Unlocking Sustainable Growth: The Transformative Impact of Green Finance on Industrial Carbon Emissions in China

Abstract

This study investigates the crucial role of green finance in addressing the imperative of reducing industrial carbon emissions for a sustainable global economy. Encompassing facets, such as green credit, insurance, investment, and governmental help for growth in green businesses. Our research on the strength of a comprehensive dataset covering China’s 30 provinces between 2010 and 2019 employs a fixed-effects regression and heterogeneity assessment, revealing an inverse association between green finance and industrial carbon emissions. This verifies the notion that green finance serves as a deterrent to carbon emissions from the industrial sector. According to the results of this study, green financing can significantly lower the CO₂ emissions from industries, which in turn can lead to an enhancement in environmental quality. Notably, our findings revealed substantial regional variations in this relationship. By proposing actionable recommendations, we advocate strategies to address regional disparities, standardize measurement protocols for green finance, optimize the environment for technological innovation, and realize industrial structures. By acknowledging these nuanced dynamics, our study not only contributes to the understanding of the impact of green finance but also offers targeted solutions to foster high-quality sustainable development in China, ensuring a more effective and comprehensive approach to mitigating carbon emissions in the industrial sector.

1. Introduction

In the 20th century, the global economy grew significantly and China developed rapidly. This growth has been achieved through the widespread use of fossil fuels, including coal and natural gas. However, the combustion of this energy also releases large amounts of greenhouse gases, such as CO₂, which constitutes a primary reason for climate change and global warming. According to the latest IPCC assessment report, due to greenhouse gases produced by human activities, in comparison to 1850 to 1900, the temperature on Earth rose by 1.1 °C between 2011 and 2020, and it is anticipated that the estimated increase in global temperature would surpass 1.5 °C by the year 2040, with rapid and widespread changes in the atmosphere, ocean, and biosphere. In February 2005, 149 developed countries and regions adopted the Kyoto Protocol to address concerns about climate change. In December 2015, the Paris Agreement in France was officially approved by the 21st United Nations International Climate Conference, providing a unified guideline for the worldwide reaction to changing climate after 2020: “Encourage stronger global climate governance while working to keep temperature increases to 1.5 °C or less”. Although global coal consumption has decreased by 0.9% compared to 2018 and some countries have also begun the exploitation of new energy sources and the application of sustainable energy, the rate of increase in natural gas and oil energy consumption is much faster than the rate of decrease in global coal consumption. Therefore, there has not been a significant drop in the output of atmospheric pollutants such as carbon dioxide. Therefore, how to effectively deal with the “carbon problem” caused by excessive industrialization has become an urgent matter.

Since the 21st century, China’s economy has steadily advanced and has gradually become the second largest economy and the largest carbon dioxide emitter on Earth. To curb these high emissions, China has become the head country on Earth and embraced a green financial system. In 2016, with the support of the central government, Guiding Opinions on Building a Green Financial System was released by the Chinese government, which outlined China’s definition of green financing. In 2017, following its decision to create a green finance reform and innovation pilot zone, the State Council of China released a document outlining the policy guidelines for construction. In 2020, China proposed a vision called “carbon peaking and carbon neutrality”, which is an inherent need to promote sustainable development. The sources of carbon emissions include production, electricity, agriculture, and transportation. Among these, production and manufacturing account for the largest proportion. Because China is a large manufacturing country, it is important to reduce carbon emissions in the industrial sector. It also indicates that, to achieve sustainable growth, the nation needs to build an industrial system and accelerate the transition of industry to low-carbon production. Therefore, progress in green finance has become a core issue in addressing this problem. China’s green finance has flourished in the last few years; as of September 2023, the green credit balance in China was 28.58 trillion yuan, an increase of 36.7% over the previous year, ranking first in the world, mainly invested in storage, transportation, and logistics services, as well as electric power, heat, gas, renewable energy, and clean energy projects. The domestic green bond market is second globally with a balance of 1.98 trillion yuan.

The purpose of green finance is to support economic endeavors that help mitigate climate change, improve the environment, and efficiently use resources. Thus, the production preferences of various industries can be changed, and the flow of funds can be directed to promote emission reduction and energy conservation, ultimately achieving green transformation and sustainable development. Some literature studies have pointed out that among the top ten largest nations which encourage green finance, the carbon emissions of these 10 economies are negatively impacted by green finance, in other words, the growth of green finance could decrease the production of carbon dioxide, but the relationship may vary depending on the market conditions of different countries and green finance [1], and this study also confirmed that the best financial strategy for reducing carbon dioxide emissions is green finance. Therefore, improving the advancement of green finance for China has become a core issue to ensure energy conservation and emission reduction and to develop a transformed economy. An analysis of the impact of green finance on industrial carbon dioxide emissions can provide insight into the degree of green finance advancement and the effectiveness of emission reduction in China.

Consequently, the objective of this study was to discuss the influence of green finance on industrial carbon production. This study considers four major dimensions—green credit, green insurance, green investment, and government support—to build an indicator system to gauge the degree of progress in green finance. The industrial carbon production intensity measurement method was applied to analyze the industrial carbon emissions. Controlled variables such as technological innovation and industrial structure were also introduced. A fixed-effects model was used to explore the influence of the heterogeneity of green finance on carbon production from industries in China.

The primary contributions of this study are as follows. Current domestic and international research on green finance mostly concentrates on the theoretical level in terms of theoretical significance, such as its connotation, policies, mechanisms, and systems. However, relatively little quantitative research has been conducted and the association between green finance and carbon emissions from industries has not received much attention. This study proposes combining green finance with industrial carbon emissions, combs, and summarizes theories of green finance, and demonstrates whether green finance affects industrial carbon emissions using models, which holds important theoretical significance for enriching the research field of green finance. This study determines China’s green finance growth level by utilizing theoretical research on the externality of green financing, combines the actual situation in China’s industrial carbon emissions, and examines its effects on industrial carbon dioxide emissions and regional heterogeneity through empirical analysis. It also offers suggestions on how a nation’s provinces may advance green finance expansion progressively.

The remainder of the paper is structured as follows: the Section 2 presents a literature review, the Section 3 presents the data and methodology, the Section 4 presents the results and discussion, and the last section concludes the paper with policy suggestions.

2. Literature Review

2.1. The Conception and Measurement of Green Finance

Green finance, also referred to as low-carbon or sustainable finance, considers financial matters from the environmental preservation perspective. Scholars have studied green finance evaluation systems successfully. As the uninterrupted deepening of research on green ecological finance, first, Lee and Hussain (2023) used a Simple Linear Regression model to analyze the impact of socio-economic indicators on green finance, covering family age, education level, household size, knowledge of green finance, monthly income and expenditure, and the amount of wood and natural gas used [2]. Zheng et al. (2022) brought in neural networks in the assessment framework for urban green finance progress by constructing an assessment indicator system for the growth level of a city’s green financing, which included standard-level environmental, financial, and social factors and optimized the model through Genetic Algorithm [3]. The empirical analysis shows that the state of urban economic development, manner and effectiveness of capital allocation, and level of capital and policy support for the preservation of the environmental sector significantly influence the growth of the urban economy. Wang et al. (2021) developed an approach to measurement based on the fuzzy mathematics principle that may be employed to gauge the extent of advancement in green finance, and the enhanced analytic hierarchy process (AHP) and the entropy approach were used to calculate the indicator weights. The results show that the model can accurately assess the degree of green finance development, and that green financing indicators in China have generally shown a pattern of strong expansion over the previous nine years [4].

Over the last few years, with the uninterrupted deterioration of the ecological environment, governments worldwide have begun to strengthen the popularization of green concepts and actively advocate a green outlook on life. Therefore, scholars have not only focused on defining the concept of “green finance” but have also begun to study issues such as the effectiveness of green financial instruments. In the 21st century, international experts have extensively studied the concepts, instruments, and impacts of green finance. Lyu et al. (2022) used the logarithm of the discrepancy between the overall interest expenses of each provincial industrial sector and those of six energy-consuming industries to compute green credit. Additionally, a dynamic panel data model was developed to investigate how green credit affects carbon emissions at national and regional scales and the underlying mechanism [5]. These findings indicate that implementing green credit substantially reduces carbon emissions. Notably, the eastern region demonstrated the most pronounced emission-reduction effect. Except for the western section, green finance exerts a significant and favorable influence on disruptive low-carbon innovation. Sinha et al. (2021) used quantitative regression and quantile estimation to explore the impact of green bonds and financing on the preservation of the environment and sustainability in society. These results suggest that the green finance mechanism may have a progressively detrimental effect on social and environmental accountability [6]. Ronaldo et al. (2022) used 220 valid responses for data analysis, and the results showed that green finance can stimulate the advancement of environmentally friendly technology and the growth of small-scale green businesses, further fulfilling Sustainable Development Goals by means of sustainability for the environment and economy [7]. Abbas et al. (2023) analyzed the data of 50 Chinese energy companies from 2012 to 2021 through quantitative regression and dynamic analysis techniques. The findings indicate that the provision of funds for environmentally friendly projects and the implementation of taxes specifically targeting environmental concerns have a substantial influence on the progress and advancement of renewable energy initiatives [8].

Based on previous research, we created a system to assess the degree of development in green finance from four aspects—green credit, green insurance, green investment, and government support—to examine the degree of green finance development in 30 Chinese provinces and cities, and calculate the green finance development index for each province and city (see

Table 1. Indicator system of green finance development level.

Primary Indicators	Characteristic Index	Indicator Description	Indicator Attribute	References
Green Credit	Proportion of interest expenses in high-energy-consuming industries	Interest expenses for six high-energy-consuming industrial industries	-	Song, et al. (2021) [9]
Green Insurance	Depth of crop insurance	Income from crop insurance/total agricultural output value	+	Li and Xia,2014 [10]; Lin, 2023 [11]
Green Investment	Proportion of pollution control expenditure to GDP	Pollution control expenditure/GDP	+	Ren, et al. (2022) [12]
Government Support	Proportion of fiscal environmental protection	fiscal expenditure on environmental protection/fiscal expenditure	-	Zhou et al. (2020) [13]

).

Table 1 provides a detailed explanation of the index system used to determine the degree of green finance development.

First, green credit applies the measurement method proposed by Song et al. (2021) and uses the percentage of interest costs in highly energy-intensive industries as a negative indicator [9]. It is believed that, in the current situation, where the gap in the industry loan interest rates in China is comparatively small, the changes in industry interest expenses are mostly connected to the loan scale, and the changes in the proportion of interest expenses indirectly reflect the changes in the proportion of loan scale. Simultaneously, the percentage of interest costs in highly energy-intensive industries reflects commercial banks’ initiatives to maintain environmental resource degradation. This study uses the interest expenses of six high-energy-consuming industrial industries as the measurement standard, and the energy consumption and emissions of these industries have a significant impact on the environment, constituting a significant percentage of the total domestic carbon dioxide emissions. By measuring the interest expenses of these industries, the role of green credit can be evaluated more accurately to determine whether it has promoted the modernization and restructuring of these highly energy-intensive businesses towards energy saving and environmental preservation, thereby promoting sustainable development. At the same time, this evaluation method can avoid simply using the reduction in energy consumption, such as coal, as the evaluation criterion, while ignoring the actual situation of industrial transformation and upgrading. The data on green credit come from the China Statistical Yearbook and the Statistical Yearbooks of various provinces.

Second, crop insurance is used as the measurement standard for green insurance indicators mainly because the risks involved in crop insurance are closely related to the environment. These risks include climate change, natural disasters, diseases, and pests, all of which are intricately connected to the environment. Meanwhile, crop insurance also needs to consider the environmental consequences of agricultural production, such as the application of chemical insecticides and fertilizers and the use of water resources. Therefore, the role of risk and environmental factors in green finance can be considered more comprehensively by measuring green insurance using crop insurance indicators. Therefore, this study refers to Li and Xia (2014) practice of using crop insurance as a substitute, and uses the crop insurance premium income/total agricultural output value to reflect the characteristics of the current advancement stage of green insurance in China [10]. Lin et al. (2023) also used this indicator for green insurance to measure green finance [11]. Data on green insurance were sourced from the China Insurance Yearbook.

Third, green investment is an effective investment and financing method that may enhance the ability of green projects to attract investment and financing. The proportion of pollution control expenditure in green investment provided by Ren et al. (2022) reflects the progress of green investment in China [14]. The data on green investment come from the China Statistical Yearbook and China Environmental Statistical Yearbook.

Finally, regarding government support, according to the financing allocation function of the financial market, Zhou et al. (2020) provided an effective incentive theory that government departments should guide the market economy in green financial technology innovation to promote economic development [13]. Therefore, an indicator of government support was added to assess the degree of advancement in green finance, and fiscal environmental protection expenditures were selected to reflect the level of government support.

2.2. Concept and Measurement of Industrial Carbon Emissions

According to the Interim Measures for the Administration of Carbon Emissions Trading, carbon emissions refer to greenhouse gas emissions caused by fossil resource incineration, industrial production links, soil use changes, or forestry activities, including greenhouse gas emissions caused by purchased power and heat [15].

In 2021, carbon dioxide emissions generated by the burning of energy in globalized industries and manufacturing accounted for nearly 89% of total emissions, far exceeding other factors and becoming an important cause of global climate change. Arguments have been made regarding the measurement of the carbon emissions from various industries. First, we selected the calculation methods. According to certain academics, the energy consumption method is the most reliable, because there is a direct connection between energy consumption and carbon emissions. Other scholars believe that the exhaust emission method is more accurate for measuring industrial carbon emissions because it can directly measure the carbon emissions from exhaust gases [16]. Second is parameter estimation. The quantification of carbon emissions from industry requires the estimation of multiple parameters such as energy consumption and exhaust emission concentration. Some scholars tend to use measured data, whereas others prefer methods, such as model simulation and statistical inference [17]. In addition, the comparison and evaluation methods for carbon emissions from industries in different areas, industrial sectors, and time periods have limitations. Some scholars believe that unified indicators and methods should be used for comparison and evaluation in order to obtain comparability. However, other scholars believe that the specificity of different regions and industrial sectors should be considered and suitability methods should be used for comparison and evaluation [18]. There are various uncertainties in the measurement of carbon emissions from industries; therefore, this study focuses on industrial carbon dioxide emissions. Because human activities are the source of industrial carbon emissions, especially fuel consumption during industrialization, these activities result in enormous carbon emissions. Therefore, we define industrial carbon emissions as carbon dioxide generated by the incineration of fossil fuels during industrial production, and focus on the empirical analysis of statistical data from China from 2010 to 2019.

2.3. Research on the Correlation between Green Finance and Industrial Carbon Emissions

Scholars have investigated the relationship between green finance and carbon dioxide emissions to achieve Sustainable Development Goals (SDGs) through economic sustainability. Yu et al. (2022) used the mixed-group mean estimation method to explain how green digital finance influences the mitigation of climate change from the perspective of resource constraints on panel data of 60 emerging and non-emerging economies from 2010 to 2020. The results indicate that promoting digital green financing facilitates the advancement of green and renewable energy sources while mitigating carbon dioxide emissions [19]. Nenavath (2022) conducted a comprehensive survey on the effect of green finance-related policies using the SDID method, a text analysis method, and panel data of 28 states and eight federal districts from 2010 to 2020. The results show that India’s industrial carbon dioxide emissions have dropped significantly because of green finance-related regulations [20]. Mamun et al. (2022) analyzed a substantial sample comprising 46 countries, and the results showed that carbon emissions can be drastically decreased by green finance, both immediately and over time, and in mature credit markets, the green finance’s effect on carbon emissions is more noticeable in economies with higher innovation success rates and climate change risks [21]. Umar et al. (2023) underlined the importance of green innovation and financing in attaining sustainable development. It was concluded through a quantitative Law of Return (MMQR) analysis that green finance remarkably reduced the carbon emissions of OECD countries, while the consequences of green innovation showed a significant passive correlation with trade-adjusted carbon dioxide emissions [22]. Lan et al. (2023) uses a fixed utility model to study the relationship between green finance and industrial pollution in China’s provinces and cities. Using panel data regression, it was concluded that green finance is negatively correlated with industrial carbon emissions [23].

Previous studies have investigated the effects of green finance on carbon emissions in China. Based on the econometric models and panel data of 30 provinces in China from 2005 to 2020, Ran and Zhang (2023) empirically found that green finance can significantly promote carbon emissions reduction [24]. Wang et al. (2023) used provincial panel data from 2008 to 2019 and revealed that the advancement of green finance significantly reduces carbon emissions through the optimization of the energy consumption structure and the upgrading of the industrial structure [25]. However, most of these studies mainly focused on carbon emissions at the national level, neglecting carbon emissions from China’s industry, or only looking at a part of green finance [26,27].

Based on the above literature, we propose the following assumptions regarding the four dimensions of green credit, green investment, green insurance, and government support for green finance:

H1. 

Green credit can reduce industrial CO₂ emissions.

H2. 

Green insurance can reduce industrial CO₂ emissions.

H3. 

Green investment can reduce industrial CO₂ emissions.

H4. 

Government support can reduce industrial CO₂ emissions.

3. Methodology and Data

3.1. Measurement of Green Finance

Based on the existing literature, this study establishes a widespread assessment index system for assessing the amount of green finance development based on the four distinct characteristics of green credit, green insurance, green investment, and government support to study the extent of green finance advancement in 30 provinces and cities in China and calculates provincial and municipal green financing development indices. The steps are described below.

First, to ensure consistency between various technical indicators in the system, it is necessary to preprocess the original data information to eliminate the differences between them so that they have a unified standard and to exclude the impact of positive and negative indicators on the data information.

Positive indicators:

$$V_j(it)={\displaystyle \frac{x_j\left(it\right)-{min}_{1\le i\le n}{min}_{1\le t\le T}\left(x_j\right(it\left)\right)}{{max}_{1\le i\le n}{max}_{1\le t\le T}\left(x_j\right(it\left)\right)-{min}_{1\le i\le n}{min}_{1\le t\le T}\left(x_j\right(it\left)\right)}}$$

(1)

Negative indicators:

$$V_j(it)={\displaystyle \frac{x_j\left(it\right)-{min}_{1\le i\le n}{min}_{1\le t\le T}\left(x_j\right(it\left)\right)}{{max}_{1\le i\le n}{max}_{1\le t\le T}\left(x_j\right(it\left)\right)-{min}_{1\le i\le n}{min}_{1\le t\le T}\left(x_j\right(it\left)\right)}}$$

(2)

where V_j(it) represents the standardized value of index j for province and city i during period t and Xj(it) represents the initial value of index j for province and city i during period t.

Second, the entropy method is employed to ascertain the weights of the indicators.

Jaynes (1982) showed that information entropy has a significant impact on the choice of the assessment index weights [28]. Therefore, we used the entropy method to calculate the indicator weights to better reflect the actual situation and effectively improve evaluation.

$${\mathrm{Z}}_{\mathrm{j}}(\mathrm{i}\mathrm{t})={\displaystyle \frac{{\mathrm{v}}_{\mathrm{j}}\left(\mathrm{i}\mathrm{t}\right)}{\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{v}}_{\mathrm{j}}\left(\mathrm{i}\mathrm{t}\right)}}$$

(3)

$${\mathrm{E}}_{\mathrm{j}}=-{\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{t}=1}^{\mathrm{T}}{\mathrm{z}}_{\mathrm{j}}\left(\mathrm{i}\mathrm{t}\right)\ln {\mathrm{z}}_{\mathrm{j}}\left(\mathrm{i}\mathrm{t}\right)}{\ln \mathrm{n}\mathrm{T}}}$$

(4)

$${\mathrm{W}}_{\mathrm{j}}={\displaystyle \frac{1-{\mathrm{e}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^{\mathrm{m}}1-{\mathrm{e}}_{\mathrm{j}}}}$$

(5)

In this study, Z_j(it) represents the proportion of influence on the j index of province and city i during period t. where V_j(it) = 0, Z_j(it)lnZ_j(it) = 0, E_j represents the entropy value of index j, W_j is the weight of the index, and m is the number of technical indicators. The weight formulas for the various green finance indicators were obtained by calculating the weights of these technical indicators; the conclusions are listed in

Table 2. Calculation of index weights of green finance.

Criterion Layer	Indicator Layer	Weight
Green credit	Proportion of interest expenses in high-energy-consuming industries	11.56%
Green insurance	Depth of crop insurance	50.53%
Green investment	Proportion of pollution control expenditure to GDP	33.15%
Government support	Proportion of fiscal environmental protection	4.75%

.

3.2. Measurement of Industrial Carbon Emissions

As the country has not yet released official carbon emission data for industrial production, this study refers to previous studies and adopts a carbon emission accounting method from the Intergovernmental Panel on Climate Change (IPCC), which originates from the “2006 IPCC Guidelines for National Greenhouse Gas Inventories” [29,30]. It takes panel data for energy consumption of industrial production in 30 provinces from 2010 to 2019, and carries out comprehensive research on energy data of other regions in China to obtain accurate carbon emissions from industrial production. According to the regional energy consumption balance table in the Statistical Yearbook of Energy Consumption of China 1993–2019, energy consumption in industrial production can be divided into nine types: natural gas, electricity, gasoline, diesel oil, fuel oil, coke, raw coal, and fuel oil. Currently, there are generally two methods in the academic community for estimating carbon dioxide emissions: modeling and emission coefficient methods. Because energy consumption is displayed in physical quantities, it is necessary to convert this energy into standard coal when calculating carbon emissions in order to estimate carbon dioxide emissions more accurately.

$$T_{rj}=\sum _{i=1}^9{E}_{irj}{F}_i{C}_i$$

(6)

where T_rj represents carbon emissions generated by energy consumed in the r-th region in the past year, E_irj refers to the demand for Class i energy used by the r-th region in the past year, F_i is the calculation relation of China’s i-class resource benchmark coal (see

Table 3. Conversion standard coal coefficient Fi of various energy sources.

Type	Conversion Coefficient of Standard Coal
raw coal	0.7143 tce/t
gasoline	1.4714 tce/t
fuel oil	1.4286 tce/t
coke	0.9714 tce/t
kerosene	1.4714 tce/t
natural gas	13.300 tce/104 m3
crude oil	1.4286 tce/t
diesel oil	1.4571 tce/t
electricity	1.229 tce/104 kw·h

), C_i is the carbon emission coefficient of China’s i-class resource, and is the carbon emission coefficient provided by the IPCC (2006) (see

Table 4. Carbon emission coefficient Ci of various energy sources.

Type	Carbon Emissions Factors (t Carbon/tce)
Raw coal	0.7476
Gasoline	0.5532
Fuel oil	0.6176
Coke	0.1128
Kerosene	0.3416
Natural gas	0.4479
Crude oil	0.5854
Diesel oil	0.5913
Electricity	2.2132

).

3.3. Model Construction and Variable Selection

3.3.1. Construction of Econometric Models

To explore the effect of green finance on industrial carbon emissions, this study used panel data collected from 30 provinces and cities in China from 2010 to 2019. As there are significant individual differences in the data analysis of these provinces and cities, which are due to a number of causes like geographical location, political environment, folk customs, and climate. Therefore, this study examines fixed-effects and random-effects models, both of which believe that individual effects exist, but that their ways of existence are different. Random effects assume that individual effects are not directly related to the explanatory and control variables, whereas fixed effects assume that at least one factor directly affects individual effects, thereby directly affecting their outcomes.

The random-effects model is as follows:

$$C_{i,t}=\alpha +\beta {GS}_{i,t}+z_i+{\epsilon }_{i,t}$$

(7)

$$C_{i,t}=\alpha +{\beta }_1{GS}_{i,t}+{\beta }_2{LP}_{i,t}+{\beta }_3{IS}_{i,t}{+\beta }_4{lnGDP}_{i,t}+{\beta }_5{FDI}_{i,t}+{\beta }_6{EP}_{i,t}+z_i+{\epsilon }_{i,t}$$

(8)

where C_i,t represents the degree of industrial carbon emissions; GS_i,t represents the degree of development in green finance; and X_i,t represents indicators that control other factors, including technological innovation, industrial structure, economic development level, foreign direct investment, and environmental protection intensity. Z_i is an individual fixed effect term, ε is a disturbance term in the model, representing other factors that will affect the intensity of industrial carbon emissions. α is a constant term and β is the coefficient of the variable reflecting the degree of influence of the variable on the intensity of industrial carbon emissions.

The fixed-effects model is as follows:

$$C_{i,t}={\alpha +\beta GS}_{i,t}+{\nu }_t+{\epsilon }_{i,t}$$

(9)

$$C_{i,t}={\alpha +\beta }_1{GS}_{i,t}{+\beta }_2{LP}_{i,t}+{\beta }_3{IS}_{i,t}+{\beta }_4{lnGDP}_{i,t}+{\beta }_5{FDI}_{i,t}+{\beta }_6{EP}_{i,t}+z_i+{\epsilon }_{i,t}$$

(10)

$$C_{i,t}={\alpha +\beta }_1{GS}_{i,t}{+\beta }_2{LP}_{i,t}+{\beta }_3{IS}_{i,t}+{\beta }_4{lnGDP}_{i,t}+{\beta }_5{FDI}_{i,t}+{\beta }_6{EP}_{i,t}+{\mu }_i+{\nu }_t+{\epsilon }_{i,t}$$

(11)

In the above equation, $C_{i,t}$ and ${GS}_{i,t}$ represent the dependent variable and core explanatory variable in order, while the control variables are technological innovation LP, industrial structure optimization IS, economic level ln GDP, etc., in order; μ, ν, and ε, respectively, represent urban fixed effects, time fixed effects, and random error terms.

3.3.2. Variable Selection

• Dependent Variable

Industrial carbon emissions (C) were used as the dependent variable, reflecting the relationship between the economy and carbon dioxide pollution. By measuring the amount of carbon dioxide released by industrial industries, we can more accurately evaluate how economic activity affects the environment, allowing us to better balance the interests of environmental preservation and economic growth. The decline in carbon emissions shows that a country or economy has made significant progress in implementing low-carbon development strategies (see Table 3 and Table 4).

2.

Independent variable

The green finance development level (GS) was considered the key independent variable. Through an in-depth study of 30 provinces and cities in China, combined with previous studies, this study establishes a system with four levels of green credit, insurance, investment, and government support and evaluates the degree of green finance advancement in China’s provinces and cities (see Table 1 and Table 2).

3.

Control variables

Technological innovation (LP): Technological innovation takes the invention of high and new technology as the main development goal, relies on scientific and technological intellectual property rights and the natural resources it produces, and is realized by the development of environmental protection funds. It is more than dramatically enhancing the level of the ecological environment’s overall condition, further improving the degree of economic and social construction, bolstering energy effectiveness, and reducing the emissions of environmental pollutants, such as high-temperature exhaust gas and sulfur dioxide. Green finance can indirectly boost the quality of the ecological environment, and with the advancement of green technological innovation, the effect of this promotion will also change. This study considers the number of authorized green patents as an important reference index for evaluating green technological innovation ability. This reflects the patent achievements in green technology, environmental protection technology, clean energy technology, and other related areas, which are directly related to the improvement of energy efficiency and the reduction of carbon emissions in the industrial production process [31].

Industrial structure (IS): This index evaluates the quality of national economic growth. With the deepening of China’s economic transformation, it has implemented a range of actions to adjust its structure. However, there is still a gap between its organizational role and the scale effect. In addition, industrial added value can better reflect the net effect of industrial production behavior, and most of the studied industrial carbon emissions come from secondary industries. Therefore, this study selects the ratio of the added value of the secondary industry to GDP as an index to evaluate the structural effect.

Economic development level (lnGDP): This study uses the calculation method proposed by Chao (2014) and others and uses the ratio of actual GDP to the number of long-term populations as the year ends as the index to quantify national economic growth. This study chose the economic development level as the control variable [32].

Foreign direct investment (FDI): The pollution haven hypothesis states that capital flows tend toward countries with low environmental standards. That is, developed countries transfer polluting industries to economically backward countries to prevent their environment from being destroyed in economic development and to reduce the cost of environmental governance and resource constraints. China has formulated environmental regulation policies that are suitable for its national conditions at different stages. Based on the panel dataset for 30 provinces before 2009, scholars have provided support for the existence of a pollution haven within China [33]. The pollution halo hypothesis states that foreign direct investment brings advanced management systems, technology, and a series of experiences to economically weak countries through a demonstration effect. Weaker economies can also improve the utilization of productive resources by learning from others. The government also adjusts local environmental policies according to the current situation and ultimately achieves a carbon reduction. Therefore, the environmental impact of FDI can be both positive and negative. In this study, it is expressed by foreign direct investment in each region/gross domestic product.

Environmental protection intensity (EP): Energy conservation and environmental protection expenditure refers to the total investment used to preserve, repair, safeguard, and enhance the environment to uphold current environmental norms and avert ecological harm. The proportion of environmental protection expenses in the GDP will have an important effect on the quality of environmental protection. Therefore, in theory, an increase in investment in energy savings and environmental protection is beneficial for decreasing carbon dioxide emissions. This study used provincial energy conservation and environmental protection expenditure/GDP data.

3.3.3. Data Sources and Description of Main Variables

The data were obtained from the China Industrial Statistics Yearbook, China Energy Statistics Yearbook, China Statistical Yearbook, and official website of the China Bureau of Statistics. Because some index data from 2020 to 2021 have not been fully published in the process of data screening, and China, along with the rest of the world, has experienced the COVID-19 pandemic since 2020, the data are affected by many factors; therefore, index data from 2010 to 2019 were selected. By sorting the data, descriptive statistics for each factor were calculated, and the explanatory, explained, and control variables were carefully studied.

As

Table 5. Descriptive statistics of main variables.

Variable	Minimum	Maximum	Average	Standard Deviation	Description
C	5.460	8.752	7.466	0.561	Logarithm of industrial carbon emissions
GS	0.047	0.796	0.211	0.095	Green finance development level index
LP	0.000	1226.000	254.723	274.099	Number of green patents authorized
IS	0.242	0.590	0.451	0.061	Added value of secondary industry/GDP
lnGDP	7.188	9.721	8.488	0.475	GDP/year-end resident population
FDI	0.000	0.121	0.022	0.020	Foreign direct investment/local GDP
EP	0.012	0.058	0.031	0.009	Energy conservation and environmental protection expenditure/GDP

shows, notable disparities exist in the levels of carbon dioxide emissions from industries across various locations in China. with the highest value of 8.752 and the lowest value of 5.460, demonstrating a regional disparity in carbon dioxide emissions from industries in China. It can be inferred that there is an imbalance in industrial development, which may be caused by issues such as lopsided regional industrial development, population distribution, and the lagging growth of the industry for environmental protection. When considering the green finance indicators, the average value was 0.211, the maximum value was 0.796, and the minimum value was 0.047, with a significant difference. The advancement of green finance in China has been unbalanced. For the control variables, the maximum value of the technological innovation index was 1226, and the minimum value was 0. Provinces and cities differ significantly in terms of technological innovation indicators, which indicates that the capital investment intensity gap is large and regional investment in technological innovation and R&D is still insufficient. From an industrial structure standpoint, the average share of China’s secondary industry is 0.451 and the standard deviation is 0.061. Although still high, the difference is not significant, indicating that China is gradually improving, transforming, and upgrading its industrial structure. The logarithmic average per capita GDP is 8.488, indicating the stability and sustainability of China’s economic development. On average, the investment level is 0.022 with a standard deviation of 0.475, indicating that the level has high reliability and comparability. The average amount of environmental protection was 0.021 and the standard deviation was 0.012. As each province and city has its own unique set of hard and soft characteristics, the degree of impact needs to be further studied and explained.

4. Results and Discussion

4.1. Regression Results

The data were subjected to regression analysis and subsequently estimated according to the settings of the random- and fixed-effects models. The results are shown in

Table 6. Benchmark regression results based on panel model.

Variable	(1) RE	(2) RE	(3) FE	(4) FE	(5) Bidirectional Fixation
GS	0.453 ** (2.253)	−0.763 ** (−3.335)	0.463 ** (2.265)	−0.859 *** (−3.919)	−0.972 *** (−4.283)
LP		−0.000 (−1.222)		−0.000 ** (−2.100)	−0.000 *** (−2.804)
IS		−0.629 ** (−2.053)		−0.696 * (−2.377)	−0.599 * (−1.936)
lnGDP		0.374 *** (6.568)		0.414 *** (7.436)	0.233 ** (2.034)
EP		−1.316 * (−0.910)		−0.908 * (−0.657)	−0.894 (−0.602)
FDI		−0.145 (−0.177)		0.082 (0.104)	0.884 (0.965)
R2	0.054	0.274	0.054	0.277	0.103
N	300	300	300	300	300

Note: t-statistics are in parentheses; * p < 0.1, ** p < 0.05, and *** p < 0.01.

. Columns (1)–(5) correspond to Equations (7)–(11).

Table 6 shows the regression analysis results based on the random- and fixed-effects models. Columns (1) and (3) illustrate the influence of green finance on the level of carbon emissions from industrial activities, without considering the control variables, whereas Columns (2), (4), and (5) show the results after including the control variables.

GS was used as the independent variable, and C was the dependent variable. Column (1) in Table 6 is a random-effects model that does not consider the control variables. The coefficient of green finance reached 0.453, while the p-value was less than 0.05, indicating that with an improvement in the level of green finance, the degree of industrial carbon emissions increased by approximately 0.453%. Column (3) is a fixed model that does not consider control variables and the green finance coefficient is 0.463, suggesting that enhancing green finance concurrently leads to an increase in industrial carbon emissions. However, solely analyzing green finance’s influence on the intensity of carbon emissions in the industrial sector may ignore some economic and social factors. Therefore, to obtain more accurate regression results, control variables must be incorporated into the model to better reflect the actual situation.

After the control variables are included in the model, the F and BP tests are positive, and the result of the Hausmann test is still negative, indicating that in Columns (2) and (4), the random-effects model is the optimal model. However, this study is based on data collected from 30 provinces and cities, and there is no problem with random sampling. Thus, this study employed a fixed model for analysis. The goodness of fit of the regression reached 0.277, which was much higher than 0.054 when the control variables were not included (Column (3) in the table), indicating that the introduction of control variables into the model can explain industrial carbon emission capacity more effectively. According to the results of the fixed-effects model, the absolute value of the green finance (GS) coefficient is significantly higher than that of the other control variables, and p < 0.01, which suggests that green finance considerably inhibits industrial carbon emissions.

Considering the viewpoint of an individual fixed-effects model, owing to the phenomenon of missing variables (i.e., changing with time and region), it is necessary to introduce a two-way fixed-effects model to ensure the correctness of the model, avoid deviations, and consider individual or time differences. In addition, the two-way fixed-effects model can better explain the direct influence of green finance on industrial carbon dioxide emissions. The specific data for the two-way fixed-effect outcomes are presented in

Table 7. Intermediate process values for bidirectional fixed models.

Variable	Coef	Std. Err	t	p	95%CI
GS	−0.972	0.227	−4.283	0.0020 ***	−1.417~−0.527
LP	−0.000	0.000	−2.804	0.005 ***	−0.000~−0.000
IS	−0.599	0.309	−1.936	0.054 *	−1.205~0.008
lnGDP	0.233	0.114	2.034	0.043 **	0.008~0.457
EP	−0.894	1.484	−0.602	0.547	−3.803~2.015
FDI	0.844	0.875	0.965	0.335	−0.870~2.559
F(6,255) = 5.062, p = 0.000
R2 = −0.108, R2(adjusted) = 0.103

Note: t-statistics are in parentheses; * p < 0.1, ** p < 0.05, and *** p < 0.01.

.

The following conclusions were drawn based on the regression results.

• The green finance’s impact on industrial carbon dioxide emissions

Based on the index of green finance development established in the previous article, the index and amount of carbon dioxide emissions have a negative correlation. Every 1% increase in the level of green finance reduces the intensity of industrial carbon emissions by approximately 0.972%. This may be because green finance requires strict environmental and social responsibility. Therefore, enterprises and institutions should place greater emphasis on environmental and social responsibilities and reduce their carbon emissions from production and business activities. At the same time, the funds and financial instruments provided by green finance will also promote enterprises’ innovation and technological upgrading, and improve their technical level in environmental protection and energy conservation. Thus, enterprises can more effectively reduce carbon emissions from production and business activities and achieve the goal of green development. Thus, it is of utmost importance to curb industrial carbon emissions. As China continues to bolster the establishment of the green finance system, industrial carbon dioxide emissions in all provinces and cities have significantly reduced.

2.

Impact of technological innovation

The negative index coefficient of technological innovation may be because enterprises can promote R&D innovation of products, technological progress, and product upgrading and transformation; provide more efficient services; develop high-value-added products; and help reduce carbon dioxide emission intensity by strengthening investment in introducing talent. In this study, the amount of green patent authorization is used to measure technological innovation, which is negatively correlated with the degree of industrial carbon dioxide emissions. As part of technological innovation, the amount of green patent authorization is directly related to the improvement of energy efficiency and the reduction of carbon emissions in the industrial production process [31]. This means that with an increase in the amount of green patent authorization, the research investment cost will increase accordingly to provide more funds for innovation; thus, it will generate low-carbon emissions to protect the environment. Total factor productivity can also serve as an alternative indicator of technological innovation, but it may be influenced by factors other than technological innovation, such as capital investment and changes in the labor force, which could obscure the actual effects of green technological innovation. Thus, although the impact of green patents is relatively small, understanding their potential influence on carbon emissions is valuable in formulating relevant policies.

3.

Influence of industrial structure optimization

The index coefficient of industrial structure optimization is −0.599, and the percentage of China’s secondary industry’s added value in the total regional output in the index data is decreasing year by year. This demonstrates how effectively modifying the industrial structure can inhibit carbon dioxide emissions, further improve energy efficiency, and minimize the release of gases that contribute to the greenhouse effect. Therefore, to accomplish the objective of a double carton, the modernization and advancement of the secondary industry is essential for the advancement of clean and pollution-free green and/or tertiary industries.

4.

Impact of economic growth

The economic growth coefficient is positive. With continuous growth in GDP, carbon dioxide emissions from industries also display a positive correlation tendency, which is consistent with expectations. This shows that in recent years, the vigorous progress of China’s economy, the expansion of industrial production capacity, and the increase in population have increased carbon dioxide emissions to a limited degree. China’s economic development pattern remains extensive and is determined by the characteristics of heavy industry. This shows that China’s economic transformation will take some time, and the demand for clean energy and zero carbon dioxide emissions will become more urgent in the future.

5.

Impact of environmental protection

Generally, as investment in environmental protection increases, the share of energy conservation and environmental protection in GDP increases correspondingly. In the fixed effects model, an increase in environmental protection investment reduces industrial carbon dioxide emissions, and the relationship between the two is significant. However, in the two-way fixed-effects model, no association was found. This is because the fixed-effects model may not consider the dynamic relationships between variables, such as the impact of environmental protection investment on carbon dioxide emissions, which may change over time. The two-way fixed-effects model can better capture this dynamic relationship. In the dynamic period, increased investment may not be targeted at the most effective emission reduction projects, and the implementation and management efficiency of the investment may not be high.

6.

Impact of foreign direct investment

Foreign direct investment is important for the development of the national economy and the environment. However, the regression analysis found that its effect on the intensity of carbon dioxide emissions was not significant. This may be because of the policy environment of the Chinese government and the fact that foreign investment is subject to strict environmental restrictions. If the local government implements strict environmental regulations and standards, the foreign direct investment may not significantly increase industrial carbon dioxide emissions. Moreover, foreign direct investment may be concentrated in certain areas and the environmental impact of these areas may be offset by lower emissions in other regions [34]. The scale and type of investment also affect the environmental impact. Small-scale or industry-specific investments may not have a significant impact on the overall carbon dioxide emissions.

4.2. Heterogeneity Analysis

Considering the variations in how the socioeconomic environment affects the regression results across different locations, this study delves deeper into the disparities and analyzes the influence of green finance on industrial carbon emissions in various locations. Beijing, Hebei, Tianjin, Liaoning, Shanghai, Zhejiang, Jiangsu, Fujian, Shandong, Hainan, and Guangdong are among the cities in the eastern region of China. Shanxi, Inner Mongolia, Heilongjiang, Jilin, Hubei, Hunan, Anhui, Henan, and Jiangxi were among the cities in the central area. The western region includes Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, and Chongqing.

Table 8. Regional heterogeneity analysis of green finance on industrial carbon emissions.

Term	Nationwide	East Region	Central Region	West Region
GS	−0.972 *** (−4.283)	−1.071 *** (−3.246)	−2.576 *** (−4.290)	−0.937 * (−1.960)
LP	−0.000 *** (−2.804)	−0.000 * (−1.581)	0.000 ** (2.335)	−0.000 *** (−2.726)
IS	−0.599 * (−1.936)	−0.606 (−0.921)	−1.547 *** (−3.014)	−0.143 (−0.332)
lnGDP	0.233 ** (2.034)	0.172 (0.701)	0.174 (0.831)	−0.404 * (−1.914)
FDI	−0.894 (−0.602)	0.373 (0.302)	8.132 * (1.941)	4.097 (1.432)
EP	0.884 (0.965)	1.566 (0.567)	2.250 (0.548)	−1.788 (−0.712)
R2(adjusted)	0.103	0.048	0.009	−2.545
sample size	300	110	90	100

Note: t-statistics are in parentheses; * p < 0.1, ** p < 0.05, and *** p < 0.01.

presents the results of the analysis.

• Eastern region

The green finance coefficient in the eastern region is negative, which proves that the advancement of green finance in the eastern region significantly suppresses industrial carbon emissions. Economic development in the eastern region is relatively mature, with a more complete industrial chain and technological support than in the central region. Simultaneously, there are many investments and applications in green finance. Therefore, enterprises in the eastern region are more likely to accept and apply green finance and carry out projects in areas such as the circular economy, clean energy, energy saving, and environmental protection, thereby reducing industrial carbon emissions. In addition, some cities in the eastern region actively promote low-carbon development, enterprise transformation, and upgrading through policy guidance and market mechanism incentives, increasing investment in green industries, and reducing industrial carbon emissions. The technological innovation indicator is negative and significant, indicating that technological innovation is beneficial for preserving the environment and reducing regional emissions to a certain extent.

2.

Central region

The central region’s industrial carbon emissions are affected by green financing development in a manner similar to that of the eastern region. The significant coefficient of green finance is negative, indicating that green finance advancement in the central district has a remarkable inhibitory effect on local industrial carbon emissions. Among the three major regions, green finance in the central region had a substantial influence on industrial carbon emissions. This may be because the central region is a traditional industrial hub in China, with a single industrial structure and high industrial carbon emissions. Green finance mainly targets areas of preservation of the environment and the promotion of sustainable development, including the circular economy, renewable energy, conservation of energy, and preservation of the environment. The development of these fields in the central region is correspondingly feeble, so the promotion and application of green finance could encourage the growth of these fields, reduce industrial carbon emissions, and greatly influence the amount of carbon emissions produced by industries. In addition, the central regional government’s policy support and financial incentives for sustainable growth and environmental preservation are also obvious, which can stimulate the enthusiasm and innovation of enterprises. There is a negative coefficient for industrial structure at the 1% significance level, indicating that optimizing industrial composition in the central area will reduce industrial carbon emissions. The degree of economic advancement in the central area is positively correlated with industrial carbon emissions. As the economy develops, the scale of industrial production may expand, leading to the increased processing of raw materials and manufacturing of products, which is often accompanied by an increase in carbon emissions. The coefficient for technological innovation in the central region is positive, whereas those for the nationwide, eastern, and western regions are negative. This indicates that an increase in green patent authorization in the central region will lead to an increase in industrial carbon emissions, whereas the results are the opposite in the nationwide, eastern, and western regions. This is because under normal circumstances, the application of green patents can improve energy efficiency and reduce industrial carbon emissions. However, if most of these patents have not been commercialized or put into practical use, their direct impact on carbon emissions may be limited and could even lead to an increase in industrial carbon emissions due to increased costs.

Another point worth noting is that the control variable FDI was not significant for industrial carbon dioxide emissions in either the fixed-effects model or the two-way fixed-effects model. However, in the heterogeneity model (Table 8), its impact on the industrial carbon dioxide emissions of the whole country and the eastern and western regions was not significant, but an increase in FDI in the central region could significantly increase industrial carbon dioxide emissions. The reason, as previous research has found, is that the impact of FDI on China’s carbon emissions has obvious regional differences, and the foreign investment access policy has no significant impact on the carbon emissions of the eastern region [34], while the total carbon emissions in the central region will significantly increase with the introduction of FDI [35] With the implementation of the central region’s rise policy, it has taken over industrial transfer from the eastern coastal areas. These industries are often energy intensive and have high emissions, leading to an increase in carbon dioxide emissions. In addition, some studies have shown that the long- and short-term effects of FDI on carbon emissions are significantly different, and this difference is mainly due to the larger scale effect and the smaller technological progress effect [36], which may offset each other, leading to the impact of FDI on national industrial carbon dioxide emissions being insignificant.

3.

Western Region

In contrast to the central and eastern districts, the importance attributed to the progression of green finance in the western district on industrial carbon emissions is not as high, but it also shows a strong inhibitory effect, with a coefficient of −0.937 and p < 10%. This may be due to the policy tilt in the western region; more support from the government, finance, and taxation; and the concentration of its industrial development level in the resource economy. Most green finance businesses are used to support China’s sustainable development industries, which has led to the emergence of results. Therefore, the influence of green finance in the western region on industrial carbon emissions is significant, reflecting that the country is continuously promoting technological innovation while providing financial support to minimize the release of CO₂ from industries. The coefficient of the economic development level is notably adverse, indicating that the expansion of the western region’s economy has suppressed the increase in carbon emissions from industries. This could be because of the country’s strong development of clean sectors such as solar and wind energy in the western region, which has, to some extent, helped push the western economy into a green development stage.

Based on the data analysis, it was found that green finance indicators have the greatest impact on the central region, followed by the eastern region, and have a smaller impact on the western region. Although there are certain differences in parameter estimates and significance levels, the significant inhibitory influence of green finance on industrial carbon emissions has remained unchanged. In the western provinces, the GS coefficient is −0.937, with a p-value of 0.054; in the east, the p-values are 0.002, and in the west, the p-values are 0.000. This indicates that green finance indeed has an inhibitory influence on carbon emissions from industries, but it has a noteworthy inhibitory effect on the central region, with the eastern region also having an inhibitory effect, with the western region closely following. The developed economy and perfect industrial structure in the eastern region make the effects of green finance on industrial carbon emissions significant with the guidance of government policies, and most of the green financial resources in the western region are invested in large-scale industries, making the region sensitive to the impact of industrial carbon emissions. Although the central region has less inclination in national policies and finance, and the economic pressure is enormous, it exerts a substantial influence on industrial optimization and emission reduction. Therefore, it can be speculated that the significant impact of green finance on carbon emissions from industries may be indirectly improved by optimizing the industrial structure and that there are remarkable differences in the inhibitory effects of green finance on industrial carbon emissions among the three principal areas of the central, western, and eastern regions.

4.3. Robustness Testing

The impact of green finance on carbon dioxide emissions from industries is the main topic of this study. Therefore, two robustness tests were conducted to test the stability of the overall results. The first is to replace the technological innovation index with the TFP index of total factor productivity. The second method replaces the dependent variable by replacing carbon dioxide emissions from industries with per capita industrial carbon dioxide emissions. The symbols and significance of the main explanatory variables discussed in this section remain unchanged, proving the robustness of the empirical results.

• Total factor productivity

To avoid the accidental phenomenon caused by special variables in the empirical process, this study uses total factor productivity (TFP) as a substitute variable regarding advances in technology indicators to ensure the credibility of the conclusions. Total factor productivity refers to the ratio of the total output of a system to the real input of all the production factors. It captures a broader concept of technology and efficiency that includes not only patent generation, but also improvements in management, human capital, and the effective use of inputs. The calculation formula is total factor productivity = total output/total resource input. The total output is the actual GDP, while the inputs are the number of employees and capital stock. The regression results after the replacement follow next.

Table 9. Intermediate process values for bidirectional fixed models(TFP introduced).

Variable	Coef	Std. Err	t	p	95%CI
GS	−0.936	0.210	−4.461	0.000 ***	−1.167~−0.237
TFP	−0.164	0.026	−6.437	0.000 ***	−0.214~−0.114
IS	−0.175	0.300	−0.583	0.7560	−0.762~0.412
lnGDP	0.284	0.108	2.634	0.009 **	0.073~0.495
EP	−0.236	1.396	−0.169	0.866	−2.973~2.500
FDI	0.581	0.819	0.709	0.479	−1.025~2.187
F(6,255) = 11.137, p = 0.000
R2 = −0.310, R2(adjusted) = 0.323

Note: ** p < 0.05, and *** p < 0.01.

shows the findings of the regression analysis that although the estimated values of the parameters of the substitute and explanatory variables are slightly different, the plus minus sign between the estimated values of the parameters is unchanged. Under the two-way fixed-effects model, the green finance coefficient is −0.936 and p < 0.01, which shows that when the substitute variables are included in the model, green finance still has a suppressive impact on industrial carbon emissions, and both are significant at the 1% significance level, and the goodness of fit of the model is also very close. In the other control variables, the industrial structure indicators become insignificant, while the economic-level indicators show strong significance. The significance of the environmental protection intensity and foreign direct investment indicators remains unchanged, and the overall level is close to the results of the original model. Therefore, it is believed that the analysis is robust and the conclusions are reliable.

2.

Per capita industrial carbon dioxide

In addition to the intensity of carbon dioxide emissions from industries, the level of industrial carbon dioxide emissions in each province can be measured by per capita industrial carbon dioxide emissions (CA). Per capita industrial carbon emissions = total industrial carbon dioxide emissions/number of permanent residents in each region toward the end of the year.

Table 10. Intermediate process values for bidirectional fixed models (per capita industrial carbon dioxide as dependent variable).

Variable	Coef	Std. Err	t	p	95%CI
GS	−0.122	0.064	−1.910	0.057 *	−0.247~0.003
LP	−0.000	0.000	−2.605	0.010 **	−0.000~−0.000
IS	−0.175	0.087	−2.013	0.045 *	−0.345~−0.005
lnGDP	0.047	0.032	1.463	0.145	−0.016~0.110
FDI	0.334	0.246	1.360	0.175	−0.147~0.816
EP	−0.382	0.417	−0.915	0.361	−1.199~0.436
F(6,255) = 3.054, p = 0.007
R2 = 0.011, R2(adjusted) = 0.087

Note: * p < 0.1, ** p < 0.05.

presents the outcomes of the regression model after replacement. The alternative and explanatory variables have changed in the parameter estimates; however, the coefficient signs remain unchanged. Under the two-way fixed effects model, the coefficient of green finance (GS) is −0.122, and p < 0.10, demonstrating that when the alternative variables are covered in the model, green finance still has an inhibitory influence on carbon emissions from industries that is remarkable at the 10% significance level. The indicators of technological innovation and industrial structure optimization showed strong significance. Per capita industrial carbon dioxide emissions account for the impact of population size, which may alter the extent of the impact of green finance, economic development levels, and FDI on carbon emissions. For instance, a region with a smaller population but a larger industrial scale may have a higher per capita emission level, whereas in some areas, the service industry or high-tech industries may account for a higher proportion, potentially reducing per capita industrial carbon dioxide emissions. Consequently, when the dependent variable is changed to per capita industrial carbon emissions, the impact of green finance on the dependent variable remains significant; however, there is no significant association between economic development levels, FDI, and the intensity of environmental protection with per capita industrial carbon dioxide. Overall, the negative influence of green finance on industrial carbon emissions can still be obtained, and it is believed that the analysis has good robustness and the conclusions are reliable.

5. Conclusions and Policy Implications

Using data collected from 2010–2019, this paper analyses how much of an impact green finance has on China’s industrial carbon emissions reduction. The control variables such as technological innovation, industrial structure, economic development level, foreign direct investment, and environmental protection intensity have been introduced. The study uses fixed-effect regression analysis and heterogeneity analysis to confirm that green finance has a restraining effect on carbon emissions from industries. Based on the findings, green finance has the potential to effectively reduce industrial carbon dioxide emissions, thereby improving environmental quality. And the infections of green finance on suppressing industrial carbon emissions in the three major regions of China are imbalanced.

There are two main aspects in research contribution for this paper. Firstly, it contributes to the literature on the impact of green finance on industrial carbon emissions. The findings of this study are compatible with the existing relevant literature, such as Wang and Ma (2022) who also found that China’s green finance can drastically cut carbon dioxide emissions by using econometric methods [37]. To more accurately verify the correlation between green finance and industrial carbon emission, this study employed a comprehensive measurement to assess green finance in green credit, green investment, green insurance, and government support, as well as used the carbon emission coefficient provided by IPCC to measure the level of industrial carbon emission. It considered more control variables and applied a fixed-effects regression model to test the results. Secondly, this paper addresses regional heterogeneity through empirical analysis and offers suggestions on how the nation’s provinces might progressively advance the green finance’s expansion and mitigate carbon emissions in the industrial sector to foster high-quality sustainable development.

The practical implications and policy recommendations are proposed below. First and foremost, measures must be tailored to local circumstances in order to encourage the steady expansion of green finance. Due to the economic progress conditions, endowments and resource consumption levels of various provinces and cities are not the same. Given this, local governments should formulate green finance development plans that are suitable for the actual situation of each region based on their economic development situation. For example, the financial institutions and government departments in coastal areas can formulate guidelines and standards for green industry and financial development and accelerate the digital transformation of green financial services. The central region can closely link green finance with industrial transformation and upgrading, use industrial parks, innovation and entrepreneurship bases, and other forms to encourage the reform and innovation practice of green finance from top to bottom. In areas with underdeveloped economic development in the western region, a comprehensive credit approval and environmental risk management mechanism should be established to enable banking and financial institutions to provide loans with greater confidence, vigorously develop the new energy industry, and avoid pollution problems caused by the extensive economic advancement.

Secondly, unify standards and boost the stable advancement of green finance. The standard system for green finance lacks uniformity, and a unified standard has not yet been fully developed nationwide, which makes it difficult to identify green projects and have varying caliber, making financial institutions helpless and unable to provide precise support. To this end, the first step is to address the issue of unifying “green standards” to better guide “green” investment, and on this basis, to clarify the relationship between “national” and “local” to avoid confusion. Therefore, China needs to develop a unified set of audit standards, clarify the scope of business and evaluation indicators, and unify the differences between different standards. This will establish a feasible and universal credit standard system. For example, unified standards can be used to establish permission rules for green project loans, guiding bonds, stocks, and funds. At the same time, its standards should be combined with national policies and guidelines, clarifying the scope and support focus on green finance projects. The progress of green finance cannot be judged only by green credit indicators. The green bond market, Eco-investing funds, carbon finance, etc. can all play a great role in the score of green finance. To establish a diversified green financial system, financial regulators should perform actively the role of macro-control and guidance, which is very beneficial to improving the score of green finance in China’s west and central regions.

Thirdly, further promote technological innovation and optimize the layout of industrial structure. In the future, the industry will continue to be a key factor in advancing China’s economic development. Therefore, the industrial sector will still produce a substantial amount of carbon emissions. To achieve carbon neutrality and peak carbon emissions, the industrial sector must increase investment to promote low-carbon technological innovation and increase the application efficiency of green patents. It is essential to cooperate with universities and research institutions by establishing networks including industry sectors, universities, research institutions, government departments, and financial institutions, ensuring the improvement of total factor productivity. Optimize the layout of the industrial structure by guiding capital towards high-tech and high-value-added industries, thereby reducing investment in high-pollution and high-energy-consuming industries and promoting the development of a low-carbon economy. In the meantime, it is essential to extend the industrial chain by strengthening the integrated development of upstream and downstream industries to achieve zero emissions in carbon-intensive industries such as petrochemicals, fine chemicals, building materials, and non-ferrous metals.