The Impact of Green Finance on Promoting Industrial Structure Upgrading: An Analysis of Jiangsu Province in China

Abstract

Climate change is a challenge facing all countries around the world. In response to the global climate change, China has pledged a two-stage carbon reduction goal of “dual carbon” to realize sustainable development. Industrial structure upgrading driven by green finance is an important way to reduce carbon emissions and achieve sustainable development. In this work, we investigate the impact of green finance on promoting industrial structure upgrading in Jiangsu province. We construct the grey correlation degree and coupling coordination degree model to analyze the relationship between green finance development and industrial structure upgrading with data from 13 prefecture-level cities in Jiangsu province from 2010 to 2021. The results demonstrate that green finance policies inhibit the financing tendencies of high-energy consumption industries and improve the financing difficulties of high-energy enterprises, forcing high-energy industries to transform and realize industrial upgrading. In addition, the improvement in green energy consumption structure and energy production efficiency will promote an improvement in carbon emission efficiency. Moreover, the development of green finance contributes to promoting industrial structure upgrading, putting forward new requirements for the development of green finance as well. Furthermore, the promotion of green finance and low-carbon industries provides a strong driving force for industrial structure upgrading as well as high-quality economic development in Jiangsu province. Therefore, the green finance policy system, as well as innovation in green financial products, needs to be further improved to accelerate industrial structure upgrading.

1. Introduction

China proposed a two-stage carbon reduction goal of “carbon peaking” by 2030 and “carbon neutrality” by 2060 (referred to as a “dual carbon” goal), which is China’s strategy to respond to global climate change and realize sustainable development. To achieve the goal of sustainable development, China promotes industrial upgrading and the optimization of energy structure with green finance.

Green finance promotes industrial upgrading through a variety of factors. Firstly, financing constraints are the direct influencing factors of green finance on industrial structure upgrading, while scientific innovation, policy support, and green development concept are indirect influencing factors, which promote industrial upgrading by influencing the financing channels or production behaviors of enterprises themselves. Secondly, green finance supports the environmental protection industry and green low-carbon industry through diversified green financial products, policies, and positive changes in the consumer market. Moreover, green finance discourages energy-intensive industries through high interest rates and stringent lending conditions, policies to guide the low-carbon transition, and the forced transformation of consumer markets. Thirdly, green finance creates a friendly and stable financing environment through credit instruments or relevant policies and high interest rates and harsh loan conditions on non-green, high energy-consuming industries. At the same time, when the technological innovation system and application mechanism of the low-carbon industry gradually mature, there will be stable positive feedback. Finally, the concept of green development will have an impact on the research and development stage, production stage, and sales stage of the production enterprise, and will promote industrial upgrading through the whole process of enterprise management, so as to achieve the “dual carbon” goal.

In this work, we investigate the promoting effect of green finance on the industrial structure upgrading of Jiangsu. We construct an indicator system for both green finance and industrial structure upgrading and calculate their weights with the entropy method. Moreover, grey relational analysis and the coupling coordination degree model are adopted to analyze the relationships between green finance and industrial structure upgrading. The main novelty of this work includes the following aspects: (1) we build the indicator system of green finance and industrial structure upgrading and calculate the weights of the indicators; (2) we investigate the interaction between the indicators of both green finance and industrial structure upgrading.

The rest of the paper is organized as follows: In Section 2, we review the existing literature and point out the main contributions of this paper. Section 3 introduces the indicator system as well as the grey relational degree model and coupling coordination degree model. The models used to investigate the relationships between green finance and carbon emission are employed and the results are analyzed in Section 4. Further discussions are made and conclusions are drawn in Section 5 and Section 6, respectively.

2. Literature Review

Since green finance was first formally discussed in the 1990s [1], there have been a lot of practical summaries and reports on green finance [2,3]. The development of green finance is affected by both government investment and public financing [4], facilitating high-quality economic development with green innovation [5]. Krueger et al. [6] analyzed the relationship between green finance and industrial structure and concluded that green financial products and services provided by banks contributed to promoting industrial structure upgrading by affecting investors’ investment behavior. The promoting effect of green finance on industrial upgrading has aroused the interest of scholars [7,8,9,10,11]. The existing research on the promotion of green finance to industrial upgrading is mainly carried out from the following aspects.

The influence of financing constraints on industrial structure upgrading has been widely investigated. It has been discovered that green financial products could adjust the capital flow through bank intermediation, which guides the allocation of market resources [12]. Brock and Taylor [13] investigated the EKC curve and found the development of green finance had a positive impact mechanism on industrial structure upgrading. Acemoglu et al. [14] theoretically proved that environmental regulation can guide innovation resources to the green sector. Green bonds contribute to optimizing stakeholder management through environmental management to ease financing constraints [15]. Moreover, empirical studies show issuance of green bonds helps to improve environmental performance or financial performance [16,17,18,19].

Moreover, green finance encourages scientific and technological innovation to promote industrial upgrading [20,21,22]. The development of green finance will facilitate technological innovation, improve resource utilization efficiency, and promote industrial upgrading [23,24,25]. Wang and Wang [26] showed that the efficiency of green finance in promoting China’s industrial structure upgrading was high, but it showed a downward trend. Empirical results demonstrate that green finance positively facilitates industrial structure upgrading by stimulating technological innovation [27,28].

Furthermore, scholars have conducted research on the promotion of industrial upgrading by green finance policies [19,29,30,31]. Under the “dual carbon” goal, green finance policies support industrial structure upgrading and promote high-quality economic development [32]. Xu et al. [33] discovered that green finance in China contributed to reducing carbon emissions by promoting energy structure transition.

Through the review of the current literature, we can conclude that current research on the impact of green finance on industrial structure upgrading has yielded substantial results, but there is still room for further research. First, the existing research lacks the indicator system of green finance and industrial structure upgrading, which makes it is difficult to accurately describe the degree of green finance and industrial structure upgrading. Second, the existing literature on the relationships between green finance and industrial structure upgrading indicators is insufficient. Third, the existing literature lacks quantitative evaluation of energy consumption structure. Thus, in this paper, we introduce indicator systems for green finance and industrial structure upgrading and calculate their weights with the entropy method to evaluate the degree of development of green finance and industrial structure upgrading. Then, we investigate how green finance promotes industrial structure upgrading by analyzing the grey relational degree between the indicators of green finance and industrial structure upgrading. Moreover, we analyze the coupling coordination degree between green finance and industrial structure upgrading.

3. Research Design

3.1. The Indicator System

Table 1. The indicator system of green finance, carbon emission efficiency, and industrial structure upgrading.

Primary Indicator	Secondary Indicator	Tertiary Indicator	Tertiary Indicator Description	Indicator Measurement	Indicator Type
Green finance	Green credit	PIEHEI	The proportion of interest expense of high energy-consuming industries	Interest expenditure of six high energy-consuming industries/total interest expenditure of industries	−
	Green securities	MVGEPI	The proportion of market value of the green environmental protection industry	Market value of green enterprises/total market value of the stock market	+
		POVHEI	The proportion of output value of high energy-consuming industries	Total output value of six energy-consuming industries/total market value of the stock market	−
	Green investment	PPEEP	The proportion of public expenditure on environmental protection	Fiscal expenditure on green environmental protection/total fiscal expenditure	+
		PIEPC	The proportion of investment in environmental pollution control	Pollution control investment/GDP	+
Carbon emission efficiency	Carbon emission	PCCE	Per capita CO2 emissions	CO2 emissions/resident population	−
	Energy consumption	EC/GDP	Energy consumption per GDP	Energy consumption/GDP	−
		ECSG	Energy consumption structure green low carbonization level index	A comprehensive index calculated by Equations (1) and (2)	+
Industrial structure upgrading	Industrial structure	POVTI	The proportion of output value of the tertiary industry	Output value/GDP of the tertiary industry	+
		PVATI	Proportion of the value added of the output value of the tertiary industry	Value added of output value of the tertiary industry/value added of GDP	+
	Employment structure	PETI	Proportion of employment in the tertiary industry	Employment in the tertiary sector/total employment	+
	Industrial structure optimization	TI	Theil index	Comprehensive index	−
	The advanced industrial structure	RI	Research input	R&D/GDP	+

summarizes the indicator system of green finance, carbon emission efficiency, and industrial upgrading. In this paper, we incorporate three indicators into the green finance indicator system: green credit, green securities, and green investment; two indicators into the carbon emission efficiency indicator system: carbon emission and energy consumption; and four indicators into the industrial structure upgrading indicator system.

The green credit is measured by the proportion of interest expenditure of high energy consumption industries (PIEHEI) in Jiangsu province. The PIEHEI can be used as a reverse indicator to indicate the development degree of green credit. The smaller the value of the PIEHEI, the better the inhibiting effect of green credit.

In terms of green securities indicators, the proportion of the market value of the green environmental protection industry (MVGEPI) and the proportion of the output value of the high energy consumption industry (POVHEI) are selected to measure the overall impact trend of the capital market on energy conservation and environmental protection for the development of Jiangsu province. The MVGEPI can positively reflect the development degree of green securities, the higher the proportion, the better the development degree of green securities. The POVHEI reflects the development degree of green securities in the reverse direction, and the lower the proportion, the better the development degree of green securities.

In terms of green investment indicators, we choose the representative proportion of public expenditure on green environmental protection (PPEEP) in Jiangsu province and the proportion of green investment in environmental pollution control (PIEPC) as the measurement standards. The higher the proportion of these two indicators, the higher the level of green investment development.

Carbon emission efficiency is measured by the per capita carbon dioxide emission (PCCE) in Jiangsu province. Moreover, in order to further emphasize the importance of carbon emission efficiency in green finance, the energy consumption per GDP (EC/GDP) and low-carbon level of the energy consumption structure (ECSG) are selected as measurement indicators. The low-carbon index (LCI) of energy consumption structure can be computed by Equations (1) and (2), where the parameters α, β, γ are the proportion of coal, oil, and gas and other energy consumption in Jiangsu province, respectively. The LCI is an important indicator for measuring the low-carbon energy consumption structure of a country, which reflects the carbon emissions of a country’s energy consumption structure and how it can improve the low-carbon level of its energy consumption structure. The calculation method of the LCI is based on a country’s energy consumption structure, calculating the carbon emissions of each energy source to calculate the total carbon emissions of the country, and then calculating the country’s low-carbon index according to the relevant Equations (1) and (2). In this paper, the original LCI data are sourced from the China Energy Statistical Yearbook and equations (1) and (2) are based on the research of Tang et al. [34] and Liu et al. [35].

$$\cos \left({\theta }_1\right)={\displaystyle \frac{\alpha }{{\alpha }^2+{\beta }^2+{\gamma }^2}}, \cos \left({\theta }_2\right)={\displaystyle \frac{\beta }{{\alpha }^2+{\beta }^2+{\gamma }^2}}, \cos \left({\theta }_3\right)={\displaystyle \frac{\gamma }{{\alpha }^2+{\beta }^2+{\gamma }^2}}$$

(1)

$$LCI=\arccos {\left[\cos \left({\theta }_1\right)\right]}^3+\arccos {\left[\cos \left({\theta }_2\right)\right]}^3+\arccos {\left[\cos \left({\theta }_3\right)\right]}^3$$

(2)

The industrial upgrading indicators include the output value structure, employment structure, and industrial structure optimization and upgrading. In terms of output value structure, the proportion of output value of the tertiary industry (POVTI) and the proportion of added value (PVATI) are selected as the measuring indicators. In terms of employment structure, the proportion of employment in the tertiary industry (PETI) is chosen as the measurement index. In terms of industrial structure upgrading, R&D expenditure divided by GDP is used as an indicator of R&D investment (RI). The optimization of industrial structure is measured by the Theil index (TI), which can be calculated by Equation (3):

$$T^s={\sum }_{r=1}^R{\lambda }_r^{s}L{n}_{\overline{{\lambda }_r}}^{{\lambda }_r^s}$$

(3)

In Equation (3), $R$ denotes the number of regions, $s$ represents the industrial sector, ${\lambda }_r^s$ is the share of the industry $s$ in the total industry of the region $r$, and ${\lambda }_r$ is the share of the economic total of the region $r$ in the total economic total of all regions.

3.2. The Model

3.2.1. Grey Relational Degree Model

• The reference and comparison sequences

According to the calculation steps of grey correlation analysis [36,37], the reference sequence is POVTI, PVATI, RI, PETI, and TI, respectively. The comparison sequence is PIEHEI, POVHEI, PCCE, EC/GDP, MVGEPI, PPEEP, PIEPC, and ECSG, respectively.

2.

Data standardization preprocessing

The change interval of the kth indicator is set as [$j_{k_1}$, $j_{k_2}$], where $j_{k_1}$ and $j_{k_2}$ are the minimum and maximum of the kth indicator in all evaluated objects, respectively. In this paper, Equation (4) is adopted to transform the original value into standardized data.

$$X_i\left(k\right)={\displaystyle \frac{j_k^i-j_{k_1}}{j_{k_2}-j_k^i}}\in \left(0,1\right), i=1,2,\dots m,k=1,2,\dots n$$

(4)

3.

Calculation of the difference sequence, maximum and minimum difference

The difference sequence can be formalized by Equation (5):

$$\mathsf{\Delta }i\left(k\right)=\left|X_0\left(k\right)-X_i\left(k\right)\right|$$

(5)

The minimum difference $\mathrm{a}=\underset{i,k}{\min }\mathsf{\Delta }i\left(k\right)$, and the maximum difference $\mathrm{b}=\underset{i,k}{\max }\mathsf{\Delta }i\left(k\right)$.

4.

Calculation of the grey correlation coefficient

The grey correlation coefficient ${\xi }_i\left(k\right)$ can be computed by Equation (6):

$${\xi }_i\left(k\right)={\displaystyle \frac{(\underset{i,k}{\min }\left|X_0\left(k\right)-X_i\left(k\right)\right|+\mathsf{\theta }\underset{i,k}{\max }\left|X_0\left(k\right)-X_i\left(k\right)\right|)}{(\left|X_0\left(k\right)-X_i\left(k\right)\right|+\mathsf{\theta }\underset{i,k}{\max }\left|X_0\left(k\right)-X_i\left(k\right)\right|)}}$$

(6)

In Equation (6), the parameter $\mathsf{\theta }\in \left(0,1\right)$ describes the extent of resolution.

5.

Calculation of correlation degree

The correlation degree $r_i$ can be calculated by Equation (7):

$$r_i={\displaystyle \frac{1}{n}}{\sum }_{k=1}^n{\xi }_i\left(k\right),\mathrm{i}=1,2,\dots \mathrm{m}$$

(7)

After calculating the correlation degree results (the correlation degree is between 0 and 1), the relationship between the comparison item and the reference sequence can be judged by the value of the correlation degree. The closer the correlation degree is to 1, the closer the relationship between the comparison item and the reference sequence. Finally, the ranking of each comparison item is obtained through the positive ordering of the size of all comparison items.

3.2.2. Coupling Coordination Degree Model

• Data standardization

The standardization of the positive indicators in Table 1 can be calculated by Equation (8):

$$X_i^{\prime }={\displaystyle \frac{X_i-X_{\min }}{X_{\max }-X_{\min }}}$$

(8)

The standardization of the negative indicators in Table 1 can be computed by Equation (9):

$$X_i^{\prime }={\displaystyle \frac{X_{{\min }_i}-X_i}{X_{\max }-X_{\min }}}$$

(9)

2.

The index weight calculated by entropy method

The entropy method [38] is adopted to calculate the weight of each indicator of the coupling coordination degree model. Firstly, we need to calculate the proportion of the jth sample of the ith indicator in the index by Equation (10):

$$P_{ij}={\displaystyle \frac{X_{ij}}{{{\displaystyle \sum }}_{i=1}^n{X}_{ij}}}$$

(10)

Secondly, the entropy value of the indicator j is calculated by Equation (11) as follows:

$$e_j=-{\displaystyle \frac{1}{\ln \left(m\right)}}\ast {{\displaystyle \sum }}_{i=1}^m{P}_{ij}\ast Ln\left(P_{ij}\right)$$

(11)

Thirdly, the residual entropy of item j is calculated by Equation (12):

$$d_j=1-e_j$$

(12)

Finally, the weights of the evaluation indicators are calculated by Equation (13) as follows:

$$w_j={\displaystyle \frac{d_j}{{{\displaystyle \sum }}_{j=1}^m{d}_j}}$$

(13)

3.

Coupling coordination degree calculation

After the weight is determined, the coupling coordination degree model [39,40] is adopted to evaluate the coupling degree between the green finance level and industrial upgrading in Jiangsu province. Equation (14) describes the coupling degree between green finance development and industrial development.

$$C=\left[\left({\mathrm{U}}_1{\ast \mathrm{U}}_2\right)/{\left({\mathrm{U}}_1+{\mathrm{U}}_2\right)}^2\right]{ }^{{\displaystyle \frac{1}{2}}}$$

(14)

where ${\mathrm{U}}_1$ and ${\mathrm{U}}_2$ are the comprehensive indicators of green finance and industrial upgrading, respectively, calculated by the entropy method with Equations (10)–(13). $C\in \left(0,1\right)$ is the coupling degree, and the greater value of C, the better the coupling between green finance and industrial upgrading.

The Equations (15) and (16) for evaluating the coupling coordination degree and coordination indicator are as follows:

$$D=\sqrt{C\times T}$$

(15)

$$T=\alpha \cdot {\mathrm{U}}_1+\beta \cdot {\mathrm{U}}_2$$

(16)

where D is the evaluating coupling coordination degree, T is the coordination indicator, and α and β are undetermined coefficients, complying with α + β = 1. The division standard of the coupling degree is shown in

Table 2. Standard for classification of coupling coordination degree.

Value Interval of Coupling Coordination Degree	Coordination Level	Degree of Coordination
(0.0~0.3)	1	Low
[0.3~0.5)	2	Middle
[0.5~0.8)	3	High
[0.8~1.0]	4	Extremely high

.

4. Analysis of the Results

4.1. Data Sources and Descriptive Statistics

In this paper, data related to green finance, carbon emission, and industrial structure upgrading in Jiangsu province from 2010 to 2021 are selected for analysis. Relevant data come from National Bureau of Statistics of China, Statistical Yearbooks, Jiangsu Provincial Bureau of Statistics and Wind database.

Table 3. Descriptive statistics of green finance and carbon emission efficiency indicators.

Indicators	Minimum	Maximum	Average	Standard Deviation
PIEHEI	0.23	0.41	0.31	0.06
POVHEI	0.09	0.11	0.10	0.01
MVGEPI	0.01	0.02	0.01	0.01
PPEEP	0.02	0.03	0.03	0.01
PIEPC	0.01	0.01	0.01	0.01
PCCE	8.54	10.34	9.91	0.50
EC/GDP	0.31	0.62	0.43	0.10
ECSG	5.67	6.08	5.89	0.15

and

Table 4. Descriptive statistics of industrial upgrading indicators.

Indicators	Minimum	Maximum	Average	Standard Deviation
POVTI	0.41	0.52	0.48	0.04
PVATI	0.44	0.72	0.59	0.09
PETI	0.36	0.47	0.40	0.04
TI	0.04	0.08	0.06	0.01
RI	0.02	0.03	0.03	0.01

show descriptive statistics of the relevant data.

Table 3 shows the descriptive statistics of green finance and carbon emission efficiency related data of Jiangsu province from 2010 to 2021. It can be seen from Table 3 that the minimum value, maximum value, average value, and standard deviation of the PIEHEI are 22.96%, 41.49%, 31.17%, and 5.75%, respectively. Table 3 shows that in the past 12 years, the value of this indicator has fluctuated, but in general, the PIEHEI in the total industrial interest expenses is about one-third. The minimum value, maximum value, average value, and standard deviation of the POVHEI are 8.77%, 11.48%, 9.91%, and 0.88%, respectively, which suggests that energy-intensive industries account for just under 10% of output. The minimum value of the MVGEPI is 0.56%, the maximum value is 1.71%, the average value is 1.21%, and the standard deviation is 0.33%, which shows that the share of environmental protection enterprises in the total output value is relatively small, but there is some room for growth. The minimum value, maximum value, average value, and standard deviation of the PPEEP are 2.31%, 3.18%, 2.78%, and 0.23%, respectively. This shows that the local financial investment in environmental protection in Jiangsu province is increasing year by year. The minimum value of the PIEPC is 0.41%, the maximum value is 1.48%, the average value is 0.95%, and the standard deviation is 0.40%, which reflect the intensity of investment in environmental pollution control in Jiangsu province. The minimum of PCCE is 8.54 tons, the maximum is 10.34 tons, the average is 9.91 tons, and the standard deviation is 0.50 tons. This reflects the CO₂ emission of Jiangsu province. The trend indicates that the per capita CO₂ emission of Jiangsu province has not been effectively controlled. The minimum value of the EC/GDP is 0.31, the maximum value is 0.62, the average value is 0.43, and the standard deviation is 0.10, which reflects the gradual improvement in energy efficiency in Jiangsu province. For the low carbonization level indicator of energy consumption structure, ECSG, the minimum value is 5.67, maximum value is 6.08, average value is 5.89, and standard deviation is 0.15, which suggests the low-carbon level of the energy consumption structure in Jiangsu province.

Table 4 shows descriptive statistics of the industrial upgrading data of Jiangsu province from 2010 to 2021. From Table 4, it can be observed that the POVTI varies between the minimum of 41.37% and the maximum of 52.17%, with an average of 47.57% and a standard deviation of 3.76%, which indicates the POVTI value of Jiangsu province has increased in the past 12 years. The value of PVATI varied between the minimum of 43.56% and the maximum of 71.86%, with an average of 58.82% and a standard deviation of 9.13%, which shows that the PVATI value of Jiangsu province is also on the rise, and the large standard deviation indicates that PVATI fluctuates greatly. In terms of RI, the minimum value of the R&D ratio is 2.07%, the maximum is 2.93%, the average is 2.57%, and the standard deviation is 0.26%. This shows that the proportion of R&D investment in Jiangsu province is increasing year by year. The minimum, maximum, average, and standard deviation of the PETI are 35.70%, 46.80%, 39.81%, and 3.77%, respectively, which shows that over the past 12 years, the PETI has continued to grow. The minimum value of the TI is 0.04, maximum value of TI is 0.08, average value of TI is 0.06, and the standard deviation is 0.01, which shows that the industrial structure of Jiangsu province has been continuously optimized over the past 12 years.

4.2. Grey Relational Analysis

4.2.1. POVTI as a Reference Sequence

When the reference sequence is the POVTI, the output results for the grey relational degree are shown in

Table 5. The results for grey relational degree with POVTI as a reference sequence.

Assessment Indicators	Correlation Degree	Rank
EC/GDP	0.967	1
ECSG	0.931	2
PIEHEI	0.920	3
POVHEI	0.785	4
PPEEP	0.761	5
MVGEPI	0.759	6
PIEPC	0.662	7
PCCE	0.651	8

. Of all evaluation items, the EC/GDP has the highest evaluation (0.967), followed by the ECSG (0.931), which indicates that the carbon emission efficiency indicator has a greater impact on the third output value.

4.2.2. PVATI as a Reference Sequence

When the reference sequence is the PVATI, the output results of the grey relational degree are shown in

Table 6. The results for grey relational degree with PVATI as a reference sequence.

Assessment Indicators	Correlation Degree	Rank
ECSG	0.824	1
EC/GDP	0.819	2
PIEHEI	0.809	3
PPEEP	0.807	4
MVGEPI	0.798	5
POVHEI	0.75	6
PIEPC	0.729	7
PCCE	0.714	8

. According to the eight evaluation items, the ECSG has the highest evaluation (0.824), followed by the EC/GDP (0.819). From Table 6, we can find that the third value added of output has a greater impact on the carbon emission efficiency.

4.2.3. RI as a Reference Sequence

When the reference sequence is the RI, the output results of the grey relational degree are shown in

Table 7. The results for grey relational degree with RI as a reference sequence.

Assessment Indicators	Correlation Degree	Rank
EC/GDP	0.946	1
ECSG	0.924	2
PIEHEI	0.924	3
POVHEI	0.809	4
PPEEP	0.799	5
MVGEPI	0.793	6
PIEPC	0.69	7
PCCE	0.685	8

. Of all the evaluation items, the EC/GDP has the highest evaluation (0.946), followed by ECSG (0.924). From Table 7, we can discover that the carbon emission efficiency indicator has a greater impact on R&D investment, which shows that carbon emission efficiency is the most correlated and influenced for the research input of positive indicators of industrial structure upgrading, and its promotion effect is the strongest.

4.2.4. PETI as a Reference Sequence

When the reference sequence is the PETI, the output results of the grey correlation degree are shown in

Table 8. The results for grey relational degree with PETI as a reference sequence.

Assessment Indicators	Correlation Degree	Rank
ECSG	0.881	1
EC/GDP	0.867	2
PIEHEI	0.849	3
POVHEI	0.74	4
MVGEPI	0.727	5
PPEEP	0.723	6
PCCE	0.636	7
PIEPC	0.632	8

. Of all evaluation items, the ECSG has the highest evaluation (0.881), followed by the EC/GDP (0.867), indicating that the PETI has a greater impact on the carbon emission efficiency.

4.2.5. TI as a Reference Sequence

When the reference sequence is the TI, the output results of the grey relational degree are shown in

Table 9. The results for grey relational degree with TI as a reference sequence.

Assessment Indicators	Correlation Degree	Rank
PCCE	0.775	1
PIEPC	0.765	2
PPEEP	0.733	3
MVGEPI	0.703	4
POVHEI	0.678	5
PIEHEI	0.577	6
EC/GDP	0.574	7
ECSG	0.554	8

. Of all evaluation items, the PCCE shows the highest (correlation: 0.775), followed by the PIEPC (correlation: 0.765). This shows that carbon efficiency and green investment have a relatively large impact on the Thiel index. For the Thiel index, carbon emission efficiency and green investment indexes are the most correlated, and the promotion effect is the strongest.

4.2.6. Conclusions of Grey Relational Analysis

In summary, through the analysis of the grey relational degree model of green finance and the industrial upgrading, it is concluded that the LCI of the energy consumption structure has the strongest correlation with the EC/GDP in terms of the output value structure, employment structure and industrial structure upgrading. The correlation between the PPEEP and POVHEI followed closely. This shows that in the process of developing the output value structure, employment structure, and industrial structure, it is extremely important to improve the green and low-carbon energy consumption structure and the production energy efficiency, so as to promote the improvement in carbon emission efficiency. In terms of industrial structure, employment structure, and the advanced industrial structure, the ESCG has the strongest correlation with the EC/GDP, followed closely by the PPEEP and PIEHEI. The green and low-carbon energy structure reduces the proportion of energy types with high carbon emissions per unit, and the improvement in energy production efficiency also promotes the reduction in carbon emissions per unit of energy production. Thus, in the process of developing the industrial structure, employment structure, and advanced industrial structure, the green and low-carbon energy consumption structure and the improvement in production energy efficiency are extremely important, thereby promoting the improvement in carbon emission efficiency. The green finance industry in Jiangsu province promotes industrial upgrading from both the supply side and the consumption side. The less influential factors are the increase in the PPEEP and PIEHEI, indicating that the green investment and policy influence mechanism at the level of Jiangsu province are indispensable. Through green investment, the financing channels are expanded to promote environmental protection. At the same time, the PIEHEI with high correlation also indicates that the development of these industries is not conducive to industrial upgrading or the optimization of industrial structures. The reasonable path is to reduce the financing propensity of high energy-consuming industries and make it more difficult for non-green, high energy-consuming enterprises to raise funds, thereby compelling the transformation of non-green, high energy-consuming industries to achieve industrial upgrading. Meanwhile, policies have been adopted to improve the financing difficulty of high-energy enterprises, forcing high-energy industries to transform to achieve industrial upgrading.

4.3. Coupling Cordination Degree Analysis

4.3.1. Calculation of Green Finance and Carbon Emission Efficiency Indicator Weights

The weights of the green finance and carbon emission efficiency indicators with the entropy method are shown in

Table 10. The weights of green finance and carbon emission efficiency indicators with the entropy method.

Indicators	Information Entropy Value	Information Utility Value	Weight (%)
ECSG	0.895	0.105	13.594
MVGEPI	0.942	0.058	7.598
PPEEP	0.945	0.055	7.109
PIEPC	0.877	0.123	16.042
EC/GDP	0.927	0.073	9.506
PCCE	0.793	0.207	26.916
POVHEI	0.926	0.074	9.607
PIEHEI	0.926	0.074	9.627

. According to the entropy method, in the green finance indicator system, the most important factors are the PCCE, PIEPC, and ECSG, accounting for 26.9%, 16.0%, and 13.5%, respectively. It manifests that these three three-level indicators have a significant impact on the indicator system of green finance.

4.3.2. Calculation of Industrial Upgrading Indicator Weights

The weights of the industrial upgrading indicators with the entropy method are shown in

Table 11. The weights of industrial upgrading indicators with entropy method.

Indicators	Information Entropy Value	Information Utility Value	Weight (%)
POVTI	0.9	0.1	19.255
PVATI	0.915	0.085	16.351
RI	0.93	0.07	13.409
PETI	0.837	0.163	31.216
TI	0.897	0.103	19.769

. Through the entropy method, it is found that in the indicator system of industrial upgrading, the larger weights are the PETI, POVTI, and TI. It indicates that the employment structure indicator has a great impact on the indicator system of the industrial upgrading.

4.3.3. Coupling Coordination Degree between Green Finance and Industrial Upgrading

After weighting by the entropy method, the calculation results of the coupling coordination degree (C), the evaluating coupling coordination degree (D), and the coupling coordination indicator (T) of the green finance system and industrial upgrading system in Jiangsu Province can be obtained and shown in

Table 12. The results of coupling coordination degree between green finance and industrial upgrading.

Year	C	T	D	Coordination Level	Degree of Coordination
2010	0.215	0.370	0.282	1	Low
2011	0.615	0.319	0.443	2	Middle
2012	0.430	0.566	0.493	2	Middle
2013	0.848	0.388	0.574	3	High
2014	0.756	0.422	0.565	3	High
2015	0.703	0.455	0.566	3	High
2016	0.520	0.549	0.534	3	High
2017	0.637	0.542	0.587	3	High
2018	0.894	0.453	0.636	3	High
2019	0.810	0.529	0.755	3	High
2020	0.919	0.484	0.801	4	Extremely high
2021	0.911	0.496	0.823	4	Extremely high

. As can be seen from Table 12, the coupling coordination degree of green finance and industrial upgrading in Jiangsu province is stable and improving, promoting each other’s upgrading and development, and entering a virtuous cycle. On the one hand, the coupling degree (C) of Jiangsu province gradually increased from 0.215 in 2010 to more than 0.8 after 2018, which is in a high-level coupling state. This indicates that although the development degree of green finance and industrial upgrading in Jiangsu province varies in different periods, the green finance system has been continuously improved, and the level of industrial upgrading has also been continuously improved. Green finance contributes to providing more impetus for industrial upgrading, and the improvement in industrial upgrading level will also put forward new requirements for green finance. The interaction between the two systems promotes the development of green finance and industrial upgrading in Jiangsu province in a highly coupled state. On the other hand, the coupling coordination degree indicator for green finance and industrial upgrading of Jiangsu province gradually increased from 0.282 in 2010 to 0.823 in 2021, and the coupling coordination degree indicator showed a gradual growth trend on the whole, indicating that the development level of green finance and industrial upgrading in Jiangsu province are in synchronous and coordinated development, and gradually entered a highly coupled coordination state. In 2020–2021, it even rises to a state of extreme coupling coordination. It shows that with the improvement in the development level and industrial upgrading level of green finance in Jiangsu province, the upward trend of its coordination degree is obvious.

5. Discussion

In this paper, we analyze the role of green finance in industrial structure upgrading of Jiangsu in China. With grey relational analysis and coupling coordination degree model, we find that the improvement in carbon emission efficiency has a significant correlation degree and promoting effect on industrial structure upgrading, and that the grey relational degree and coupling coordination degree between green finance and industrial structure upgrading in Jiangsu province are relatively high. But, there are still some shortcomings in this work, such as the partially missing data of green insurance and green bonds. In the future, with the gradual improvement in the market and data statistics of green finance, a deep analysis can be carried out. At the same time, we take Jiangsu province as a whole and do not consider the development imbalance between different regions, which is also a place for improvement in the future. Moreover, this paper still has limitations in using correlation to imply causation. Owing to the constraints in data types and statistical methods, we have just demonstrated a strong correlation between variables using the grey correlation degree model as well as the coupling correlation degree model without carrying out causation analysis quantitatively. Future research could be conducted to prove the causation relationship by employing the Granger causality test. Different from the existing literature, we focus on how green finance promotes the upgrading of the industrial structure as well as analyzing the interrelationships among various impact mechanisms. Moreover, in terms of research methodology, we integrate grey relational analysis with coupling coordination analysis to enhance the persuasiveness of our findings through empirical examinations.

6. Conclusions

Carbon emissions are an important contributor to global warming, one that demands global efforts to cope with. Green finance, to promote the upgrading of industrial structure, is an important way to reduce carbon emissions and achieve sustainable development. The relationships between green finance, carbon emission, and industrial upgrading are investigated in this paper. The grey relational analysis and coupling coordination degree models are adopted to analyze the impact of green finance on industrial structure upgrading with data from 13 prefecture-level cities in Jiangsu province from 2010 to 2021. The results of the grey relational analysis show that green finance policy can inhibit the financing tendency of high-energy industries, improve the financing difficulty of non-green high-energy enterprises, and force non-green high-energy industries to transform and realize industrial upgrading. Moreover, the green, low-carbon energy consumption structure and the improvement in energy production efficiency contribute to promoting the improvement in carbon emission efficiency. Furthermore, the industrial structure upgrading of Jiangsu province may be near the bottleneck stage, and the first-mover advantage brought by the improvement in energy efficiency and low carbonization is gradually caught up with the growing per capita CO₂ consumption. The results of the coupling coordination degree demonstrate that the promotion of green finance and green low-carbon industry provides a strong driving force for industrial upgrading and the high-quality economic development in Jiangsu province.

The findings of this work may have policy implications in the following aspects. Firstly, the green finance policy system needs to be further improved. It is necessary to raise the level of awareness and participation efficiency of participants in green finance projects. Secondly, innovation in green financial products and services needs to increase. Through innovative mortgage guarantees and other ways, new credit products and bond products for different enterprises should be differentially launched. Thirdly, green technology innovation needs to be promoted. We need to increase funding for research and development and shorten the time for the transformation of scientific research results.

But there are still some limitations in this paper. Green finance is still in a stage of infancy in Jiangsu, with limited data. For instance, there are significant data gaps in areas such as green insurance, green bonds, and carbon finance. In future studies, with the green finance market and data statistics becoming more extensive, the model can be refined to support deeper analysis and research, yielding more pertinent and practical empirical findings. Additionally, this work treats Jiangsu province as a single unit, neglecting regional development disparities, which can be addressed in further research.