.943	0.121	0.184	0.053
Coord	143	0.038	0.981	0.271	0.211	0.209
G	143	0.129	0.774	0.470	0.135	0.491
O	143	0.040	0.803	0.187	0.181	0.104
Incl	143	0.073	0.687	0.260	0.117	0.230
GF	143	0.301	0.854	0.447	0.136	0.398
GDP	143	17,189	164,889	50,081.29	28,683.36	39,889
FDI	143	0.000	0.121	0.021	0.024	0.014
UR	143	38.200	87.550	56.772	13.521	53.470
ER	143	0.001	0.008	0.004	0.001	0.004
IS	143	0.031	5.297	1.214	1.064	0.876
GI	143	13.000	22,275.000	1467.182	3575.446	238.000

. It is observed that the inter-regional differences in green finance development are small, potentially attributable to a uniformly low level of green finance within the regions examined. Table 5 also indicates that the average HQED is 0.194, with a standard deviation of 0.159, signifying substantial disparities in HQED across the BTH regions.

3.3.2. Basic Regression Analysis

To verify the influence of green finance on HQED, we designated HQED as the dependent variable and the level of green finance as the independent variable, employing Stata 15.0 software to estimate Equation (1) with the FE estimator. To mitigate the effects of extraneous variables, GDP, FDI, environmental regulation (ER), and urbanization rate (UR) were incorporated as control variables in a comprehensive multiple regression analysis.

The Hausman test was conducted to select the appropriate model for our analysis, and its results led to the rejection of the null hypothesis, suggesting that the fixed-effects model was preferable for our regression analysis. Consequently, the fixed-effects model’s outcomes are considered the definitive results. The results of the regression are shown in

Table 6. FE estimations.

VARIABLES	Quality	I	Coord	G	O	Incl
GF	0.162 ***	0.241 ***	0.091 *	0.507 **	0.043 *	0.338 *
	(2.76)	(2.96)	(1.59)	(1.20)	(1.34)	(1.84)
GDP	0.062 **	0.462 ***	0.120 *	0.277 *	0.599 ***	0.352 ***
	(2.31)	(12.42)	(1.72)	(1.44)	(10.54)	(4.20)
FDI	0.121 ***	0.101 *	0.072 **	0.125 *	0.436 ***	0.031 *
	(10.05)	(1.04)	(2.30)	(1.43)	(16.94)	(0.81)
ER	−0.021 ***	−0.025 **	−0.016 *	−0.164 ***	−0.013 *	−0.024 *
	(−2.80)	(−2.41)	(−0.33)	(−3.03)	(−0.38)	(−0.29)
UR	0.015 *	0.086 ***	0.427 ***	0.304 *	0.033 *	0.104 *
	(0.66)	(2.64)	(7.02)	(1.80)	(0.66)	(1.42)
Constant	0.092 ***	−0.045 *	0.058 *	0.474 ***	0.280 ***	−0.012 *
	(5.21)	(−1.84)	(1.27)	(3.71)	(7.43)	(−0.21)
Observations	143	143	143	143	143	143
Number of Cities	13	13	13	13	13	13
Adj R-squared	0.643	0.791	0.622	0.121	0.826	0.527

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

. The coefficient of green finance is 0.162, significant at the 1% level. This signifies a substantial positive impact of green finance on HQED, indicating that an increase of 1 unit in green finance translates to an average increase of 0.162 units in HQED.

In addition, regressions were conducted with each of the five dimensions of HQED as explanatory variables to observe the impact of green finance on each dimension. The regression revealed that green finance has a significant positive effect on all five dimensions of HQED.

Except for environmental regulation, the regression results of other control variables align with expectations. The coefficient of GDP is significantly positive at the 5% level, indicating that an improvement in GDP can promote HQED. This improvement in economic status fosters changes in people’s consumption patterns and elevates their consumption ideals, driving a shift towards higher-quality consumption. The coefficient of opening up is significantly positive at the 1% level, indicating that a higher degree of openness increases the inflow of foreign capital. Foreign investment introduces supervision and incentives for HQED, bringing advanced technology and management practices that better the economic environment, elevate the technological capabilities of enterprises, and boost regional economic development levels. The coefficient of the urbanization rate is significantly positive at the 10% level, illustrating that an increase in urbanization rate diminishes economic disparities among regions, boosts regional economic dynamism, and enhances people’s living standards, thereby benefiting HQED. Conversely, the coefficient of environmental regulation is significantly negative at the 1% level. While environmental regulations compel enterprises to innovate and lessen industrial environmental harm, in the initial stages of HQED, enterprises may lack sufficient capability for green development. Hence, environmental regulations might augment enterprises’ burdens and exert a negative impact within certain thresholds.

3.3.3. Robustness Test

To validate the robustness of the above findings, this paper uses green finance with a one-period lag as the explanatory variable to replace the original core explanatory variable for regression. The regression results are shown in

Table 7. Regression results of robustness test.

VARIABLES	Quality	I	Coord	G	O	Incl
Lag1_GF	0.127 **	0.203 **	0.267 *	0.155 *	0.022 *	0.160 *
	(2.11)	(2.43)	(1.74)	(1.36)	(0.97)	(1.85)
GDP	0.070 **	0.469 ***	0.063 *	0.080 *	0.605 ***	0.401 ***
	(2.49)	(12.08)	(0.89)	(0.40)	(10.30)	(4.58)
FDI	0.120 ***	0.102 *	0.065 **	0.095 *	0.437 ***	0.023 *
	(9.79)	(1.15)	(2.07)	(1.08)	(16.97)	(0.79)
ER	−0.020 **	−0.023 **	−0.015 *	−0.162 ***	−0.023 *	−0.102 *
	(−2.61)	(−2.21)	(−0.57)	(−2.98)	(−0.79)	(−1.09)
UR	0.027 *	0.072 **	0.384 ***	0.128 *	0.039 *	0.155 **
	(1.16)	(2.25)	(6.52)	(1.77)	(0.80)	(2.14)
Constant	0.103 ***	−0.033 *	0.010 *	0.662 ***	0.273 ***	0.040 *
	(5.80)	(−1.35)	(0.22)	(5.20)	(7.33)	(0.72)
Observations	143	143	143	143	143	143
Number of Cities	13	13	13	13	13	13
R-squared	0.647	0.794	0.639	0.127	0.849	0.525

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

. The regression results of HQED and its five dimensions with one-period-lagged green finance are basically consistent with the basic regression conclusions. This consistency underlines the robustness of the initial estimations.

3.3.4. Mediating Effects Analysis

This paper used the Bootstrap test to find that a mediating effect of green finance on HQED through green innovation and industrial structure optimization exists. Therefore, we conducted regression analysis based on the mediation effect model, and the test results are given in

Table 8. Test results of mediating effect of green innovation.

VARIABLES	Quality	GI	Quality
GF	0.162 ***	0.424 ***	0.149 **
	(2.76)	(2.97)	(2.45)
GI			0.031 *
			(1.84)
GDP	0.062 **	0.868 ***	0.035 *
	(2.31)	(13.32)	(0.84)
FDI	0.121 ***	0.011 *	0.121 ***
	(10.05)	(0.95)	(10.04)
ER	−0.021 ***	−0.017 *	−0.020 ***
	(−2.80)	(−0.91)	(−2.72)
UR	0.015 *	0.364 ***	0.026 *
	(0.66)	(6.39)	(0.99)
Constant	0.092 ***	−0.173 ***	0.097 ***
	(5.21)	(−4.01)	(5.18)
Observations	143	143	143
Number of Cities	13	13	13
Adj R-squared	0.643	0.754	0.645

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

and

Table 9. Test results of intermediary effect of industrial structure.

VARIABLES	Quality	IS	Quality
GF	0.162 ***	0.283 *	0.122 **
	(2.76)	(1.12)	(2.61)
IS			0.141 *
			(1.62)
GDP	0.062 **	0.333 ***	0.051 *
	(2.31)	(2.88)	(1.85)
FDI	0.121 ***	0.020	0.121 ***
	(10.05)	(0.38)	(10.05)
ER	−0.021 ***	−0.078 **	−0.018 **
	(−2.80)	(−2.41)	(−2.41)
UR	0.015 *	0.256 **	0.007
	(0.66)	(2.54)	(0.29)
Constant	0.092 ***	−0.040	0.093 ***
	(5.21)	(−0.52)	(5.31)
Observations	143	143	143
Number of Cities	13	13	13
Adj R-squared	0.643	0.437	0.649

Notes: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

The results show that green finance has a significant positive impact on green innovation, and green innovation can promote HQED, where the proportion of the mediating effect is 8.11% (0.424 × 0.031/0.162). Therefore, hypothesis 2 is verified.

The results of hypothesis 3 are given in Table 9. Green finance has a significant positive impact on industrial structure optimization, and industrial structure optimization can promote HQED. The proportion of intermediary effect is 24.63% (0.283 × 0.141/0.162). Thus, hypothesis 3 is verified.

4. Discussion

As shown in Table 4, green finance has a positive influence on HQED and its five dimensions. First, green finance facilitates technological innovation among enterprises through external financing, contributing to the elevation of societal innovation levels. Thus, it plays a significant role in fostering innovation. Second, enhancements in green finance aid in refining the industrial structure and bolstering regional development, aligning with the goals of coordinated growth. Third, the synergy between green finance and green development fosters the sustainable advancement of the economy and society. Fourth, green finance, by considering both economic and environmental benefits, encourages foreign green investments, enhancing the attractiveness of the region to foreign investors and companies, thereby supporting open development. Fifth, it directs capital towards sectors valuing social benefits, mitigating income disparities and advancing the equitable distribution of basic public services, in line with inclusive development objectives.

The findings presented in Table 6 and Table 7 indicate that green innovation and industrial structure play intermediary roles in the process. Green finance facilitates capital flow towards innovative enterprises, bolstering green innovation activities. This, in turn, enhances environmental quality and optimizes the economic development model, subsequently advancing HQED. Furthermore, by financially supporting industries with lower energy costs and imposing higher economic penalties on high-polluting industries, green finance contributes to the significant upgrading and optimization of the industrial structure, which positively impacts HQED.

Future research could explore several avenues. Firstly, additional intermediary factors that play a role in the relationship between green finance and HQED, like green consumption, warrant closer examination. Secondly, an investigation into the regulatory mechanisms that influence the effectiveness of green finance in promoting HQED could provide valuable insights. Thirdly, examining the impact of green finance on HQED in other economic regions, such as the Yangtze River Economic Belt and the Yellow River Economic Belt, could offer a broader understanding of its effects across different contexts.

5. Conclusions

Utilizing data from the 13 cities within the BTH region of China from 2010 to 2020, this study quantified the level of HQED and green finance development. Through multiple regression analysis, we determined that green finance significantly influences HQED. Additionally, it positively affects all five dimensions of HQED. Moreover, using an intermediary effect model, we explored the mechanism through which green finance impacts HQED. The robustness test was carried out by means of a substitution of independent variables, and the results showed that the above conclusions were robust.

Based on the conclusions in this paper, the following policy recommendations are put forward. First, promote the development of a green financial system. Governments in the region establish a comprehensive green financial guarantee system through legislative measures and refine institutional frameworks to promote green finance. They should guide enterprises to integrate the principles of green transformation into their corporate social responsibility agendas. By increasing penalties for polluting enterprises while offering incentives to those engaged in environmental protection, these measures aim to boost participation in green financial practices. The government should further strengthen funding support for financial institutions that provide green services so that they can increase capital investment in green finance, upgrade green financial products, and continue to promote the construction of green credit, green bonds, carbon finance, and so on. In addition, it is important to increase the market attention and attractiveness of green finance. Taking small- and medium-sized enterprises and underdeveloped regions as a breakthrough to promote green finance, state-owned commercial banks take the lead in developing new green finance application industries and application scenarios.

Second, promote green innovation. Governments should nurture a commitment to green development and innovation within enterprises through comprehensive awareness campaigns, tax incentives, and direct subsidies for eco-friendly practices and innovations. Such measures are designed to encourage businesses to engage more actively with green financial products and to invest in green innovation, thereby establishing a sustainable mechanism. Financial institutions are urged to increase financial support for the green enterprise’s innovation activities to reduce transaction costs and accelerate the process of green innovation. Enterprises, in turn, should embrace their social and environmental responsibilities, capitalize on green development opportunities, pursue green innovation vigorously, and strive for efficient and sustainable development.

Third, accelerate the optimization and upgrading of industrial structure in the BTH region. It is imperative for local governments in the BTH region to proactively modify the economic growth strategy towards a more sustainable, cost-effective, and efficient paradigm. This involves bolstering the shift and advancement of the energy infrastructure via scientific and technological innovations, thereby fostering high-quality economic development (HQED). Establishing a green finance reform and innovation pilot zone could serve as a magnet for international investment and cutting-edge, eco-friendly, and low-carbon technologies, facilitating global collaboration in green finance. Regional governments must leverage geographical advantage to foster interconnected development through green finance, ultimately enhancing the HQED across the entire region.