Global Sustainability Research                                                     ISSN: 2833-986X 
https://doi.org/10.56556/gssr.v4i1.1185 

                                                                  

 

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RESEARCH ARTICLE  

The Impact of Green Finance on China's Macroeconomic Resilience and 

Sustainable Development: An Empirical Analysis Based on 2011-2022 

Provincial Panel Data 

 
Xuchen Luo1, Haiming Yu1*, Yan Wang1, Jiale Shao2, Danyang Mei3, Yunjie Tang1 

 

1School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou China 
2School of Science, Zhejiang University of Science and Technology, Hangzhou China 
3School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou China 

 

Corresponding Author: Haiming Yu: email: yhm@zust.edu.cn 

Received: 20 January, 2025, Accepted: 11 March, 2025, Published: 25 March, 2025 

 

Abstract 

Macroeconomic resilience is important tomaintain a more persistent economic growth as well as overcomes the 

external shocks. Under GMM framework, this study selects the spatial panel data of 30 provinces in China from 

2011 to 2022 to examine the macroeconomic resilience level within China. The results show that: 1) Green finance 

has a positive effect on China's macroeconomic resilience. (2) Technological innovation, industrial structure 

upgrading and risk management are important channels through which green finance can affect economic resilience. 

(3) In the process of green finance enabling economic resilience, there is a nonlinear impact on economic resilience 

and a significant threshold effect. (4) The impact of green finance on enhancing China's macroeconomic resilience 

is spatially heterogeneous, and the promotion effect is more significant in the provinces with high economic 

development level. The government should encourage and support green finance industries which will then support 

the sustainable and healthy development of China's economy and environment. 

Keywords: green finance; economic resilience; GMM difference; mediating effect; multiple threshold effect

 

Introduction

 

With the outbreak of COVID-19, economies around the world have experienced strong negative reactions such as 

stagnation or even regression of economic development, rapid rise in unemployment, decline in overseas 

investment, decrease in cross-border e-commerce trade and changes in commodity prices, which have led to a 

serious decline in the resilience of the world macro economy. As the wave fades, so does the problem of how to 

reshape the world economy. Economic resilience is an important manifestation of an economy's resistance to 

shocks, resilience, restructuring capacity and innovation and development capacity aftershocks. Compared with 

other countries, China, as the world's second largest economy, has played a key role in the process of reshaping the 



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world economy in the post-epidemic era and achieved the first positive economic growth, which further 

demonstrates that China's economy has strong potential development space and strong economic resilience. The 

report to the 20th National Congress of the Communist Party of China (CPC) stressed the importance of green 

finance, raising the building of a green financial system to a national strategy and attracting more capital to the 

green sector. By optimizing resource allocation, improving carbon emission reduction efficiency and reducing 

financial risks, green finance provides strong support for promoting economic transformation and upgrading and 

achieving sustainable development. Sustainable development is broadly defined as development that meets the 

needs of present generations without jeopardizing the ability of future generations to meet their needs. With the 

continuous changes of The Times, the content and connotation of sustainable development are gradually refined, 

and there are more detailed requirements in ecology, energy and environment.Wang et al. (2016) argued that green 

finance promotes sustainable economic development by optimising resource allocation and promoting economic 

restructuring.Yao et al. (2023) found that green finance significantly enhanced China's macroeconomic resilience, 

especially in terms of its ability to cope with shocks, organisational coordination and innovation and 

transformation.However, the research on the impact and mechanism of green finance in regulating China's 

macroeconomic resilience is still relatively insufficient. 

Based on the panel data of 30 provinces in China, this study uses the GMM difference model to explore the impact 

of green finance on China's macroeconomic resilience, and further analyzes the mediating effect, threshold effect 

and spatial heterogeneity, to provide a scientific basis for formulating and optimizing relevant policies. The rest of 

the study is as following. Chapter two summarizes the literature of this study and hypothesis. Chapter three 

discusses the methods and models used in this study. In chapter four, we present concrete empirical results and 

conclude our work in chapter five. 

 

Literature Review  

 

Research on the measurement of economic resilience 

 

The definition of economic resilience is still not in consensus. Wan (2017) used GDP to measure economic 

resilience from three aspects: the rise and fall of the overall economy, the trend comparison before and after the 

shock, and the external impact model. Tan (2020) using economic maintenance and restorative measures such as 

regional economic resilience. Briguglio et al. (2006) extended economic resilience research elaboration to the area, 

choose 86 countries and regions, building contains macro, government regulation, social operation and four 

dimensions of market allocation of index system to measure the economy toughness. Martin (2012) chose the 

measurement form of a single index, and used the change of the number of employees in a place before and after 

the shock as the measurement of economic resilience. Brakman et al (2015), Giannakis and Bruggeman (2017) 

used the GDP of a place or the unemployment rate to construct a reverse indicator to replace the variable of the 

number of employees. Polese (2015) studied the economic resilience in urban research, and found that highly 

educated human resources, rich and vast market environment, multi-form structure and other aspects were the key 

factors affecting the development of urban economic resilience, that is, they were used as the screening criteria for 

measurement indicators. Doran and Fingleton (2018) examined direction to expand into the space, based on law of 

Verdun is hit with the fact that state economic system status difference, from the Angle of view of the differences 

in size and economic recovery measure economic resilience; Ubago et al. (2019) measured the economic resilience 

of the country as a whole from the three perspectives of industry, human resources and capital.  

 



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Analysis of the impact of green finance on China's macroeconomic resilience 

 

At present, researches on macroeconomic resilience have not yet reached a consensus. First of all, the definition of 

macroeconomic resilience varies from each other. Wan (2017) used GDP to measure economic resilience from three 

aspects: the rise and fall of the overall economy, the trend comparison before and after the shock, and the external 

impact model. Tan Juntao (2020) using economic maintenance and restorative measures such as regional economic 

resilience, Briguglio et al. (2006) to economic resilience research elaboration to the area, choose 86 countries and 

regions, building contains macro, government regulation, social operation and four dimensions of market allocation 

of index system to measure the economy toughness; Martin (2012) chose the measurement form of a single index, 

and used the change of the number of employees in a place before and after the shock as the measurement of 

economic resilience; Brakman et al (2015), Giannakis and Bruggeman (2017) used the GDP of a place or the 

unemployment rate to construct a reverse indicator to replace the variable of the number of employees; Polese 

(2015) deeply studied the economic resilience in urban research, and found that highly educated human resources, 

rich and vast market environment, multi-form structure and other aspects were the key factors affecting the 

development of urban economic resilience, that is, they were used as the screening criteria for measurement 

indicators; Doran and Fingleton (2018) will research direction to expand into the space, based on law of Verdun is 

hit with the fact that state economic system status difference, from the Angle of view of the differences in size and 

economic recovery measure economic resilience; Ubago et al. (2019) measured the economic resilience of the 

country as a whole from the three perspectives of industry, human resources and capital. The definition of economic 

resilience is relatively unified. It measures the ability of national and regional economic systems to withstand 

external shocks, such as the ability to resist disturbances, the ability to restore and maintain the original state, and 

the ability to enhance public security and maintain social order. Moreover, economic resilience does not exist as an 

independent individual. It is closely related to a region's system, culture, science and technology, society and other 

aspects. It is the ballast stone of the resilience system and the barometer of the healthy and sound development of 

the economic system. In economic measure of resilience research, specific divided into use the single index such 

as GDP and employment measure, the fact that state compared with hit condition difference method, and contains 

the ability, the Angle of the composite index measure method. Is common among them, the more complex we 

measure method, related research covers the macro micro level, the internal influence mechanism, Pratt &Whitney 

financial Doberman, mediation effect, space, etc., and for how to improve the economic resilience, how to shrink 

regional economic resilience differences provide advice. In this paper, economic resilience is defined as the ability 

to cope with shocks, the ability to organize and coordinate, and the ability to innovate and transform. Adhere to the 

green sustainable development in our country, developing green finance, green finance in implementing the green 

development, boost the development of environmental protection industry, improve the pollution treatment system 

and played an important role in governance. Green financial development to optimize the capital of the guidance, 

to further enhance China's macroeconomic resilience to promote economic development in high quality. This study 

will from two aspects of direct effects and indirect effects (see research design Figure1.) green financial effect on 

our country's macroeconomic resilience theory mechanism. 

Meng et al. (2023) constructed a high-quality economic development index and a green finance index, and found 

that green finance can significantly promote high-quality economic development, enhance technological 

innovation, promote industrial structure upgrading, and help enhance the impact of green finance on high-quality 

economic development. Li et al. (2023) empirically found that green finance can improve the economic resilience 

of the region and have a positive impact on the economic resilience of the surrounding regions through spatial 

spillover effect through the construction of spatial Dubin model. Yao et al. (2023) constructed an evaluation index 

system of green finance and macroeconomic resilience, and based on the system GMM model and the mediating 



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effect model, confirmed that green finance development can significantly improve China's macroeconomic 

resilience; Shi et al. (2022) argued that green finance can promote high-quality economic development, but there 

is a nonlinear relationship between the two and there is a threshold effect. Wen et al. (2022) argued that green 

finance affects high-quality economic development by supporting green innovation. Song (2023) further 

demonstrated the positive role of green finance in promoting economic resilience through grey correlation 

analysis.Liao (2024) pointed out that the realization of sustainable development goals cannot be achieved without 

the help and support of green finance, which can be regarded as a driving force for sustainable development from 

the perspective of economic development, social progress and ecological civilization optimization.However, as far 

as current research is concerned, the evaluation indicators of green finance and economic resilience are relatively 

simple and simple. 

Analysis on transmission channels of green finance on China's macroeconomic resilience  

 

Many scholars examined the green finance impacts as well as the transmission channelsthrough which that can 

affect economic resilience. Wang et al. (2020) found that green finance enhances regional economic resilience by 

promoting industrial transformation. Green finance can provide financial support for environmentally friendly and 

sustainable industries, so that these industries have more complete green innovation technology and can adapt to 

market changes and needs. Liu et al. (2021), taking China's provincial panel data from 2010 to 2019 as an example, 

verified that green finance effectively promoted high-quality economic development through three ways: enterprise 

technology innovation, industrial structure upgrading and green consumption; Xia et al. (2023), based on the fixed 

effect model, demonstrated that green finance can significantly promote corporate green innovation by promoting 

corporate technological innovation; Zhang et al. (2023) believed that the upgrading of industrial structure played a 

significant mediating effect in the process of green finance improving the efficiency of green economic 

development. Yao et al. (2023) empirically concluded that green finance promotes industrial upgrading and 

technological innovation to improve economic resilience. 

In the process of achieving the goal of sustainable economic development, green finance plays a very important 

role as a "booster". The progress of the economy needs the assistance and guarantee of green finance, especially 

the value and role of green finance should be affirmed from the perspective of "green economy" development. In 

recent years, the rapid development and continuous progress of China's economy have had a profound direct and 

potential impact on the environment. The comprehensive strengthening of environmental protection work has also 

become an urgent key task. There is a very close connection between the proposal of the concept of "green finance" 

and the construction of an "environmentally friendly" economic development pattern. The impact of the continuous 

rise of green finance on sustainable economic progress can be analyzed from two aspects: First, it vigorously 

promotes the progress of green consumption; second, it realizes the continuous tilt and transfer of social resources 

towards green industries. The development of green finance has fundamentally replaced the coverage of the 

economy in the fields of green development and low-carbon development. While enterprises fulfill their 

responsibilities for green development, they also have more financial support and guarantees. Especially in the 

macro environment where market entities are developing towards diversification, financial institutions have shown 

more confidence and stronger motivation in the development of green finance. The comprehensive creation of a 

coordinated and interactive pattern between the financial market and the carbon market has also provided space for 

the value and role of green finance to be exerted and developed. 

First, green finance helps to optimize the allocation of resources. The traditional economic development model 

tends to over-rely on industries with high energy consumption and high pollution, leading to over-exploitation of 

natural resources and environmental damage. The rise of green finance provides an opportunity for funds to flow 

to environmentally friendly and sustainable development areas. By financial institutions providing financial tools, 



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such as green loans, green bonds, funds can be more effectively guide to clean energy, energy conservation and 

emissions reduction, recycling economy and green industry, promote the optimization of economic structure and 

transformation. This will not only help reduce environmental pressure, reduce energy consumption and pollutant 

emissions, but also promote the development of new technologies and industries, foster a more competitive 

industrial chain and enhance the resilience of the economy. 

Secondly, the guiding role of green finance can also promote the development of green consumption. While 

providing financing for green projects, green finance also promotes the supply and awareness of green products 

and services. Through the green standards, evaluation and information disclosure system established by financial 

institutions, they can provide investors with relevant information about green products and evaluate their 

environmental friendliness and sustainability. This provides consumers with a clearer choice, enabling them to 

consciously choose environmentally friendly and low-carbon products and services. This green consumption shift 

will not only help change the traditional consumption pattern of high energy consumption and high emissions, 

reduce resource waste and environmental pressure, but also promote new green industries and job creation, and 

promote sustainable economic development. Based on this, Hypothesis 1 is proposed in this paper. 

Hypothesis 1: the development of green finance can from "the ability to cope with shocks", "the organization and 

coordination ability," and "innovation transformation ability three aspects significantly increased the 

macroeconomic resilience. 

Analysis of the multi-threshold effect of green finance on macroeconomic resilience in China 

 

The existing literature discusses very fewer about the threshold effect of green finance on macroeconomic 

resilience. Xiao et al. (2023) used the dynamic generalized panel model method to conduct nonlinear impact 

analysis to explore the mechanism of green finance promoting high-quality economic development through green 

technology innovation, and analyzed the regulating effect of green finance on the relationship between green 

technology innovation and high-quality economic development. The results show that GTE plays a partial 

intermediary role between different dimensions of green finance and high-quality economic development, among 

which GTE plays the strongest role in promoting high-quality economic development through green securities, 

followed by green credit and green insurance. Zhang et al. (2020), taking the Yangtze River Delta core urban 

agglomeration as an example, used panel data model to study that green finance and industrial structure upgrading 

are the direct driving forces for high-quality economic development; Industrial structure upgrading plays a partial 

intermediary role in the process of green finance promoting high-quality economic development, and is an 

important intermediary path, but the moderating effect is not significant. Ma (2018) believed that industrial 

structure, energy structure and transportation structure were the economic causes of severe pollution and large 

amount of carbon emissions in China. Therefore, it is necessary to change the polluting economic structure, and the 

main way to change the economic structure is to change the investment structure and increase green investment. 

We should accelerate the construction of green financial system through 12 channels, such as establishing green 

development fund, supporting the development of green credit, establishing green guarantee mechanism, and 

building incentive mechanism of green finance, so as to improve the return on investment and financing availability 

of green projects, and reduce the return on investment and financing availability of polluting projects. Since the 

implementation of the Western Development policy, Chen et al. (2018), the western region has made full use of its 

resource advantages and latecomer advantages to actively undertake the industrial transfer in the central and eastern 

regions, and the industrial structure has been optimized to a certain extent. As the most important ecological barrier 

and ecologically fragile region in China, the western region should abandon the development concept of "pollution 

first, treatment later", promote the optimization and upgrading of industrial structure with green finance, and 

achieve green development and high-quality economic development. On the one hand, green finance can promote 



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industrial upgrading. The emergence of green finance, guided by innovation, environmental protection and 

sustainability, provides financing support for the development of strategic emerging industries and high-tech 

industries. These industries involve fields such as clean energy, new materials and new energy vehicles, and are 

characterized by high added value, high-tech content, low pollution and low energy consumption. Through green 

loans, bonds and funds provided by financial institutions, social funds can be transferred to these sectors to 

accelerate the upgrading and transformation of the industrial structure, narrow the technological gap with developed 

countries, and improve the level of the industrial chain and its core competitiveness. In this way, economic 

development can be accelerated, while the impact on the environment and high consumption of limited resources 

can be reduced, and economic resilience can be improved. 

On the other hand, green finance can promote technological innovation. The introduction of green finance can 

provide financial support and market promotion for innovative technologies such as new energy and environmental 

protection technologies. Research and development in these fields can not only reduce energy consumption and 

pollution, promote the development of environmental protection industry, but also foster new industries and drive 

the development of innovation. At the same time, financial institutions can also according to the green standards, 

assessment and disclosure of financial and non-financial information, form the ability of identification, risk 

evaluation and management of green, provide better protection for the technology innovation. Such green financial 

innovation not only helps to optimize the allocation of resources and promote economic development, but also 

speeds up the process of scientific and technological innovation and improves the level of industrial chain and 

market competitiveness, thus enhancing the innovation, resilience and sustainability of the economy. Based on this, 

Hypothesis 2 is proposed in this paper. 

Hypothesis 2: Technological innovation and industrial structure upgrading act as intermediaries to affect the impact 

of green finance on China's macroeconomic resilience. 

Green finance can promote the development of low-carbon and clean energy industries and promote the 

transformation and upgrading of economic structure. By guiding and supporting the development of green 

industries, the economy can improve its resource utilization efficiency and reduce its dependence on traditional 

industries with high pollution and high energy consumption, thus enhancing macroeconomic resilience. 

Secondly, green finance can provide long-term and stable financial support to help enterprises make environment-

friendly investments and innovate. This not only helps to reduce environment pollution and resource waste, can 

also enhance the enterprise the competitive ability and sustainable development ability. This non-linear impact can 

create a virtuous cycle between economic growth and environmental protection. 

In addition, green finance can also promote the construction of ecological civilization, improve the ecological 

environment quality, improve the people's feeling and happiness. This kind of nonlinear positive affect can promote 

social stability and sustainable development, macroeconomic resilience to provide strong support for our country. 

Based on this, hypothesis 3 is proposed in this paper. 

Hypothesis 3: As threshold variables, technological innovation, industrial structure upgrading and risk management 

make the impact of green finance on economic resilience have nonlinear characteristics. 

 

Spatial heterogeneity analysis of green finance on China's macroeconomic resilience 

 

There are also limited studies about the spatial differences on green finance impact on economic resilience. Xu et 

al. (2018) argued that green finance can through the enterprise capital formation mechanism, the signal transmission 

mechanism, the mechanism of feedback and credit has influence industrial structure, the empirical results show that 

the green finance mainly through direct funds to influence industrial structure, and regional differences, the eastern 

region because of its own resources advantage, the western region because of the policy advantages, Green credit 



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has significant effects on industrial structure upgrade, the effect is not obvious in the central region. Du and Zheng 

(2019) argued that green finance in developed regions played a greater role and had a significant effect on carbon 

emission reduction. Zeng (2021) set in the new crown pneumonia outbreak, from two aspects: resistance and 

resilience of the outbreak of the influence of the development of China's regional economic resilience, found in 

different quarter period, resistance have different strength, resilience, performance, the provinces domain also 

presents the different strength of economic resilience. Cheng (2022) found empirical green finance in the eastern 

economic resilience, big cities to promote effective, economic resilience to promote small and medium-sized cities 

of the Midwest, the effect is not significant, namely exists heterogeneity; Deng and Liu (2023) believed that from 

the perspective of the central and western regions, the role of green finance in promoting the high-quality economic 

development of the central and western regions was significantly higher than that of the eastern regions. Shang et 

al. (2023) used Dagum Gini coefficient and variance decomposition to reveal that the overall level of green finance 

in China is not high from the perspective of north-south space and structure, and the southern region is higher than 

the northern region. Du et al. (2023) points out that the green financial development imbalance in our country, far 

from the green level of financial development in our country overall. Song (2023) empirically found that there is a 

significant spatial correlation between green finance and economic resilience through the construction of spatial 

matrix. Most of the existing research focuses on the independent analysis of the correlation between green 

development and economic development, but the spatial research on the national scale is relatively scarce. 

Firstly, regional natural resource endowments and environmental conditions are important factors affecting the role 

of green finance. There are differences in resource distribution and environmental status in different regions, which 

lead to different effects of green finance in different regions. On the one hand, in areas with rich resources and good 

ecological environment, such as coastal areas and ecological reserves, the application of green finance can better 

promote the rational use of resources and environmental protection, and improve the resilience of the economy. On 

the other hand, in areas with poor resources and serious environmental pollution, the application of green finance 

may face greater challenges and its effect on improving economic resilience may be relatively weak. 

Second, the region's industrial structure and development level will also affect the role of green finance. There are 

differences in the industrial structure of different regions. Some regions may rely more on industries with high 

pollution and high energy consumption, while other regions may have realized the optimization of industrial 

structure and green transformation. Therefore, the application effect of green finance in different regions will also 

be different. For those regions that rely on industries with high pollution and energy consumption, the introduction 

of green finance can promote industrial transformation and upgrading in these regions and improve their resilience. 

However, for those regions that have optimized their industrial structure, the application of green finance has a 

relatively small effect on improving their resilience. In addition, the regional policy environment and the level of 

financial market development will also affect the role of green finance. There are differences in policy support and 

financial market development levels in different regions, which will directly affect the development and application 

of green finance. Some regions may have established a sound green finance policy and regulatory framework, 

providing better financial services and support, and green finance may have better application effects in these 

regions. While some regions may lack relevant policies and financial market development, and the application of 

green finance may face more difficulties. Based on this, hypothesis 4 is proposed in this paper. 

 

Hypothesis 4: green finance function in the macroeconomic resilience of spatial heterogeneity. 

 

 



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Methodology 

 

Data and Variables 

 

Considering the availability of data, due to the Tibet autonomous region of the data in China 2011 years ago and 

the existence of a large number of the problem of the missing, so in this paper, based on 2011-2022 data of 30 

provinces and autonomous regions municipalities directly under the central government data sample space for 

research. The data on environmental pollution, infrastructure, human capital, resource endowment, technological 

innovation and industrial structure upgrading come from China Statistical Yearbook, China Financial Statistical 

Yearbook, Financial Basic Data Center of the People's Bank of China, China Research Data Platform, China Public 

Policy and Green Development Database and Wind Database. The data related to the construction of the green 

finance evaluation system are from the Global Green Finance Big Data Platform of Central University of Finance 

and Economics, the websites of the Ministry of Science and Technology, the National Bureau of Statistics, the 

People's Bank of China and other authoritative statistical yearbooks, including the national and provincial statistical 

yearbooks, environmental status bulletin and some professional statistical yearbooks. "China financial yearbook", 

"China statistical yearbook of science and technology of the China energy statistical yearbook of China agricultural 

statistics yearbook" China industrial statistics yearbook "statistical yearbook of China's third industry. Some 

missing values were obtained by interpolation method. 

One core explanatory variable, one core explained variable, two mediating variables and four control variables were 

selected. 

 

Explained variables 

Economic Resilience Evaluation Index (EcoRes, ER), according to the existing literature, there are two main 

methods to measure economic resilience: The first is the single index method, which is used by Chen et al. (2020) 

to define regional economic resilience based on the growth rate of the actual Gross Domestic Product (GDP) of 

each province. 

The second method is to construct a comprehensive index system for measurement. The results reflected by the 

single index method vary greatly according to the changes of selected indexes, which cannot fully reflect economic 

resilience. In this study reference Yao et al （2023）method, on the basis of using the "ability to cope with shocks", 

"organization coordinated ability", "innovation transformation ability" three secondary index evaluation system to 

build economic resilience, the ability to cope with shocks "refers to the object of economy in the face of shock 

response capacity, "Organization coordinated ability" refers to the object of economy in the face of economic 

recession on technology updates, capital allocation, and labor to make the new configuration, transformation of 

"innovation ability" refers to the object of economy after a hit from the outside world, choice of transformation of 

the development of new methods and technology ability. The entropy method is used for calculation. The evaluation 

system is shown in Table 1. 

Which is obtained by entropy value method "ability to cope with shocks" weight 0.218, organization and 

coordination ability, weight is 0.347, "transformation of innovation ability" weight is 0.435. The weight coefficients 

are shown in Table 2. 

Core explanatory variables 

Green finance level index (GFI) is based on the green finance evaluation system constructed by Zeng et al. (2014), 

which includes green credit, green insurance, green securities and green investment. This paper constructs a green 



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finance level system (see Table 3) from seven levels: green credit, green investment, green insurance, green bond, 

green support, green fund and green equity. Fyone, 2020), using the entropy TOPSIS method of green samples in 

31 provinces, cities and autonomous regions municipalities directly under the central government to the financial 

index as an evaluation of local green financial level measurement data. Entropy weight TOPSIS method is a method 

formed by the combination of entropy weight method and TOPSIS model. It is a mathematical method used to 

judge the dispersion degree of an index. Its mathematical principle is divided into the following three steps: 

 

Table 1. Evaluation system of economic resilience 

First-level indicators Secondary indicators Calculation method 

 

Economic Resilience (ER) 
 

 

 

Ability to cope with shocks (ER-A) 

A1. Per capita GDP (100 million 

yuan) 

A2.GDP (100 million yuan) 

A3. Number of people 

participating in unemployment 

insurance (ten thousand) 

A4. Number of people 

participating in urban basic 

medical insurance (ten thousand) 

A5. Foreign trade dependence (%) 

 

 

Organization and coordination 

ability (ER-B) 

A6. Loan-to-deposit ratio of 

financial institutions (%) 

A7. Urbanization rate (%) 

B1. Per capita disposable income 

of residents (Yuan) 

B2. Fiscal self-sufficiency rate (%) 

B3. Unemployment rate (%) 

B4. Per capita GDP growth rate 

(%) 

Innovation and transformation 

capacity (ER-C) 

C1. Sales revenue of new products 

of industries above designated size 

(RMB '000) 

C2. Share of technology Market 

contract turnover (%) 

C3. Share of R&D Expenditure 

(%) 

 

 

 

 

 



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Table.2 Weight results of economic resilience indicators using entropy weight method 

item Information entropy 

value e 

Information utility value 

d 

Weight coefficient w 

Ability to cope with shocks 

(ER-A) 

0.8393 0.1607 0.218 

Organization and 

coordination (ER-B) 

0.7987 0.2013 0.347 

Innovation and transformation 

ability (ER-C) 

0.9079 0.0921 0.435 

 

First, the original matrix is forward transformed, that is, all indexes are transformed into very large indexes. Then, 

the matrix standard is forward transformed to eliminate the influence of different index dimensions. Suppose that 

there are n evaluation objects and m evaluation indicators (all of which have been positive), the positive matrix is 

as follows: 

𝑋 =

[
 
 
 
 
 
𝑥11 𝑥12 𝑥13 𝑥14 … 𝑥1𝑚

𝑥21 𝑥22 𝑥23 𝑥24 … 𝑥2𝑚

𝑥31 𝑥32 𝑥33 𝑥34 … 𝑥3𝑚

⋯ ⋯ ⋯ ⋯ ⋯ ⋯
𝑥𝑛1 𝑥𝑛2 𝑥𝑛3 𝑥𝑛4 …

𝑥𝑚𝑚 ]
 
 
 
 
 

 

After that, the normalized matrix of each element is Z, where each element in Z is:.𝑧𝑖𝑗 = 𝑥𝑖𝑗/√∑ 𝑥𝑖𝑗
2𝑛

𝑖=1 Finally, we 

first determine the most value for subsequent calculation. 

To define the maximum value: 

𝑍𝑚𝑎𝑥 = (𝑍1𝑚𝑎𝑥, 𝑍2𝑚𝑎𝑥 , … , 𝑍𝑚𝑎𝑥) = 

（𝑚𝑎𝑥{𝑧11, 𝑧21, 𝑧31, … , 𝑧𝑛1},𝑚𝑎𝑥{𝑧12, 𝑧22, 𝑧32, … , 𝑧𝑛2},…𝑚𝑎𝑥{𝑧1𝑚, 𝑧2𝑚, 𝑧3𝑚, … , 𝑧𝑛𝑚}) 

Define the minimum:𝑍𝑚𝑖𝑛 = (𝑍1𝑚𝑖𝑛, 𝑍2𝑚𝑖𝑛, … , 𝑍𝑚𝑖𝑛) = 

（𝑚𝑖𝑛{𝑧11, 𝑧21, 𝑧31, … , 𝑧𝑛1},𝑚𝑖𝑛{𝑧12, 𝑧22, 𝑧32, … , 𝑧𝑛2},…𝑚𝑖𝑛{𝑧1𝑚, 𝑧2𝑚, 𝑧3𝑚, … , 𝑧𝑛𝑚}) 

 

The score is then calculated from the base formula and normalized. 

 

The greater the degree of dispersion is, the greater the influence of the index on the comprehensive evaluation is. 

The comprehensive evaluation system is as follows: 

 

 



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Table 3. Green finance level evaluation system 

First-level 

indicators 

Secondary index Tertiary indicators Calculation method 

 

 

 

 

green 

Finance 

Level 

Index 

(GFI) 

 

Green Credit 

 

Percentage of credit for 

environmental protection 

projects 

Total credit for environmental 

protection projects in the 

province/total credit for the 

province 

Green investment Proportion of investment in 

environmental pollution 

control in GDP 

Investment in environmental 

pollution control /GDP 

Green insurance Degree of promotion of 

environmental pollution 

liability insurance 

Income from environmental 

pollution liability insurance/total 

premium income 

Green bonds Degree of green bond 

development 

Total amount of green bonds 

issued/total amount of all bonds 

issued 

Green support Fiscal environment 

Share of conservation 

spending 

Fiscal expenditure on 

environmental protection/general 

budget expenditure 

 Green fund Percentage of green funds Total market value of green 

funds/total market value of all 

funds 

 Green equity Depth of development of 

green rights 

Total transaction amount of carbon 

trading, energy use rights trading, 

emission rights trading/equity 

market 

 

 

Intermediary variables 

Technological innovation, industrial structure upgrading and risk management. Green finance is to promote 

technological innovation, guide the flow of capital to the direction of scientific and technological innovation, 

develop advanced industrial technology, improve production efficiency, and achieve green and sustainable 

economic development. The technological innovation index of this study is measured by the expenditure of local 

finance on science and technology, and the upgrading of industrial structure is an important reflection highlighting 

the capital flow and the reallocation of production factors among different industries. The upgrading index of 

industrial structure in this study is measured by the ratio of the total output value of the local tertiary industry and 

the secondary industry. Risk management ability is an important embodiment of capital allocation, which is 

measured by the ratio of working capital to assets in this study. 

Control variables 

In this study, environmental pollution, infrastructure, human capital and resource endowment are selected as control 

variables and upgraded by referring to the calculation method of Wang Qizheng and Zhu Yingming. Environmental 



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pollution is measured by local financial expenditure on environmental protection, infrastructure is measured by 

local highway mileage, and human capital is measured by local resident population aged 14-65. Resources 

endowment in terms of local electric power consumption. 

 

Descriptive statistics 

 

Table4. Descriptive statistics 

Variable type Variable name 
Symbols Sample 

size 
Maximum Minimum Average 

Standard 

deviation 
Median 

Explained variable 

Economic 

resilience 

ER 
362 1.97 -0.63 0.975 0.388 1.05 

Ability to cope 

with shocks 

ER-A 
362 0.733 0.309 0.412 0.078 0.392 

Organizational 

and coordination 

skills 

ER-B 

362 0.622 0.233 0.412 0.086 0.395 

Innovation and 

transformation 

capability 

ER-C 

362 0.740 0.339 0.512 0.064 0.332 

Explanation/endogenous 

variables 

Green Finance 

Index 

GFI 
362 0.768 0.073 0.26 0.14 0.237 

Control/instrumental 

variables 

Environmental 

pollution 

EP 
362 6.874 5.327 6.104 0.27 6.108 

Infrastructure BI 362 5.608 4.082 5.082 0.369 5.198 

Human capital HC 362 7.961 3.532 4.734 0.958 4.448 

Endowment of 

resources 

RE 
362 4.891 2.268 3.232 0.314 3.213 

Mediating variable 

(Med) 

Technological 

innovation 

TI 
362 7.068 4.575 5.906 0.475 5.889 

Upgrading of 

industrial structure 

UIS 
362 5.297 0.518 1.266 0.642 1.247 

Risk management RM 362 8.149 6.181 7.313 0.847 7.147 

 

Table 4. Descriptive statistical analysis shows that this study evaluates the data of different variables so as to 

understand their distribution and relative differences. The standard deviations of economic resilience, green finance 

index, environmental pollution, infrastructure, human capital, and resource endowment are all smaller than the 

mean, and the absolute values differ greatly. The standard deviation data in the table are generally small, indicating 

that the distribution of sample data is relatively concentrated. In addition, except for human capital, the variance of 

other variables is small, indicating that the fluctuation degree of the data is small and the stability is high. 



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Econometric model 

 

System GMM model 

 

System GMM model using Generalized Moment estimator (Generalized Method of Moment, GMM) estimator 

Method in the construction of the random variables follow specific Moment assumption, rather than on the entire 

distribution assumption, the assumption is called the Moment conditions. In 𝑘GMM estimation, the number of 

parameters to be estimated is assumed to be, and the number of moment conditions is assumed to be. 𝑙According 

to the exact identification property, it can be obtained 𝑘 = 𝑙that when, the number of parameters to be estimated is 

equal to the number of moment conditions; According to the over-identification property, the number of parameters 

to be estimated is less than the number of moment conditions. 𝑘 < 𝑙GMM is a generalization of moment estimation. 

0, GMMIn precisely identify cases, the objective function of minimum value is equal to the estimator and estimator 

equivalent; MM Under the condition of excessive recognition, however, is no longer applicable, GMM can be 

effectively combined moment conditions, make the GMM is more effective than the MM.MM 

In the GMM estimation, the parent moment condition is:, and the sample moment condition is:. The GMM mean 

estimate is obtained by solving the sample moment condition:  

 

 

𝐸[𝑦] − 𝑢 = 0      
1

𝑁
∑𝑖=1

𝑁  𝑦𝑖 − �̂�G𝑀𝑀 = 0, 

�̂�𝐺𝑀𝑀 =
1

𝑁
∑𝑖=1

𝑁  𝑦𝑖 

 

This study to explore the green financial impact on our country economic resilience, and test hypotheses (1), build 

econometric benchmark model type (1) as follows: 

EcoRes𝑖𝑡 = α1 + β1EcoRes𝑖𝑡−1 + Ψ2GFI𝑖𝑡 + θ1X𝑖𝑡 + 𝜀𝑖𝑡（1）
 

Among them, and represent the province and the year of the first provincial I t years toughness index of economy, 

for the first t provinces I economic toughness index in the lag issue of variables, for the first t in the provinces I 

green financial index, for the first t in the provinces I control variables, as random perturbation 

terms.𝑖𝑡EcoRes𝑖𝑡EcoRes𝑖𝑡−1GFI𝑖𝑡X𝑖𝑡𝜀𝑖𝑡 

Mediating effect model 

 

In order to explore the mechanism of green finance affecting China's macroeconomic resilience, this study adopts 

two mediating variables, technological innovation and industrial structure upgrading, and constructs two models 

(2) -(3) based on Wen Zhonglin and Ye Baojuan. 

 

Med𝑖𝑡 = α2 + β2Med𝑖𝑡−1 + Ψ2GFI𝑖𝑡 + θ2X𝑖𝑡 + 𝜀𝑖𝑡(2) 

EcoRes𝑖𝑡 = α3 + β3EcoRes𝑖𝑡−1 + Ψ3GFI𝑖𝑡 + 



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                                                   Ψ4Med𝑖𝑡 + θ3X𝑖𝑡 + 𝜀𝑖𝑡（3） 

WhereMed𝑖𝑡, is the mediating variable of province i in year tΨ1, and the rest of the variables are the same as in 

Equation (1). To regression equation (1) to test whether green finance to the total effect of macroeconomic resilience 

significantly. If it is significant and positive, it indicates that the total effect of mediating effect exists. 2. Run 

regression on Equation (2) to test whether the coefficient of green finance on mediating variable is significant. Ψ2If 

it is significant and positive, it indicates that green finance promotes mediating variables. The third step is to conduct 

regression on Equation (3). If the coefficient, is significantly positive and the absolute value of the coefficient is 

less than the absolute value of the coefficient, it indicates that there is a partial mediating effect. Ψ3Ψ4Ψ3Ψ1If it 

is not significant or significant, it indicates that the mediating variable has played a full mediating role.Ψ3Ψ4 

Table5. Types of GMM variables 

Name Variable type 

ER Dependent variable 

GFI Endogenous variables 

EP Instrumental variables 

BI Instrumental variables 

HC Instrumental variable 

RE Instrumental variables 

 

Threshold effect model 

In order to further explore the nonlinear relationship between green finance and economic resilience through 

technological innovation, industrial structure and risk management, the panel threshold model is constructed as 

shown in (4) - (5). 

yit = 𝜇i + 𝛽1
′xit + 𝜀itif𝑞𝑖𝑡 ≤ 𝛾

yit = 𝜇i + 𝛽1
′xit + 𝜀it, if𝑞𝑖𝑡 ≥ 𝛾

(4) 

In the above formula, is the threshold variable (which can be part of the explanatory variables), is the 

threshold value to be estimated, and follows independent and identically distributed. 𝑞𝑖𝑡𝛾𝜀itThe equation in the 

above equation can be written as follows: 

yit = 𝜇i + 𝛽1′xit ∙ I(q𝑖𝑡 ≤ 𝛾) + 𝛽2′xit ∙ I(q𝑖𝑡 > 𝛾) + 𝜀it（5） 

Where () is the indicative function, is the variable (which can be part of the explanatory variables), is the 

threshold value to be estimated, and follows independent and identically distributed. I𝑞𝑖𝑡𝛾𝜀itIts estimate principle 

is mainly based on the theory of minimum sum of squared residuals (SR). 

 

 

 

 

 

 



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Results and Conclusions 

 

Research on the impact of green finance on China's macroeconomic resilience 

 

GMM estimation results 

 

Table 6. GMM estimation results 

 
Non-standardized 

coefficients 

Coefficient of 

standardization 
Z P R² 

Adjust 

R² 
Wald 

 B 
Standard 

error 
Beta 

Const 1.054 0.064 - 16.503 0.000*** 
0.85 0.87 2.338 

GFI 0.319 0.209 0.116 1.989 0.000*** 

Dependent variable: Economic resilience 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

 

Table 6 above shows the final results of GMM estimation. Firstly, the model passed the Wald chi-square test 

(Wald=2.338, p=0.000<0.05), which means that the model is valid. Secondly, the P value met the conditions and 

showed significant correlation. Finally, R squared were 0.85 and 0.87, the standard curve fitting degree of original 

data is very good, green financial macroeconomic resilience in the provinces of our country has positive role in 

promoting, China should continue to intensify efforts to unswervingly promote the development of green finance. 

Excessive identification test 

Table 7. Over-identification test results 

inspection Statistics P 

Hansen J test 10.006 0.819 

Note: * * *, * *, *, respectively 1%, 5% and 10% significance level 

 

The over-identification test is used to test whether the instrumental variables are exogenous variables. There are 

four instrumental variables involved in this study, namely "environmental pollution", "infrastructure", "human 

capital" and "resource endowment". As can be seen from Table 7, the over-identification Hansen J test shows that 

the null hypothesis is accepted (p=0.819) 0.05. Meanwhile, the over-identification Hansen J test shows that the null 

hypothesis (P = 0.819) is accepted. It proves that there is a strong correlation between the selected instrumental 

variables and the endogenous variables, and these instrumental variables can effectively explain the changes of the 

endogenous variables. This further verifies the rationality and reliability of our model, and provides an important 

reference for future policy making and decision-making. 



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Endogeneity test 

 

Table 8. Endogeneity test results 

Test Statistics P 

Statistics 1.545 0.014** 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

 

In this study, we conducted endogenous test, endogenous test results such as Table 8. , as is shown in the results 

show that the satisfactory results. After Hausman test and Durbin - Wu - Hausman test verification, we found that 

in the model there is no significant correlation between variables and the error term. This means that we study the 

causation is not interference by endogenous problems. 

Parallel mediation effect 

Considering the economic dependent and independent variables green financial index to toughness index regression 

coefficient, the influence of financial index and green by technology innovation and upgrade of industrial structure 

and risk management ability, determines the technological innovation, industrial structure upgrade and risk 

management ability for the intermediary variable. For the comprehensive analysis of the independent variable will 

pass intermediary variable to affect the dependent variable, this study will be technological innovation, industrial 

structure and risk management capacity upgrade as a intervening variable, build parallel mediation effect of 

regression model, judge the mediation effect, finally complete mediation effect test. In addition, to ensure the 

integrity of the mediation effect, this study will be technological innovation and upgrade of industrial structure and 

risk management ability as intermediary variable substitution, results such as Table 9-10. As shown in Table 9-10. 

Table 9. Coefficient table of mediating effect regression model 

 ER TI UIS RM ER’ 

Constant 0.567 0.434 -0.239 0.447 0.442 

GFI 0.624 0.378 0.913 0.379 0.619 

EP -0.106 0.911 1.544 0.717 -0.179 

BI 0.279 -0.487 -1.114 -0.583 0.292 

HC -0.007 0.023 0.04 0.017 -0.008 

RE -0.154 0.673 -0.832 0.682 -0.389 

TI     0.236 

UIS     -0.092 

RM     0.247 

Sample size 362 362 362 362 362 

R² 0.057 0.698 0.522 0.517 0.09 

Adjust R² 0.044 0.693 0.514 0.509 0.07 

F 
F(5,356)=4.317, 

P=0.001*** 

F(5,356)=164.735

, P=0.000*** 

F(5,356)=77.751

, P=0.000 

F(5,356)=89.633, 

P=0.000 

F(7,354)=5.022, 

P=0.000*** 

Note: ***, ** and * represent the significance level of 1%, 5% and 10%, respectively 



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It can be seen that the green finance level index has a positive role in promoting technological innovation, industrial 

structure upgrading and risk management ability, and the economic resilience evaluation index can also promote 

technological innovation, industrial structure upgrading and risk management ability; The level of green finance is 

determined by the level of technological innovation; But the three intermediary path negative influence on 

infrastructure development, high quality and economic development. The coefficient of the square term of the 

mediating variable is positive and significant at the level of 5%, indicating that its impact on the quality of economic 

development has significant nonlinear characteristics.  

Table10. Summary results of mediating effect test 

item 

c 

Total 

effect 

a a(p-value) b b(p value) 

a*b 

mediati

ng 

effect 

value 

a*b (Boot 

SE) 

A * b 

(z) 

a*b (p-

value) 

a*b 

(95%BootCI

) 

c' direct 

effect 

Test 

conclusi

ons 

GFI=>

TI=>E

R 

0.624 
0.37

8 
0.000*** 0.236 0.002*** 0.089 0.032 2.759 0.006*** 0.041 - 0.167 0.619 

Fully 

mediated 

GFI=>

UIS=>

ER 

0.624 
0.91

3 
0.000*** -0.092 0.022 * * -0.084 0.047 -1.792 0.034** 0.202-0.018 0.619 

Fully 

mediated 

GFI=>

RM=>E

R 

0.624 
0.37

9 
0.000*** 0.247 0.001 * * * 0.077 0.034 1.247 0.003*** 0.053-0.179 0.619 

Full 

mediatio

n 

According to the coefficient of product inspection, by the above-mentioned P values, P values < 0.05, presents the 

significance, through the Bootstrap sampling inspection at the same time, this study found that in a * b the regression 

coefficient of 95% confidence interval does not include the number 0, further indicating partial mediating role. 

Specifically, in the impact of green finance on China's macroeconomic resilience, technological innovation and 

industrial structure upgrading as well as risk management ability as mediating variables show a full mediating effect 

in the impact of green finance on macroeconomic resilience. These results indicate that technological innovation 

and industrial structure upgrading as well as risk management capability play an important role in the process of 

green finance playing a role, which further strengthens our understanding of the relationship between these 

variables. 

The threshold effect empirically 

 

Threshold effect test 

 

The results of this model are obtained under 300 bootstrapping (BS) sampling. For innovative technology, in a 

double threshold estimation, P value is 0.15, not through the test of significance, namely the model does not exist 

double threshold effect; In a single threshold estimation, F value is 65.83, significant under 1% level, the 95% 

confidence interval [6.5281, 6.5910], so the model is a single threshold, threshold effect analysis can be performed. 

For the upgrading of industrial structure, in a double threshold regression, P value is 0.00, F valuewere 96.05 and 

80.75, significant under 1% level. In the first threshold value, the 95% confidence interval [0.9139, 0.9489]; In the 

second threshold value, the 95% confidence interval [1.4326, 1.4590]. So when the threshold variable is upgrade 



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of industrial structure, the model with double threshold, double threshold effect analysis can be performed. For the 

risk management ability, in a double threshold estimation, P value is 0.09, not through the test of significance, 

namely the model does not exist double threshold effect; In a single threshold estimation, F value is 74.74, 

significant under 1% level, the 95% confidence interval [7.5013, 7.5200], so the model is a single threshold, 

threshold effect analysis can be performed. 

 

Threshold regression results 

 

Threshold effect further, this research on panel test used to test whether a hypothesis 3, level of technological 

innovation and upgrade of industrial structure and risk management capability respectively through the test of single 

threshold, more than the threshold. On this basis, we set a single threshold regression model with technological 

innovation level and risk management ability as threshold variables, and set a multiple threshold model with 

industrial structure upgrading as threshold variables. The outcome of the model based on the threshold of the 

threshold of each variable regression as shown in Table11. 

Table 11. Threshold regression model results 

variable 
 ER  

TI UIS RM 

EP 0.086*** (2.85) 0.050*** (2.09) 0.062*** (1.69) 

BI 
0.766*** 

(3.00) 

0.490*** 

(2.76) 

0.742*** 

(2.91) 

HC 
-0.003*** 

(-1.39) 

-0.011*** 

(-3.92) 

-0.002*** 

(-0.96) 

RE 
0.202** 

(1.63) 

0.117*** 

(1.24) 

0.172** 

(1.36) 

GFI(INNO<gamma1) 
0.091*** 

(3.44) 

0.063*** 

(2.40) 

0.095*** 

(2.42) 

GFI(gamma1<INNO<gamma2) 
0.308*** 

(5.88) 

0.336*** 

(10.95) 

0.406*** 

(5.31) 

GFI(INNO>gamma2) - 
0.650*** 

(9.13) 

- 

 

Constant term 
-4.82** 

(-5.04) 

-2.93*** 

(4.49) 

-4.49** 

(-4.80) 

Sample size 360 360 360 

Number of provinces 30 30 30 

R2 0.705 0.779 0.711 

 

Taking the benchmark regression results of threshold effect based on industrial structure upgrading in Table 11 as 

an example, the influence of green finance on economic resilience due to differences in industrial structure 

upgrading is significantly positive: when the innovation level is lower than the first threshold value of 0.9396, the 

influence coefficient of green finance on regional economic resilience is 0.636, which is significant at the level of 

5%; When the threshold variable between first threshold and second threshold value, the core explanation variables 



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to explain the influence is positive significant, influence coefficient is 0.336, P value is 0.000, significant at 1% 

level; When innovation level is greater than the threshold value of 1.4491, the influence coefficient is 0.650, P value 

is 0.000, significant under 1% level. However, as the threshold value first increases and then decreases, the 

threshold variable has an optimal interval, that is, the threshold value is within [0.9574,1.4491]. 

At the same time, in order to intuitively present the regression effect and threshold effect, Figure 1 is the threshold 

effect test diagram based on each threshold variable. 

 

 

 

Figure 1. Threshold effect test diagram 

Robustness test of threshold effect 

The results of the robustness tests were all obtained under 300 bootstrapping (BS) sampling. For the threshold 

variables of technological innovation capability and risk management capability, both of them are significant at the 

level of 1% in the single threshold estimation, with 95% confidence intervals of [6.5281,6.5910] and 

[7.5013,7.5200], respectively. For industrial structure upgrading, in the double threshold estimation, the 95% 

confidence interval is [0.9116,0.9489] and [0.9272,0.9617]. Therefore robustness model has a single threshold for 

threshold effect analysis.  

Table 12 uses the method of deleting the control variable "human capital". From the regression results, it can be 

seen that the threshold values change weakly, which are 6.5809, 0.9396, 0.9574 and 7.5120 respectively. Secondly, 

it can be seen from the observation coefficient that when the threshold variable is lower than or higher than the 

threshold value, the positive promoting effect of the growth unit of green finance development on economic 



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resilience is improved compared with the regression results of the threshold benchmark, and the significance level 

does not change much. 

 

Table12. Robust regression results of threshold effect 

Variables 
 ER  

TI UIS RM 

EP 0.083*** (2.70) 0.046*** (1.85) 0.060*** (1.61) 

BI 
0.749*** 

(2.99) 

0.447*** 

(2.41) 

0.731*** 

(2.93) 

RE 
0.206** 

(1.65) 

0.137*** 

(1.34) 

0.175** 

(1.38) 

GFI(INNO<gamma1) 
0.089*** 

(3.35) 

0.065*** 

(2.49) 

0.093*** 

(2.35) 

GFI(gamma1<INNO<gamma2) 
0.305*** 

(5.86) 

0.318*** 

(11.06) 

0.405*** 

(5.33) 

GFI(INNO>gamma2) - 
0.607*** 

(8.06) 
- 

Constant term 
-4.75** 

(-5.10) 

-2.81*** 

(-4.15) 

-4.44** 

(-4.87) 

Sample size 360 360 360 

Number of provinces 30 30 30 

R2 0.704 0.766 0.711 

At the same time, in the coefficients of other control variables, green financial development is still positively 

promoting, and the coefficient and t value change little. In summary, the results of the robustness test and the 

benchmark regression results of the threshold effect have little change in the coefficients of the core explanatory 

variables, and there is no change in the significance and sign, so the benchmark regression results of the threshold 

effect are robust.  

Spatial heterogeneity 

Based on the spatial location of 30 provinces in China, according to the division method of the National Bureau of 

Statistics, the 30 provinces are divided into the eastern, central, western and northeastern provinces.  

Table13. Two-way fixed effect results of the three provinces in Northeast China 

Variables 
Coefficien

ts 

Standard 

errors 
t P R² F 

const 69.299 20.764 3.337 0.005*** 

within=0.053 

between=0.017 

overall=0.035 

F=15.313, 

P=0.000 
GFI 3.932 2.202 1.786 0.004*** 

EP -0.429 0.544 -0.787 0.443 



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BI -6.805 2.78 -2.448 0.027** 

HC -1.75 1.999 -0.875 0.395 

RE -5.75 5.111 -1.125 0.278 

TI -0.929 0.275 -3.383 0.004**8 

UIS -0.906 0.151 -6.01 0.000*** 

RM -0.703 0.187 -5.02 0.001***   

Dependent variable: Economic resilience 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

 

Table14. Results of two-way fixed effects in the east 

Variables of 

interest 
Coefficients Standard errors t P R² F 

const -0.272 0.676 -0.403 0.688 

 

 

 

within=0.009 

between=0.569 

overall=0.192 

 

 

 

F=3.119, 

P=0.005 

GFI 0.175 0.216 0.809 0.029** 

EP 0.022 0.153 0.142 0.888 

BI 0.453 0.131 3.465 0.001*** 

HC -0.014 0.03 -0.478 0.634 

RE -0.6 0.174 -3.443 0.001*** 

TI 0.168 0.128 1.311 0.193 

UIS -0.037 0.045 -0.839 0.404 

RM 0.179 0.106 1.208 0.027**   

Dependent variable: Economic resilience 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

 

 

 

 

 

 



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Table15. Results of two-way fixed effects in western China 

Variables Coefficients 
Standard 

errors 
t P R² F 

const 2.878 1.002 2.872 0.005*** 

within=0.052 

between=0.308 

overall=0.156 

F=2.426, 

P=0.025 

GFI -0.096 0.286 -0.334 0.039** 

EP -0.95 0.34 -2.794 0.006*** 

BI 0.573 0.222 2.579 0.012** 

HC 0.013 0.036 0.356 0.722 

RE -0.254 0.265 -0.956 0.341 

TI 0.333 0.15 2.221 0.029** 

UIS -0.176 0.117 -1.513 0.134 

RM 0.079 0.149 1.264 0.037**   

Dependent variable: Economic resilience 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

Table16. Results of two-way fixed effects in the middle part 

Variables Coefficients 
Standard 

errors 
t P R² F 

const -7.966 2.089 -3.814 0.000*** 

within=0.322 

between=0.774 

overall=0.47 

F=8.097, 

P=0.000 

GFI 0.864 0.339 2.549 0.013** 

EP 0.706 0.325 2.176 0.033** 

BI 1.551 0.413 3.757 0.000*** 

HC 0.02 0.036 0.556 0.580 

RE -1.394 0.314 -4.441 0.000*** 

TI 0.246 0.155 1.59 0.117 

UIS -0.829 0.187 -4.441 0.000 * * * 

RM 0.162 0.143 1.112 0.039**   

Dependent variable: Economic resilience 

Note: ***, ** and * represent significance levels at 1%, 5% and 10%, respectively 

 



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This study uses four groups of sample data to pass the two-way fixed effect test, and the test results are as follows: 

Table 15-18. Among them, the central and eastern provinces are the most significant, followed by the eastern 

provinces, and finally the western provinces. It shows that the development of green finance has a positive 

promoting effect on local economic resilience, and the positive promoting effect is the largest in the central and the 

three eastern provinces, followed by the eastern region and the weakest in the western region. Based on the analysis 

of the model conclusions, this study holds that different regions have different environmental resource endowments 

and basic economic development status, and the demand degree of green finance for environmental protection and 

sustainable use will also be different. The three provinces in central China and Northeast China have rich natural 

resources, and due to the prominent environmental problems, such as soil erosion, the demand for green finance is 

relatively large. The eastern region has more perfect infrastructure related to green finance, and follows the principle 

of green and high-quality development. Compared with the western region, the eastern region has more complete 

traditional financial and green financial systems, and more sufficient financial support, which can better play the 

positive role of green finance in promoting regional macroeconomic resilience. 

Conclusions and policy recommendations 

Using the relevant data of 30 provinces in China from 2011 to 2022, this paper examines the impact of green finance 

on China's macroeconomic resilience and the mediating effect of technological innovation and industrial structure 

upgrading. First, using the latest provincial data, this study proves that the development of green finance can greatly 

improve China's macroeconomic resilience and promote economic stability and sustainable development. Secondly, 

this study verifies that technological innovation, industrial structure upgrading, and risk management, as mediating 

effects, can further enhance China's macroeconomic resilience. Finally, green finance shows strong spatial 

heterogeneity in the process of promoting China's macroeconomic resilience. Based on the above conclusions, this 

study puts forward the following policy recommendations: The development of green finance can significantly 

enhance China's macroeconomic resilience, especially in terms of coping with shocks. Through diversified 

economic structure, efficient allocation of resources, risk management and reduction, green finance provides 

important support for sustainable economic development and resisting external shocks. By encouraging and 

supporting the development of green industries, green finance can promote the transformation of the economy to a 

low-carbon, environmentally friendly and sustainable direction, so as to reduce the vulnerability of the economy to 

specific industries or fields and improve the resilience of the overall economy to external shocks. Through the 

support of green finance, environmental protection and sustainable projects can obtain more funds and investment, 

so as to promote the effective use and conservation of resources to cope with the shortage of resource supply and 

price fluctuations; By introducing green financial instruments, such as green bonds and green insurance, enterprises 

and financial institutions can better assess and manage risks related to climate change, natural disasters, etc., reduce 

the vulnerability of the economy, and improve the ability to withstand shocks. The development of green finance 

can significantly enhance the resilience of China's macro economy in terms of organizational and coordination 

capabilities. Policy framework and standards, the participation of financial institutions, the establishment of 

multilateral cooperation and partnership can help improve the organization and coordination ability and promote 

the development of green finance, promote the sustainable development of economy, the efficient allocation of 

resources and risk management, improve the macroeconomic resilience and competitiveness. Government to 

formulate and implement green financial policy, for example, to set up the green financial fund, make green bonds 

issue guidelines, etc., provide direction and specification for the development of green finance, organization and 

coordination of all parties. Financial institutions such as banks, securities companies and insurance institutions 

promote the financing and investment of environmentally friendly and sustainable projects by providing green 

financial products and services. Financial institutions participate in the green finance market to achieve effective 



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allocation of resources and management of risks, and improve the resilience of the overall economy. All 

stakeholders, including governments, financial institutions, enterprises, social organizations and the public, should 

establish partnerships to jointly promote the development of green finance. For example, the government can 

provide policy support and market guidance, financial institutions can provide financing and risk management, 

enterprises can implement green projects, and social organizations and the public can provide supervision and 

participation. The development of green finance can significantly enhance the resilience of China's macro economy 

in terms of innovation and transformation capacity. Through innovative economic model and transformation 

capacity cultivation, green finance can promote the economy to achieve innovative transformation, improve 

adaptability and competitiveness, and enhance the economy's ability to resist risks and sustainable development. 

Enterprises and entrepreneurs should be encouraged to innovate in green technology, clean energy, environmental 

protection and other fields to foster new economic growth points and improve the economy's innovation and 

transformation capacity, to enhance macroeconomic resilience. Through the support of green finance, enterprises 

can obtain funds and resources to promote the transformation towards environmental protection and sustainability. 

Green finance can provide support services such as training and consulting to help enterprises improve their 

environmental management and sustainable development capabilities, so that they can better adapt to market 

changes and environmental requirements, enhance their ability to resist risks, and thus improve macroeconomic 

resilience. 

By innovating the ways and means of green financial development, guiding financial capital to help the new track 

of green development, can provide support for the sustainable development of more industries and industries in our 

country, promote the progress of green technology, and have a positive role in transforming green technology into 

actual results. At the same time, we should further adhere to and increase scientific and technological innovation, 

and encourage green finance participants to innovate in technology. Financial institutions may set up a special 

department to financial product innovation, in order to satisfy the demands of enterprise technology innovation 

fund, the relevant government departments should strengthen the support of enterprise technology innovation, set 

up special funds or implement preferential tax policies, reduce the financing cost of enterprises, green technology 

innovation for enterprises to create a good financial environment. We should promote the resilience of the economy 

and form a replicable, promotable and sustainable green finance model. We will establish a sound green financial 

system, introduce capital into green and environmentally friendly industries with low energy consumption, promote 

sustainable and green economic development, increase economic growth potential and enhance economic 

resilience. At the same time, we will promote diversification of green products and accelerate the upgrading of 

green industries. With investment and financing support from green finance, traditional industries with high 

pollution and high energy consumption can be transformed and upgraded to green industries and clean technologies. 

The upgrading of industrial structure helps to improve the degree of resource utilization efficiency and 

environmentally friendly economy, reduce reliance on limited resources, improve the ability of the sustainable 

development of the economy. At the same time, the upgrade of industrial structure will also be able to develop new 

industries and jobs, to improve the toughness of the overall economy and innovation ability. 

We will further consolidate the macroeconomic system and strengthen economic integration and complementarity 

among regions. Green finance for toughness at a higher level of regional economy region promoting effect is more 

effective and more prominent, the higher market openness of the eastern region will strengthen the green financial 

system innovation; In the resource-rich central region, it can mobilize social forces to promote the expansion of 

green finance financing scale; In the western regions along the Belt and Road, we can continue to increase 

investment in environmental governance with the help of preferential policies. The eastern region with developed 

green finance can guide the central and western regions to realize diversified green finance. Under the effect of 

green finance on economic resilience, it can promote a virtuous cycle of economic resilience intensity, form a 



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benign competition among various regions, build a high-level linkage system for green finance development, and 

subtly transfer the concept of green finance into the economic system. Thus, it can better promote economic 

resilience and generate high-high aggregation effect. In general, it is necessary to maintain a stable macroeconomic 

environment, optimize the information sharing mechanism, promote the reasonable aggregation, efficient flow and 

balanced diffusion of resources, promote the coordinated development of regional economy, get rid of the 

development pattern of weak resistance, and form a virtuous cycle and spatial optimization. 

 

Declaration  

 

Acknowledgment: 

Throughout the writing of this dissertation, I have received a great deal of support and assistance. 

I would first like to thank China National University Student Innovation & Entrepreneurship Development Program 

which  provide fund support for this research. I would particularly like to acknowledge my team members, Yan 

Wang，Jiale Shao，Danyang Mei for their wonderful collaboration and patient support. I would also like to thank 

my tutors, Haiming Yu and Yunjie Tangfor their valuable guidance throughout my studies. You provided me with 

the tools that I needed to choose the right direction and successfully complete my dissertation. 

In addition, I would like to thank my parents for their wise counsel and sympathetic ear. You are always there for 

me. Finally, I could not have completed this dissertation without the support of my friends, Xiaoyi Chen, Yijun He, 

Zhengtao Wu, who provided stimulating discussions as well as happy distractions to rest my mind outside of my 

research. 

 

Funding: China National University Student Innovation & Entrepreneurship Development Program  

Code：202411057058X and ZUST German-speaking Country and Regional Studies Project Code: 2022DEGB010 

 

Conflict of interest: The authors declare that they have no known competing financial interests or personal 

relationships that could have appeared to influence the work reported in this paper. 

 

Ethics approval/declaration: This study did not involve human or animal subjects, and thus, no ethical approval 

was required. The study protocol adhered to the guidelines established by the journal.  

 

Consent to participate: All of the authors listed above were involved in this study. 

 

Consent for publication: All the authors listed above have agreed to publish their work in Global scientific 

Research Journals. 

 

Data availability: Data openly available in a public repository.  

 

Authors contribution: Xuchen Luo：Methodology 、Formal analysis、Writing - Original Draft 

Haiming Yu：Conceptualization、Writing - Review; Editing, Yan Wang：Writing - Original Draft, Jiale Shao：

Visualization, Danyang Mei：Data Curation, Yunjie Tang：Writing - Review; Editing 

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