The Development of Green Finance and the Rising Status of China’s Manufacturing Value Chain

Abstract

Green finance has gradually become an imperative engine for the high-quality development of the manufacturing industry in the new era. Using 30 provincial panel data sets of China from 2011 to 2020, this paper explores whether green finance will effectively impact the rise of China’s manufacturing value chain using green finance as the breakthrough point. The results show that: (1) The implementation of green finance policy has a promoting effect on the rise of the manufacturing value chain. (2) It has significant regional heterogeneity characteristics in the process of green finance that influence the manufacturing value chain. In the transformation and upgrading of the manufacturing industry, the role of green finance in regions with developed economic bases is superior to those in comparatively backward areas. (3) Innovation and technological development are significant factors in the development of the manufacturing industry, and the intensity of innovative development inputs as well as the labour factor of highly skilled workers have a moderating role in the process of green finance for climbing up the value chain status of manufacturing. Based on the empirical evidence, policy implications are suggested.

1. Introduction: More Focus on the Green Development

Countries have taken numerous actions to ameliorate and control the problem of climate change, which has generated considerable concern throughout the world. In February 2005, 149 states and regions adopted the Kyoto Protocol, which became the first international agreement that set mandatory emission reduction targets. The Paris Agreement, which established detailed plans for the world’s response to climate change after 2020, was approved during the 21st United Nations Climate Change Conference in 2015. In addition to the carbon reduction goal set at the Copenhagen Conference in 2009, it is anticipated that carbon dioxide emissions per unit of GDP will decrease by 40% to 50% in 2020 compared to 2005. To address climate change in China, General Secretary Xi Jinping announced in 2020 at the 75th UN General Assembly General Debate that China’s CO₂ emissions would strive to peak by 2030 and become carbon neutral by 2060. In March 2021, the Carbon Peaking and Carbon Neutrality Goals were officially incorporated into the 14th Five-Year Plan and Vision 2035. The stated target of China’s Intended Nationally Determined Contributions (INDCs) is to peak carbon dioxide emissions by 2030 and work toward becoming carbon neutral by 2060, which is a significant step in combating global climate change and a significant challenge for China. As China’s industrialization process accelerates, one of the main problems facing this sector is the coordination between production and the environment. A sustainable and green development environment is essential for the development of high-tech industries at home and abroad, and China’s manufacturing industry is also confronted with environmental endogenous problems. The consequences of these problems include increased costs for enterprises, administrative penalties, and other risks, such as increased financial pressure from environmental management, clean technology upgrading, and environmental agreement costs. From the perspective of financial development, achieving the goal of carbon neutrality requires adequate financial support. Replying to the three major functions of financial development, which are resource allocation, risk management, and market pricing, social capital is guided to participate in the green transformation and green industry growth in terms of green credit, green investment, and green industry support policies. Today, with the gradual establishment of the climate change governance system and the gradual economic recovery of China, green development is the main theme and the development of green finance can help reduce the financial risks that may be faced in the process of transforming and upgrading the development of manufacturing industry brought about by the climate and environment change, thereby achieving effective assistance for the high-quality development of the manufacturing industry.

2. Literature Review

2.1. Qualitative Research of the Green Finance

According to Salazar’s [1] definition of green finance, combined with Lv et al.’s [2] study on the pathways of green finance in economic operation, green finance is an efficient investment and financing activity based on ecological and environmental benefits, which has the aim of promoting sustainable development, relying on effective government policy support and playing its role as a resource regulator in the market economy, and playing an imperative role in bridging the gap between new and sustainable development. Chami et al. [3] show evidence that the development of green finance in relation to long-term social development is beneficial. By using Dixit’s [4] game of the prisoner’s dilemma—given in the Presidential Address to the Econometric Society—to analyze economic traders’ attitudes towards social responsibility from an opportunistic perspective, it can be reasoned that the promoters of green finance, or the agents of economic activity, can contribute to the enhancement of public welfare in response to the policy.

The development of green finance-related theory and policy practice research can be traced back to the Superfund Act of 1980 in the United States, which was one of the world’s earliest acts to adopt a fund approach to industrial waste pollution. As scholars in various countries continue to pay attention to environmental issues, their views on green finance have gradually evolved. In the early research, green finance is considered as a bridge between the financial industry and the environmental industry, an interdisciplinary subject between green economy and finance, and an important means of financing for developing a green economy [1,5]. Furthermore, Labatt and White [6] argue that green finance is a tool for improving environmental quality and transferring environmental risks by engaging in market mechanisms. Thinking from the financial marketisation, green credit, Aizawa and Yang [7] examine the development of green finance in China. According to their argument, the success of green financial policies contributes to environmental sustainability while enabling China to meet current international challenges with the necessary experience.

2.2. Quantitative Research on the Green Finance

As the definition of green finance has become clearer, more and more scholars have begun to measure the extent of green finance development. They have also begun to analyze it from a quantitative perspective. Due to the late development of green finance, there is no unified standard for measuring the degree of green finance development from the international community. Therefore, many scholars have started to quantify and analyze the green finance development index from different perspectives and the influencing factors in the process of green finance development. From the existing literature, the core indicators include the degree of green innovation [8], the degree of integration of energy-saving and environmental protection policies with green financial policies [9], the impact of new energy on green economic development [10], and the degree of green financial policy response including green investment, green credit, etc.

Because green technology innovation is an effective means to achieve both economic development and environmental protection, Chen et al. [11] conducted an empirical test of green finance on green technology innovation at the firm level using panel data through a mediating effect approach, dividing the sample of firms into state-owned firms (including internal high-intensity control firms and start-up firms) and private firms to explore the importance of green finance implementation and firm green technology innovation. Jin et al. [9] used the credit level of the energy conservation and the environmental protection industry as the focus of green finance research, and analyzed the interaction effect of the heterogeneity between the financing efficiency of enterprises and the degree of economic development through a sample of listed ECEP enterprises in China from 2010 to 2019, with the implementation of green finance policies promoting financing more significantly in the central and western regions, i.e., having a positive effect on the economic development of less developed regions both at the national regulatory level and at the enterprise scale level. Lv et al. [2] constructs indicators of green finance development with a policy and market orientation, analyzing the spatial–regional variation of green finance within provincial units in China, and found significant regional variation in the development of green finance in China. The variation was analyzed in terms of absolute and relative differences based on the feedback of economic agents such as enterprises on green finance policies and the efficiency of the government’s green financial effectiveness policies, and a rational explanation for the resulting differences was given in terms of geographical divisions in the East and West. In terms of the prevalence of green finance index measurement, Wang et al. [10] measured the development index of green finance from a macro perspective. Its index data was based on green credit, and the entropy value method and the improved hierarchical analysis method (AHP) were used to determine the index weights and develop a model to evaluate the level of green finance development in China over a certain duration. The model was constructed to evaluate the level of green finance development in China over a certain period. It was also constructed to forecast the future level of green finance development based on its indexes.

2.3. Literature Assessment and the Marginal Contribution of the Research

Through the review and summary of the above literature [1,2,3,4,5,6,7,8,9,10], we can find that although many scholars have conducted qualitative and quantitative analyses of green finance from various perspectives, studied its theoretical construction and the impact mechanism on regional economy, the research on the link between green finance and manufacturing is relatively rare. As the world’s factory, China’s manufacturing industry plays an important role in economic development, but few studies on the impact mechanism of green finance on the industry have explored the impact of green finance on the development of the manufacturing industry, so the marginal contributions of this paper are as follows:

Firstly, in terms of the impact of green finance on high-quality socio-economic development, unlike most of the current literature, which explores the impact of green finance on the green transformation of enterprises at the micro level or the policy recommendations of green finance development on achieving high-quality development at the macro policy level. This paper explores the role of green finance in achieving the rising status of the manufacturing value chain from an industrial perspective, which not only enriches the existing literature but also provides new policy recommendations for achieving the “carbon peaking and carbon neutrality“ goal in the new era.

Secondly, in order to achieve the research target, this paper designs a multiple research level to continue the quantitative empirical analysis, from the geographical areas from the natural partitions, the market development echelon, and the green economic development level. To evidence the importance of green finance on the manufacturing value chain further, this paper carries out the moderating effect from the facts of technology innovation and high-tech talents.

Thirdly, exploring the possible direct effects of green finance on the manufacturing value chain from the perspectives of locations, marketisation, and green economic development, we provide a more detailed and creative research route on the theoretical mechanism.

This study will explore the role of green finance in the process of climbing the value chain of the manufacturing industry from multiple perspectives and provide effective insights and suggestions for the better use of green finance policies to stimulate the real economy, promote the climbing of the manufacturing value chain, and thus achieve high-quality economic development.

3. Theoretical Analysis and Research Hypothesis on Green Finance Impact Mechanism

Green finance, as an emerging financial concept, is not only an optimization and innovation of traditional finance, but also a future trend in financial development. It is an inevitable choice to achieve high-quality economic development [12]. The emergence of green finance has promoted green technology innovation in the manufacturing industry, improved resource allocation efficiency, and promoted the transformation and upgrading of the manufacturing industry’s industrial structure. It is of great significance for the climbing of the value chain in the manufacturing industry.

First of all, from the perspective of the overall structure of the manufacturing industry, green finance policies provide corresponding financial support for green optimization and upgrading of the industry structure [1,5]. Green finance provides a large amount of green innovation funding for the manufacturing industry, encouraging enterprises to innovate in technology and production modes. As the scale of green finance gradually expands, it continuously guides funding to support the green development of the manufacturing industry, and the shift in investment preferences of financial institutions has led to the continuous elimination of traditional high-polluting enterprises, directly promoting the green transformation of the manufacturing industry as a whole. Existing research shows that the green finance policy implemented by the central bank enables enterprises issuing green bonds to significantly reduce their credit spreads and relatively increase the credit spreads of enterprises issuing brown bonds (Godlewski et al., 2013; Hacıömeroğlu et al., 2022) [13,14], creating incentives for green enterprises from the perspective of financing while forcing brown enterprises to green their transformation and upgrading (Chen et al., 2021) [11]. At the same time, the implementation of green finance is often accompanied by certain policy effects, which may cause a certain financial agglomeration phenomenon, and the knowledge spillover effect brought about by the aggregation of funds will continuously indirectly promote the transformation and upgrading of the manufacturing industry structure [15]. That is, green finance promotes the green transformation of the regional manufacturing industry structure as a whole, enhances the added value of the regional manufacturing industry, and helps to climb up the value chain of the regional manufacturing industry. Secondly, for manufacturing enterprises, green finance brings more financing channels and opportunities. On the one hand, green finance provides a large amount of funding support for manufacturing enterprises to carry out production process reform and green process R&D, promoting the improvement of product quality and technological level. As the production scale of enterprises continues to expand, in order to maintain their core competitiveness and attract more green financial resources, enterprises will pay more attention to innovative R&D of green technologies, and this virtuous cycle will continue to promote the green development of the regional manufacturing industry, improve the added value of the regional manufacturing industry, and become an important driving force for the climbing of the value chain of the regional manufacturing industry [16]. On the other hand, the implementation of green finance policies provides additional funding support for the normal operation of enterprises. The increase in fixed operating costs in enterprise operating capital will enhance the operating capacity and production capacity of enterprises. According to the financial leverage effect of operating capital, the intervention of financial products such as green bonds will increase the coefficient of enterprise financial leverage, which increases expected earnings but also increases enterprise financial risk. However, the development of green finance usually relies on national policies, and its profit increase for enterprises is often greater than the financial risk it brings. That is, green finance can stimulate more manufacturing enterprises to transform towards green development through financial leverage, promote the high-quality development of the regional manufacturing industry, and achieve the climbing of the value chain of the regional manufacturing industry. In summary, whether from the perspective of the overall transformation of the regional manufacturing industry structure or from the perspective of manufacturing enterprises themselves, green finance will have a significant impact on the climbing of the value chain of the regional manufacturing industry. Therefore, this article makes the following hypothesis:

Hypothesis 1:

Green finance development has a positive effect on the improvement of the manufacturing value chain status.

Since the reform and opening-up, China’s social and economic development has gradually shown significant spatial heterogeneity, with the East being strong and the West weak. Although strategies such as the development of the West have continued to be promoted, the absolute differences within the provincial areas are still large [17]. From the perspective of the financial structure, as the real economy presents different differences in a different time and space scales, the financial market structure, the degree of financial openness, and other financial services in the regional scope divided by East and West have different direct impact effects on the development of the real economy [18]. In terms of the actual development of the local real economy, the flow of financial resources from neighboring regions provides more convenient conditions for the development of non-core regions, and neighboring regions are to a certain extent more likely to share and complement each other in terms of financial resources such as deposits and loans. China’s financial resources are generally concentrated in the Yangtze River Delta, the Pearl River Delta, and the Bohai Sea region. The extent of green economy development and the endowment of financial infrastructure has substantial advantages over the western and central regions. Therefore, based on the existing studies, China’s green financial policies have differences due to the degree of financial infrastructure construction. In addition, there are also differences in the pace of its influence on the regional manufacturing value chain. Based on the differences in the degree of marketization and green economy development in China, the provincial areas are divided from multiple perspectives, such as East–West and North–South, with the major cities as the centre, and there exists at least one city with faster economic development and a higher degree of financial development in the region as the centre to radiate the surrounding areas. In East China, for example, Shanghai is definitely the centre of the region, and as the leader in financial development nationwide, the extent of its green finance development will inevitably lead to the emulation and implementation of policies in other provinces in the range, such as Anhui, Shandong, and Jiangsu, making the neighboring provinces more efficient in learning and learning from the new policies, and their impact on the local economy and policy tilt will thus accompany each other, thus promoting the transformation and upgrading of the manufacturing industry and achieving a climb in the value chain. Therefore, this paper puts forward the following hypothesis.

Hypothesis 2:

There may be regional heterogeneity in the development of green finance in the regional context based on factors such as the conditions for its development and the level of economic development, which affects the pattern of effects of green finance on the climbing of the manufacturing value chain in China.

Based on existing research, the transmission effect of green finance development on the climbing of manufacturing value chains is considered in terms of the factors influencing the upgrading of manufacturing as follows. Firstly, technological innovation is an important factor for micro-entities in manufacturing enterprises to achieve value chain climbing [18]. Financial support is a necessary condition for micro-entities to carry out technological innovation. Green finance provides policy inclination and financial support for high technology and low-carbon technology through green investment, green insurance, green securities, and green credit, providing richer financing channels for innovation and development, which in turn creates incentives for enterprises to carry out innovative technological activities [19]. Research has shown that there is a correlation between the input of skilled labor and the outcome of technological innovation and that the law of decreasing returns to scale and the marginal diminishing benefits of R&D have led to labour intensification in most high-technology firms [20]. Therefore, the training and introduction of high-tech talents become a key factor for enterprises to improve their competitiveness and enhance their core technological innovation capability. Green finance, by providing special support to specific enterprises in the form of funds, will guide the flow of funds to enterprises, improve the funding for the introduction of talents and technology, and reduce the entry constraint of high-skilled labour in the process of manufacturing transformation. The following hypothesis is proposed.

Hypothesis 3:

The development of green finance may have an impact on the climbing of the manufacturing value chain through the transmission of the role of technological innovation and the introduction of talent.

4. Research Method and Data

4.1. Model Settings

Based on the above analysis, this paper mainly constructs a relationship model of the impact of green finance development on the manufacturing industry, with the manufacturing value chain climbing index as the explanatory variable and the level of green finance development as the core explanatory variable. Because the process of green finance’s impact on the manufacturing industry will be influenced by green development and other related factors, factors such as innovation capacity and the degree of environmental regulation are incorporated into the model in the form of control variables in the hope of obtaining a more accurate relationship between the two, and the basic regression model is set as follows:

$$\begin{array}{c}{\mathrm{DVAR}}_{\mathrm{it}}=\mathrm{c}+{\mathsf{\beta }}_1{\mathrm{GF}}_{\mathrm{it}}+\gamma {\mathrm{X}}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}\end{array}$$

(1)

Among them, the explanatory variable${\mathrm{DVAR}}_{\mathrm{it}}$ is the manufacturing value chain climbing index; the core explanatory variable ${\mathrm{GF}}_{\mathrm{it}}$ indicates the level of green financial development of each place; the control variable ${\mathrm{X}}_{\mathrm{it}}$ includes the number of patents, high technology output value, advanced industrial structure, gross domestic product, total factor productivity, and degree of environmental regulation in each province; and ${\mathsf{\epsilon }}_{\mathrm{it}}$ is the random error term of the model.

4.2. Variable Selection and Data Descriptions

4.2.1. Explanatory Variable: Manufacturing Value Chain Index

Drawing on Kee and Tang [21] to measure the value chain index, the total domestic component income of each province, ${\mathrm{PY}}_{\mathrm{i}}$, consists of profit ${\pi }_{\mathrm{i}}$, wage level ${\mathsf{\omega }\mathrm{L}}_{\mathrm{i}}$, cost of capital ${\gamma \mathrm{k}}_{\mathrm{i}}$, and raw material cost ${\mathrm{PM}}_{\mathrm{i}}$, expressed by the Formula (2).

$$\begin{array}{c} \mathrm{P}{\mathrm{Y}}_{\mathrm{i}}={\pi }_{\mathrm{i}}+\mathsf{\omega }{\mathrm{L}}_{\mathrm{i}}+\gamma {\mathrm{k}}_{\mathrm{i}}+\mathrm{P}{\mathrm{M}}_{\mathrm{i}}\end{array}$$

(2)

${\mathrm{PM}}_{\mathrm{i}}$ consists of two parts, namely the cost of domestic raw materials and the cost of imported raw materials from abroad. $\delta$ refers to the material production value and $\mathrm{q}$ refers to the raw material value. Since it is possible that some of the domestic raw materials (${\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{D}}$) contain foreign value, ${\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{D}}$ equals to domestic raw material values ${\mathrm{q}}_{\mathrm{i}}^{\mathrm{D}}$ plus foreign raw material production value ${\delta }_{\mathrm{i}}^{\mathrm{F}}$. The imported domestic raw materials also contain domestic products ${\delta }_{\mathrm{i}}^{\mathrm{D}}$, which means that the imported raw material cost (${\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{I}}$) consists of a domestic material production value ${\delta }_{\mathrm{i}}^{\mathrm{D}}$ and an import raw material value ${\mathrm{q}}_{\mathrm{i}}^{\mathrm{F}}$. All of these parts are considered as a whole and is represented by ${\mathrm{PM}}_{\mathrm{i}}$.

$$\begin{array}{c}\mathrm{P}{\mathrm{M}}_{\mathrm{i}}={\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{D}}+{\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{I}}={\mathrm{q}}_{\mathrm{i}}^{\mathrm{D}}+{\delta }_{\mathrm{i}}^{\mathrm{F}}+{\delta }_{\mathrm{i}}^{\mathrm{D}}+{\mathrm{q}}_{\mathrm{i}}^{\mathrm{F}}\end{array}$$

(3)

Similarly, each province’s domestic value-added DVA is equal to profits, wages, capital rental cost, and domestic raw material cost company with the material production value, expressed by the Formula (4).

$$\begin{array}{c} \mathrm{D}\mathrm{V}\mathrm{A}={\pi }_{\mathrm{i}}+\mathsf{\omega }{\mathrm{L}}_{\mathrm{i}}+\gamma {\mathrm{k}}_{\mathrm{i}}+{\mathrm{q}}_{\mathrm{i}}^{\mathrm{D}}+{\delta }_{\mathrm{i}}^{\mathrm{D}}\end{array}$$

(4)

Furthermore, for processing trade firms, their exports (EXP) are equal to their total revenue, and their imports (IMP) are equal to the sum of the cost of imported raw materials ${\mathrm{PM}}_{\mathrm{i}}{}^{\mathrm{I}}$ and imported capital ${\delta }_{\mathrm{i}}^{\mathrm{k}}$, so the above equation can be transformed into the Formulas (5) and (6).

$$\begin{array}{c} {\mathrm{EXP}}_{\mathrm{i}}{}^{\mathrm{p}}={\mathrm{DVA}}_{\mathrm{i}}{}^{\mathrm{P}}+{\mathrm{IMP}}_{\mathrm{i}}{}^{\mathrm{p}}-{\delta }_{\mathrm{i}}^{\mathrm{D}}+{\delta }_{\mathrm{i}}^{\mathrm{F}}-{\delta }_{\mathrm{i}}^{\mathrm{k}}\end{array}$$

(5)

$${\mathrm{DVA}}_{\mathrm{i}}^{\mathrm{p}}=({\mathrm{EXP}}_{\mathrm{i}}{}^{\mathrm{P}}-{\mathrm{IMP}}_{\mathrm{i}}{}^{\mathrm{p}})+{\delta }_{\mathrm{i}}^{\mathrm{D}}-{\delta }_{\mathrm{i}}^{\mathrm{F}}+{\delta }_{\mathrm{i}}^{\mathrm{k}}$$

(6)

Based on the above equation, the ratio of value added is obtained, which is the manufacturing value chain climbing index (Formula (7)).

$$\begin{array}{c}{\mathrm{DVAR}}_{\mathrm{i}}=1-\frac{{\mathrm{P}}_{\mathrm{i}}-{\delta }_{\mathrm{i}}}{{\mathrm{PY}}_{\mathrm{i}}}\end{array}$$

(7)

4.2.2. Explanatory Variables: Green Finance Index

At present, there is no absolute uniformity in the measurement of green finance in the academic community. Drawing on the practice of some scholars [22,23], green investment, green insurance, green securities, and green credit are selected as secondary indicators, and the entropy value method and the coefficient of the variation method are used to synthesise a comprehensive green finance development index, as shown in

Table 1. Green Finance Indicators System.

First Level Index	Second Level Index	Data Description	Source of Data
$\mathrm{Green}\mathrm{finance}\left(\mathrm{GF}\right)$	Green investment	Ratio of energy saving and environmental protection expenditure to government fiscal expenditure	China Yearbook of Environmental Statistics
	Green insurance	Ratio of agriculture insurance expenses and agriculture insurance revenues	Statistical Yearbook from 30 provinces, Wind database
	Green securities	Ratio of the total market capitalisation of the six energy-intensive industries 1 to the total market capitalisation of A shares	China Stock Market and Accounting Research Database (CSMAR)
	Green credit	Interest expenses in high energy-consuming industries	Statistic Yearbookfrom 30 provinces, China Yearbook of Industrial Statistics, Economy Prediction System (EPS)

¹ The six high-energy-consuming industries include electricity, heat production and supply, non-metallic mineral products, ferrous metal smelting and rolling processing, chemical raw materials and chemical products manufacturing, petroleum processing, coking and nuclear fuel processing, and non-ferrous metal smelting and rolling processing.

.

4.2.3. Control Variables and Other Variables

Based on existing studies and other factors that have an impact on the process of climbing and greening the manufacturing value chain, the following factors are added as control variables:

Green Innovation Capability (PAT). Green technology innovation is beneficial for improving product quality and the position of manufacturing companies in the global value chain, as well as for enhancing product added value. Chen et al. [24] found in their research that the performance of green product innovation and green process innovation in enterprises is positively correlated with their competitiveness. In addition, Ghisetti et al. [25] also found in their research that green technology innovation can effectively reduce production costs and increase profitability for enterprises. Therefore, this article includes regional green innovation capabilities as a control variable. At the same time, following the ideas presented by Fang [26] in their research, the number of practical green patents obtained by each province is used to measure their green innovation capability.

Innovative Technology Production Capability (HP). Innovative technology production capability is a summary reflection of a region’s advanced production technology, advanced production equipment, and high-quality personnel. The level of innovative technology production capability to some extent reflects the manufacturing industry’s independent innovation ability in the region. Strong independent research and development and innovation capabilities can significantly improve product added value and market competitiveness, promoting the value chain ascension of the manufacturing industry in the region. Referring to the existing studies [27], this article measures the region’s innovative technology production capability using the output value of high-tech industries in each province.

Advanced Industrial Structure (advstr). Industrial advancement is an important indicator of upgrading and transformation of the regional industrial structure. On the one hand, industrial advancement helps to improve the manufacturing industry’s production efficiency and quality level in the region, promoting high-quality development of the manufacturing industry. On the other hand, industrial advancement can achieve high-quality, high-efficiency, and sustainable development of the manufacturing industry by continuously optimizing the industrial structure, improving the production factor system, promoting innovation and collaborative development, thereby promoting the continuous ascension of the manufacturing industry value chain. This article refers to the method proposed by Au and Henderson [28] to measure the level of industrial advancement by the proportion of the output value of the secondary and tertiary industries in the region.

Gross Domestic Product (GDP). As an indicator for measuring the overall economic development of a region, the level of economic development is the most direct reflection of the region’s development stage and economic strength. The higher the level of economic development in a region, the better the environment it provides for the development of the manufacturing industry, and the more likely it is to form manufacturing agglomeration. Manufacturing agglomeration can effectively accelerate industrial technological progress. Enterprises aggregate in high-demand regions to pursue economies of scale, and the high profits brought about by agglomeration, in turn, promote enterprise innovation. Meanwhile, through forward linkage effects, agglomeration promotes industrial clustering and forms a virtuous innovation mechanism [29]. Therefore, the level of economic development in a region may be an important factor affecting the ascent of the manufacturing industry value chain.

Total factor productivity (TFP). In contrast to indicators such as gross domestic product, total factor productivity focuses on the comprehensive production capacity of each region, emphasizing the quality of regional economic development. It can more comprehensively reflect the production efficiency and economic characteristics of cities. Considering that factors such as production efficiency may have a certain promoting effect on the development of the regional manufacturing industry, in order to control the factors that may affect the rise of the manufacturing industry value chain, this article uses total factor productivity (TFP) as a control variable to avoid a series of problems caused by omitted variables and to ensure the accuracy of the estimation results.

Environmental regulation intensity (ER). The impact of environmental regulations on the rise of the manufacturing industry value chain can be divided into two aspects: On the one hand, the strength of environmental regulations is closely related to the pollution control cost of manufacturing enterprises. Stronger environmental regulations will to some extent increase the pollution control costs of enterprises, but it will also force enterprises to innovate in production technology. When faced with higher pollution control costs, enterprises will consider improving the technological added value and market competitiveness of their products through technological innovation, so as to increase their profits to compensate for the pollution control costs. Therefore, environmental regulations can continuously encourage manufacturing enterprises to upgrade production technology, ultimately leading to the rise of the manufacturing industry value chain. On the other hand, high-intensity environmental regulations can help accelerate the elimination of backward manufacturing enterprises in the region. Environmental regulations can increase the market share of new and technology-intensive industries, thus benefiting the rise of the manufacturing industry value chain. Referring to the existing studies [30], this article uses the proportion of industrial pollution control investment completion to the second industry output value to measure the regional environmental regulation intensity.

In addition, referring to the existing literature [31,32], this paper selects the proportion of R&D expenditure to GDP as an indicator of the intensity of investment in innovation development ($\mathrm{RD}$) and the average number of workers employed in high-tech industries as an indicator of highly skilled personnel ($\mathrm{PHE}$) as moderating variables.

4.2.4. Descriptive Statistics

The paper use data from the Statistical Yearbook from 30 provinces, China Statistical Yearbook on Environment, China Industry Statistic Book, China Stock Market and Accounting Research Database (CSMAR), etc. The data for the years 2011–2020 were selected and the data were standardized for extreme differences. Specific descriptive statistics for each variable are shown in

Table 2. Summary Statistics.

VarName	Obs	Mean	SD	Min	Median	Max
DVAR	300	1.0693	0.216	0.46	1.07	1.52
GF	300	0.1907	0.113	0.06	0.16	0.84
pat	300	0.0805	0.122	0.00	0.04	1.00
hp	300	0.0909	0.156	0.00	0.04	1.00
advstr	300	2.1296	6.110	0.52	1.09	84.23
GDP	300	0.2179	0.187	0.00	0.16	1.00
TFP	300	1.6466	0.755	0.16	1.74	2.90
er	300	0.0029	0.003	0.00	0.00	0.02
RD	300	1.7296	1.127	0.41	1.40	6.44
PHE	300	0.1075	0.191	0.00	0.05	1.00

.

4.2.5. Correlation Coefficient Matrix and Variance Inflation Factor (VIF) Test

Before conducting a regression analysis, this article first examined the Pearson correlation coefficient matrix and the variance inflation factors (VIF) among variables to determine whether there is multicollinearity between explanatory variables and control variables. The specific test results can be seen in

Table 3. Pearson correlation coefficient matrix of major variables.

	DVAR	GF1	Pat	Hp	Advstr	Gdp	Tfp	Er
DVAR	1
GF1	0.036	1
pat	−0.071	0.566 ***	1
hp	−0.241 ***	0.445 ***	0.763 ***	1
advstr	0.076	0.285 ***	0.065	0.048	1
gdp	−0.293 ***	0.464 ***	0.850 ***	0.847 ***	0.045	1
tfp	−0.216 ***	−0.088	−0.124 **	−0.051	0.142 **	−0.076	1
er	0.342 ***	−0.292 ***	−0.262 ***	−0.247 ***	−0.014	−0.303 ***	−0.117 **	1

** p < 0.05, *** p < 0.01.

and

Table 4. Variance Inflation Factors (VIF).

Variable	VIF	1/VIF
gdp	5.63	0.1775
pat	4.29	0.2329
hp	3.65	0.2738
GF1	1.71	0.5840
er	1.18	0.8456
advstr	1.15	0.8727
tfp	1.08	0.9239

. It can be easily observed from the results that the maximum VIF among all variables is 5.63, and based on empirical judgment, the possibility of multicollinearity among variables is very low, thus a regression analysis can be performed. It should be noted that the Pearson correlation coefficient matrix here shows that there is no significant correlation between green finance and the value chain of the manufacturing industry, but subsequent empirical research results show significant correlation. Based on existing empirical research [33,34], this article believes that the inconsistency between the two results is because the result of the correlation coefficient matrix was obtained without controlling for other factors that affect the value chain of the manufacturing industry (such as regional fixed effects, etc.). Therefore, the conclusions of subsequent empirical research results are more robust.

5. Empirical Testing

5.1. Basic Regression Analysis

Based on the above theoretical mechanism analysis, the implementation of green finance has a positive effect on the climbing of the manufacturing value chain. Therefore, a benchmark regression was first conducted to verify the relationship between green finance and the manufacturing value chain index. After conducting OLS mixed regression, fixed effects tests, and random effects tests on the panel data (the result is shown in

Table 5. Basic regression.

	(1)	(2)	(3)
	Ols Mixed Reg	Fix Effect	Random Effect
Variable	DVAR	DVAR	DVAR
${\mathrm{GF}}_{\mathrm{it}}$	0.2444 **	0.9278 ***	0.5629 ***
	(2.0189)	(3.0114)	(2.9588)
pat	0.9506 ***	0.0420	0.5863 ***
	(5.3597)	(0.2286)	(3.5264)
hp	−0.1151	−0.4736	−0.5051 ***
	(−0.9006)	(−1.6487)	(−2.6027)
advstr	0.0024	0.0018	0.0025 *
	(1.2871)	(1.2892)	(1.6872)
GDP	−0.7752 ***	0.5517 *	−0.1919
	(−5.8417)	(1.9271)	(−0.9773)
TFP	−0.0490 ***	−0.0541 ***	−0.0543 ***
	(−3.4005)	(−4.8846)	(−4.6190)
er	21.1886 ***	3.5976	8.9631 **
	(5.2767)	(0.8987)	(2.2175)
_cons	1.1397 ***	0.8866 ***	1.0604 ***
	(26.6734)	(15.3764)	(20.6004)
adj. R2	0.303	0.176
N	300	300	300

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

), Hausman test was conducted and the p-value of the test result was 0.0044, which proved the validity of fixed effects at 0.01 level of significance. From Table 5, it can be found that the coefficients of the impact of green finance on the manufacturing value chain index are all positive at the 5% significance level, indicating that the development of green finance has promoted the climbing of the status of the manufacturing value chain, and the implementation of green finance policies has had a backward promoting effect on the key financial support for the transformation and upgrading of the manufacturing industry.

The possible reasons could be explained as follows: From the perspective of the transmission path, compared to before the development of green finance, the manufacturing industry is mostly resource-consuming and labor-intensive industries. Due to the limited capital and high sunk costs, it is difficult to support the capital and investment required for technological progress in the short term, and the willingness to carry out innovation activities is low, and the process of transformation and upgrading of the manufacturing industry progresses slowly and remains in the middle and low-value chain for a long time. However, the development of green finance has created more convenient policies and financial conditions for enterprises to carry out green transformation terms of policy support and financing conditions. Based on this, the capital cost of manufacturing enterprises in the process of innovation and transformation is reduced, the risk of trial and error is reduced, and it is more conducive to the manufacturing industry to invest in introducing high technology, carrying out innovation activities, enhancing the added value of their own production products, and thus promoting the status of the value chain to climb up to the middle and high end. Therefore, the implementation of green financial policies is necessary to form a guiding mechanism for high technology and green industries with the relaxation of financial support policies for specific industries, and to promote the transformation and upgrading of traditional manufacturing industries to higher value-added, ecological and advanced, thus promoting the high-quality development of the economy, and at the same time, verifying the correctness of Hypothesis 1 in this paper.

Observing the estimation result of the control variables, in the more significant models (1) OLS regression and model (3) random effect test, the significant coefficients of neo-green innovation capacity, total factors productivity, and the degree of environmental regulation on the climbing of the manufacturing value chain show significant at the 10% level, and especially the coefficients of green innovation capacity and the degree of the advanced industrial structure shows positive at 10% level significant. The situation illustrates that technical support is an important link in the process of transforming and upgrading the manufacturing industry from the middle and low-end value chain to the high-end. However, the coefficient of total factor productivity is negative at the 1% significance level, and total factor productivity is negative at the 1% significance level, and total factor productivity refers to the impact of technology, efficiency, and scale effect on the economy excluding tangible investments such as labor, capital, and land. Therefore, although some of the technology-related indicators are positively influenced, we cannot rule out the influence of China’s large economic volume, which still has a certain labor force advantage and demographic dividend, while the core attack and strangle technology still exist. In addition, the coefficient of the impact of GDP in fixed effects is 0.5517 at a lower significance level of 10%, and the coefficient is negative in the OLS mixed regression and random effects tests, which may be explained by the following aspects. On the one hand, the pressure of China to lay out and build an independent and controllable modern industrial system and promote the transformation and upgrading of the modern manufacturing system is an important factor that affects the stability of China’s manufacturing added value to GDP. On the other hand, the unreasonable intervention of local governments, especially the intervention and control on the implementation of financial policies, the improvement of capital market, the rational allocation of financing funds and pricing power in various jurisdictions, makes the impact of GDP on the rise of manufacturing value chain not significant.

5.2. Robustness Tests

5.2.1. Replacement of Explanatory Variables

In order to ensure the robustness of the results obtained in this paper, the algorithm for the core explanatory variables green finance index was re-evaluated using the coefficient of the variation method to verify the robustness of the panel data, and the empirical results are as follows: according to the results of the Hausman test, the p-value is 0.000, which obviously rejects the original hypothesis, and the results of the fixed effects are chosen to explain the model, and the coefficients of the core explanatory variables are positive at the 10% significance level is positive, and the significance of the control variables in the fixed effects model is significantly better than that of the original entropy value method of calculating the core explanatory variables, as shown in

Table 6. Robustness test result.

	Replace the Explanatory Variables			Replace Estimation Method
	OLS	Fixed Effect	Random Effect	GMM
Variable	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{DVAR}}_{\mathrm{it}-1}$				0.3822 ***
				(6.2316)
${\mathrm{GF}}_{\mathrm{it}-1}$				4.3470 **
				(2.7521)
${\mathrm{GF}}_{\mathrm{it}}$	0.0520	0.3239 **	0.2795 **	−3.7979 ***
	(0.4679)	(2.3680)	(2.1775)	(−2.8154)
$\mathrm{pat}$	1.0758 ***	0.1670	0.7461 ***	0.5850 ***
	(6.4337)	(0.8776)	(4.5228)	(3.1436)
$\mathrm{hp}$	−0.0991	−0.6101 **	−0.5100 ***	−0.1397
	(−0.7722)	(−2.1290)	(−2.6070)	(−0.6340)
$\mathrm{advstr}$	0.0034 *	0.0022	0.0030 **	0.0016
	(1.8904)	(1.5698)	(2.0347)	(1.1585)
$\mathrm{GDP}$	−0.8060 ***	0.8022 ***	−0.1794	−0.4407 **
	(−6.0245)	(3.0605)	(−0.9009)	(−2.1537)
$\mathrm{TFP}$	−0.0524 ***	−0.0637 ***	−0.0616 ***	−0.0702 ***
	(−3.6283)	(−5.6836)	(−5.1941)	(−6.1901)
$\mathrm{er}$	19.5222 ***	0.4341	6.1761	19.7753 ***
	(4.9412)	(0.1064)	(1.5191)	(2.6814)
_cons	1.1657 ***	0.8855 ***	1.0422 ***	0.7140 ***
	(19.7925)	(13.7202)	(16.2306)	(8.1065)
adj. R2	0.294	0.165
Hausman		55.29 ***
AR(1)-P				0.000
AR(2)-P				0.065
Hansen test				1.000
N	300	300	300	270

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

, so basically the panel data selected for this paper can be considered robust.

5.2.2. Replacement Estimation Method

In order to ensure the robustness of the results obtained in this paper, a dynamic panel model is further introduced to analyse the impact of green finance development on the climbing status of the manufacturing value chain. The GMM model is used to estimate the dynamic panel. The lagged first order ${\mathrm{DVAR}}_{\mathrm{it}-1}$ which is the explanatory variables is added to the model as an instrumental variable, as well as the lagged first order ${\mathrm{GF}}_{\mathrm{it}-1}$ which is the core explanatory variables for regression. The regression results are shown in Table 6. The first-order autocorrelation of the difference of the nuisance terms was judged to exist based on the significant value of AR(1), but AR(2) was not significant i.e., there was no second-order autocorrelation, so the original hypothesis of no autocorrelation of the nuisance terms was accepted. The p-values of the Hansen tests were all above 0.5, indicating that there was no over-identification of the regression results.

As can be seen from Table 6, the lagged one-period manufacturing price value chain index is significantly positive at the 1% level in the GMM model, i.e., there is a significant positive effect of the manufacturing value chain status climbing index in the previous period on that index in the latter period, indicating that the climbing of the manufacturing value chain status index is time-coherent. Adding the lagged one-period of the explanatory variables and the lagged one-period green finance index, the regression coefficients of the indices of green finance and its lagged one-period are −3.7979 and 4.3470 respectively. There is a difference in the sign of the correlation coefficient, which means that when the green finance policy is proposed and measures are started to be implemented, every 1% increase in its development level does not immediately reflect on the manufacturing value chain index in the current period and may even have a negative impact. The possible reason for this is that at the beginning of the green finance policy implementation, the concept of green finance was vague to all sectors of society, resulting in the interpretation of the policy and the utilization of favorable support policies not keeping pace with the launch of the policy, coupled with the fact that green finance is still in its infancy in China and the support for green industries was insufficient at the beginning of its implementation, which resulted in the negative value of the correlation coefficient in the current period. With the progress of time, as the micro-economic subjects manufacturing enterprises gain a better understanding of the green financial policy and the financial support policy, more manufacturing enterprises seek new financing avenues in the process of green financial policy implementation, and then rely on financial support to carry out innovation and achieve transformation, so the correlation coefficient of the lagging period shows a significant increase. In terms of the control variables, the growth in the number of green utility patents and the increase in the degree of environmental regulation have a positive contribution to the climbing of the manufacturing value chain status index at the 5% significance level, with coefficients of 1.0758 and 19.7753 respectively, but total factor productivity has a negative effect at the 1% significance level, indicating that China, after excluding the economic volume factor, in the key technology link the transformation and upgrading of the manufacturing industry still lacks momentum. The empirical results show that although financial policy support for green industries has been implemented, the solution to the difficult technical problems of the attack is still constrained. Further increasing the financial support for the relevant industries and relaxing the restrictions on financing conditions may be one of the solutions to the lack of incentive for enterprises to innovate, solving the financial problems of enterprises to carry out innovative activities, improving the anti-risk ability to carry out trial and error during the transformation of the manufacturing industry, and giving enterprises the capital to dare to innovate and achieve the climbing up of the value chain status. The above results, in summary, indicate that the results obtained in this paper are more robust.

5.3. Heterogeneity Analysis

5.3.1. Regional Heterogeneity Test

(1) Four major regional heterogeneity tests

Due to the regional heterogeneity of China’s uneven economic development, the development of green finance in different regions is bound to differ, and the degree of its impact on the manufacturing value chain will also differ. Therefore, firstly, Chinese regions are divided into four major regions, namely, the eastern coastal region, the central region, the north–eastern region and the western region, and a sub-sample fixed-effects test is conducted; the specific results are shown in

Table 7. Four regional heterogeneity tests.

Variable	East	Middle	West	Northeast
	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.4662 *	0.1105	3.3595 ***	7.7604 **
	(1.9299)	(0.2049)	(5.3683)	(5.8774)
$\mathrm{pat}$	−0.1391	0.7149 **	0.1142	−1.0463 **
	(−1.0850)	(3.4711)	(0.3568)	(−6.6927)
$\mathrm{hp}$	−0.6001 **	1.0126	0.3580	−12.4303
	(−2.7890)	(1.4876)	(0.4545)	(−1.7022)
$\mathrm{advstr}$	0.0019 *	0.0123	0.0096 *	0.0157 **
	(2.1106)	(1.4801)	(2.0938)	(7.5959)
$\mathrm{GDP}$	0.9919 ***	0.2376	−1.2974 *	−1.8810 *
	(3.8280)	(0.6925)	(−2.0728)	(−3.3299)
$\mathrm{TFP}$	−0.0800 ***	−0.0371 **	−0.0366 ***	0.0038
	(−4.6446)	(−3.9978)	(−4.0891)	(0.3875)
$\mathrm{er}$	−4.0071	20.7542	6.0366	79.6751 *
	(−0.3690)	(1.9555)	(1.5696)	(3.4493)
_cons	0.8091 ***	0.7786 ***	0.8699 ***	0.3494
	(8.1584)	(10.1145)	(11.0255)	(0.9729)
Area	Yes	Yes	Yes	Yes
adj. R2	0.334	0.310	0.170	0.525
N	100	60	110	30

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

From Table 7, it can be found that the coefficient of the development of green finance in the Northeast region on the development of manufacturing value chain is 7.7604 at the 5% significance level, and the positive coefficient effect is significant. The reason may be that compared with the other three regions, Northeast China only includes three provinces and its geographical location is relatively concentrated, which makes the data in this region obvious to some extent. On the other hand, it shows that Northeast China, as an old industrial base, has an obvious promotion effect on the rising of the manufacturing value chain, which provides a powerful explanation for Northeast China to vigorously promote the development of green finance. Furthermore, it shows that the Northeast region as an old industrial base. The promotion effect of the implementation of the green finance policy on the climbing of the manufacturing value chain is obvious, providing a strong illustration for the vigorous promotion of green finance in the northeast region. The coefficient of green finance development in the western region on the climbing of manufacturing value chain is 3.3595 at the 1% significance level, which is a significant promotion effect. The possible reasons for such institution may be that the western region includes Xinjiang and Guizhou, which are the provinces where China’s green financial reform and innovation pilot zones are located, and according to the 2019 Local Green Financial Development Index Score, their green financial development evaluation results are in the top tier of China, improving the competitiveness of the western region in comparison with other regions in terms of overall green financial development. In addition, Xinjiang and Guizhou rank in the top 10 in terms of local policy specific measures scores during the evaluation cycle with 2017–2019, which is at the leading level nationwide. From the perspective of policy implementation effectiveness, the green finance development in this region will have a more significant promotion effect on the climbing of manufacturing value chain. Furthermore, the coefficient of green finance development in the eastern region is 0.4662 at the 10% significance level on the climbing of manufacturing value chain. As a region with a higher level of economic development in China, the eastern region is in the leading position nationwide in terms of the normal degree of implementation of green finance and the progress of transformation and upgrading of the manufacturing industry, and the basic conditions of green finance development and the high level of economic development provide advantageous conditions for promoting industrial greening transformation. However, the eastern region includes more provinces, and in addition to the Beijing–Tianjin–Hebei region and the Yangtze River Delta region, it also includes areas such as Guangxi and Hainan where the development of green finance and manufacturing is relatively weak, so the overall level of positive effects in the eastern region is not at the highest level. Compared with other regions, the coefficient of the effect of green finance on the climbing of manufacturing value chain in the central region is positive, but the significance is lower than 10%, indicating that the promotion effect of green finance development on the climbing of manufacturing value chain in the central region is not significant, and the effectiveness of policy implementation is lower compared with other regions.

In terms of control variables, the level of advanced industrial structure in the eastern, western, and north–eastern regions has a positive influence effect on the climbing of the manufacturing value chain at the 10% significance level, indicating that the change in regional industrial structure is an important influencing factor in achieving green transformation in the process of the positive and positive effect of the development of green finance on the manufacturing value chain. The other noteworthy control variable, GDP, is represented by the eastern region, with a positive coefficient of 0.9919 at the 1% significance level, compared to the negative coefficient effect of the western and north–eastern regions at the 10% significance level, reflecting the overall high level of economic development in the eastern region as a concentrated distribution of economic development provinces and cities, as well as the importance of the economic base in the development of green finance and its stimulating effect on the upgrading of manufacturing industries. It also reflects the importance of the economic base in the process of green finance development and stimulating the upgrading of manufacturing industries.

(2) Seven regional heterogeneity tests

Based on the heterogeneity tests of the above four major geographic divisions, it can be found that there are large differences between regions, especially the eastern, north-eastern, and western regions show obvious performance. In order to further explore the effect of geographic location factors in generating the effect of green finance, the Chinese region is subdivided into seven geographic divisions of central, southern, eastern, northern, northwestern, southwestern, and northeastern regions, and further sub-sample regressions are conducted; specifically, the results are shown in

Table 8. Test for heterogeneity among the seven regions.

Variable	East China	Central China	South China	North China	Northwest	Southwest	Northeast
	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.9866 ***	−0.8632	0.1736	−0.0643	2.5486 ***	7.1461 **	7.1461 **
	(6.5291)	(−0.3362)	(0.1474)	(−0.4929)	(9.1135)	(5.2746)	(5.2746)
$\mathrm{pat}$	−0.2257 *	−0.3299	0.9425	1.4797	0.2399	−0.0089	−0.0089
	(−2.2861)	(−1.4858)	(2.0610)	(1.7651)	(0.3954)	(−0.0214)	(−0.0214)
$\mathrm{hp}$	−0.8821 *	0.3305	0.1899	−2.9626	−0.7463	−1.3222	−1.3222
	(−2.5229)	(0.2727)	(0.1715)	(−1.7560)	(−0.7970)	(−1.4363)	(−1.4363)
$\mathrm{advstr}$	0.0005	0.0065	0.0328 ***	0.0027 **	0.0118	−0.0064	−0.0064
	(0.7980)	(0.8617)	(11.6593)	(3.9434)	(2.0156)	(−0.4967)	(−0.4967)
$\mathrm{GDP}$	0.9895 **	0.7701	0.3127	0.8419	−1.1044 **	−1.8604	−1.8604
	(3.4856)	(0.5456)	(0.8567)	(0.6744)	(−3.4116)	(−1.9789)	(−1.9789)
$\mathrm{TFP}$	−0.0542 **	−0.0984	−0.0375 *	−0.0676 **	−0.0418 *	−0.0375 *	−0.0375 *
	(−3.9544)	(−2.1065)	(−2.8532)	(−2.9875)	(−2.4202)	(−2.9421)	(−2.9421)
$\mathrm{er}$	15.7730 **	−37.2991	41.5306	13.7207 **	3.9495	25.4129	25.4129
	(2.8077)	(−2.2459)	(1.0684)	(2.9501)	(0.8881)	(1.9507)	(1.9507)
_cons	0.7212 ***	0.7362	0.6501 **	1.1092 ***	1.1233 ***	0.5901 ***	0.5901 ***
	(24.0050)	(2.3793)	(4.3238)	(8.2726)	(12.2667)	(10.1957)	(10.1957)
Area	Yes	Yes	Yes	Yes	Yes	Yes	Yes
adj. R2	0.392	0.480	0.494	0.430	0.454	0.439	0.439

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

From the results in Table 8, we can find that East China has the most outstanding performance, and the coefficient of green finance on the climbing index of manufacturing value chain is 0.9866 at 1% significance level, which is consistent with the status quo of the socio–economic development of East China as the most developed region in China’s economy. The coefficients of GDP and the degree of environmental regulation are positive at the 5% significance level, which indicates that the socio–economic development level of East China is consistent with the current situation of manufacturing development in terms of industrial structure distribution in the region, and as the financial center of the country, the efficiency of policy implementation and the degree of implementation are better than those of other regions, which means that the green financial development of East China in promoting the rise of manufacturing value chain is in the leading position nationwide. However, the coefficients of green utility patents and high technology output value shows a negative effect at the 10% significance level, which indicates that there are technical bottlenecks in the process of transformation and upgrading of China’s manufacturing value chain, which restricts the movement of China’s manufacturing value chain to the middle or higher end.

The other representative regions are the northwest, southwest, and northeast, where the impact of green finance on the rise of the manufacturing value chain is positive at the 5% level of significance, which is related to China’s current strategic policies of western development and revitalization of the old industrial bases in the northeast. The implementation of green finance policies in these regions are relatively more relaxed, creating a better environment for SME financing. In addition, the southwest region contains Chongqing, Chengdu, and other cities, and the financial infrastructure construction has gradually caught up with the eastern region since the implementation of the financial inclusion policy in recent years, coupled with the increased vitality in the process of transformation and development of their own cities, the implementation of the green finance policy brings favorable policies for the development of enterprises in the region again, and promotes the transformation and upgrading of manufacturing enterprises relying on the policy and financial support. At the same time, the impact of technology-related control variables in the southwest, northwest, and northeast regions is insignificant, and most regions shows negative effect, indicating that, while green finance in the region has positively contributed to the manufacturing value chain, the overall economic level is still at a relatively backward stage, and technological innovation in the process of manufacturing transformation and upgrading is at an inappropriate level. Finally, the development of green finance in central, southern and northern China has not significantly contributed to the upgrading of the manufacturing value chain compared to other regions. Southern China relies on the Guangdong–Hong Kong–Macao Greater Bay Area to have a positive impact, while Central and Northern China have a negative impact. Possibly the reason for this is that compared to Eastern and Southern China, Central and Northern China have relatively weaker geographical location advantages and less favourable resource endowment conditions, coupled with the fact that the Yangtze River Delta city cluster is at a high level of economic development overall, with Shanghai, the national financial and economic centre of eastern China, as the core radiating to the surrounding areas. Since the reform and opening-up, the South China region’s economy has grown, with the developed cities of Guangzhou and Shenzhen serving as the country’s nerve centres, and it now outperforms central and northern China in terms of capital, technology, and labour quality.

In conclusion, from an overall perspective, the level of green finance development can still play a positive role in promoting the rise of the manufacturing value chain, with the overall effect being better in the east than in the west and in the south than in the north, and there are significant differences between regions, verifying hypothesis 2. The development of green finance in different regions has geographical heterogeneity on the climbing of the manufacturing value chain in the region, indicating that regional heterogeneity is still a concern in the process of realising the climbing of the manufacturing value chain status in China, and thus in promoting high-quality economic development. In response to the issues, corresponding measures should be taken to take large urban agglomerations as the centre, with faster-developing regions forming a wider radiation to surrounding urban agglomerations through assistance or policy support and technology export, so that green finance policies can benefit more regions and promote the climbing of China’s manufacturing value chain.

5.3.2. Market-Oriented Development Heterogeneity Test

From the study of Dong and Feng [35], it can be obtained that the increase of marketization, especially the transformation of the marketization of financial structure, can promote the development of economic quality method by accelerating the transformation and upgrading of industries. According to Wang et al. [36], “China’s marketization index report by province (2021)”, the marketization index ranking of China’s 30 provinces except Tibet, China’s provincial areas are divided into three major categories according to the marketization index score in 2019. As shown in Figure 1, the first tier is for provinces ranked 1–10 on the Marketization Index, the second tier is for provinces 11–20, and the third tier is for provinces 21–30.

The three echelon regions were tested separately for fixed effects, and the results are shown in

Table 9. Heterogeneity test of the degree of market development Results.

Variable	First Echelon	Second Tier	Third Echelon
	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.5434 *	2.6307 **	3.1364 ***
	(1.9175)	(3.0498)	(3.6082)
$\mathrm{pat}$	−0.0949	1.1062 ***	0.1438
	(−0.9345)	(4.3519)	(0.1642)
$\mathrm{hp}$	−0.5306 **	−1.3124	−3.6807
	(−2.5949)	(−1.5182)	(−1.3434)
$\mathrm{advstr}$	0.0013	0.0310 ***	0.0124 *
	(1.3466)	(3.9730)	(1.8735)
$\mathrm{GDP}$	0.9272 ***	−0.7003	0.3401
	(4.1638)	(−1.5137)	(0.3386)
$\mathrm{TFP}$	−0.0622 ***	−0.0431 ***	−0.0490 **
	(−4.5640)	(−4.4753)	(−2.5748)
$\mathrm{er}$	13.5681	26.7061 ***	4.9652
	(1.4718)	(3.4069)	(1.1199)
_cons	0.6916 ***	0.7353 ***	0.8736 ***
	(7.4919)	(11.7517)	(8.4657)
Area	Yes	Yes	Yes
adj. R2	0.330	0.297	0.211
N	100	100	100

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

. The coefficient level of the first echelon is 0.5434, which is lower than the coefficient levels of the second and third echelons. Combined with the level of the control variable GDP, it can be found that only the coefficient level of the effect of GDP of the first echelon is significantly positive at the 1% significance level, so it indicates that the first echelon, which includes Guangdong, Shanghai, Zhejiang, Beijing, and Tianjin, belongs to the regions with a strong economic foundation, the development of the market economy starts earlier, and the level of marketization is higher. At the same time, the transformation process of its manufacturing industry started early. The manufacturing value chain in these regions is in the process of transformation and upgrading, and the proportion of which has completed the transfer to the middle and higher-end value chain is larger and the speed is faster than other regions. So, it could explain that due to the continuity of policy and economic development, its systematization is higher, the development space is smaller, and the coefficient level is relatively lower than that of regions with a backward economic development level and lack of endowment advantages, under the 10% significance level of core explanation variables. In the second and third echelons, the development of green finance on the climbing of manufacturing value chain status is significantly positive at 5% and 1% significance levels, which are 2.6307 and 3.1364, respectively. Since the effect of GDP of provinces in these echelons on the climbing of manufacturing value chain is relatively low, the process of transformation and the upgrading of the manufacturing industry is relatively slow, indicating that this part of the region should make more efforts to develop green finance. There is still space to improve the effect of the role of green finance on the climbing status of the manufacturing value chain.

5.3.3. Heterogeneity Test of Green Economy Development Level

Since environmental issues have received widespread attention from all sectors of society, green economic development has become an important aspect of economic sustainability, and China’s economic development is gradually changing from the “three highs” of high pollution, high consumption, and high emissions to intensive, efficient, and green quality development. In the process of green transformation, the development of green finance has a stimulating effect on the green transformation of the manufacturing industry. According to Xu et al. [37], the provincial green economy development level is measured by assessing social development, economic efficiency, innovation drive, ecological construction, and fairness for the people, and according to its measurement results, the 30 provinces outside Tibet are divided into three green economy development level regions, as shown in Figure 2.

On the basis of the above grouping, regressing the three levels of regions separately for fixed effects, the results are shown in

Table 10. Heterogeneity test of green economy development level.

Variable	First Level	Second Level	Third Level
	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.5800 *	2.7556 *	3.1392 ***
	(2.1304)	(2.0290)	(3.7302)
$\mathrm{pat}$	0.0537	−0.1784	0.6633
	(0.2944)	(−0.6183)	(1.6464)
$\mathrm{hp}$	−0.3809 **	−0.8983	0.7444
	(−2.3310)	(−1.7634)	(1.0756)
$\mathrm{advstr}$	0.0024 **	0.0205 ***	0.0094
	(2.3478)	(5.0086)	(1.2081)
$\mathrm{GDP}$	0.4826	−0.0108	−0.7636
	(1.6076)	(−0.0134)	(−0.9475)
$\mathrm{TFP}$	−0.0896 ***	−0.0457 ***	−0.0255 **
	(−6.3195)	(−5.1227)	(−2.4612)
$\mathrm{er}$	−11.9033	6.9114	12.0477 **
	(−1.0232)	(1.4731)	(2.4202)
_cons	0.9322 ***	0.7276 ***	0.7539 ***
	(8.7028)	(6.9419)	(8.3597)
Area	Yes	Yes	Yes
adj. R2	0.288	0.323	0.220
N	100	100	100

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

. The results show that no matter the level of regional green economy development, green finance will significantly affect the rise of the manufacturing value chain. Specifically, in areas with a high level of green economy development, the impact of green finance on the rise of manufacturing value chain is 0.5800 at the level of 10% significance, and the regression coefficient is obviously smaller than that in areas with low level of green economy development. The possible reason is that the high-level areas, mainly developed provinces such as Beijing, Shanghai, and Zhejiang, have a high degree of adaptation to the policy environment of financial innovation and green development, and are already in a relatively stable development stage. For the regions with a relatively low level of green economy development, the implementation and development of green finance are restricted to some extent because of the weak development of the regional economic foundation. This result also shows that regions with relatively backward economic development have more space to develop green finance, which can better play the role of green finance development in promoting the transformation and upgrading of the regional manufacturing industry, and promotes the climbing of the manufacturing value chain.

6. Further Analysis: Test for Moderating Effects

In addition to the factors discussed above that influence the climbing position of manufacturing value chains, this paper argues that there are other modes of transmission in green finance that have an impact on the changing position of manufacturing value chains. Therefore, based on the key technological factors affecting the transformation and upgrading of the manufacturing industry, the proportion of R&D investment to GDP is chosen as the input intensity of innovation development ($\mathrm{RD}$) and the number of workers employed in high-tech industries as the indicator representative of highly skilled personnel ($\mathrm{PHE}$) to verify the moderating effect of both on the process of green finance in promoting the rise of the manufacturing value chain.

6.1. Testing the Moderating Effect of Input Intensity of Innovation Development

Before conducting the test for moderating effects, the moderating variables are interacted to obtain the interaction terminteract₁, and the interaction terms were put into the LSDV fixed effects model for regression, which interacted as follows.

$$\begin{array}{c}\mathrm{D}\mathrm{V}\mathrm{A}{\mathrm{R}}_{\mathrm{it}}=\mathrm{c}+{\alpha }_1\mathrm{G}{\mathrm{F}}_{\mathrm{it}}+{\mathsf{\beta }}_1\mathrm{R}\mathrm{D}+\gamma {\mathrm{X}}_{\mathrm{it}}+\mathsf{\theta }{\mathrm{interact}}_{\mathrm{i}}+{\delta }_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{it}} \end{array}$$

(8)

$$\begin{array}{c}{\mathrm{interact}}_1=\mathrm{G}{\mathrm{F}}_{\mathrm{it}}\times \mathrm{R}\mathrm{D}\end{array}$$

(9)

It can be found through columns (2) and (3) of

Table 11. Test of moderating effect of innovation development input intensity.

	(1)	(2)	(3)	(4)
Variable	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.9278 *	0.9858 *	2.5878 ***	2.0623 ***
	(1.8922)	(1.9051)	(4.5430)	(4.5527)
pat	0.0420	−0.0270	0.0597	0.0597
	(0.3061)	(−0.2066)	(0.4126)	(0.4126)
hp	−0.4736 ***	−0.4190 ***	−0.3174 ***	−0.3174 ***
	(−2.7629)	(−2.9439)	(−2.7588)	(−2.7588)
advstr	0.0018 *	0.0016 **	0.0011	0.0011
	(1.9884)	(2.1760)	(1.2744)	(1.2744)
GDP	0.5517 *	0.7718 **	0.3339	0.3339
	(2.0066)	(2.7272)	(1.0777)	(1.0777)
$\mathrm{TFP}$	−0.0541 ***	−0.0587 ***	−0.0515 ***	−0.0515 ***
	(−5.9691)	(−5.8009)	(−5.2787)	(−5.2787)
er	3.5976	3.0533	5.3995	5.3995
	(1.2067)	(0.9416)	(1.5395)	(1.5395)
RD		−0.0881 **	−0.0166	−0.0745 *
		(−2.1839)	(−0.4040)	(−1.7328)
interact1			−0.3038 ***
			(−4.3456)
interact2				−0.3038 ***
				(−4.3456)
_cons	0.7699 ***	1.0251 ***	0.6863 ***	0.7865 ***
	(5.8648)	(5.3324)	(4.8456)	(5.9600)
Area	Yes	Yes	Yes	Yes
adj. R2	0.610	0.614	0.627	0.627
N	300	300	300	300

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

that the significance of the moderating variable decreases after adding the moderating variable innovation development input degree, so the moderating variable and the core explanatory variables are centralized and multiplied to get the new interaction term interact₂, and the model is obtained as shown in column (4). At the 1% level of significance, the development of green finance has a positive effect on the climbing of the manufacturing value chain, with a significant positive coefficient for the core explanatory variable GF and a negative coefficient for the interaction term, indicating that the intensity of innovation development inputs inhibits the impact of green finance on the climbing of the manufacturing value chain and has a negative moderating effect. The reason may be that the level of R&D innovation and research and development investment in the process of manufacturing development is low, even though the process of financial support and policy support of green finance for high-tech industries is limited by the level of technological innovation and cannot obtain financial support, which makes the landing efficiency of the green finance development process low and produces negative regulation.

6.2. Moderating Effect of Highly Skilled Personnel

Similar with the process described above, an interaction term was first applied to the moderating variable, the average number of workers in high-tech industries, and the Green Finance Index.

$$\begin{array}{c}\mathrm{D}\mathrm{V}\mathrm{A}{\mathrm{R}}_{\mathrm{it}}=\mathrm{c}+{\alpha }_1\mathrm{G}{\mathrm{F}}_{\mathrm{it}}+{\mathsf{\beta }}_1\mathrm{P}\mathrm{H}\mathrm{E}+\gamma {\mathrm{X}}_{\mathrm{it}}+\mathsf{\theta }{\mathrm{interact}}_{\mathrm{i}}+{\delta }_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{it}}\end{array}$$

(10)

$$\begin{array}{c}{\mathrm{interact}}_3=\mathrm{G}{\mathrm{F}}_{\mathrm{it}}\times \mathrm{P}\mathrm{H}\mathrm{E}\end{array}$$

(11)

Based on model (3) (in

Table 12. Moderating effects of highly skilled personnel.

	(1)	(2)	(3)	(4)
Variable	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$	$\mathbf{D}\mathbf{V}\mathbf{A}{\mathbf{R}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{GF}}_{\mathrm{it}}$	0.9278 *	0.9475 *	0.9798 *	0.7814
	(1.8922)	(1.8191)	(1.8409)	(1.5128)
$\mathrm{pat}$	0.0420	−0.0760	0.0004	0.0004
	(0.3061)	(−0.5504)	(0.0025)	(0.0025)
$\mathrm{hp}$	−0.4736 ***	−0.2060	0.2588	0.2588
	(−2.7629)	(−0.9369)	(0.7900)	(0.7900)
$\mathrm{advstr}$	0.0018 *	0.0017 *	0.0018 *	0.0018 *
	(1.9884)	(1.9078)	(1.9033)	(1.9033)
$\mathrm{GDP}$	0.5517 *	0.5953 **	0.5531 *	0.5531 *
	(2.0066)	(2.1641)	(1.9024)	(1.9024)
$\mathrm{tfp}$	−0.0541 ***	−0.0576 ***	−0.0574 ***	−0.0574 ***
	(−5.9691)	(−5.9220)	(−5.7978)	(−5.7978)
$\mathrm{er}$	3.5976	3.1227	3.3340	3.3340
	(1.2067)	(1.0312)	(1.0925)	(1.0925)
$\mathrm{PHE}$		−1.0681	−1.1372	−1.4891 **
		(-1.4621)	(−1.6019)	(−2.2904)
${\mathrm{interact}}_3$			−1.8451 *
			(−2.0389)
${\mathrm{interact}}_4$				−1.8451 *
				(−2.0389)
_cons	0.7699 ***	0.8772 ***	0.8827 ***	0.9205 ***
	(5.8648)	(5.6399)	(5.7770)	(6.3438)
Area	Yes	Yes	Yes	Yes
adj. R2	0.610	0.612	0.612	0.612
N	300	300	300	300

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

), the moderating variables and core explanatory variables are centralized and multiplied again to obtain the new interaction term ${\mathrm{interact}}_4$, obtaining the model as shown in column (4). It could be found that the significance of the interaction variables is not in a high significance level which could only be explained in the 10% significance level. Since the interaction term contains the influence of the core explanatory variable GF on the explanatory variable DVAR, there may be cointegration affecting the significance of GF in model (4). From the significance and coefficient situation of the interaction term, the coefficient shows negative at 10% significance level, and the coefficient of green finance becomes lower, indicating that there is a negative moderating effect of talent as a moderating variable. In order to evidence for the effect efficiency of interact variable, the author calculated the marginal effects of the interaction term. From the marginalisation plot in Figure 3, it could be seen that the marginal effect of ${\mathrm{interact}}_4$ decreases to 1 when GF equals to 0.6, which marginal effect is lower than the interaction term before being centralised. The marginalisation plots illustrate the similar outcome which is that the interaction term contains the high skilled talents have negative moderating effect on the development of manufacture industry transfer to higher end. Therefore, it can be concluded that although green finance has a positive effect on the rise of the manufacturing value chain, there is a shortage of highly qualified labour to meet the technical support requirements to facilitate the transformation of the manufacturing value chain with the support of green finance. As a result, China should increase its efforts to cultivate talent to ensure that companies have the talent under the conditions of financial incentives and technology as the core condition to carry out innovative activities and achieve value chain position advancement.

7. Conclusions and Recommendations

The development of manufacturing industry is an important symbol representing the comprehensive national power of today’s states. As a large manufacturing country, the climbing up of the value chain from the middle and lower end to the middle and higher end is crucial for China to achieve high-quality economic development. As a result, China should increase its efforts to cultivate talent to ensure that companies have the talent under the conditions of financial incentives and technology as the core condition to carry out innovative activities and achieve value chain position advancement. This paper investigates the effect of green finance on the climbing status of manufacturing value chain through a dynamic panel regression estimation of panel data from 2011 to 2020, and obtains the following main conclusions: (1) Green finance has a facilitating effect on the climbing status of manufacturing value chain during the sample period, and the conclusions are proved to be valid in the baseline regression, robustness test, and GMM dynamic panel estimation. (2) The effect of green finance on the growth of the manufacturing value chain is consistent with the degree of regional economic development, from the standpoint of regional heterogeneity. The relative promoting effect is more significant in economically developed regions such as East China and South China, while the promoting effect is less significant in less developed places such as Central China. As the regional classification criteria, the conclusion primarily applies to the heterogeneity of the level of marketization development and the level of green economy development. The higher the level of marketization and the level of green economy development, the more favourable it is for the regional manufacturing value chain to achieve rapid growth. (3) Regions with a higher degree of marketization and greening of their economic development experience faster economic growth, and the stimulating effect of green finance is limited, whereas green finance development in relatively less developed regions has much room for improvement in its role in promoting the climbing of the manufacturing value chain. (4) The key restriction of China’s current stage of green financing on the ascension of the industrial value chain status, according to the moderating impact analysis, is the degree of technology and talent circumstances. The challenge of depending on highly qualified labour requires immediate financial policy support in order to prevent cost risks in the process of performing technology innovation and talent introduction.

The following policy insights are drawn from the above findings.

Firstly, continue to improve the construction of green financial system and enhance the efficiency of green finance in the process of promoting the rise of the manufacturing value chain. Green finance will be developed collaboratively from various perspectives such as investment, insurance, securities, and credit, providing a more convenient and relaxed financing environment for manufacturing enterprises to carry out innovative activities, reducing the risk of trial-and-error capital, motivating manufacturing enterprises to actively carry out green and sustainable transformation, achieving a climb in the value chain, and thus promoting a faster and brisk growth.

Secondly, focusing on the reduction of regional heterogeneity, coordination needs to be done at a national level. Implementing green financial policies according to local conditions to promote the rising status of manufacturing value chain. The regions with faster development of green finance form demonstration points to provide more reference experience for the later development regions. At the same time, while learning from the experience of developed regions, the developing regions should combine their own regional development overall strategy and the reality of economic or social development. For example, the Western Development in the western region and the revitalisation strategy of the old industrial bases in the north–eastern region will guide enterprises that are strategically focused and have regional characteristics. As a region with rapid economic development, the southeast coastal region can make full use of its inherent advantages, such as sufficient capital, perfect infrastructure construction, and pilot policies to promote the development of the real economy in order to promote the transformation and upgrading of the manufacturing industry and thus become a leader in the region and radiate its advantages to other relatively slow-developing urban areas in the vicinity.

Thirdly, policies should be continued to stimulate technical innovation and the introduction of talents, with a focus on training talents in the field of attack, to offer the fundamental circumstances for the manufacturing industry to engage in innovative activities. Simultaneously, financial system reform should be pushed further in order to make green financial policies more compatible and targeted with the development of high-tech industries, in order to more effectively play the regulatory function of talents and technology in the process of boosting the rise of the manufacturing value chain, releasing the key role of green financial development in stimulating the transformation and upgrading of the manufacturing value chain.