Does Green Finance Promote Green Total Factor Productivity? Empirical Evidence from China

Abstract

Green economic growth is a major challenge for countries, as it requires achieving both ecological protection and economic development goals simultaneously. It can be expressed as the continuous growth of green total factor productivity (GTFP), which is the core indicator reflecting the simultaneous achievement of environmental and economic sustainability. This study provides an in-depth understanding of how green finance contributes to GTFP with data from 30 Chinese provinces between 2006 and 2021. The results reveal that green finance has a significant promotion effect on GTFP as well as a significant spatial spillover effect. By splitting GTFP into green technological progress and green efficiency improvement, green finance can improve the former but not the latter. Green finance can influence GTFP through the mechanisms of technological innovation and industrial structure upgrading, both of which can also only drive green technological progress but not green efficiency improvement. Regional heterogeneity suggests that the promotion effect of green finance on GTFP tends to be stronger and can significantly contribute to green efficiency improvement in regions with higher economic levels. The heterogeneity of natural resource endowment reveals that this promotion effect is more significant in resource-rich areas, but green finance still cannot significantly promote green efficiency improvement in these regions. In contrast, green finance can significantly enhance both green technological progress and green efficiency improvement in resource-general areas. The heterogeneity of the information technology level shows that this promotion is more significant in regions with higher levels of information technology, and in particular, the enhancement of green efficiency improvement by green finance in these regions is significantly positive. The findings provide valuable recommendations.

1. Introduction

Leading to ecological degradation and resource over-exploitation, rapid economic growth worldwide is facing the dual pressures of energy consumption and pollution emissions. An essential strategy for nations across the world to achieve a balance between the environment and the economy is green economic growth, which allows nations to continue economic growth by resolving any conflict between humans and nature without creating any environmental or social difference. The United Nations Environment Program launched the Green Economy Initiative in 2008. The World Bank further states that an inclusive green economy enables countries to maintain growth while protecting the environment and is a sustainable economic model [1,2]. Despite the positive effects of green economic growth, there are some challenges to achieving it. The lack of sufficient funds to develop green projects and thus achieve green economic growth is discussed as an important issue because green projects often have late-returning and low profit, and private-sector investors are reluctant to participate. One of the key and innovative approaches to address this issue is the use of green financing instruments [3].

Green finance is essentially an innovative financial paradigm that focuses on incorporating ecological and social factors into the financial system without conflicting with the original financial principles [4,5]. It effectively aggregates funds through credit, bonds, funds, insurance, carbon finance, and other financial instruments and directs them to green industries, such as energy conservation, environmental protection, and clean energy industries [6,7,8]. From the perspective of economic growth, the capital support effect of green finance can accelerate the rationalization of economic structure [9,10]. From the perspective of environmental protection, the resource allocation effect of green finance can guide enterprises to engage in technology improvement and green production, which can strengthen their social and environmental responsibility [11,12].

Green total factor productivity (GTFP) is a critical statistic that measures the extent to which a country can address environmental issues and promote long-term economic growth [13,14]. The meaning of GTFP is based on the concept of total factor productivity (TFP), which is the productivity remaining after removing factor input growth from total output growth. It is regarded as the only driver of sustained economic growth by the neoclassical economic growth theory, whose main sources are technological progress and efficiency improvement [15]. By incorporating pollution emission and resource consumption indicators into the TFP calculation framework, GTFP, as a new type of TFP indicator that integrates economic growth, resource conservation, and environmental protection, can profoundly reflect the core and purpose of green economic growth and thus can be used to measure it [16,17,18,19,20].

Based on the previous review, there are numerous separate discussions on green finance and GTFP, but direct and in-depth studies on the relationship between them are relatively lacking. Therefore, the main contribution of this study includes focusing on examining and analyzing the impact of green finance on GTFP and empirically testing its impact mechanism. In addition, the heterogeneity of this impact is explored from three perspectives: region, natural resource endowment, and information technology level, providing a new approach to regional policy research.

The structure of this study is composed of several phases. “Literature review” deals with evidence regarding green finance and GTFP considering previous studies. “Theoretical analysis and hypotheses” examines the direct effects, mediating effects, and heterogeneity analysis of green finance on GTFP and presents the research hypotheses. “Study design” introduces the model setting, variable selection, and data. “Empirical testing”, “analysis of mechanisms”, and “heterogeneity analysis” include the outputs and explanations of the empirical study. This paper ends with conclusions and recommendations and further points out the limitations of this study as well as directions for future research.

2. Literature Review

2.1. Financial Development and Economic Growth

The financial system is the core of the modern economy. The expansion of financial development, the adjustment of financial structure, and the improvement of financial efficiency can all contribute to economic growth [21,22,23,24]. A well-functioning financial system can effectively reduce information and transaction costs, influence investment-saving rates, and ultimately boost TFP by promoting technological upgrading and innovation [25,26,27]. Calderson and Liu [28] and Beck and Levine [29] found through empirical evidence that financial development significantly enhances TFP. A study by Rioja and Valev [30] showed that there are significant differences in the contribution of the financial system to TFP at different levels of financial development.

However, when a country pursues economic growth, it cannot manage environmental sustainability because rapid economic growth requires extensive use of resources and energy and generates pollution emissions. Appropriate institutions and policies are needed to achieve economic growth without damaging the environment. Therefore, green innovation activities such as green finance have emerged and become an important way to achieve this goal.

2.2. Green Finance, Economic Growth, and Environmental Protection

Green finance is an innovation in financial instruments that adjusts the goals of economics from the perspective of environmental protection [31,32]. Different from traditional finance, green finance supports the development of green industries through optimal allocation of resources while limiting the growth of high-pollution and high-energy consumption industries [33,34]. Differentiated green finance policies and services enable more sustainable growth of green enterprises and green industries by promoting technological innovation and industrial structure upgrading [35]. Green finance spreads green ideas to the market and guides green investment and green consumption, thus improving investment and consumption structures. It can be said that green finance has altered the business direction and resource allocation of traditional finance motivated by commercial interests only, taking into account both economic growth and environmental protection and solving the problem of the “double failure” of markets and policies in achieving these two goals simultaneously.

In addition to its financial features, green finance also has environmental regulatory functions. It can use market incentive mechanisms to supplement the gap in government management, thus forming an effective complement to traditional environmental regulation. The environmental compensation theory holds that appropriate government environmental regulation can stimulate enterprises to carry out technological innovation, thus generating technological compensation, which is conducive to promoting technological progress and industrial restructuring [36,37,38,39]. The cost compliance theory argues that green finance raises business costs, indirectly inhibits technological progress and industrial transformation, and is detrimental to enterprise development and economic growth [40,41,42,43].

2.3. Green Finance and GTFP

GTFP is an economic efficiency indicator obtained by incorporating energy consumption and undesired outputs, such as pollution emissions, into the production function [18]. Most empirical findings show that total factor productivity considering energy inputs and undesired outputs is significantly lower than when they are ignored [18,44], so studying GTFP is crucial for measuring green economic growth. The focus of GTFP is input and output. On the input side, green finance reduces dependence on fossil energy and increases energy efficiency. Li and Jia [45] and Glomsrød and Wei [46] revealed that green finance can reduce fossil fuel consumption and diminish environmental deprivation. On the output side, green finance controls pollution emissions such as carbon dioxide emissions and promotes environmentally friendly economic activities [47]. In terms of the calculation method, Meeusen and Van Den Broeck [48] split total factor productivity into technological progress and technical efficiency, which also provided a way for studying GTFP. In the next study, GTFP was split into green technological progress and green efficiency improvement [49]. Wang and Wang [50] showed that green finance could promote technological innovation and thus help improve the industrial and energy structure. Mahat et al. [51] suggested that green finance can contribute to the upgrading of industrial structures, which leads to industrial efficiency improvement.

Previous studies have provided us with many meaningful theoretical foundations and conclusions. However, there are still some gaps. The role of green finance in influencing GTFP is currently unclear, and the mechanisms also need to be thoroughly explored and systematically investigated. In addition, there is a relative lack of empirical studies on the heterogeneity analysis of local issues. Moreover, previous studies have not discussed the role, mechanisms, and heterogeneity of this impact in more detail by splitting GTFP into green technological progress and green efficiency improvement, as such discussions can lead to more effective conclusions and policy recommendations. Therefore, this study empirically explores the above issues using panel data for 30 Chinese provinces from 2006 to 2021, and the findings can bridge the gaps left by previous studies.

3. Theoretical Analysis and Hypotheses

3.1. The Impact of Green Finance on GTFP

There are several ways in which green finance affects GTFP. First of all, green finance has a capital supply effect, which can guide capital into green industries and restrict capital from entering industries with high energy consumption and high pollution so as to realize the improvement of resource allocation efficiency. Secondly, green finance has a technological innovation effect, which helps enterprises diversify their innovation risk and make them focus on technological innovation [52], and by strongly supporting low-emission and high-efficiency enterprises to achieve green efficiency through technological innovation, high-emission and low-efficiency enterprises will be squeezed out of the market [53]. Thirdly, green finance supervises and strictly controls the funds invested in enterprises to ensure that they fulfill their environmental responsibilities. Moreover, the widespread popularity of green concepts will continue to increase market demand for green products, and green enterprises and green industries will receive more market support. The government promotes green consumption by popularizing green concepts and regulating green markets, and the continuous growth of green consumption will further stimulate green enterprises to expand the scale of green production and achieve sustainable growth of GTFP. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 1.

Green finance has a significant impact on GTFP.

According to the calculation of GTFP, it can be further split into green technological progress and green efficiency improvement. Green finance directly drives green technological progress by providing sufficient financial support and reducing R&D risks, which can push the frontiers of enterprise production outward. By raising environmental costs and strictly controlling the production process, green finance directs capital to high-efficiency enterprises while forcing low-efficiency enterprises to carry out technological transformation and upgrading, and continuously eliminating their backward production capacity, ultimately promoting green efficiency improvement. However, green efficiency improvement requires the long-term practice of green technology and the accumulation of production experience through “learning by doing”. Therefore, green finance can direct the flow of funds to promote green technological progress, but it may not have an impact on green efficiency improvement in the short term. At the same time, the expense of environmental management may inhibit green efficiency improvement. On the basis of the above discussion, the following hypotheses are proposed:

Hypothesis 2.

Green finance has a positive effect on green technological progress.

Hypothesis 3.

Green finance has a positive effect on green efficiency improvement.

3.2. Spatial Spillover Effect

The development of green finance and its impact on GTFP are closely related to local policies and economic levels, and the development of green finance in one region can affect not only the local GTFP but also the GTFP of neighboring regions. Green enterprises and green industries in one place can attract green capital and green technology from neighboring regions to achieve resource sharing and synergistic cooperation, and the scale and scope of green enterprises and green industries can be expanded to bring about a wider range of green production and green consumption, resulting in an increase in GTFP in local and neighboring regions. Green financial policies, especially green credit policies, as a mild compensatory environmental policy, have an incentive effect on the green transformation of traditional industries, thus becoming an effective policy tool for local governments to gain benefits for enterprises. When local governments face both economic and environmental pressures, especially when the development of green finance in one region achieves an increase in GTFP, it is more likely to cause neighboring regions to imitate it. The unified green finance platform and service standards create a good market environment for green enterprises and green industries, radiating to neighboring regions and promoting cross-regional cooperation, all of which have a positive impact and a positive spatial spillover effect on GTFP. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 4.

There is a significant spatial spillover effect of green finance on GTFP.

3.3. Mechanisms by Which Green Finance Promotes GTFP

Green finance promotes technological innovation by providing funds and directing the flow of social capital and promoting industrial structure upgrading through optimal resource allocation and strict regulation [54,55,56]. Technological innovation and industrial structure upgrading can contribute to the improvement of GTFP, so they both become important mediating mechanisms for the impact of green finance on GTFP.

3.3.1. Technological Innovation Effect

The key factor for technological progress and efficiency improvement is technological innovation. Green enterprises and green industries are mostly technology-intensive or capital-intensive and often require more capital to support high levels of technological innovation. Green finance influences the technological innovation of enterprises by providing financing for their innovation activities [57]. Green finance provides financing preferences for green enterprises while raising the financing costs of high-pollution and high-energy consumption enterprises so that more funds can be directed to green enterprises for technological innovation. Technological innovation can effectively reduce energy consumption and pollution emissions per unit of output, which further improves GTFP in the long run [58]. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 5.

Green finance can promote GTFP by encouraging technological innovation.

3.3.2. Industrial Structure Upgrading Effect

Structural transformation is one of the important features of modern economic growth [59], and the financial system plays a very important role in the process of structural transformation. The financial system influences the transformation of industrial structures by regulating supply and demand. The demand driver is called the “income effect”. As income increases, the consumption structure changes, pulling the industrial structure upgrade from the demand side [60,61,62]. Green finance popularizes the green concept and motivates the public to choose green products, which promotes industrial structure upgrades from the demand side. The supply driver is called the “substitution effect”. With technological progress and capital deepening, the relative costs between sectors and the relative prices of products change, which promotes industrial structure upgrading from the supply side [63,64]. Green finance guides the flow of production factors from traditional industries to green industries and promotes green production while forcing traditional enterprises to continuously carry out technology transformation and upgrading, gradually eliminating backward production capacity and promoting industrial structure upgrading. The ecological and advanced industrial structure can further achieve the improvement of GTFP. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 6.

Green finance can promote GTFP by stimulating industrial structure upgrades.

3.4. Heterogeneity Analysis

In reality, uncertainty regarding the impact of green finance on GTFP may be closely related to the differences in geographical location, resource endowment, and infrastructure level. This paper examines heterogeneity in three dimensions: regional heterogeneity, resource endowment heterogeneity, and information technology level heterogeneity.

3.4.1. Regional Heterogeneity

Currently, there are differences between regions in China in terms of ecological conditions, environmental regulations, economic development levels, and the implementation of government policies [65], resulting in significant heterogeneity in the impact of green finance on GTFP. Economic growth in eastern China is ahead of other regions due to its geographical location. The developed market system and fierce market competition in this region force enterprises to constantly introduce new technologies and make technological innovations. Industries in eastern China were also the first to start the transformation from traditional industries to green industries. Other regions in China are far less capable of technological innovation, industry development, and relevant policy support than eastern China, meaning that these regions lack the corresponding environmental and technological innovation support, so the impacts of green finance on GTFP are not as strong as in eastern China. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 7.

The impact of green finance on GTFP is most significant in eastern China.

3.4.2. Resource Endowment Heterogeneity

Natural resources, as the basic input factor for human production activities, have an important impact on economic growth. Resource-rich areas tend to prioritize the development of resource-based industries [66], which leads to path-dependent and locked-in effects of industrial development, resulting in the crowding out of incentives for green economic growth [67] and more serious resource and environmental problems [68], and thus GTFP is usually low in these regions. By making it more difficult to obtain funds, green finance motivates enterprises to make green transformations in resource-rich areas, gradually break the path dependence of industrial development, and adjust the industrial structure to be technology-intensive. At the same time, green finance brings more resources into green industries, promotes technological innovation and industrial structure upgrades in these areas, and ultimately achieves an increase in GTFP. In resource-general areas, due to the constraints of resource endowment conditions, most enterprises achieve their own growth and development through technological progress and efficiency improvement, and their GTFP is usually high. Green finance gradually removes technologically backward and inefficient enterprises from the market, thus continuously improving green efficiency. Government regulators in these areas supervise enterprises more strictly in resource utilization and technology application, which can further promote green efficiency improvement. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 8.

The impact of green finance on GTFP is more significant in resource-rich areas, but the impact of green finance on green efficiency improvement is more significant in resource-general areas.

3.4.3. Heterogeneity of Information Technology Level

Digitalization and information technology transformation are regarded as the “new engines” of economic growth. Studies have shown that enhancing information technology can improve the level of information asymmetry in financial markets and the efficiency of financial resource allocation [69,70,71]. Information technology can be a good way to improve green efficiency due to its high permeability, rapidity, and sustainability. As the level of information technology increases, all kinds of costs and expenses will fall to some extent. The widespread use of the Internet has given rise to online work, which has reduced commuting time and energy consumption and improved efficiency. Information technology, such as big data, alleviates information asymmetries and can make relevant decision-making information more accessible to investors, consumers, and policymakers, improving efficiency issues. Information technology can be deeply integrated with traditional industries, promote the upgrading of traditional industries, and form efficient production models and industrial structures. In addition, digital information technology can better break down boundaries and make the scale effect from technology diffusion more obvious, which is conducive to achieving green efficiency improvement. On the basis of the above discussion, the following hypothesis is proposed:

Hypothesis 9.

The impact of green finance on GTFP is more significant in areas with high levels of information technology, and in particular, it can promote green efficiency improvement.

4. Study Design

4.1. Model Setting

Based on the above theoretical analysis, the following baseline regression model was constructed to investigate the impact of green finance on GTFP. GTFP can be split into green technological progress and green efficiency improvement.

$$GTF{P}_{it}={\alpha }_1+{\beta }_{1}G{F}_{it}+{{\displaystyle \sum }}_j{\gamma }_j^1{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it},$$

(1)

$$TEC{H}_{it}={\alpha }_3+{\beta }_{3}G{F}_{it}+{{\displaystyle \sum }}_j{\gamma }_j^3{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it},$$

(2)

$$EFF{E}_{it}={\alpha }_2+{\beta }_{2}G{F}_{it}+{{\displaystyle \sum }}_j{\gamma }_j^2{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}.$$

(3)

The subscripts i, t in the Formulas (1)–(3) indicate province and year, respectively. $GTF{P}_{it}$ is the regional green total factor productivity; $TEC{H}_{it}$ is regional green technological progress; $EFF{E}_{it}$ is regional green efficiency improvement; $G{F}_{it}$ is the regional green finance level; $X_{it}$ is a series of other control variables. In addition, ${\mu }_i$ is the individual fixed effect; ${\nu }_t$ is the time fixed effect; ${\epsilon }_{it}$ is random disturbance terms. According to the previous theoretical analysis, $\beta$ is expected to be positive, which means that green finance can increase GTFP.

4.2. Variable Setting

(1)

The explanatory variable: Level of green finance development. We selected indicators to construct the green finance development index (GF). The study by Muganyi et al. [31] classified the green finance system into green credit, green securities, green investment, green insurance, and carbon finance. China’s green finance started late, so there are fewer years of data on green credit, and the publicly available provincial data are not complete. For green insurance, China only started to enforce enterprise environmental pollution liability insurance at the end of 2013; while the participation rate of enterprises is also low, there is a lack of systematic statistics, and only the total scale at the national level is provided. However, no matter what kind of green finance resources are available, the economic and environmental benefits must be achieved through the production and operation of enterprises. Therefore, this paper mainly focuses on the green proportion of financial assets to select relevant indicators and adopt the entropy value method to determine the weight of each variable to measure the green finance development index (GF). Based on the actual situation in China, the availability of data and the types of financial instruments, this paper divides the green finance development index into four dimensions. ① Green credit mainly refers to the financial support given to green technology research and development. In this paper, the ratio of the interest expense of six high-energy-consuming industries (the six high-energy-consuming industries in the National Economic Classification issued by China include: chemical raw materials and chemical products manufacturing, non-metallic mineral products industry, ferrous metal smelting and rolling processing industry, non-ferrous metal smelting and rolling processing industry, petroleum processing and coking and nuclear fuel processing industry, electricity and heat production, and supply industry) to total industrial interest expense is used as a reverse indicator of green credit. ② Green investment is mainly in green bonds, mainly in the areas of pollution prevention and treatment, energy saving and emission reduction, circular economy products, ecological resources protection, and environmental climate change restoration. In this paper, it is measured in terms of investment in environmental pollution control as a share of GDP. ③ Green insurance is represented by the depth of agricultural insurance, which mainly solves the problem of unclear liability for environmental pollution. In this paper, it is measured by the share of agricultural insurance revenue to total agricultural output. ④ Government support is the financial expenditure arranged mainly for environmental protection issues. In this paper, it is measured by the ratio of fiscal environmental protection expenditure to fiscal general budget expenditure.

(2)

The explained variable: Green total factor productivity (GTFP). According to Campisi et al. [72], Malmquist productivity measures are split into two components: efficiency change and technical change index. It is also the changes in productivity over time (or efficiency change) and the changes in technology over time (or technology change) [73]. In other terms, the relative efficiency of a decision-making unit over time will depend on both its position relative to the corresponding frontier and the position of the frontier itself [74]. The Luenberger productivity index proposed by Chambers et al. [75] is more general than the Malmquist productivity index because it can take into account both the reduction of inputs and the increase in outputs without the need to choose the measurement perspective. Therefore, referring to Liu and Xin [76], the green total factor productivity of each province (GTFP) is measured using the SBM directional distance function and the Luenberger productivity index, taking into account energy consumption and undesired output, and its decomposition terms, green technological progress (TECH), and green efficiency improvement (EFFE) are measured.

(3)

Control variables: In addition to green finance, regional GTFP depends on a variety of factors. Regional development foundations, such as the level of economic development and infrastructure, regional development inputs such as human capital, and exogenous factors affecting regional development, such as the level of openness, marketization, and environmental regulations, can also have an important impact on regional GTFP. In this paper, all control variables are lagged by one period to mitigate possible endogeneity problems between contemporaneous variables. ① The level of economic development (agdp) is measured by the logarithm of GDP per capita for each province. ② The level of infrastructure (facility) is measured by the logarithm of road area per capita for each province. ③ The human capital level (hum) is measured by the average year of education in labor by province. ④ The level of external opening (open) is measured by the ratio of the amount of actual foreign investment used by each province in the year to GDP. ⑤ The level of marketability (market) is measured by the marketability index through principal component analysis [77]. ⑥ Environmental regulation (ER) is measured by the logarithm of the ratio of environmental terms to the total number of words in the full government report. The text statistics and analysis of provincial government work reports were carried out using R software. The 27 environmental terms that reflect the importance of environmental protection in the government were selected by word separation of 30 Chinese provincial government work reports from 2006 to 2021, and their ratio to the total number of words in the government reports was calculated to reflect the importance of environmental issues in the government.

4.3. Data Sources and Sample Selection

The research object of this paper is 2006–2021 provincial panel data from China, excluding Hong Kong, Macao, Taiwan, and Tibet, which have a higher rate of missing data. The China Statistical Yearbook was used to compile data on major data in each province. The other variables were derived from the China Environmental Statistical Yearbook, the China Energy Statistical Yearbook, the China Science and Technology Statistical Yearbook, and the province-level statistical yearbooks. A small amount of missing data was compensated for using the interpolation method. The descriptive statistics for each variable are shown in

Table 1. Descriptive statistics of variables.

Variables	Observations	Average	Standard Deviations	Minimum Values	Maximum Values
GTFP	480	1.591	1.014	0.318	5.844
TECH	480	1.690	0.739	0.904	7.759
EFFE	480	0.991	0.221	0.259	2.155
GF	480	0.171	0.113	0.050	0.827
agdp	480	1.398	0.625	−0.456	3.052
facility	480	2.632	0.380	1.400	3.453
hum	480	1.783	0.181	1.354	2.453
open	480	0.292	0.345	0.009	1.721
market	480	1.861	0.318	0.846	2.539
ER	480	−5.741	0.282	−6.651	−5.063

Source: Authors’ elaboration.

.

5. Empirical Testing

5.1. Baseline Regression

Empirical methods were used to further verify the impact of green finance on the GTFP by the settings of the previous Equation (1). Fixed effect models were selected based on the results of the Hausman test. The standard errors were clustered at the province level. All models were controlled for both time-fixed effects and individual fixed effects.

Table 2. Impact of green finance on GTFP: baseline regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	GTFP	GTFP	TECH	TECH	EFFE	EFFE
GF	6.470 ***	4.229 ***	6.243 ***	3.542 ***	0.005	0.059
	(15.557)	(7.513)	(19.382)	(13.167)	(0.034)	(0.268)
agdp		0.045		0.232 ***		0.024
		(0.344)		(2.909)		(0.465)
facility		0.865		0.032		0.151
		(0.397)		(0.526)		(0.421)
hum		1.114 **		0.123		0.405 *
		(1.972)		(0.558)		(1.842)
open		0.571		0.301 ***		0.228 ***
		(0.256)		(3.362)		(3.340)
market		0.457 *		0.516 ***		0.199 **
		(1.871)		(6.233)		(2.092)
er		0.073		−0.150		0.039
		(0.727)		(−0.094)		(0.994)
_cons	0.482 ***	0.828	0.620 ***	0.641	0.990 ***	1.852 ***
	(6.563)	(0.615)	(10.903)	(1.057)	(35.425)	(3.535)
N	480	480	480	480	480	480
R2	0.868	0.881	0.852	0.782	0.600	0.623

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

contains the baseline regression results.

The regression results of green finance on GTFP are shown in columns (1) and (2) of Table 2. The coefficient of the effect of green finance on GTFP in column (1) is significantly positive at the 1% level. Column (2) shows small fluctuations in the coefficients after adding the control variables, but all are still significantly positive at the 1% level, thus verifying Hypothesis 1. It shows that green finance can contribute to an effective increase in GTFP. The coefficient of the effect of green finance on green technological progress in columns (3) and (4) is significantly positive at the 1% level, indicating that green finance can significantly promote green technological progress, thus verifying Hypothesis 2. The positive but non-significant coefficient on the effect of green finance on green efficiency improvement in columns (5) and (6) indicates that the current level of green finance does not contribute significantly to green efficiency improvement and therefore does not confirm Hypothesis 3. Green finance can support green R&D and achieve “incremental” progress in green technology, but it is difficult to control the application of green technology and thus improve green efficiency.

The coefficients of the control variables’ level of human capital (hum) and level of the market (market) on GTFP are all significantly positive. It shows that upgrading human capital and increasing marketability are effective paths to promote GTFP. The coefficients of the effects of economic development (agdp), external opening (open), and level of marketability (market) on green efficiency improvement are significantly positive. This indicates that a higher level of economic development, opening up to the outside world, and increasing marketability are effective ways to achieve green efficiency improvement. The coefficients of the effects of human capital (hum), external opening (open), and level of marketability (market) on green technological progress are significantly positive. This indicates that upgrading human capital, opening up to the outside world, and creating a more developed market can stimulate innovation and promote green technological progress.

This paper makes use of quantile regression to examine the differences in the effects of various levels of green finance on GTFP. To compare the differences in the impact of green finance on GTFP at various quartiles, we used the Fama–French factorial model to differentiate the sample by factorial and choose three significant quartiles, such as 0.3, 0.6, and 0.9, to measure the low, medium, and high levels.

Table 3. Impact of green finance on GTFP: quantile regression.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	GTFP			TECH			EFFE
Variables	0.30	0.60	0.90	0.30	0.60	0.90	0.30	0.60	0.90
GF	3.914 ***	5.835 ***	10.188 ***	1.706 ***	3.131 **	8.335 ***	0.048	0.317	0.909
	(4.663)	(12.191)	(5.650)	(4.066)	(2.160)	(8.205)	(0.589)	(0.422)	(0.247)
agdp	0.546 ***	0.283 ***	0.796 **	0.338 ***	0.403 ***	0.051	0.044	0.130 **	0.281 ***
	(4.846)	(3.953)	(2.330)	(6.115)	(3.051)	(0.393)	(1.512)	(2.530)	(4.299)
facility	−1.331 ***	−0.920 ***	−3.105 ***	−0.048	−0.010	1.110 ***	−0.087	−0.059	−0.061
	(−3.743)	(−3.779)	(−2.877)	(−0.273)	(−0.037)	(2.865)	(−0.725)	(−0.395)	(−0.203)
hum	−0.139	0.046	0.680 **	0.173 **	0.150 *	0.423 ***	−0.009	0.011	−0.068
	(−1.630)	(0.459)	(2.435)	(2.419)	(1.650)	(4.749)	(−0.372)	(0.311)	(−0.874)
open	−0.134	−0.274 ***	−0.344	−0.397 ***	−0.379 ***	−0.492 ***	0.063 ***	−0.083 ***	−0.179 ***
	(−1.521)	(−2.832)	(−0.811)	(−4.850)	(−3.411)	(−4.840)	(2.597)	(−2.987)	(−3.343)
market	0.025	0.071	0.820 ***	−0.228 ***	−0.367 ***	−0.605 ***	0.050	0.137 ***	0.053
	(0.244)	(0.886)	(3.825)	(−3.957)	(−3.415)	(−5.410)	(1.381)	(3.660)	(0.456)
er	−0.129	−0.121	−0.422 ***	−0.075	−0.158 **	−0.416 ***	0.039	0.057	0.053
	(−1.161)	(−1.555)	(−2.692)	(−1.476)	(−2.325)	(−4.365)	(1.259)	(1.536)	(0.733)
_cons	1.818 *	0.918	−0.439	0.422	0.086	−3.490 ***	1.110 ***	1.068 ***	1.544 ***
	(1.824)	(1.534)	(−0.287)	(0.949)	(0.121)	(−4.814)	(3.966)	(3.738)	(2.587)
N	480	480	480	480	480	480	480	480	480
R2	0.396	0.329	0.470	0.337	0.369	0.540	0.778	0.848	0.922

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

displays the results of the regression.

As can be seen in columns (1)–(3) of Table 3, the coefficients of the effects of green finance on GTFP in the 0.3, 0.6, and 0.9 quartiles are all significantly positive at the 1% level, and the coefficients rise as the level of GTFP rises. The coefficients of the effects of green finance on green technological progress in columns (4)–(6) in the 0.3, 0.6, and 0.9 quartiles are all significantly positive, and the coefficients rise as the level of green technological progress rises. The coefficients of the effects of green finance on green efficiency improvement in the 0.3., 0.6, and 0.9 quartiles in columns (7)–(9) are insignificant and consistent with the results of the benchmark regression.

5.2. Analysis of Robustness

5.2.1. Instrument Variable Method

In order to exclude possible endogeneity issues in the model from affecting the accuracy of the measurement results, we attempted to mitigate endogeneity issues using the instrument variables method. We used the one-period lagged green finance development index as an instrument variable to address the endogeneity issue in the model. The one-period lagged green finance development index not only correlated with the level of green finance development in the region but also had a low correlation with other economic variables in the province. Therefore, it became a more appropriate instrument variable. The regression was conducted using the 2SLS method, and the results are shown in

Table 4. Impact of green finance on GTFP: regression results for instrument variables.

	(1)	(2)	(3)	(4)
	First	Second	Second	Second
Variables	GF	GTFP	TECH	EFFE
L.GF	0.945 ***
	(41.27)
GF_IV		5.521 ***	1.592 ***	0.513
		(8.780)	(7.936)	(0.812)
agdp	0.831	0.481 ***	0.263	0.160 ***
	(0.151)	(3.367)	(1.075)	(3.848)
facility	−0.607	0.229 *	0.734 ***	−0.032
	(−1.236)	(1.794)	(3.360)	(−0.867)
hum	0.670 ***	1.383 ***	4.542 ***	−0.045
	(3.841)	(2.954)	(4.221)	(−0.331)
open	0.467	0.353 **	1.315 ***	0.087 *
	(0.740)	(2.151)	(4.784)	(1.836)
market	0.652	0.617 ***	1.578 ***	0.944 **
	(1.069)	(3.868)	(5.119)	(2.041)
er	−0.486	−0.203	0.568 **	0.139 ***
	(−0.904)	(−1.456)	(2.551)	(3.428)
_cons	−0.126 ***	−0.372	4.578 **	1.660 ***
	(−2.801)	(−0.314)	(2.042)	(4.819)
N	450	450	450	450
R2	0.940	0.499	0.740	0.522

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

In this paper, we used instrument variables two-stage least squares (IV-2SLS) for regression. The results of the “weak instrument variables” test show that the instrument variable, the green finance development index (L.GF), lags by one period, has an F-value of 20.37 in its first stage regression, and passes the Stock/Yogo test. This indicates that there are no weak instrument variables and that the instrument variables all meet the basic conditions for relevance as instrument variables. The results of the over-identification test show that the Sargan and Basmann statistics for the instrument variable have a p-value of 0.309, ensuring the basic conditions for exogeneity as an instrument variable. From the above tests, it can be seen that the lagged green finance development index (L.GF) passes both the “weak instrument variable” test and the “unidentifiable” test.

The coefficients on the core explanatory variables were estimated using instrument variables with two-stage least squares (IV-2SLS). As can be seen from column (1) of Table 4, the first stage instrument variable is significantly positively correlated with green finance (GF) for each province at the 1% level. In the second-stage regression in column (2), the influence coefficient of the fitted value of the green finance instrument variable (GF_IV) on GTFP (GTFP) is significantly positive at the 1% level. In the two-stage regression in column (3), the influence coefficient of the fitted value of the green finance instrument variable (GF_IV) on green technological progress (TECH) is significantly positive at the 1% level. In column (4), the influence coefficient of the fitted value of the green finance instrument variable (GF_IV) on green efficiency improvement (EFFE) remains insignificant in the two-stage regression. The results of Table 4 are consistent with the baseline results and validate the robustness of the baseline results in this paper.

5.2.2. GMM Dynamic Panel Analysis

The static panel model may suffer from endogeneity and estimation bias as the level of GTFP in the previous period may have an impact on GTFP in the current period; in other words, there may be inertia in GTFP. A dynamic panel model is a model that reflects dynamic lag effects by introducing explained variables with lags in the explanatory variables of a static panel model. Therefore, to ensure the robustness of the estimation results, this paper used a dynamic panel regression model to examine the impact of green finance on GTFP. The generalized method of moments (GMM) can effectively overcome the problems of endogeneity of explanatory variables and heteroskedasticity of residuals. It yields unbiased and consistent estimators and is widely used in dynamic panel data model estimation. The two main types of GMM methods are differential-GMM and system-GMM. Differential-GMM treats the model as a first-order difference and estimates the difference equation using lagged variables as instrument variables. System-GMM combines difference equation and level equation for GMM estimation, which can improve the efficiency of estimation compared to the differential-GMM. Due to the differences between differential-GMM and system-GMM methods, both of them were used for estimation in this paper. The regression results are shown in

Table 5. Impact of green finance on GTFP: results of GMM regressions.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	GTFP	GTFP	TECH	TECH	EFFE	EFFE
	Differential-GMM	System- GMM	Differential-GMM	System- GMM	Differential-GMM	System- GMM
L.GTFP	0.823 ***	0.908 ***
	(51.283)	(28.777)
L.TECH			0.972 ***	0.969 ***
			(57.038)	(33.338)
L.EFFE					0.707 ***	0.902 ***
					(13.146)	(23.878)
GF	5.494 ***	2.510 ***	8.481 ***	6.109 ***	1.307	0.349
	(35.815)	(7.436)	(29.213)	(20.155)	(0.864)	(0.791)
agdp	−0.286 ***	0.140 **	−0.563 ***	−0.118 *	0.073	0.022
	(−7.247)	(2.069)	(−6.054)	(−1.918)	(1.458)	(0.863)
facility	0.184 ***	−0.547 ***	0.631 ***	−0.219 **	−0.138 **	−0.011
	(2.647)	(−5.346)	(5.570)	(−2.284)	(−2.358)	(−0.282)
hum	−0.198 **	0.041	−0.650 ***	−0.084	0.460 ***	0.026
	(−2.483)	(0.161)	(−3.334)	(−0.361)	(3.476)	(0.276)
open	1.125 ***	0.586 ***	1.480 ***	1.364 ***	−0.139 ***	−0.072 **
	(23.536)	(5.994)	(20.118)	(16.015)	(−4.156)	(−1.993)
market	0.167 ***	0.400 ***	−0.383 ***	−0.120	0.072 ***	−0.028
	(3.466)	(4.526)	(−14.865)	(−1.515)	(3.739)	(−0.826)
er	−0.010	−0.074	−0.092 ***	−0.217 ***	0.034 ***	0.020
	(−0.719)	(−1.259)	(−4.300)	(−3.902)	(3.930)	(0.951)
AR(1)	0.000	0.001	0.000	0.000	0.000	0.000
AR(2)	0.375	0.630	0.827	0.512	0.902	0.663
Sargan test p-value	1.000	1.000	1.000	1.000	1.000	1.000
N	420	420	420	420	420	420

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

Table 5 shows the results of the GMM model regression of green finance on GTFP. The coefficients on GTFP for the lag period (L.GTFP) in Table 5 are all positive and all significant at the 1% level. The GTFP in the current period is positively correlated with the ones in the previous period, indicating that the GTFP is cumulative and persistent. The first-order series AR(1) passed the 1% significance test in the perturbation term autocorrelation test, while the second-order series AR(2) is not significant, so the original hypothesis of no autocorrelation of the perturbation term is accepted. The p-values corresponding to the Sargan tests are all 1, indicating that the regression results are not over-identified. The coefficients of the effects of green finance (GF) on GTFP in columns (1) and (2) are both significantly positive at the 1% level, indicating that there is a significant contribution of green finance (GF) to GTFP. The coefficients of the effects of green finance (GF) on green technological progress (TECH) in columns (3) and (4) are both significantly positive at the 1% level. In columns (5) and (6), the coefficient of the effect of green finance (GF) on green efficiency improvement (EFFE) is positive and insignificant. This indicates that green finance can achieve improved green technological progress, and the baseline results are again validated.

5.2.3. Exogenous Shock Test

The estimation results need to exclude the effects of current affairs and other shocks to accurately identify the impact of green finance on GTFP. In 2012, the former China Banking Regulatory Commission (CBRC) issued the Green Credit Guidelines (later referred to as the Guidelines), which formally set out the requirements and assessment criteria for banking and financial institutions to conduct green credit business. In 2016, the People’s Bank of China and seven other departments issued the Guiding Opinions on Building a Green Finance System (later referred to as the Opinions), marking China as the first country to have a green finance system promoted by the central government. These two documents, as national policy documents of the Chinese government to promote the development of green finance, are a relatively exogenous shock for regions to promote their green finance development. The regional differences in policy implementation mean that the Guidelines and Opinions can be used as a natural experiment to assess policy effects. The COVID-19 outbreak in late 2019 caused an unprecedented shock to the normal functioning of the economy and society and may have affected the relationship between green finance and GTFP.

Given this, the study selects three policy shocks in 2012, 2016, and 2019 to estimate the effects of green finance on GTFP using the difference-in-differences (DID) method. The core explanatory variables in the commonly used DID models for studying policy impacts are interaction terms for dummy variables and time dummy variables, which are binary dummy variables that make it difficult to capture the differences in treatment levels between treatment groups. However, DID models are not limited to the use of dichotomous dummy variables to distinguish between treatment and control groups. Instead of altering the original nature of the model, continuous DID can present a richer sample heterogeneity. It also avoids the bias associated with subjectively setting treatment and experimental groups, minimizing the problem of endogeneity in the model [78]. In this study, promoting green finance is not a question of developing green finance versus not developing green finance but rather a question of high levels of green finance development versus low levels of green finance development. Specifically, this paper uses the continuous variable “level of green finance” as a proxy for grouping variables of the model without explicitly setting treatment and control groups and constructs a continuous DID model as follows:

$$GTF{P}_{it}={\eta }_1+{\delta }_{1}G{F}_{it}\times Pos{t}_{it}+{{\displaystyle \sum }}_j{\tau }_j^1{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it},$$

(4)

$$TEC{H}_{it}={\eta }_3+{\delta }_{3}G{F}_{it}\times Pos{t}_{it}+{{\displaystyle \sum }}_j{\tau }_j^3{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it},$$

(5)

$$EFF{E}_{it}={\eta }_2+{\delta }_{2}G{F}_{it}\times Pos{t}_{it}+{{\displaystyle \sum }}_j{\tau }_j^2{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}.$$

(6)

In Equations (4)–(6), $Pos{t}_{it}$ is the point-in-time variable. The value is taken as 1 after the policy shock and 0 before. The coefficient $\delta$ on $G{F}_{it}$ × $Pos{t}_{it}$ reflects the impact of the shock. If it is significantly positive, it indicates that GTFP growth is more pronounced in provinces that are more exposed to shocks. In other words, green finance can significantly contribute to GTFP. The results of the DID estimation are shown in

Table 6. Impact of green finance on GTFP: DID regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Year ≥ 2013			Year ≥ 2017			Year ≥ 2020
Variables	GTFP	TECH	EFFE	GTFP	TECH	EFFE	GTFP	TECH	EFFE
GF × Post	2.236 ***	3.805 ***	−0.005	3.228 ***	3.852 ***	0.036	3.294 ***	5.626 ***	0.085
	(6.020)	(12.527)	(−0.034)	(10.544)	(15.127)	(0.284)	(8.833)	(22.125)	(0.573)
agdp	0.166	−0.032	0.027	−0.014	−0.188 *	0.024	0.032	−0.261 ***	0.021
	(1.262)	(−0.298)	(0.545)	(−0.115)	(−1.819)	(0.467)	(0.253)	(−2.997)	(0.405)
facility	−1.033 ***	−0.408 ***	−0.159 ***	−0.797 ***	−0.300 **	−0.152 **	−0.916 ***	−0.206 **	−0.146 **
	(−6.502)	(−3.137)	(−2.625)	(−5.375)	(−2.432)	(−2.494)	(−6.056)	(−1.999)	(−2.432)
hum	1.658 ***	0.589	−0.377 *	1.552 ***	1.103 ***	−0.395 *	1.880 ***	0.960 ***	−0.407 **
	(2.949)	(1.281)	(−1.760)	(3.094)	(2.645)	(−1.918)	(3.660)	(2.741)	(−1.991)
open	−0.441 **	1.031 ***	0.220 ***	−0.445 ***	0.715 ***	0.228 ***	−0.930 ***	0.199 *	0.224 ***
	(−2.257)	(6.455)	(2.955)	(−2.693)	(5.205)	(3.363)	(−5.807)	(1.821)	(3.523)
market	0.593 **	−0.588 ***	0.204 **	0.668 ***	−0.401 **	0.202 **	0.696 ***	−0.412 **	0.201 **
	(2.395)	(−2.904)	(2.160)	(2.930)	(−2.115)	(2.158)	(2.958)	(−2.571)	(2.149)
er	0.059	−0.155 *	0.038	0.129	−0.101	0.039	0.150	0.000	0.041
	(0.579)	(−1.864)	(0.966)	(1.363)	(−1.274)	(1.002)	(1.522)	(0.007)	(1.057)
_cons	0.223	1.210	1.816 ***	0.337	0.456	1.840 ***	0.340	1.421	1.868 ***
	(0.163)	(1.081)	(3.482)	(0.269)	(0.438)	(3.575)	(0.262)	(1.606)	(3.617)
N	480	480	480	480	480	480	480	480	480
R2	0.876	0.844	0.623	0.893	0.861	0.623	0.886	0.901	0.623

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

Columns (1) to (3) of Table 6 show the results of the regressions of the “Guidance” shock on GTFP. The results of column (1) in Table 6 can be obtained from Equation (4). The coefficient of the effect of GF_it × Post_it on GTFP is significantly positive at the 1% level, which again confirms the causal relationship between green finance and GTFP in the benchmark regression results, validating Hypothesis 1. The results of column (2) in Table 6 are obtained from Equation (5). The coefficient of the effect of GF_it × Post_it on green technological progress is significantly positive at the 1% level. The coefficient of the effect of GF_it × Post_it on green efficiency improvement is not significant. Columns (4) to (6) of Table 6 show the results of the regression of the “Opinion” shock on GTFP. The coefficients of the effects of GF_it × Post_it on GTFP and green technological progress are both significantly positive at the 1% level, while the coefficient of the effect of GF_it × Post_it on green efficiency improvement is insignificant. Columns (7) to (9) of Table 6 show the results of the regression of the “COVID” shock on GTFP. The coefficients of the effects of GF_it × Post_it on GTFP and green technological progress are both significantly positive at the 1% level, while the coefficient of the effect of GF_it × Post_it on green efficiency improvement is insignificant. The causal relationship between green finance and GTFP is validated.

5.3. Spatial Spillover Effect

Green finance in one region may affect GTFP in other regions, causing bias in the ordinary econometric model. In this paper, the spatial spillover effect of green finance on GTFP was tested. The Global Moran’s I index was used to construct an economic distance weight matrix to test the spatial autocorrelation of green finance and GTFP. The results are shown in

Table 7. Global Moran’s I for green finance and GTFP.

Year	GF	GTFP	Year	GF	GTFP
2006	0.197 ***		2014	0.279 ***	0.067 **
	(3.131)			(4.413)	(2.030)
2007	0.194 ***	0.140 ***	2015	0.277 ***	0.065 *
	(3.164)	(2.706)		(4.421)	(1.695)
2008	0.221 ***	0.115 **	2016	0.236 ***	0.072 *
	(3.533)	(2.181)		(3.980)	(1.741)
2009	0.240 ***	0.086 *	2017	0.210 ***	0.107 *
	(3.784)	(1.896)		(3.695)	(1.700)
2010	0.282 ***	0.093 *	2018	0.258 ***	0.119 *
	(4.321)	(1.758)		(4.212)	(1.797)
2011	0.314 ***	0.089 *	2019	0.257	0.143 **
	(4.719)	(1.633)		(4.184)	(2.054)
2012	0.317 ***	0.062 *	2020	0.278 ***	0.150 *
	(4.802)	(1.625)		(4.371)	(2.127)
2013	0.319 ***	0.076 *	2021	0.125 *	0.085
	(4.848)	(1.832)		(2.205)	(0.317)

Source: Authors’ elaboration. Note: Since all GTFP was 1 in 2006, its Moran’s I index was not calculated. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

As can be seen from Table 7, the Global Moran’s I for both green finance and GTFP under the economic distance weight matrix has a high significance. Therefore, the dynamic spatial Durbin model was tested to verify the spatial spillover effect of green finance on GTFP in this paper. The model is set up as follows:

$$GTF{P}_{it}=\varphi +\phi GTF{P}_{i,t-1}+\lambda \left(WGTF{P}_{it}\right)+\sigma \left(WGTF{P}_{i,t-1}\right)+{\rho }_{1}G{F}_{it}+{\rho }_2\left(WG{F}_{it}\right)+{{\displaystyle \sum }}_j{\omega }_j{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}.$$

(7)

In Equation (7), $W$ is the spatial weight matrix, and $\varphi$ denotes the constant term. ${\mu }_i$ and ${\nu }_t$ denote the spatial and temporal effects. ${\epsilon }_{it}$ is the random perturbation term. Other symbols have the same meaning as in Equation (1). The results of the dynamic spatial Durbin model effect decomposition under the economic distance weight matrix are shown in

Table 8. Impact of green finance on GTFP: spatial Durbin model effect decomposition.

	Short-Term Effect (SR)
GTFP	direct effect	indirect effect	total effect
GF	2.035 ***	2.740 ***	4.775 *
	(3.091)	(9.287)	(1.916)
control variables	control	control	control
	long-term effect (LR)
GTFP	direct effect	indirect effect	total effect
GF	2.894	4.190	7.084
	(0.135)	(0.047)	(0.158)
control variables	control	control	control

Source: Authors’ elaboration. Note: * and *** represent significance at the 10% and 1% levels, respectively.

.

As can be seen from Table 8, the direct effect of green finance on GTFP is significantly positive in the short term, while the indirect effect is also significantly positive. This indicates that green finance can not only effectively promote GTFP in one region but also in neighboring regions. In other words, there is a spatial spillover effect of green finance on GTFP. In the short term, the total effect is still significantly positive. In the long term, although the direct, indirect, and total effects of green finance on GTFP are all positive, they are not significant.

6. Analysis of Mechanisms

6.1. Mediating Effect Model

The previous results show that green finance has a significant contribution to GTFP. This paper constructs a mediating effects model to test the mechanisms of technological innovation and industrial structure upgrading. The following empirical model is set up:

$$GTF{P}_{it}=a_1+a_{2}G{F}_{it}+{{\displaystyle \sum }}_j{a}_j{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it },$$

(8)

$$M_{it}=b_1+b_{2}G{F}_{it}+{\displaystyle \sum }_j{b}_j{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it},$$

(9)

$$GTF{P}_{it}=c_1+c_{2}G{F}_{it}+c_3{M}_{it}+{\displaystyle \sum }_j{c}_j{X}_{jit}+{\mu }_i+{\nu }_t+{\epsilon }_{it}.$$

(10)

In Equations (7)–(9), M is the mediating variable, and the other variables are consistent with the previous equations.

6.2. Mediating Variables

Technological innovation (TI). Technological innovation is measured using the number of patent applications per 10,000 people in each province.

Industrial structure upgrading (HIS). In this paper, we refer to the method of Fu et al. [79] to calculate industrial structure upgrading (HIS).

6.3. Analysis of Results of Mediating Effects

Based on the setting of the previous econometric model Equations (8)–(10), this paper empirically tests the mechanisms. The regression results are shown in

Table 9. Impact of green finance on GTFP: regression results with technological innovation as a mediating variable.

	(1)	(2)	(3)	(4)
Variables	TI	GTFP	TECH	EFFE
GF	2.045 ***	5.378 ***	8.059 ***	0.100
	(5.921)	(9.138)	(14.984)	(0.461)
TI		0.111 *	0.146 **	0.093
		(1.776)	(2.556)	(0.690)
agdp	1.304 ***	0.124	−0.345 ***	0.064
	(15.528)	(0.929)	(−2.826)	(1.243)
facility	0.203 ***	−0.342 **	0.508 ***	−0.131 **
	(2.625)	(−2.074)	(3.376)	(−2.125)
hum	−0.755 ***	1.868 ***	1.295 ***	−0.369 *
	(−2.707)	(3.706)	(2.811)	(−1.700)
open	0.182 *	−0.427 **	0.744 ***	0.298 ***
	(1.949)	(−2.462)	(4.698)	(4.262)
market	1.069 ***	0.635 ***	−0.191	0.218 **
	(11.188)	(3.970)	(−1.305)	(2.322)
er	0.346 ***	−0.216 **	−0.599 ***	0.043
	(4.126)	(−2.382)	(−7.213)	(1.133)
_cons	0.661	−4.004 ***	−6.461 ***	1.838 ***
	(0.938)	(−4.004)	(−7.068)	(3.558)
N	480	480	480	480
R2	0.871	0.853	0.769	0.635

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

and

Table 10. Impact of green finance on GTFP: regression results with industrial structure upgrading as a mediating variable.

	(1)	(2)	(3)	(4)
Variables	TI	GTFP	TECH	EFFE
GF	1.740 ***	6.805 ***	7.151 ***	0.296
	(6.165)	(10.266)	(13.232)	(1.332)
HIS		0.297 ***	0.550 ***	0.171
		(3.472)	(6.301)	(0.159)
agdp	−0.064	0.462 ***	−0.174	−0.023
	(−1.106)	(2.864)	(−1.638)	(−0.448)
facility	0.217 ***	0.080	0.470 ***	−0.169 ***
	(2.806)	(0.663)	(3.280)	(−2.759)
hum	0.721 ***	−0.970**	1.217 ***	−0.438 **
	(3.095)	(−2.124)	(2.813)	(−2.027)
open	−0.660 ***	−0.453 **	1.129 ***	0.165 **
	(−7.937)	(−2.537)	(6.919)	(2.397)
market	0.396 ***	0.327 *	−0.335 **	0.181 *
	(5.261)	(1.812)	(−2.352)	(1.943)
er	−0.071	0.024	−0.531 ***	0.021
	(−1.641)	(0.171)	(−6.668)	(0.539)
_cons	−1.768 ***	1.331	−6.140 ***	2.145 ***
	(−3.836)	(1.106)	(−7.131)	(4.132)
N	480	480	480	480
R2	0.931	0.545	0.785	0.638

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

.

The regression results with technological innovation as the mediating variable are presented in Table 9. The results in column (1) show that the coefficient of the effect of green finance (GF) on technological innovation (TI) is positive and significant at the 1% level. This indicates that green finance can effectively enhance technological innovation. The coefficient of the effect of technological innovation (TI) on GTFP in column (2) is positive and significant at the 10% level. This indicates that there is an indirect effect of green finance on GTFP through technological innovation. The coefficient of green finance (GF) on GTFP in column (2) is positive and significant at the 1% level. This indicates that there is a direct effect. Taken together, the coefficient of green finance enhancing GTFP through technological innovation is consistent with the sign of the coefficient of green finance (GF) on GTFP in column (2). This implies that there is a partial mediating effect, suggesting that green finance can promote GTFP by driving technological innovation. The coefficient of technological innovation (TI) on green technological progress (TECH) in column (3) is positive and significant at the 1% level. This indicates that there is a direct effect. Taken together, the coefficients of green finance enhancing green technological progress through technological innovation are consistent with the sign of the coefficient of green finance on green technological progress in column (3). It implies that there is a partial mediating effect, suggesting that green finance can promote green technological progress by driving technological innovation. The coefficient of the effect of technological innovation (TI) on green efficiency improvement (EFFE) in column (4) is not significant, and the coefficient of the effect of green finance (GF) on GTFP is still not significant. This suggests that green finance does not contribute to green efficiency improvement through technological innovation. The results in Table 9 validate Hypothesis 5, which states that green finance, by providing financial support for technological innovation, promotes green innovation activities, thereby increasing GTFP and accelerating green technological progress. However, the current innovation activities induced by green finance are only biased towards new technologies and do not lead to green efficiency improvement.

We further employed the Bootstrap method for mediating effect robustness tests. The results show that the indirect mediating effects of green finance on GTFP and green technological progress through technological innovation are both significantly positive. The 95% confidence intervals of the indirect effects are (0.006, 0.032) and (0.459,0.988), respectively, which do not contain 0, and the mediating effects are significant. The indirect mediating effect of green finance on green efficiency improvement through technological innovation is not significant. The 95% confidence interval for the indirect effect is (−0.196, 1.123).

The regression results with industrial structure upgrading as the mediating variable are shown in Table 10. The results in column (1) show that the coefficient of the effect of green finance (GF) on industrial structure upgrading (HIS) is positive and significant at the 1% level, indicating that green finance can upgrade industrial structures. The coefficient of the effect of industrial structure upgrading (HIS) on GTFP in column (2) is positive and significant at the 1% level, indicating that there is an indirect effect of green finance on GTFP through industrial structure upgrading. The coefficient of green finance (GF) affecting GTFP in column (2) is positive and significant at the 1% level, indicating a direct effect. Taken together, the coefficient of green finance enhancing GTFP through industrial structure upgrading is consistent with the sign of the coefficient of green finance on GTFP in column (2), suggesting that there is a partial mediating effect with green finance promoting GTFP through industrial structure upgrading. The coefficient of the effect of industrial structure upgrading (HIS) on green technological progress (TECH) in column (3) is positive and significant at the 1% level, indicating that there is an indirect effect of green finance on green technological progress through industrial structure upgrading. The coefficient of green finance (GF) affecting green technological progress (TECH) in column (3) is significant at the 1% level, indicating a direct effect. The coefficient of green finance enhancing green technological progress through industrial structure upgrades is consistent with the sign of the coefficient of green finance on green technological progress in column (3). This means that there is a partial mediating effect, suggesting that green finance enhances green technological progress through industrial structure upgrades. The coefficient of the effect of industrial structure upgrading (HIS) on green efficiency improvement (EFFE) in column (4) is not significant, and the coefficient of the effect of green finance (GF) on GTFP is still insignificant, indicating that green finance fails to achieve green efficiency improvement by industrial structure upgrading. The results in Table 10 confirm Hypothesis 6. Green finance improves GTFP and green technological progress by adjusting the allocation of production factors between industries and optimizing the industrial structure. Similarly, the current industrial structure upgrades induced by green finance are still focused on green technological progress and fail to achieve green efficiency improvement.

Similarly, we used the Bootstrap method for mediating effect robustness tests. The results show that the indirect mediating effects of green finance on GTFP and green technological progress through industrial structure upgrades are both significantly positive. The 95% confidence intervals of the indirect effects are (0.097, 0.995) and (0.438, 1.032), respectively, which do not contain 0, and the mediating effects are significant. The indirect mediating effect of green finance on green efficiency improvement through industrial structure upgrades is not significant, with an indirect effect 95% confidence interval of (−0.133, 1.082).

7. Heterogeneity Analysis

7.1. Regional Heterogeneity

This paper divides the 30 provincial units into four regions, namely the eastern region, the middle region, the western region, and the northeastern region, based on the division criteria of the National Bureau of Statistics. The differences in the impact of green finance on GTFP in different regions are examined separately. The sample sizes of the four major regions are different. To avoid estimation errors caused by the different sample sizes, this paper refers to Qiang and Jian [80] to construct regional dummy variables. The cross-product term of the green finance index and the regional dummy variables are used as the explanatory variables for the regression. The explanatory variables are the eastern region (GF_e), the middle region (GF_m), the western region (GF_w), and the northeastern region (GF_n) (the eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The northeast region includes Liaoning, Jilin, and Heilongjiang).

Table 11. Impact of green finance on GTFP: results of regional heterogeneity regressions.

	(1)	(2)	(3)
Variables	GTFP	TECH	EFFE
GF_e	5.564 ***	7.282 ***	2.158 ***
	(8.315)	(14.428)	(4.617)
GF_m	4.378 ***	5.616 ***	−0.423
	(2.781)	(8.710)	(−1.428)
GF_w	3.124 ***	2.904 ***	0.838
	(3.802)	(2.857)	(0.649)
GF_n	3.263	0.003	0.832
	(1.439)	(0.019)	(1.052)
agdp	0.189	0.068	0.141 ***
	(1.198)	(0.570)	(3.066)
facility	−0.939 ***	−0.133	−0.199 ***
	(−5.815)	(−1.093)	(−3.267)
hum	1.209 **	0.283	−0.156
	(2.093)	(0.651)	(−0.894)
open	−0.054	1.097 ***	0.205 **
	(−0.216)	(5.829)	(2.180)
market	0.496 **	−0.671 ***	0.128 **
	(2.033)	(−3.644)	(2.269)
er	0.087	−0.116	0.071 **
	(0.865)	(−1.535)	(2.190)
_cons	0.416	0.637	1.657 ***
	(0.295)	(0.600)	(4.814)
N	480	480	480
R2	0.885	0.877	0.611

Source: Authors’ elaboration. Note: ** and *** represent significance at the, 5% and 1% levels, respectively.

reports the regression results of the impact of green finance on GTFP based on regional heterogeneity.

As can be seen from the columns of Table 11, green finance has a significant positive impact on GTFP, green technological progress, and green efficiency improvement in the eastern region. The impact coefficient is higher than the results of the baseline regression and other regions. As the region with the highest level of economic development in China, green finance in the eastern region is conducive to the concentration of innovative factors that can achieve green technological progress and green efficiency improvement and thus enhance GTFP. The effects of green finance in the middle region and green finance in the western region on GTFP and green technological progress are both significantly positive, but the effect on green efficiency improvement is not significant. The impact of green finance on GTFP, green technological progress, and green efficiency improvement in the northeast region is not significant. The relatively low level of green finance in the northeast region resulted in none of the effects being significant. Hypothesis 7 is verified.

7.2. Heterogeneity of Resource Endowment

There may be heterogeneity in the impact of green finance on GTFP due to differences in resource endowment across regions. This paper divides the sample into resource-rich and resource-general areas (resource-rich areas include: Tianjin, Shanxi, Inner Mongolia, Jilin, Heilongjiang, Guizhou, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The other provinces are resource-general areas) based on the number of employees in the extractive industries in each region, reflecting the regional resource endowment [81], and is used to analyze the heterogeneous impact of resource endowment from green finance on GTFP. Resource-rich areas are provinces with natural resource endowments above the 50% quantile in 2021, and resource-general areas are provinces with natural resource endowments below the 50% quantile. There are 11 resource-rich areas and 19 resource-general areas in China. In this paper, regional dummy variables are constructed according to regional resource abundance, and the cross-product of the green finance development index and regional dummy variables is used as an explanatory variable in the regression, with each explanatory variable being resource-rich (GF_r) and resource-general (GF_g).

Table 12. Impact of green finance on GTFP: results of regressions on heterogeneity of resource endowments.

	(1)	(2)	(3)
Variables	GTFP	TECH	EFFE
GF_r	8.235 ***	6.646 ***	2.484
	(6.975)	(7.106)	(0.513)
GF_g	4.656 ***	5.777 ***	0.331 ***
	(8.431)	(15.490)	(6.573)
agdp	0.173	−0.225 **	0.102 **
	(1.297)	(−2.123)	(1.995)
facility	−0.769 ***	−0.154	−0.099
	(−4.844)	(−1.225)	(−1.628)
hum	0.682	−0.133	−0.677 ***
	(1.211)	(−0.298)	(−3.151)
open	−0.601 ***	0.753 ***	0.216 ***
	(−3.506)	(5.542)	(3.296)
market	0.218	−0.786 ***	0.056
	(0.882)	(−4.014)	(0.598)
er	0.062	−0.129 *	0.029
	(0.630)	(−1.664)	(0.776)
_cons	1.320	2.010*	2.152 ***
	(0.996)	(1.914)	(4.255)
N	480	480	480
R2	0.886	0.866	0.652

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

reports the regression results of the impact of green finance on GTFP based on the heterogeneity of resource endowments.

As can be seen from column (1) of Table 12, green finance in resource-rich areas (GF_r) and resource-general areas (GF_g) has a significant impact on GTFP. The coefficients are greater in resource-rich areas than in resource-general areas. Since GTFP is generally lower in resource-rich areas, the promotion effect of green finance on GTFP has a strong marginal effect and is more obvious. Therefore, green finance has become an effective way to promote GTFP in these areas. In column (2), the coefficients of the effects of green finance on green technological progress in resource-rich and resource-general areas are both significantly positive. The coefficients are larger in resource-rich areas than in resource-general areas. This may be due to the fact that resource-rich areas that rely on traditional industries need to be transformed, so green finance is prioritized for the “incremental” advancement of green technology. As can be seen from column (3), the coefficient of green finance on green efficiency improvement is not significant in resource-rich areas, but the coefficient of green finance on green efficiency improvement in resource-general areas is positive and significant at the 1% level. Due to its industrial structure, it is difficult to achieve green efficiency improvement in resource-rich areas. Compared with resource-rich areas, the advanced industrial structure and sound financial system in resource-general areas are more conducive to green efficiency improvement. Hypothesis 8 is verified.

7.3. Heterogeneity in the Level of Information Technology

Improvements in information technology can alleviate information asymmetry in the financial market, optimize the allocation efficiency of financial resources, and facilitate efficient matching between the supply and demand of green financial products. Information technology plays a positive role in promoting GTFP, so it is necessary to take it into account in the model [82]. To explore the different effects of heterogeneity in information technology level on the impact of green finance on GTFP, this paper divides the sample areas into regions with higher and lower information technology levels (areas with high levels of information include Beijing, Inner Mongolia, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan, Chongqing, Shaanxi, and Ningxia. Other provinces are areas with low levels of information). Areas with a high level of information technology are provinces with an internet penetration rate above the 50% quantile in 2021, while areas with a low level of information technology are provinces with an internet penetration rate below the 50% quantile. Similarly, to avoid estimation errors caused by different sample sizes, this paper uses the cross-product of the green finance development index and the regional dummy variables as explanatory variables for the regressions, with each explanatory variable being regions with a high level of information technology (GF_h) and regions with a low level of information technology (GF_l).

Table 13. Impact of green finance on GTFP: regression results for heterogeneity of information technology level.

	(1)	(2)	(3)
Variables	GTFP	TECH	EFFE
GF_h	4.271 ***	7.167 ***	0.386 **
	(6.536)	(14.269)	(2.456)
GF_l	2.181 ***	6.842 ***	0.289
	(6.189)	(13.176)	(1.507)
agdp	0.045	−0.226 **	0.131 ***
	(0.346)	(−2.239)	(3.428)
facility	−0.868 ***	−0.155	−0.027
	(−5.350)	(−1.245)	(−0.756)
hum	1.114 **	−0.260	−0.082
	(1.970)	(−0.598)	(−0.639)
open	−0.553 **	0.854 ***	−0.068
	(−2.453)	(4.930)	(−1.508)
market	0.457 *	−0.805 ***	0.097 **
	(1.868)	(−4.286)	(2.228)
er	0.072	−0.138 *	0.118 ***
	(0.718)	(−1.796)	(3.092)
_cons	0.827	2.135 **	1.596 ***
	(0.613)	(2.061)	(4.948)
N	480	480	480
R2	0.881	0.868	0.714

Source: Authors’ elaboration. Note: *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.

reports the regression results of the impact of green finance on GTFP under the heterogeneity of the information technology level.

As can be seen from column (1) of Table 13, green finance (GF_h) has a significant effect on GTFP in both regions with a high level of information technology (GF_l) and regions with a low level of information technology (GF_l). The coefficient is higher in regions with high information technology levels. In column (2), the coefficients of green finance on green technological progress are both significantly positive. Column (3) shows that the coefficient of green finance on green efficiency improvement is not significant in regions with low information technology levels (GF_l), but the coefficient of green finance on green efficiency improvement is positive and significant at the 5% level in regions with high information technology levels (GF_h). Table 13 shows that the contribution of green finance to GTFP is more significant in regions with high information technology levels, which fully indicates that a higher level of information technology is a favorable condition for green finance to enhance GTFP. More meaningful is the fact that green finance can achieve green efficiency improvements in regions with high levels of information technology. Information technology helps green finance accelerate the reintegration of factor resources to form an efficient and green production model. Hypothesis 9 is verified.

8. Discussion

In this paper, we explored the relationship between green finance and GTFP using a sample of 30 provinces in China for the period 2006–2021. The regression results indicate that green finance significantly increases the level of GTFP. Previous studies also agree on the positive impact of green finance on GTFP [83] and point out that green finance development has a positive impact on sustainable economic growth [84]. Previous studies have focused more on the ability of green finance to improve the financial structure and efficiency and hence the GTFP, and have similarly found that green finance has a greater positive impact on the GTFP when economic and social conditions are better. This paper splits GTFP into green technological progress and green efficiency improvement with a fresh perspective and method and finds that green finance promotes the improvement of GTFP actually mainly promotes green technological progress and has little effect on green efficiency improvement, while the mediating effects of both technological innovation and industrial structure upgrading can only realize green technological progress. In order to investigate the impact of green finance on green efficiency improvement, this paper conducted a heterogeneity analysis and found that green finance in more economically developed regions, regions with general resources, and regions with higher information technology levels can achieve green efficiency improvement. This finding complements previous studies, reveals an effective way for green finance to enhance GTFP, and provides a new reference value for policy formulation.

9. Implications for Theory, Practices, and Policymaking

The world economy must be driven to transition to a green and sustainable direction through a green revolution. However, these processes cannot be fully realized without the support of green finance. The key point for China to achieve green economic growth is how to effectively use green finance to facilitate the transformation of production methods and economic growth patterns. This study analyses the provincial GTFP development by the Luenberger productivity index based on the SBM directional distance function and quantitatively estimates the impact of green finance on GTFP. The results of GTFP and its decomposition term show that the contribution of green finance to GTFP is mainly due to green technological progress rather than green efficiency improvement. From a theoretical point of view, the mediating effect of technological innovation and industrial structure upgrading can also only promote green technological progress but not green efficiency improvement. Heterogeneity analysis theoretically provides an effective way for green finance to realize green efficiency improvement, which is to improve economic development, resource utilization efficiency, and information technology development.

In practice, we should objectively and correctly view the economic development gap between different regions of China and formulate reasonable differentiated policies according to local conditions. The higher level of economic development in eastern China is conducive to the gathering of innovation factors, and the role of green finance in promoting GTFP can be given full play. The middle and western regions of China should strengthen green efficiency improvement, and the northeast region should focus on improving green finance development. The government and relevant financial institutions should pay special attention to the continuous promotion of green finance in resource-general areas and give full play to the role of green finance in enhancing GTFP. In resource-rich areas, they should focus on examining the application of green technological innovation to promote green efficiency improvement. The overall improvement of information technology will facilitate the financing of green enterprises and industries and the market application of green technologies, especially for the systematic improvement of green efficiency, so the focus should be on the improvement of local information technology in practice.

The above findings provide us with several important policy implications. As practitioners, enterprises should strengthen their awareness of the green concept, adopt sustainable practices in the development of technological innovation, actively introduce professional talents, enhance the efficiency of resource utilization, and gradually improve their GTFP. The governments, as regulators, should give full play to the role of macro control. They should develop long-term strategies and incentives for green finance development and expand its positive contribution to green economic growth; they should establish appropriate market incentives and protection measures to improve technological innovation and thoroughly explore its great potential; they should focus on supporting industrial structure upgrades, optimizing the resource allocation function of green finance, and boosting the degree of inclination toward green industries in all ways; they should strictly limit the market access conditions for resource-based industries and gradually eliminate the backward production capacity; they should focus on strengthening the information technology infrastructure to enhance the overall level of information technology in the whole society. Financial institutions, as third parties, should carry out multi-level green finance services to gradually expand the positive contribution of green finance. They should improve the reward and punishment mechanism, continue to promote the reform of the green finance management system, provide more green financial instruments for technology-intensive enterprises and high-tech industries, and actively guide the gathering of financial resources in the green field.

10. Conclusions

This study explores the causal relationship and impact mechanism between green finance and GTFP and further conducts heterogeneity analysis. This study highlights some interesting findings. Green finance can promote GTFP, but when splitting GTFP into green technological progress and green efficiency improvement, green finance can significantly improve the former but has a very limited effect on the latter. Green finance can drive green technological progress through the mechanisms of technological innovation and industrial structure upgrading, but neither of them can achieve green efficiency improvement. The heterogeneity analysis shows that a higher level of economic development, a well-developed industrial structure, a sound financial system, and a high level of information technology are more conducive to green efficiency improvement. This study empirically examines the effectiveness of green finance in promoting green technological progress and green efficiency improvement and then proposes specific measures to enhance GTPF. This analysis provides persuasive results; however, it also has limitations that provide room for further research. This study only explores 30 Chinese provinces to empirically estimate the promotion effect of green finance on GTFP due to the lack of available data from prefecture-level cities. In addition, future research should include how different types of green financial instruments can effectively promote GTFP and how to match appropriate green financial instruments with corresponding green financial policies to the characteristics of different regions.