How to Improve Industrial Green Total Factor Productivity under Dual Carbon Goals? Evidence from China

Abstract

This paper focuses on the relationship between green credit and industrial green total factor productivity under the dual carbon target. In recent years, weather extremes that break historical extremes have occurred frequently around the world, and the resulting loss of life and property has deepened people’s concern about climate change. As a responsible developing country, China has set the goal of reaching peak carbon emissions and reducing carbon intensity by 60–65% by 2030. In this context, based on China’s provincial-level data from 2006 to 2019, this paper first measures the growth rate of industrial green total factor productivity using the SBM-ML model, and then analyzes the impact of green credit on industrial green total factor productivity under the double carbon target by constructing the transmission mechanism of the energy consumption structure and the regulation mechanism of environmental regulation on green credit. We then analyze the impact of green credit on industrial green total factor productivity under the dual carbon target by constructing the transmission mechanism of the energy consumption structure and the regulation mechanism of environmental regulation on green credit. We find that green credit can improve the energy consumption structure and thus increase industrial green total factor productivity. In addition, the study finds that the interaction effect of green credit and environmental regulation suppresses the positive impact of green credit on industrial green TFP. This paper provides empirical evidence and policy implications for the orderly promotion of carbon peaking and carbon neutral efforts to effectively improve industrial green total factor productivity and promote high-quality economic development.

1. Introduction

In recent years, extreme weather events that have broken historical extremes have been occurring around the world, and the resulting loss of life and property has heightened concerns about climate change. The latest report of the United Nations Intergovernmental Panel on Climate Change (IPCC) states that human activities are already causing warming of the atmosphere, oceans and land. The impact of global warming on the entire climate system is unprecedented in comparison to the impact of global warming in the past centuries and even millennia. The warming of the climate system is expected to continue through the mid-21st century. As a responsible developing country, China has constructively participated in and led international cooperation to address climate change and promoted the implementation of the United Nations Framework Convention on Climate Change and its Paris Agreement. In September 2020, President Xi Jinping announced at the 75th General Debate of the United Nations General Assembly that China will increase its autonomous national contribution, adopt more vigorous policies and measures, strive to peak CO2 emissions by 2030 and strive to achieve carbon neutrality by 2060.

Over the past 40 years of reform and opening up, industry, the mainstay of China’s real economy, has contributed greatly to economic growth in the context of rapid development, and industrial reform has led to fundamental changes in China’s economic and social development prospects. However, while the industrial economy has provided an important material guarantee for China’s rise, risks and challenges have also emerged, among which the increasingly serious problem of environmental pollution brought about by the industrial development model characterized by excessive and rough development is one of the most difficult challenges. Despite great efforts, China’s industry in general has not yet got rid of the development mode of high input, high emission and high consumption, with prominent ecological and environmental problems and large consumption of resources and energy, and still faces many challenges to achieve the goal of reaching peak carbon emission and reducing carbon intensity by 60–65% by 2030. At the same time, there is a large gap in climate finance and China is facing increasing environmental pressure under the enhanced transparency framework of the Paris Agreement. How to achieve high-quality economic growth under the dual carbon target is both a key area of concern for scientific research and a realistic challenge to be addressed in addressing climate change.

The core of high-quality economic development is green total factor productivity [1,2]; however, the current growth of green total factor productivity in China is not optimistic. When environmental factors are taken into account, China’s green TFP growth is much slower than GDP [3,4,5]. Given that China is in the difficult situation of being at the end of the era of high economic growth and environmental degradation, China’s economic development model urgently needs to make a green transformation and find a high-quality development path that combines economy and ecology, and this path is the path of China’s greening process with the purpose of improving green total factor productivity [6,7]. Therefore, the rapid increase of green total factor productivity is a difficult problem facing China today and needs to be solved urgently.

In this context, in 2006, China issued the paper “Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks”. This opinion responded to the trend of high-quality green development of China’s economy and introduced the concept of green credit for the first time, and in 2015, the document “Energy Efficiency Credit Guidelines” was issued to establish the determination of green development in China. Therefore, by studying the specific role of the relationship between green credit and the industrial green total factor productivity, it can help promote better implementation of green credit policies and high-quality economic development in China and other transition economies.

Frankel and Rose [8] show that the development of green credit helps green enterprises to obtain funds to invest in green projects at a low interest rate, which relieves their financing pressure, promotes the improvement of their environmental processes, improves the efficiency of capital use, and ultimately contributes to the improvement of the green total factor productivity. Olsen and Fenhann [9] found that green credit can stimulate the development of green industries, thus providing a large number of jobs and promoting economic development. Parthan et al. [10] suggest that the government should give more green funds to enterprises to encourage them to spend more on R&D, carry out green innovation, and actively search for renewable energy to achieve sustainable development. Markandya et al. [11] studied the impact of green finance on the economy in different countries by constructing a dynamic general equilibrium model, and found that the implementation of carbon emission tax can not only achieve the effect of energy saving and emission reduction to protect the environment but also lead to the improvement of green total factor productivity. Shi et al. [12] studied the relationship between green credit and economic growth, and found that green credit can promote the development of environmental protection technology and clean industry, which in turn can promote economic growth. Antimian et al. [13] studied the impact of green finance on the economy in different countries by constructing a dynamic general equilibrium model, and found that the implementation of carbon emission tax not only can achieve the effect of energy saving and emission reduction to protect the environment but also can bring about the improvement of green total factor productivity. Li et al. [14] point out that the development of green credit can help green enterprises obtain funds at low interest rates and invest in green projects, which can ease the financing pressure of enterprises, promote the improvement of their environmental protection processes, improve the efficiency of capital use, and ultimately contribute to the improvement of green total factor productivity. Gao et al. [15] argue that green credit serves the new energy and energy conservation and environmental protection industries, which can guide the flow of capital and improve the efficiency of capital allocation, thus promoting the development of green total factor productivity. Zhu et al. [16] study the positive effects of green finance from three aspects: economic transformation, industrial structure, and enterprise management, and point out that green finance not only improves the industrial structure and changes the current traditional energy consumption pattern in China but also broadens the avenues of corporate financing, solves the difficulties of corporate green project financing, and is conducive to the sustainable development of China’s economy.

In this study, the relationship between green credit and green total factor productivity leads to a generally consistent conclusion that green credit can promote green total factor productivity, but there is little research conducted by scholars on the industrial factor, a key influence of green credit on green economic development, and it is not clear how green credit works on green total factor productivity in terms of the related transmission mechanism and the regulation mechanism. It is also unclear how green credit acts on green total factor productivity. Therefore, this paper intends to study the impact of green credit on industrial green total factor productivity from the perspective of green credit by constructing a reasonable transmission mechanism and regulation mechanism.

The main contribution of this paper is threefold. First, this paper explores the relationship between green credit and industrial green TFP from the perspective of green credit, which complements the existing literature and adds a new theoretical basis to the factors influencing industrial green TFP. Second, this paper adds a new perspective to the existing research by using the energy consumption structure as a mediating variable in the theoretical and empirical analysis. Third, this paper incorporates two major green development policies, green credit and environmental regulation, into a single model for analysis in the context of China’s current green development policies, and considers the impact of their interaction on industrial green total factor productivity. Our findings help to enrich the literature, lay the theoretical foundation and empirical basis for China’s green economy development, and provide meaningful policy recommendations for achieving the goal of carbon peaking and carbon neutrality.

Based on the data at the provincial level in China from 2006 to 2019, this paper first applies the SBM-ML model to calculate the growth rate of industrial green total factor productivity in each province of China from 2006 to 2019. Then, by constructing the transmission mechanism of the energy consumption structure, the impact of green credit on industrial green total factor productivity under the dual carbon target is analyzed. Then, we construct the regulation mechanism of environmental regulation on green credit and explore the impact of the interaction effect of environmental regulation and green credit on the promotion effect of green credit on industrial green total factor productivity. Finally, based on the research findings and analysis, policy recommendations are proposed.

2. Theoretical Analysis and Research Hypothesis

Total factor productivity is not a simple factor weighting, but the result of the interaction of all factors, which is a concrete manifestation of the utilization efficiency of production factors and can not only reflect the technical level of enterprises but also indirectly measure the comprehensive management efficiency and talent quality [17,18,19]. However, with the deteriorating ecological environment, it is no longer sustainable to use total factor productivity to measure the development of enterprises only, and green total factor productivity adds environmental measures to the original model, which is a new productivity combining technology and environment and meets the requirements of high-quality green development of China’s economy [20,21]. Referring to the existing literature, this paper defines green total factor productivity as a new type of productivity that adds resource factors and pollution output to measure the level of green technology on the basis of total factor productivity.

Green credit is a financial instrument that promotes the harmonious development of the economy and environment through policy guidance and economic leverage. Green credit has two goals: to support environment-friendly enterprises by offering them credit, i.e., incentive green credit; to restrain highly polluting enterprises by restricting the credit offered to them, i.e., punitive green credit. In other words, banks and other financial institutions will unify loan projects, loan companies and environmental protection-related information into a comprehensive review system, implement differentiated lending, increase interest rates for highly polluting enterprises, and even withdraw existing loans, forcing “two high and one leftover” enterprises to make technological improvements. We will also provide more favorable loan rates to enterprises engaged in new energy development, green environmental protection and circular economy through policy guidance, so as to achieve the “green optimal allocation” of credit resources. As the most important economic tool to promote high-quality green economic development, green credit, through the regulation of economic leverage and the implementation of a differentiated interest rate model, guides credit resources to green industries, reducing pollution emissions in high-polluting industries, and forcing them to carry out green technological innovation, thereby achieving the purpose of improving green total factor productivity. Based on the above analysis, the following research hypotheses are proposed:

Hypothesis 1:

Green credit will increase industrial green total factor productivity.

Green credit mainly acts on the energy consumption structure through three aspects: structural effect, innovation effect and policy guidance. In terms of structural effects, banks and other financial institutions will fully consider China ‘s green development policy when implementing green credit policy, and assess the environmental risks faced by enterprises in the production process, which will strictly limit the capital inflow of traditional “two high and one surplus” enterprises, forcing them to transform their energy consumption and achieve the goal of improving their energy consumption structure [22,23,24,25]. For the new energy industry and green environmental protection enterprises, due to China’s current vigorous advocacy of new energy development and green credit will give new energy enterprises sufficient development funds, alleviating the dilemma of capital shortage in the development of new energy enterprises, thus promoting green low-carbon development from the supply side and reducing the proportion of traditional fossil energy consumption [26,27]. In addition, the green credit policy will promote the development of green environmental protection enterprises, and as the demand side of new energy, the rapid development of green environmental protection enterprises will in turn promote the continuous development of new energy from the demand side, and the benign interaction between the two will eventually promote the improvement of the energy consumption structure [28]. In terms of the innovation effect, green credit will not only strengthen the technological innovation ability of green enterprises but also improve the production process of the traditional “two high and one surplus” enterprises [29,30]. On the one hand, green credit has brought a lot of financial support to new energy enterprises and green environmental protection enterprises and solved their financing difficulties in the process of green technology innovation; on the other hand, the green property of green credit limits the capital inflow of traditional “two high and one surplus” enterprises, which makes these enterprises face a serious bankruptcy crisis. Therefore, in order to alleviate their own survival pressure and obtain more credit resources, traditional “two high and one surplus” enterprises have to reduce their dependence on traditional fossil energy, implement clean production, and finally achieve the purpose of optimizing their energy consumption structure. In terms of policy guidance, green credit is proposed to promote the process of green development in China, and the investment direction is biased towards new energy and green environmental protection enterprises. Therefore, green credit itself has a strong policy-oriented attribute [31,32]. This attribute not only broadens the external financing channels of green enterprises but also solves the financing difficulties of enterprises in the process of realizing clean production, thus actively promoting the construction and development of a new energy industry and green environmental protection industry, and ultimately increasing the proportion of new energy use.

The proportion of traditional fossil energy and clean energy consumption will have a significant impact on industrial green total factor productivity [33,34]. On the one hand, in China’s energy consumption structure, the consumption of fossil resources such as coal occupies a large share [35]. The higher the coal consumption the lower the utilization efficiency, which will seriously hinder the improvement of green total factor productivity. On the other hand, compared with traditional fossil energy, new energy has the characteristics of recycling, high efficiency and environmental protection, which can effectively improve green total factor productivity [36,37]. Therefore, reducing the dependence on fossil energy such as coal and actively developing and utilizing new energy can greatly promote the process of green development in China. Based on the above analysis, the following research hypothesis is proposed:

Hypothesis 2:

Green credit will increase industrial green total factor productivity by improving the energy consumption structure.

Under the influence of the government’s environmental regulation policy, the cost of pollution control will be significantly increased, and enterprises will choose to use part of their green credit funds to cover the cost of pollution control because they cannot afford the high cost themselves, thus inhibiting their green technology innovation. [38,39]. In addition, due to the differences in resource endowments and economic development among provinces in China, strong environmental regulations will force the traditional “two high and one surplus“ enterprises in some provinces to divest their business, prompting enterprises to carry out a high-pollution business. The “pollution transfer” between provinces makes enterprises use part of the green credit funds for the divested high-pollution business to build factories. Although this measure alleviates the pollutant emissions of enterprises in the province to a certain extent, it will have adverse consequences for the green technological innovation of enterprises themselves and the green economic development of other provinces [40]. Based on the above analysis, the following research hypothesis is proposed:

Hypothesis 3:

The interaction effect of environmental regulation and green credit will inhibit the promotion effect of green credit on industrial green total factor productivity.

3. Materials and Methods

3.1. Methodology

The Data Envelopment Analysis (DEA) model can be used not only to account for economic efficiency or green economic efficiency but also to calculate productivity indices. Moreover, the productivity index calculated by the DEA model is often used as a proxy for green total factor productivity in academia.

First, assume that there exist K decision units and denote them as ($a_1$, $a_2$ … $a_k$), where each decision unit has N different types of input elements, denoting them as ($x_1$, $x_2$, … $x_n$). Each decision cell also has two different output vectors, which are the M desired outputs and the I undesired outputs, which are denoted as ($y_1$, $y_2$, … $y_m$) and ($b_1$,$b_2$....$b_i$). Based on the three premises and assumptions of weak disposability of output, zero binding and free disposability, according to the DEA method, this paper expresses it as:

$$\begin{gathered} p^t\left(x^t\right)=\left\{\left(y^t,b^t\right):{\sum }_{k=1}^K{a}_k^t{y}_{km}^t\ge y_{km}^t,\forall m;\right. \\ {\sum }_{k=1}^K{a}_k^t{b}_{ki}^t=b_{ki}^t,\forall i; \\ {\sum }_{k=1}^K{a}_k^t{x}_{kn}^t\le x_{kn}^t,\forall n; \\ \left.{\sum }_{k=1}^K{a}_k^t=1,a_k^t\ge 0,\forall k\right\} \end{gathered}$$

In the above equation, $a_k^t$ represents the weight of each decision unit observation. ${\sum }_{k=1}^K{a}_k^t=1,a_k^t\ge 0,\forall k$ is the variable scale payoff, as defined by Tone [41], and the Slacks-Based Measure (SBM) function that incorporates the resource environment into the model can be expressed as

$$\begin{array}{cc}\hfill \overrightarrow{S^{G,k^t}}& =\left(x^{G,k^t},y^{G,k^t},b^{G,k^t},g^{G,k^t,x},g^{G,k^t,y},g^{G,k^t,b}\right)\hfill \\ \hfill & =\underset{S^x,S^y,S^b}{max}\frac{\frac{1}{N}{\sum }_{n=1}^N\frac{s_n^{G,k^t,x}}{g_n^{G,k^t,x}}+\frac{1}{M+I}\left({\sum }_{m=1}^M\frac{s_m^{G,k^t,y}}{g_m^{G,k^t,y}}+{\sum }_{i=1}^I\frac{s_i^G,k^t,b}{g_i^{G,k^t,b}}\right)}{2}\hfill \\ \hfill \mathrm{s}.\mathrm{t}.& {\sum }_{k=1}^K{a}_k^t{x}_{n,k}^t+s_n^{G,k^t,x}=x_{n,k}^t,\forall n;\hfill \\ \hfill & {\sum }_{k=1}^K{a}_k^t{y}_{m,k}^t-s_m^{G,k^t,y}=y_{m,k}^t,\forall m\hfill \\ \hfill & {\sum }_{k=1}^K{a}_k^t{b}_{i,k}^t+s_i^{G,k^t,b}=b_{i,k}^t,\forall i;s_n^{G,k^t,x}\ge 0,\forall n;\hfill \\ \hfill & s_m^{G,k^t,y}\ge 0,\forall m;s_i^{G,k^t,b}\ge 0,\forall i\hfill \end{array}$$

In the above equation, $S^{G,k^t}$ represents the SBM function. $x^{G,k^t},y^{G,k^t},b^{G,k^t}$ denote the input vector, the desired output vector, and the undesired output vector for each province, respectively; $g^{G,k^t,x},g^{G,k^t,y},g^{G,k^t,b}$ denote the decrease in inputs, the increase in the desired output, and the decrease in the undesired output in each province, respectively; $s_n^{G,k^t,x},s_m^{G,k^t,y},s_i^{G,k^t,b}$ denote the slack vector of inputs, the slack vector of desired outputs, and the slack vector of non-desired outputs, respectively. Then, based on the Malmquist-Luenberger (ML) index, the function of industrial GTFP growth rate is derived as

$$M{L}_t^{t+1}={\left\{\frac{\left[1+\overrightarrow{S_t}\left(x_{t+1},y_{t+1},b_{t+1},g_{t+1}\right)\right]}{\left[1+\overrightarrow{S_t}\left(x_t,y_t,b_t,g_t\right)\right]}\times \frac{\left[1+\overrightarrow{S_{t+1}}\left(x_{t+1},y_{t+1},b_{t+1},g_{t+1}\right)\right.}{\left[1+\overrightarrow{S_{t+1}}\left(x_t,y_t,b_t,g_t\right)\right]}\right\}}^{\frac{1}{2}}$$

If ML < 1, it means that industrial green total factor productivity has increased; if ML > 1, it means that industrial green total factor productivity has relatively decreased, and if ML = 1, it means that industrial green total factor productivity is unchanged.

3.2. Variable Selection and Data Sources

In 2006, China issued the paper “Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks”, which first introduced the concept of green credit. Subsequently, green credit has been rapidly developed in China. Therefore, the panel data of 30 provinces in mainland China (except Tibet) from 2006 to 2019 are selected for analysis in this paper, with the following variable indicators:

3.2.1. Explanation of Variable-Industrial Green Total Factor Productivity (IGTFP)

Among the many methods for measuring green total factor productivity, the DEA method does not need to set a specific function form, and is more mature in application and in line with China’s green development model. Therefore, this paper draws on the measurement methods of most scholars, and uses the SBM function and ML index to measure industrial green total factor productivity.

• Factor input. Resource input, expressed as the total energy consumption of each province. Labor input is expressed by the number of employees in industrial towns. Capital investment is measured by the net value of industrial fixed assets.
• Expected output. Since in the process of industrial production, the emission of pollutants is not only attributed to a certain moment, but throughout the production process, the total industrial output value is selected and the price is reduced to measure the expected output.
• Undesired output. Measured by industrial wastewater, industrial SO2 and industrial soot emissions.

Finally, the growth rate indicator is transformed into a productivity indicator: assume that the productivity in 2006 is 1, the productivity in 2007 is equal to the productivity in 2006 multiplied by the growth rate in 2007, etc.

3.2.2. Core Explanatory Variable-Green Credit (GL)

From the definition of green credit, the direction of bank loans can be roughly divided into “two high and one surplus“ industries and non- “two high and one surplus“ industries. The six high energy-consuming industries are typical representatives of the “two high and one surplus“ industries, and the interest expenditure has a strong correlation with the total amount of loans. Therefore, the interest expenditure of high energy-consuming industries can indirectly reflect the development of green credit in China. At the same time, in order to facilitate the following empirical analysis, this paper forwards the data indicators, that is, (the proportion of interest expenditure of six high energy-consuming industries) to measure green credit.

3.2.3. Intermediary Variable-Energy Consumption Structure (ECS)

Compared with other fossil energy sources, the process of coal combustion will produce more pollutant emissions, and China is rich in coal resources. China’s coal consumption accounts for more than 50% of fossil energy consumption. Therefore, this paper uses the ratio of coal consumption to total energy consumption in each province to represent the energy consumption structure.

3.2.4. Moderating Variables

Environmental regulation (ER): Environmental regulation represents the degree to which the government attaches importance to the quality of the ecological environment. This paper uses the ratio of pollution control investment to local GDP to represent environmental regulation.

3.2.5. Control Variables

• Foreign direct investment (FDI): First, according to the annual average exchange rate between China and the United States, the dollar is converted into RMB units, and the ratio of the actual utilization of foreign investment in each province to the GDP of the region is used to represent foreign direct investment.
• Regional economic development (ED): expressed as the ratio of provincial per capita GDP to national per capita GDP.
• Marketization index (MI): China’s marketization index consists of five aspects: the relationship between government and market; the development of non-state-owned economy; the development degree of product market; the development degree of factor market; the development of market intermediary organizations and the legal environment.
• The scale of state-owned enterprises (SOE): expressed as the ratio of the total output value of state-owned and state-controlled enterprises in each province to the GDP of the region.

The above indicators are taken as the natural logarithm to eliminate the heteroscedasticity, denoted as $lnIGTFP$, $lnGL$, $lnED$, $lnFDI$, $lnMI$, $lnSOE$, $lnECS$, and $lnER$. The data are from China Statistical Yearbook, China Energy Statistical Yearbook, National Bureau of Statistics, Wind database and China Provincial Marketization Index Report.

3.3. Empirical Model

In order to test the impact of green credit on industrial green total factor productivity, this paper constructs the following econometric model:

$$\mathrm{l}\mathrm{n}IGTF{P}_{i,t}={\alpha }_0+{\alpha }_1\mathrm{l}\mathrm{n}G{L}_{i,t}+{\alpha }_2\mathrm{l}\mathrm{n}FD{I}_{i,t}+{\alpha }_3\mathrm{l}\mathrm{n}E{D}_{i,t}+{\alpha }_4\mathrm{l}\mathrm{n}{\mathit{MI}}_{i,t}+{\alpha }_5\mathrm{l}\mathrm{n}{SOE}_{i,t}+{\epsilon }_{i,t}$$

(1)

Based on the above theoretical analysis, on the basis of the original model, the energy consumption structure is added as an intermediary variable to further construct the intermediary effect model, as follows:

$$\mathrm{l}\mathrm{n}{ECS}_{i,t}={\alpha }_0+{\alpha }_1\mathrm{l}\mathrm{n}G{L}_{i,t}+{\alpha }_2\mathrm{l}\mathrm{n}FD{I}_{i,t}+{\alpha }_3\mathrm{l}\mathrm{n}E{D}_{i,t}+{\alpha }_4\mathrm{l}\mathrm{n}{\mathit{MI}}_{i,t}+{\alpha }_5\mathrm{l}\mathrm{n}{SOE}_{i,t}+{\epsilon }_{i,t}$$

(2)

$$\begin{gathered} \mathrm{l}\mathrm{n}IGTF{P}_{i,t}={\alpha }_0+{\alpha }_1\mathrm{l}\mathrm{n}{ECS}_{i,t}+{\alpha }_2\mathrm{l}\mathrm{n}G{L}_{i,t}+{\alpha }_3\mathrm{l}\mathrm{n}FD{I}_{i,t}+{\alpha }_4\mathrm{l}\mathrm{n}E{D}_{i,t}+ \\ {\alpha }_5\mathrm{l}\mathrm{n}{\mathit{MI}}_{i,\mathit{t}}+{\alpha }_6\mathrm{l}\mathrm{n}{SOE}_{i,t}+{\epsilon }_{i,t} \end{gathered}$$

(3)

Finally, in order to test the interaction between green credit and environmental regulation, the interaction indicators of environmental regulation and environmental regulation and green credit are added to the above model as follows:

$$\begin{gathered} \mathrm{l}\mathrm{n}IGTF{P}_{i,t}={\alpha }_0+{\alpha }_1\mathrm{l}\mathrm{n}G{L}_{i,t}+{\alpha }_2\mathrm{l}\mathrm{n}FD{I}_{i,t}+{\alpha }_3\mathrm{l}\mathrm{n}E{D}_{i,t}+{\alpha }_4\mathrm{l}\mathrm{n}{\mathit{MI}}_{i,t}+{\alpha }_5\mathrm{l}\mathrm{n}{SOE}_{i,t}+ \\ {\alpha }_6\mathrm{l}\mathrm{n}{ECS}_{i,t}+{\alpha }_7\mathrm{l}\mathrm{n}{ER}_{i,t}+{\alpha }_8\mathrm{l}\mathrm{n}{GL}_{i,t}\times \mathrm{l}\mathrm{n}{ER}_{i,t}+{\epsilon }_{i,t} \end{gathered}$$

(4)

In the above formula, subscripts i and t represent provinces and years, respectively. IGTFP represents industrial green total factor productivity, GL represents green credit, FDI represents foreign direct investment, ED represents regional economic development, MI represents marketization index, SOE represents state-owned enterprise scale, ECS represents energy consumption structure, ER represents environmental regulation, and ε is a random error term.

4. Results and Discussion

4.1. Descriptive Statistics

Table 1. Descriptive statistical results of main variables.

Variable	Obs	Mean	Std. Dev.	Min	Max
FDI	420	0.0222	0.0175	0.0001	0.0819
ER	420	0.0014	0.0013	0.0001	0.0099
ER	420	1.0779	0.5352	0.3435	3.4239
MI	420	6.4350	1.8754	2.3300	11.7100
SOE	420	0.5119	0.1772	0.1400	0.8396
ECS	420	0.9461	0.4089	0.0248	2.4609
IGTFP	420	1.7750	0.7683	0.6427	6.1078
GL	420	0.4725	0.1467	0.0940	0.8230

shows the descriptive statistical results of the main variables. Among them, the average value of the national industrial green total factor productivity is 1.7750, and its standard deviation is 0.7683, indicating that China’s industrial green technology level has made a breakthrough, and industrial green transformation has made some progress. The mean value of green credit is 0.47252, its standard deviation is 0.1467, and it fluctuates in the range of 0.09409–0.8230, indicating that there is obvious heterogeneity in the development of green credit in China’s provinces, and green credit still has considerable room for development. Other control variables are basically consistent with the results of the related literature.

4.2. Basic Analysis

Table 2. The impact of green credit on IGTFP.

Variables	lnIGTFP
lnGL	0.251 ***
	(3.52)
lnFDI	−0.175 ***
	(−8.05)
lnED	0.732 ***
	(7.09)
lnMI	−0.139 ***
	(−2.83)
lnSOE	1.070 ***
	(9.79)
Constant	1.036 ***
	(7.15)
Observations	420
R-squared	0.460

Note: *** denotes a significant level of 1% and standard error is in brackets.

lists the full sample regression results of the impact of green credit on industrial green total factor productivity. It can be seen that green credit will promote industrial green total factor productivity. For every 1% increase in green credit, industrial green total factor productivity will increase by 0.251%. This result shows that green credit will significantly increase industrial green total factor productivity. This may be because external financing is one of the most important financing channels for enterprises. The level of financing can indirectly affect the level of R&D of enterprises, and green credit can implement differentiated interest rates through economic leverage to increase the threshold of corporate financing and reduce the financing quota of high-polluting enterprises, so as to force them to change their original production mode, and ultimately improve the structure of energy consumption, promote industrial integration and the improvement of green innovation level of enterprises. At the same time, green credit is guided by policy guidance, strongly supports low-carbon environmental protection and new energy enterprises, provides financial support for enterprises to implement the innovation of green technology, accelerates the pace of production and construction, optimizes the industrial structure and ultimately promotes the growth of industrial green total factor productivity.

4.3. Analysis of Transmission Mechanism

The regression results listed in

Table 3. Regression results of transmission mechanism.

	(2)	(3)
Variables	lnECS	lnIGTFP
lnECS		−0.503 ***
		(−9.50)
lnGL	−0.234 ***	0.134 **
	(−3.78)	(2.04)
lnFDI	0.025	−0.163 ***
	(1.30)	(−8.28)
lnED	−0.014	0.725 ***
	(−0.15)	(7.80)
lnMI	0.122 ***	−0.078 *
	(2.87)	(−1.74)
lnSOE	−0.151	0.994 ***
	(−1.59)	(10.06)
Constant	−0.581 ***	0.743 ***
	(−4.63)	(5.54)
Observations	420	420
R-squared	0.100	0.563
Number of province	30	30

Note: ***, **, * denotes a significant level of 1%, 5%, 10% respectively, and standard error is in brackets.

show that in Model (2), the GL coefficient is negative and significant. In Model (3), the ECL coefficient is negative and the GL coefficient is positive and both are significant, indicating that green credit can improve the energy consumption structure of enterprises and thus improve the industrial green total factor productivity. The energy consumption of high-pollution industries in China is dominated by fossil energy, but it is worth noting that the combustion of traditional fossil energy is not sufficient, and there are many pollution emissions, which will undoubtedly have a negative impact on the protection of the ecological environment. The green attribute of green credit inhibits the construction and development of high-pollution industries. In order to obtain more credit funds, high-pollution industries must change their energy consumption patterns and embark on the road of clean production. In addition, green credit invests heavily in new energy construction, supports new energy enterprises to actively develop, uses new energy instead of fossil energy, optimizes China’s energy consumption structure from the source, promotes the realization of dual-carbon goals and achieves high-quality green development of the economy.

4.4. Analysis of Regulatory Mechanism

It can be seen from

Table 4. Regression results of regulatory mechanism.

Variables	lnIGTFP
lnECS	−0.450 ***
	(−8.74)
lnGL	0.148 **
	(2.34)
lnFDI	−0.138 ***
	(−7.22)
lnED	0.693 ***
	(7.79)
lnMI	−0.084 *
	(−1.96)
lnSOE	0.809 ***
	(8.22)
lnER	−0.125 ***
	(−6.39)
lnGL × lnER	−0.084 **
	(−2.01)
Constant	−0.121
	(−0.65)
Observations	420
Number of province	30
R-squared	0.605

Note: ***, **, * denotes a significant level of 1%, 5%, 10% respectively, and standard error is in brackets.

that the ER coefficient is negative and significant, which indicates that environmental regulation will inhibit the improvement of industrial green total factor productivity. This may be because if the government adopts strict environmental regulation measures, the cost of pollution control in traditional high-pollution industries will increase sharply, and as the profits of traditional high-pollution industries are low, they may even face the situation that they cannot make ends meet. At this time, enterprises will lack funds for the innovation of green technology. The interaction coefficient of GL and ER is negative, indicating that environmental regulation will reduce the promotion effect of green credit on industrial green total factor productivity. This may be due to the increase in the intensity of government environmental regulation. Because high-polluting enterprises are not supported by policies, enterprises have to use credit funds with high capital costs for sewage treatment, and passively cope with government policies. Although pollution emissions have been reduced to a certain extent, their green technology level has not been strengthened. If the bank has lax supervision and mistakenly believes that enterprises have achieved green technology innovation and give them additional financial support, the significance of green credit policy will be greatly reduced.

5. Conclusions and Policy Recommendations

5.1. Conclusions

Based on the literature and policies issued, this paper expounds the mechanism of green credit on industrial green total factor productivity, and uses the SBM-ML model to measure the growth rate of industrial green total factor productivity in China from 2006 to 2019. In order to further test the impact of green credit on industrial green total factor productivity, the basic regression model, the mediating effect regression model and the moderating effect regression model are constructed, and empirical research based on the theoretical analysis are conducted. The conclusions are as follows:

First, green credit will increase industrial green total factor productivity.

Second, green credit will increase industrial green total factor productivity by improving the energy consumption structure.

Third, the interaction between environmental regulation and green credit will reduce the promotion effect of green credit on industrial green total factor productivity.

5.2. Policy Recommendations

First, improve the relevant legal system, and vigorously promote and implement the green credit policy. Green development is one of the five major development concepts in China. However, it can be seen from the relevant documents that most of the green credit documents appear in the form of departmental regulations, and the specific implementation of the documents involves multiple departments, which brings about problems such as unclear division of responsibility and poor coordination, which undoubtedly hinder the implementation of the policy. Therefore, we should actively implement the concept of green development in China, appropriately improve its legislative level, strengthen the binding force of the law on enterprises to actively fulfill their social responsibilities, provide a strict legal basis for the better implementation of the green credit policy and inject impetus into the improvement of industrial green total factor productivity.

Second, encourage enterprise technological innovation, gradually reduce fossil energy consumption and promote industrial green process. It is necessary to deeply understand that innovation is the primary driving force for development, encourage enterprises to actively promote the innovation of green technology, improve the technological process of enterprises from the technical level, achieve technological progress, promote the green development of industry and at the same time strengthen the supervision of the energy use of enterprises, urge enterprises to achieve technological improvement in traditional production processes, improve the efficiency of the use of fossil energy, reduce pollutant emissions and promote industrial green upgrading.

Third, differentiated environmental protection policies should be formulated to maximize the role of green credit. Environmental protection policies need to be formulated and implemented according to the specific development conditions of the region. In some areas, the environmental regulation should be moderately liberalized, the environmental regulation intensity should be applied to adapt to the characteristics of industrial development, the environmental credit evaluation mechanism should be promoted and enterprises should be encouraged to disclose environmental information independently. On this basis, administrative penalties for polluting enterprises should be appropriately reduced to alleviate the shortage of green innovation funds and promote the transformation of enterprises’ environmental awareness from forced to voluntary.

5.3. Deficiencies of the Study

First, this paper ignores the possible spatial spillover effects of industrial green total factor productivity, which makes the research findings somewhat limited.

Second, due to the lack of comprehensive expertise, this paper may not analyze the results of the impact of green credit on industrial green total factor productivity sufficiently deeply.