Introduction

Due to global warming and frequent extreme weather, air pollution is increasing, which seriously affects human life. A World Health Organization report indicates that over 13 million people worldwide die annually from preventable environmental issues. Additionally, global climate governance has increasingly become a key factor limiting the sustainable development of national economies1. China is the world’s maximal emitter of carbon, and the situation of ecological and environmental protection remains challenging. Achieving the goals of establishing a fine China and reaching carbon peak and carbon neutrality (“Dual carbon” goals) still requires substantial progress and effort2. At the 20 th National Congress of the Communist Party of China, General Secretary Xi proposed: “Coordinate carbon reduction, pollution reduction, green expansion, and growth, and facilitate ecological priority, conservation, and intensive, green and low-carbon development.” In the context of the new circumstances, tasks, and requirements of ecological civilization construction, it is imperative to facilitate the collaborative governance of pollution reduction and carbon reduction (CGPRCR), given that environmental pollutants and carbon emissions are highly rooted in the same source.

On account of the negative externality of environmental governance, the environmental governance effect of government regulation has been widely recognized. Given the negative externalities inherent in environmental governance, the role of government regulation in enhancing environmental governance effectiveness has been widely acknowledged. In terms of economic policies to strengthen CGPRCR, Implementation Plan of Collaborative Governance of Pollution Reduction and Carbon Reduction jointly announced by the Ministry of Ecology and Environment and other seven departments on June 10, 2022, proposes the following: “It is imperative to enhance fiscal policy support for green and low-carbon investment projects as well as collaborative technology applications. Financial departments should ensure adequate funding for pollution and carbon reduction initiatives. Additionally, efforts should be made to promote the development of green finance (GF), effectively utilize monetary policy tools for carbon emission reduction (CER), and guide financial institutions and social capital to increase their support for environmental conservation.” Therefore, China must leverage the benefits of the prevailing green financial environment to transform CGPRCR into a consistent source of strength, addressing ecological environment-related problems. In this process, the advancement of GF is likely to become a key force in facilitating the CGPRCR in China. So, can the development of GF facilitate the CGPRCR? If the answer is yes, then the GF impact on reducing carbon reduction, and what of the transmission mechanism of collaborative governance? Furthermore, based on the characteristics of regional heterogeneity in China, can the influence of GF on the CGPRCR show unique rules and characteristics? In-depth analysis of the influence of GF on the CGPRCR, and formulation of targeted development policies will create significant theory and applicatory values for the establishment of ecological civilization and environment protection.

Different from previous studies, this paper aims to deeply explore the complex impact of GF on CGPRCR. There may be the following three marginal contributions: Firstly, this paper employed the coupling coordination degree model to assess the level of CGPRCR. Additionally, the projection pursuit method was utilized to measure the development index of GF across five dimensions: five dimensions: green credit, green securities, green insurance, green investment, and carbon finance. This multi-dimensional approach enhances the reliability of the research conclusions. Secondly, this paper includes GF and CGPRCR in the analysis model. It examines the intricate relationship between these two factors and investigates the impact of heterogeneity based on geographical location and regional differences in energy endowment. Finally, this paper analyzes the non-linear effects between GF and CGPRCR from three aspects of GF, green technology innovation (INNO), and new energy industry development (NEID). The findings provide valuable insights for the construction of ecological civilization in China.

After the introduction, the remaining sections of this paper are structured as follows: The second section combs the related research. The third section elaborates on this paper, the theoretical analysis, and the study hypothesis. The fourth section introduces the data’s source, model building, and related variables. The fifth part makes a comprehensive analysis of the results of this paper. Finally, the paper summarizes the results and makes relevant recommendations.

Literature review

Relevant on green finance

The concept of GF was initially introduced in 1991. Developed economies in the West started to investigate and implement GF initiatives aimed at addressing issues as early as the 1970s. Since then, numerous researchers have examined the idea of GF, but there has been a lack of consensus on its definition. In 2016, GF’s notion was first defined in Guidance on Building a Green Financial Systemissued by the People’s Bank of China and other seven ministries: “GF refers to financial activities that support environmental improvement, climatic change mitigation, and resource-efficient utilization in economic activities. It encompasses investment and financing in areas such as environmental protection, energy-saving initiatives, clean energy projects, and green building projects, as well as providing financial services related to item operation, risk management, and more.” The integration of sustainable development and finance, along with the enhancement and advocacy of GF policies, has led to significant growth in GF in recent years, and academic research on the subject has been progressively advancing. In existing studies, certain scholars assess the index of GF using four dimensions: green credit, green securities, green insurance, and green investment3,4. Meanwhile, other researchers argue that the green credit index solely reflects the development level of GF5,6. Secondly, many scholars have researched the effect and transmission path of GF, emphasizing the role of GF on green total factor productivity7, green innovation8,9renewable energy investment efficiency10, energy conservation, and emission reduction11,12. There is also significant emphasis on the conduction way of GF to high-quality economic development13and the measurement of its development level14,15.

Research on green finance for environmental governance

Controlling carbon emissions to combat environmental pollution is essential for green development. As GF continues to rise and develop, scholars have increasingly focused on its influence on the environment16. However, the academic world has not achieved a direct conclusion on the environmental governance effect of GF, and the energy-saving and emission-reduction influence of GF provides a reference experience for this paper. On the one hand, previous studies have suggested that GF can produce CER effects by regulating the economic activities of local enterprises17, supporting regional innovation18, promoting green technology innovation (INNO), and inhibiting investment in polluting industries19, all of which contribute to the CGPRCR. On the other hand, the effect of GF on carbon emissions is influenced by many factors, such as economic growth, industrial structure11, green innovation20,21, and so on. The effect of GF on CER varies depending on the different factors and their levels. And it has a different impact on reducing pollution to decarbonize collaborative governance.

Research on collaborative governance of pollution reduction and carbon reduction

In 1971, Hermann Haken was the first to formally introduce and propose the notion of synergy, and in 1991, Weidlich introduced “Synergetic Theory” into the field of social science research. The research on CGPRCR originates from the intersection of the synergy effect and public environmental governance. Based on the “Collaborative Governance Theory”, this approach emphasizes the necessity of transitioning the subsystems for reducing pollutant emissions and greenhouse gas emissions from an uncoordinated to a more organized and collaborative approach. In 2010, the United States, Italy, and others entered a stable stage of CER. The emphasis on building an ecological civilization in China has entered a new stage with the introduction of the CGPRCR concept, which was officially presented at the National Conference on Ecological and Environmental Protection in 2021. Scholars have explained the concept and connotation of CGPRCR from the perspectives of pollution sources and pollution treatment. To be specific, on the one hand, China’s ecological and environmental challenges can be traced back to its economic structure characterized by high energy consumption, high emissions, and high pollution. The primary contributors to these issues are the burning of energy, as well as activities related to industrial manufacture and logistics, which are responsible for the majority of pollutant and conservatory pneuma emissions. On the other hand, reducing pollution and carbon reduction control objects have consistency, and carbon pollution governance has the same frequency, efficiency, and route features. These two collaborative efforts enhance the theoretical viability of the initiatives. In addition, existing studies have confirmed the promotion effect of energy-saving technology22, energy structure23,24,25, and CER policies26,27 on pollution reduction or carbon reduction governance. Concerted efforts to CGPRCR will have an active impact on improving people’s health benefits and promoting the green transition of industrial engineering.

According to the above studies, there is still room for improvement in the studies on GF and CGPRCR. First, scholars generally use the AHP to decrease the component of the GF index system, but there is a certain subjective bias in the weight assignment, and few scholars accurately measure CGPRCR at the regional level. Second, few scholars have explored the deep relationship between regional GF and CGPRCR, as well as the impact of regional heterogeneity, and the relevant demonstration at the empirical level is even less. Third, given the regional heterogeneity, the complex non-linear relationship between GF and CGPRCR still needs to be explored.

Mechanism analysis and the research hypothesis

Direct conduction mechanism and research hypothesis

According to the “Sustainable Development Theory”, GF is a pivotal way to realize the coordinated advancement of the economy, society, and environment28. Building upon the existing studies, this paper believes that GF comprehensively measures the harmonious progression level between economy, society, and environment in the course of regional GF’s development. Furthermore, it offers support for CGPRCR through various avenues, including investment, financing, and financial innovation. The characteristics of GF, such as sociality, externality, policy, and innovation, as well as its influence, can directly affect the CGPRCR up-bottom and bottom-up.

China’s GF has the characteristics of “top-level design”, which plays an up-bottom signal transmission role that guides the market to respond to policies. It has a strong demonstration and driving effect, effectively leveraging private capital to flow into the new energy industry. Green credit, for example, through the part of its enterprise implementation of punishment and incentive measures, the warning or encouraging signals to similar enterprises. This encourages them to adjust their development models, supports the NEID, and exerts a green low-carbon effect, thereby promoting CGPRCR29. Therefore, GF through policy and market mechanism design, up-bottom to companies pass the “green signal”. This approach not only guides enterprises to accelerate the development of their green innovation technologies30 and provides financial resources for ecological and environmental governance but also raises the financing thresholds for “two high and one surplus” enterprises. By controlling the scale of development of polluting enterprises, GF serves as an essential financial instrument for achieving CGPRCR.

GF widely and deeply affects the trading patterns of market participants, through the act of investing and financing activities that support environmentally friendly, bottom-up to reduce carbon reduction collaborative governance. The establishment and operation of the carbon trading market represent a significant implementation form of GF in China. “Two low and one high” enterprises will be green economic achievements, realizing the marketization of the same kind of enterprise environmental behavior motivation, which can overcome the “tragedy of the commons” type of environmental governance predicament31. Secondly, GF can internalize the negative externalities of environmental degradation by assigning market-based pricing to environmentally harmful behaviors. Through the price mechanism, it regulates the decision-making of economic agents, thereby intensifying the financing constraints faced by polluting enterprises and, consequently, suppressing polluting investments19,32. Finally, GF can mobilize the enthusiasm of the whole society to economize energy and decrease carbon through environmental protection and public welfare projects such as “Carbon account” and “Enjoy carbon for all”33. These initiatives guide the public toward green consumption behaviors and promote the long-term and sustainable development of environmental protection and public welfare activities. Therefore, GF can attract commercial investment in environmental public welfare projects bottom-up, form a green allocation way of resources, and thus facilitate the CGPRCR.

According to the upper analysis, this paper presents hypothesis 1: GF can facilitate up-bottom and bottom-up CGPRCR.

Influence of regional heterogeneity and research hypothesis

Different regions in China have great differences in economic development level7,34, marketization level35, and environmental regulation36. These disparities may lead to heterogeneous the effect of GF on CGPRCR37. Therefore, the influence of GF on CGPRCR may also be different, and its influence may have regional heterogeneity. In the western regions, such as Sichuan, due to environmental pressure and environmental protection needs, the construction of “one place and three districts” has been accelerated, and the “1 + N” policy framework of carbon summit and carbon impartiality has been built in line with regional characteristics. Through the pilot areas of GF’s reformation and invention, the advancement of GF has been further facilitated38, and good results have been achieved in promoting the CGPRCR. In addition, Jiangxi, Zhejiang, Guangdong, and other regions have also actively established GF reform and innovation experimental areas, aiming to facilitate green development and low-carbon economy through GF. In energy-rich areas with abundant clean energy and low carbon emission energy as the dominant energy, due to the improvement of environmental awareness and the strengthening of financial support39. Financial institutions in these regions are more likely to provide greater opportunities and support for GF, thereby exerting a stronger supportive effect on CGPRCR. However, in non-energy-rich regions where high-carbon energy sources dominate and clean energy is scarce, resources of all kinds are relatively limited. These areas usually need more policy guidance and technical support to facilitate the progress of GF and realize the target of CGPRCR.

Based on the above exposition, this paper proposes research hypothesis 2: The effect of GF on CGPRCR is diverse because of various geographical locations and energy endowments.

Nonlinear conduction mechanism and research hypothesis

In the GF practice process, GF is likely to CGPRCR exists nonlinear effect, which may be due to some important variables after reaching a certain level or threshold value, the influence degree of direction or change.

Green finance (GF). This paper studies the influence of GF on CGPRCR, and there may also be a nonlinear relationship between them. In the stage of low GF development, due to the limited capital scale, GF projects are selected more accurately and can be quickly invested in projects with significant pollution and carbon reduction effects40, and play a large marginal effect41. With the expansion of the scale of GF, the scope of project selection is broadened, and funds may be dispersed to projects with relatively weak effects, resulting in a decline in the overall marginal effect.

Green technology innovation (INNO). Lin and Zhang (2023)42found that INNO is an important factor affecting CER. Based on Schumpeter’s “Innovation Theory”, at low level of INNO, financial institutions have limited awareness and mastery of green technology, and a relatively low willingness to invest in green projects43, which makes it difficult to have a positive impact on GF on the CGPRCR insufficiently, also it may have negative effects. And when the level of INNO increases, it plays a significant inhibitory role in carbon output44. The characteristics of the green financial rely on its informatization. It makes more financial institutions effectively facilitate the advancement of the environmental conservation industry and achieve the goal of CGPRCR45.

New energy industry development (NEID). New energy has become a major player in energy conversion and will dominate carbon neutrality, contributing to CER46. According to the Economic Development Theory”, when the level of the NEID is relatively low, the new energy industry investment efficiency is poor. Meanwhile, GF resources are predominantly allocated to the new energy industry, which is characterized by low energy consumption, low emissions, and low pollution. On the one hand, this allocation creates a crowding-out effect on non-green industries, thereby inhibiting their transformation and upgrading47. On the other hand, since the NEID has the features of the long investment cycle, high cost, and slow effect, the improvement effect of GF on the synergy effect of CGPRCR cannot be highlighted in the case of the low progression level of the NEID, and the improvement effect may be very limited, and even have a negative impact. As the advancement level of the NEID continues to improve, the physical and human capital of the NEID continues to accumulate48, the forms of GF products are diversified, and the development level of GF is gradually improving. Elevated research and development efforts exert influence on natural resources, while financial constraints necessitate the transformation of non-green industries. Therefore, GF has begun to significantly facilitate the improvement of CGPRCR.

Drawing from the analysis provided above, this paper puts forward research hypothesis 3: There is a nonlinear relationship between GF and CGPRCR. In the case of a low GF development level, high INNO, and high NEID, GF can play a more favorable and active impact.

Figure 1 shows the theoretical framework of this paper. (Fig. 1 is drawn by Visio2019 software, and the software of the URL to https://www.microsoft.com/zh-cn/microsoft-365/visio/flowchart-software.)

Fig. 1
figure 1

Theoretical framework of GF’s impact on the CGPRCR.

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Model building and variables measurement

Model building

For the sake of examining the three hypotheses presented above, the baseline regression model and threshold regression model were employed to analyze the direct effect, heterogeneity effect, and non-linear effect of GF on CGPRCR. The two-way fixed considers both the individual fixed effect and the time fixed effect, effectively controlling the heterogeneity of both the individual and the time, to estimate the causal relationship between variables more accurately49,50. The paper introduced GF into the analytical framework of CGPRCR, and constructs the baseline regression model:

$$\:{CGPRCR}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{GF}_{it}+{\alpha\:}_{n}{X}_{it}+{\lambda\:}_{i}+{u}_{t}+{\epsilon\:}_{it}$$
(1)

where, \(\:i\) and \(\:t\) are provinces and years. \(\:{CGPRCR}_{it}\) is the explained variable in this paper, representing collaborative governance of pollution reduction and carbon reduction. \(\:{GF}_{it}\) is the central variable representing the development level of green finance. \(\:{X}_{it}\) is the control variable in the model, including five kinds of variables: level of informatization (\(\:{INFOR}_{it}\)), government intervention (\(\:{GOV}_{it}\)), research input (\(\:{INPUT}_{it}\)), market size (\(\:{MAR}_{it}\)) and urbanization level (\(\:{CITY}_{it}\)). In addition, in Eq. (1), \(\:{\alpha\:}_{0}\) represents the intercept term, \({\alpha}_{1}\) represents the parameter to be estimated, and \({\alpha}_{n}\) represents the parameter vector to be estimated. \(\:{\lambda\:}_{i}\) is the unobservable individual fixed effect, \(\:{u}_{t}\) is the unobservable time fixed effect, and \(\:{\epsilon\:}_{it}\) is the stochastic disturbing term.

Further, the threshold regression model can flexibly capture the nonlinear relationship between the predicted explanatory variable and the explained variable, accurately calculate the threshold value, and carry out significance test for the threshold variable51. Compared with the dynamic threshold regression model, the static threshold regression model has the advantages of a simple model structure, extensive data practicability, and strong stability. Therefore, the static threshold regression model is chosen in this paper to test the nonlinear relationship between GF and CGPRCR. In this paper, green finance (\(\:{GF}_{it}\)), green technology innovation (\(\:{INNO}_{it}\)) and new energy industry development (\(\:{NEID}_{it}\)) were introduced as threshold variables to establish threshold model, which are as follows:

$$\:{CGPRCR}_{it}={\mu\:}_{i}+{\theta\:}_{11}GF_{it}\cdot\:I(threshold_{it}\le\gamma)+\theta_{12}GF_{it}\cdot\:I(threshold_{it}>\gamma)$$
$$\:+{\theta\:}_{n}{X}_{it}+{\lambda\:}_{i}+{u}_{t}+{\epsilon\:}_{it}$$
(2)

where, \(u_{i}\) represents the intercept term; \(\theta_{11}\) and \(\theta_{12}\) represents the parameter to be estimated, and \(\theta_{n}\) represents the parameter vector to be estimated. \(\:I\left(\bullet\:\right)\) is the instruction function, and the value is 1 if corresponding conditions are valid, otherwise it is 0. In the threshold model, \(\:\gamma\:\) is the threshold value. Other variables are consistent with Eq. (1).

Variables measurement and description

Explained variable

Considering atmospheric pollutants emission and carbon dioxide emissions have same the roots as the characteristics of the process, CGPRCR is feasible. In this paper, reducing the pollution in the major selection and NOxemissions of sulfur dioxide and fuel dust to measured. Carbon reduction was selected to measure carbon dioxide emissions. The coupling coordination model is used to analyze the coordinated development level of different systems. The interactions and mutual influences between two or more systems can be represented by the coupling degree. Therefore, the evaluation of CGPRCR using the coupled coordination model to study the differences in the degree of coordination between air pollutant emission control and carbon reduction across different regions. Learn from the method of Wang et al. (2021)52, the paper divides the coupling factor and coupling harmony degree levels and revises the model. The following model calculation methods are obtained:

$$\:C=\frac{2\sqrt{{U}_{1}{U}_{2}}}{{U}_{1}{U}_{2}}$$
(3)
$$\:T=a{U}_{1}+b{U}_{2}$$
(4)
$$\:CGPRCR=\sqrt{C\times\:T}$$
(5)

Among them, \(\:{U}_{1}\) is the pollutant emission, \(\:{U}_{2}\) is the carbon emission. This paper adopts the range standardization method to treat the index without dimension; \(\:CGPRCR\) indicates the collaborative governance of pollution reduction and carbon reduction’s coupling coordination degree, which takes value [0,1]. The better the value of \(\:CGPRCR\), the better coordination between pollutant emission control and carbon discharge decrease systems and the stronger the degree of collaboration. The lower the value of \(\:CGPRCR\), the worse coordination between pollutant discharge control and carbon discharge decrease systems, and the weaker degree of collaboration; \(\:C\) represents the coupling degree of two frames; \(\:T\) expresses the comprehensive coordination indicator of the two systems; Both \(\:a\) and \(\:\text{b}\)are specific gravity. The paper draws on the study of Wang et al. (2021)52, \(\:a=b=0.5\) is taken, that is, air pollutant emission control and CER are equally important.

Core explanatory variable

According to the definition of GF, GF consists of five aspects: green credit, green securities, green insurance, green investment, and carbon finance. GF’s development indicator is calculated to represent the GF’s development level. According to the study ideas of Yang et al. (2015)53and Zhang et al. (2022)54, the three-level indicators were designed by considering the effectiveness and availability of data. As indicated in Table 1.

Table 1 GF development rating system.
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There is an indicator under green credit. Due to the issue of inconsistent statistical standards and incomplete green credit data from commercial banks, this paper has chosen to express green credit by using the rate of interest expenditure of six high energy-consuming industries in the total industrial interest expenditure.

There is a three-level target under green securities, which is the percentage of market value of environmental protection enterprises. Green securities mainly reflect the financing level of China’s energy-saving and environmental protection industries through equity issuance in the capital market. Given that the green bond market is relatively nascent and provincial-level data are difficult to obtain, this indicator is represented by the proportion of the market value of environmental protection enterprises, which is calculated as the ratio of the total market value of environmental protection enterprises to the total market value of A-shares.

There are two third-level indicators under the green insurance indicator, namely, the scale of agricultural insurance and the loss rate of agricultural insurance. The proportion of agricultural insurance scale is expressed by the rate of agricultural insurance expenditure to total insurance expenditure. The loss rate of agricultural insurance is shown by the rate of agricultural insurance expenditure to agricultural insurance income. Because of the limitation of the availability of data, China began to enforce the implementation of firm environmental responsibility insurance at the end of 2013, which lacks systematic statistical data. Agriculture is considerably influenced by the natural environment. Therefore, agricultural insurance’s scope and agricultural insurance loss ratio can express the approximate green insurance development.

There are two indicators under green investment: the scale of public expenditure on environmental conservation, and the ratio of investment on environmental contamination. The former is shown by the proportion of financial disbursement on environmental protection industries in general pecuniary expenditure, while the latter is expressed by the ratio of regional investment in environmental pollution regulation in GDP.

There is an index under carbon finance, namely the balance index of local and peregrin currency freight forwarders, which is presented as the ratio of regional equilibrium of local and foreign currency freight forwarders to carbon emissions.

As the index of GF is multi-dimensional, this paper uses the projection pursuit method based on the improved accelerated genetic algorithm to measure the development level of GF, that is, by optimizing the projection direction \(\:a(j{)}_{t}\), high-dimensional data is projected onto the low-latitude subspace through certain combinations, reflecting the original high-dimensional data structure or features to the greatest extent. The projection direction of the original high-dimensional data is globally optimized to achieve the optimal value of the projection index function \(\:Q\left(a\right)\), and then the one-dimensional optimal output projection value \(\:z(i{)}_{t}\)of the GF index is obtained. As a comprehensive evaluation model, the projection pursuit model can overcome the influence of the “curse of dimensionality” and has the advantages of good robustness and strong anti-interference55. The application of an accelerated genetic algorithm based on real number coding can increase the optimization space and avoid the problem of premature convergence, so the obtained results have the advantages of robustness and science56. The specific equation is as follows:

$$\:\left\{\begin{array}{c}{max}Q\left({a}_{t}\right)={S}_{z}{D}_{z}\\\:s.t.{\sum\:}_{j=1}^{3}{a}^{2}(j{)}_{t}=1\end{array}\right.$$
(6)
$$\:z(i{)}_{t}={\sum\:}_{j=1}^{p}a\left(j{)}_{t}y\right(i,j{)}_{t}$$
(7)

where, \(\:z(i{)}_{t}\) is the projected value of the development level of GF, \(\:a(j{)}_{t}\) is the projection direction of different indicators, \(\:y(i,j{)}_{t}\) is the index value, \(\:{S}_{z}\) is the standard deviation of \(\:z(i{)}_{t}\), and \(\:{D}_{z}\) is the local density of \(\:z(i{)}_{t}\).

Threshold variables

According to the foregoing analysis, this paper selects GF, INNO, and NEID as the threshold variables of the empirical study. It studies the threshold effects of the three variables on the CGPRCR.

Green finance (GF). In this paper, GF not only appears as an independent variable but also is studied as a threshold variable. For the sake of measuring the GF’s developmental level, using above mentioned GF’s development index.

Green technology innovation (INNO). This index is set as the number of green inventions independently and jointly applied by enterprises in the year. Using green patents to measure the green innovation performance of enterprises can’t directly quantify the market value of green innovation of enterprises, but can indirectly reflect the activity of INNO and the research and development ability of enterprises in the process of operation and management57. In recent years, although the number of applications for environmentally friendly patents has been increasing, the innovation quality has declined. It may be due to the green patent including the green inventions, utility models, and green design, green and only invented by substantive examination. Therefore, in the measure of INNO only think about green inventions. Reference Li and Zheng’s (2016)58 research, this paper chooses green invention applications rather than grants. Because the authorization of the patent lag, and green technology in the enterprise may have already started to play a role in the process, applying for patent filings can reflect the true level of INNO enterprise.

New energy industry development (NEID). In consideration of data availability, this paper consulted the practice of Chen et al. (2018)59. It chooses the ratio of new energy power generation to regional total power generation to measure. Among them, the new energy incorporates solar power, wind power, and others.

Control variables

It is necessary to control other elements that may influence the CGPRCR to roundly analyze the role of GF on CGPRCR. For the sake of reducing error as much as possible, INFOR, GOV, INPUT, MAR, and CITY were selected as the control variables that affect the degree of CGPRCR in this paper. The details of the variables are presented in Table 2.

Table 2 Variable definition and description.
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The following control variables for simple description: (1) INFOR. In November 2021, the 14 th Five-Year Plan for the Deep Integration of Informatization and Industrialization released by the Ministry of Industry and Information Technology indicated that it is necessary to “implement the ‘Internet +’ green manufacturing action, carry out dynamic monitoring, precise control and optimized management of resources, energy and pollutants in the whole process, and facilitate CER to help achieve ‘carbon peak’ and ‘carbon neutrality’”. In July 2022, the Ministry of Industry and the National Development and Reform Commission jointly issued Implementation Plan for Carbon Peak in the Industrial Sector, which further emphasized the use of industrial Internet, big data, and other technologies, coordinated sharing of low-carbon information basic data and industrial big data resources, and actively promoting the “industrial Internet + green and low carbon”. The continuity of policies means that promoting information creation has turned into a vital strategic policy to facilitate high-quality economic progression and CGPRCR in China. Therefore, this paper selects Internet broadband access users to describe the INFOR. (2) GOV. Enterprise implementation of carbon reduction, the fund is one of the biggest difficulties. To facilitate CGPRCR without GOV, this paper selects the fiscal expenditure on general public services to express the index of GOV. (3) INPUT. Technological advancement is the crux of reducing energy consumption and CER control. However, technological advancement needs vast spending on research and development into a lot of money and energy60. Under the circumstances, amplifying INPUT can enhance production efficiency, reduce carbon intention, and facilitate CGPRCR61. In this paper, the scale of the inner expenditure of study trial funds to the GDP of every province is chosen to assess INPUT. (4) MAR. As the development of the carbon trading market continues to mature, the market scale also continues to expand. The market scale has an active influence on promoting national CGPRCR and high-quality economic development. Population activity and agglomeration usually influence carbon emissions, this paper selects the urban population density index to measure the MAR. (5) CITY. With the advancement of urbanization, environmental pollution has become increasingly serious, and urbanization has become an important factor affecting environmental governance62. This paper uses the proportion of the urban population to the resident population to represent the CITY.

Data sources and descriptive statistics

In the case of sufficient example size and data availability, 30 provinces in China from 2013 to 2022 were selected, except Hong Kong, Macao, Taiwan, and Tibet, as the research samples. The data of variables are selected from the China Statistical Yearbook, the China Statistical Yearbook of Science and Technology, and the provincial statistical yearbook. Carbon emissions data from China’s provincial carbon emissions of carbon accounting database is based on the apparent accounting listing. Energy generation from the China Energy Statistical Yearbook, such as GDP and economic data by the GDP deflator (in 2013) for the base period converted into constant, the INFOR data from the National Bureau of Statistics. Bank deposits and loans come from the PBC and the China Finance Association. Table 3 below shows the descriptive statistics of each variable.

Table 3 Descriptive statistics of each variable.
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Analysis of experimental results

Analysis of benchmark regression results

After the Hausman test, the fixed effect is more reasonable. Table 4 lists the baseline regression consequence of the effect of GF on CGPRCR. Model (1) only controlled for year and province-fixed effects without adding other control variables. On this basis, five control variables including INFOR, GOV, INPUT, MAR, and CITY were added to the model (2) to control the possible result bias.

The results of model (1) demonstrate that the core explanatory variable’s regression coefficient is observably active at the significant level of 0.05 in the absence of control variables. After appending the control variables in the model (2), GF’s influence coefficient on the CGPRCR is 0.090, which is more prominent, showing that the progression of GF has an active influence on CGPRCR in general. It proves hypothesis 1 of the paper. The possible reason is that China’s GF has the feature of “top-level design”. By formulating GF policies, the government, and relevant institutions provide clear development directions and signals for the market. This guides financial institutions, enterprises, and investors to pay attention to environmentally friendly projects and support the development of energy conservation and environmental protection, thus promoting the CGPRCR from top to bottom. At the same time, with the increasing attention of society to environmental protection and sustainable development, the public has reduced pollutant emissions through green consumption and green investment, thus promoting the CGPRCR from bottom to up. For example, in 2017, Fujian Province issued the Implementation Plan for the Construction of Fujian Green Finance System, which promoted the activation and development of the GF market. This initiative has directed social capital towards green industry development, effectively advancing the construction of ecological civilization and the coordinated sustainable development of the economy and society. It has provided robust momentum for green and low-carbon development.

In terms of control variables, although the influence coefficient of INFOR on the CGPRCR is negative, it can’t be explained that the improvement of INFOR has an inhibitory effect on the CGPRCR. This paper believes that the relevant policies and systems of informatization may have some problems such as lag and low efficiency in realizing the CGPRCR, and cannot be timely transformed into effective governance effects. In contrast, the coefficient of GOV on the CGPRCR is positive, indicating a significant and beneficial impact on the process. The reason may be that the government can promote the in-depth implementation of CGPRCR by formulating and implementing a series of policies and regulations on pollution reduction and carbon reduction, adopting economic means such as financial subsidies, and investing in infrastructure construction related to pollution reduction and carbon reduction. This is precisely the reason Xiang et al. (2023)63 believe that GOV is the reason for affecting carbon emissions. The influence of INPUT on the CGPRCR is negative, which may be because too much INPUT is used for the improvement of traditional energy technologies, rather than the research and development of clean energy or energy saving and emission reduction technologies, so the CGPRCR is not ideal. The influence coefficient of MAR is significantly positive at the 0.1 level. The expansion of MAR means that more resources are invested in environmental protection, which helps to promote the research development and application of green and low-carbon technologies. The influence coefficient of CITY on the CGPRCR is 0.687 and the effect is significant. With the improvement of CITY, the agglomeration of population and economic activities tends to generate economies of scale, which improves resource utilization and reduces resource consumption and pollutant emission. For example, the construction and operation of public facilities such as urban central heating can significantly reduce energy consumption and emissions compared with dispersed rural areas, and play a positive role in the CGPRCR.

Table 4 Baseline regression results.
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Endogeneity test

To solve the endogeneity problem caused by missing variables and reverse causation, the instrumental variable method and dynamic panel model difference GMM are used to estimate the model. The endogeneity test was conducted using industrial enterprise size (\(\:iv\)) as an instrumental variable. As an important structural indicator of economic activities, the industrial enterprise size is closely related to the development of GF and its implementation effect, but it is not directly affected by the results of CGPRCR and meets the requirements of the correlation of selected instrumental variables. The test results are shown in Table 5.

According to the two-stage least square regression result model (3), the first stage regression shows that both instrumental variables and endogenous GF are significantly correlated, and the F statistic is greater than the empirical value 10, which verifies the correlation hypothesis of instrumental variable. The results of the second stage regression shows that the coefficient of the GF was significantly positive at the level of 1%, and the Kleibergen-Paap rk LM statistic was significant at the level of 1%, indicating that the selected instrumental variable could be identified. At the same time, the Kleibergen-Paap rk Wald F statistic is greater than the 15% critical value of 8.96, indicating that it passes the weak instrumental variable test64. According to the results of the difference GMM model (5), the AR test shows that the first-order sequence of the model is correlated but the second-order sequence is not, indicating that the error terms of the original model do not have significant serial correlation. At the same time, the Hansen test value is greater than 0.05, indicating that the over-recognition constraint is effective. It is worth noting that after dealing with the endogenous problem, both models show that GF has a significant positive impact on the CGPRCR, which further confirms hypothesis 1.

Table 5 Results of endogeneity test.
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Heterogeneity inspection

Analysis of regional differences based on geographical location

Given the noteworthy regional heterogeneity in China, the promoting effect of GF on the CGPRCR might be various in different regions. Firstly, according to the geographical division of China, the study sample was divided into four regions to further test the impact of GF on the CGPRCR. In Table 6, models (6) to (9) represent the test results of four regions. As evident from the table, GF’s coefficient in each area is different, but all is positive. GF to carbon reduction for reducing the collaborative management will have a positive impact, further verifying hypothesis 1. The influence of GF on CGPRCR is in the order of “Central > Western > Eastern > Northeast” from Table 6. This conclusion echoes the research results of Lei et al. (2020)65, that is, the central is stronger and the east is weaker. This conclusion verified hypothesis 2.

This paper argues that the reason might be that central and western regions also have advantages in the GF’s development, for instance, policy support. The Overall Plan for the Construction of Green Finance Reform and Innovation Experimental Area in Lanzhou New Area of Gansu Province, released in 2019, proposed to vigorously facilitate monetary support for green industries. Explore formation has the characteristics of the western region green and high-quality financial services entity economy development of a new way of new features, Chongqing, Guizhou, and other areas have also set up the GF reforms and innovation experimental zones, significantly improving financing efficiency66, and promoting regional exploration of GF and the construction of ecological civilization. Data show that by the end of 2022, the GDP of the central and western regions increased the most year-on-year, the balance of green loans in the western region increased 30.7% year-on-year, and the level of GF continued to improve. The central and western regions are currently in a phase of comprehensive rectification and enhancement of GF, which explains why GF in these regions has the strongest promoting effect on the CGPRCR. East GF to CGPRCR has a positive role, but not significant. This paper argues that the reason lies in the economically developed eastern region, which is in green difficult period of transformation and industrial upgrading. Development of the early GF cannot significantly enhance the effectiveness of the existing environmental policy, which aligns with the views expressed by Zhu et al. (2021)67and Wang et al. (2023)68 corresponding to the published paper. Therefore, the eastern region needs to increase the investment in GF, and it needs to increase the guiding role of government policies or pollution control investment quotas to facilitate the further development of CGPRCR. As an old industrial base, the northeast region has a huge demand for funds to facilitate industrial light, and the development of GF is also in its primary stage, and the energy use right market and carbon trading market are not perfect. Therefore, the role of GF in the northeast region in the CGPRCR is not prominent.

Table 6 Heterogeneity test results based on geographical location.
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Based on the analysis of energy endowment regional differences

Energy endowment is an important factor affecting energy supply and consumption in a region. Significant disparities exist among different regions in terms of energy reserves and production, energy consumption construction, and energy intensity. The regional differences not only cause regional carbon emissions and pollution of the environment condition is different but also can affect the GF’s development to play an active role in CGPRCR. In this paper, the study example is divided into energy-rich areas and non-energy-rich areas, and Table 7 below shows the test results. The impact of GF on CGPRCR in the two regions is positive. The green transition of energy significantly impacts the journey towards low-carbon and sustainable development. This conclusion echoes the research results of Wu et al. (2021)39. The above hypothesis 2 got better. This paper argues that the reason is that the energy enrichment region of a single industrial structure and economic development rely on conventional energy sources. These energy uses will produce large amounts of carbon emissions, GF can provide financial support to help these areas transform and develop clean energy, to achieve the goal of CGPRCR. In contrast, non-energy-rich regions have more diversified industrial structures and higher initial technological endowments. Their financial markets are already relatively mature, and the potential for GF instruments such as carbon finance, green credit, and green securities to contribute to pollution control is limited.

Table 7 Heterogeneity test results based on energy endowment.
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Analysis of threshold model estimation results

This paper used the Stata15 software (URL: https://www.stata.com/) to test the threshold variables using the panel threshold regression program. After 300 repeated sampling, the specific \(\:F\) values, \(\:P\) values, and threshold estimates were obtained (see Table 8 below), and results are shown in Table 9. According to the inspection result, it can be concluded that GF, INNO, and NEID have an obvious threshold effect on the CGPRCR.

In specific terms, the threshold variable constraint function is as follows: (1) Model (12) indicates the model estimation results with GF as the threshold variable. It can be concluded that although the impact of GF on the CGPRCR is positive, it decreases with the improvement of the development level of GF. This paper believes that the reason may be that in the early stage of the development of GF, it has a significant role in promoting CGPRCR. The initial investment in GF can quickly fill the funding gap of green technology and environmental protection projects, promote the rapid development of clean energy, energy conservation, and emission reduction projects, and thus significantly reduce carbon emissions and pollution levels. However, with the continuous development of GF, when the development level of GF exceeds the threshold value, its marginal effect will gradually decrease69. The reason may be that when GF reaches a certain scale, the promotion effect of the increase of capital investment on pollution reduction and carbon reduction will gradually weaken, that is, the effect of pollution reduction and carbon reduction will gradually decrease with each additional unit of GF investment. (2) Model (13) said at the time of INNO as the threshold variable model to estimate the results. It can be concluded that as INNO crosses the threshold value of 7.9649, the influence of GF on the CGPRCR has changed from negative to positive, from insignificant to significant. It can be considered that in areas with higher levels of INNO, GF has greater potential in CGPRCR. (3) Model (14) is under the NEID adjustment of threshold regression estimation results. When NEID is lower than the threshold value, the influence coefficient of GF on CGPRCR is − 0.027. It may be because the NEID has the characteristics of a slow effect, and in the initial stage of development, the new energy industry has not yet formed a scale effect, and its potential for CGPRCR has not yet been fully released. This conclusion is in line with the view of Han et al. (2024)70. When the NEID crosses the threshold value, the influence coefficient is positive, and the effect is significant. That is, with the continuous development and growth of the new energy industry, the high NEID can further promote the effect of GF on the CGPRCR. This paper believes that the reason is that as an emerging industry, the core competitiveness of the new energy industry lies in technological innovation. When the NEID reaches a certain level, its technological innovation ability and market competitiveness will be significantly improved. This not only facilitates the sustainable development of the new energy industry itself but also provides GF with a greater number of high-quality investment targets and financing projects. By supporting these new energy projects with high-technology content and market prospects, GF has further promoted the effect of CGPRCR. According to the above analysis, the above hypothesis 3 is well validated.

Table 8 Threshold effect test results.
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Table 9 Threshold regression results.
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Robustness test

To check the robustness of the results, this section conducts a robustness test on the effect of GF on CGPRCR from four aspects: one period lag explained variable, adjusting research samples, replacing explanatory variables, and exogenous policy impact.

First, one period lag explained variable. Considering the time delay of GF, this paper selects the explained variable with a delay of one period as the new indicator and uses the baseline regression model for testing. The test results are shown in model (15) in Table 10. The regression coefficient and significance level of the model were largely consistent with the research findings mentioned earlier, indicating that the results in the above have passed the robustness test.

Second, the study sample was adjusted to handle the possible bias of outliers on the results. The sample areas of the largest and smallest 1%, 5%, and 10% of the CGPRCR were deleted successively, and three regression tests were conducted for 28, 26, and 24 provinces in China respectively. The regression coefficients and significance levels of explanatory variables in the results are similar to the test outcomes mentioned earlier and there is no significant difference, which manifests that the above results are robust. (Because of space restriction, this paper only lists the results of 24 regions, see Table 10 Model (16).)

Third, replace the explanation variable. In this paper, another indicator of explanatory variables is chosen to represent. That is, GF’s developmental scale is employed to represent the GF. The scale of GF development is expressed by green credit5. The test results are presented in Model (17) in Table 10 below. The regression coefficient and significance level of \(\:finance\) are consistent with the baseline regression results described above, indicating the robustness of the results.

Fourth, exogenous policy impact. When studying the impact of GF on the CGPRCR, the implementation of other policies may affect the estimated results. To ensure the robustness of the conclusion, this paper discusses two policies of GF. (1) Green finance reform and innovation pilot zone pilot policy (\(\:Pilot\)). The establishment of the green finance reform and innovation pilot zone can promote the research and development and application of green and low-carbon technologies, promote the optimization and transformation of the energy structure, and contribute to the realization of the goal of carbon neutrality. (2) Guidance on building a green financial system policy (\(\:Guidance\)). Through the development of green credit, green bonds, and other financial instruments, the policy guides social capital to invest in environmental protection, energy conservation, clean energy, and other fields, supports the construction of green industries and ecological civilization, thus promoting the CGPRCR, and promotes the transformation of the economy to green. To eliminate the possible estimation bias, this paper added the Green finance reform and innovation pilot zone pilot policy and Guidance on building a green financial system policy into the regression model for re-estimation, and the results are shown in models (18) and (19) in Table 10. It can be found that after considering the impact of GF policies, the results are consistent with the above regression results, which proves the robustness of the conclusion.

Table 10 Robustness test results.
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Conclusions and recommendations

Conclusions

After measuring the GF development index including green credit, green securities, green insurance, green investment, and carbon finance, this paper uses the panel baseline regression model and threshold regression model. It also takes GF, INNO, and NEID as threshold variables, and the INFOR, GOV, INPUT, MAR, and CITY as control variables. From across the country and province level research on the GF impact on CGPRCR. The three hypotheses presented above are proved and the robustness of the results is examined, and the following conclusions are drawn: (1) GF to CGPRCR showed a significant positive effect directly. (2) According to the geographical location, the influence of GF on CGPRCR showed an order of “Central > Western > Eastern > Northeast”. The influence coefficients of the four regions are positive, but not prominent in the eastern and northeastern regions. Based on the energy endowment zoning, GF had an active effect on the CGPRCR in energy-rich and non-energy-rich areas, but it was not significant in non-energy-rich areas. (3) Considering regional characteristics of heterogeneity, GF has non-linear effects on CGPRCR, GF, INNO, and NEID for CGPRCR has a threshold effect. In the case of a low GF development level, high INNO, and high NEID, GF can play a more favorable positive impact.

Recommendations

According to the above research conclusion, this paper suggests the following aspects:

(1) Increase international cooperation and exchanges. Countries should build a cooperation network on global climate governance to more effectively address the challenge of global climate change. Governments, international organizations, scientific research institutions, and enterprises should strengthen cooperation in the field of pollution reduction and carbon reduction. By organizing international forums, seminars, and technical exchanges, they can share successful technologies and valuable experiences in the CPRCR. This will promote the global process of CGPRCR and contribute to sustainable development and environmental protection.

(2) Adjust measures to local conditions for CGPRCR policies. The previous study found that the impact of GF on the CGPRCR has regional heterogeneity in China, and may also have regional heterogeneity in other countries. Therefore, governments should adopt different measures according to different emission reduction conditions to achieve the goal of CGPRCR. For example, India’s huge consumption of coal, accounting for a high proportion of total energy consumption, increases carbon emissions, so the Indian government can increase investment in research and development of clean technology and energy improvement technology, promote energy-efficient technology and equipment, and formulate strict energy efficiency standards and other specific measures to achieve CER.

(3) Improve the level of INNO effectively. The previous study found that under the high level of INNO, the positive impact of GF on the CGPRCR is more significant. Therefore, the country should vigorously develop and support the technological innovation and technological breakthroughs of low-carbon and environmental protection-related industries or enterprises. Specific policies such as R&D funds, tax incentives, technology transfer, and personnel training can be provided to encourage enterprises to engage in INNO and promote technological innovation. This will drive technological innovation and breakthroughs in the energy and environmental fields, providing technological.

(4) Increase government support. Considering the significant positive impact of GOV on the CGPRCR, the government should increase the capital investment in related projects to ensure that enterprises have sufficient funds to implement the projects of CGPRCR. These goals can be achieved by setting up special funds and providing loans with interest discounts, to encourage enterprises to carry out more related projects and achieve CGPRCR.

(5) Promote the implementation of the strategy for transforming the energy structure. The previous study found that under the condition of a high level of NEID, GF has a more positive and significant effect on the CGPRCR. Therefore, we can facilitate coal consumption to peak as soon as possible by strengthening industrial structure adjustment, regulating production capacity and output scale, and optimizing production technology and technical flow. At the same time, on the premise of ensuring national energy security, strictly restrict foreign investment in projects with high energy consumption and high emissions, use preferential policies to encourage and guide foreign investment in the field of renewable energy, and achieve the goal of CGPRCR.

Deficiency and prospect

Although this paper measures and analyzes the mechanism of GF affecting the CGPRCR, and puts forward the applicability according to the practical situation of the regional development strategy of differentiation, has strong theoretical and practical significance. However, there are still certain limitations to consider: first, the paper takes provincial panel data as the research sample, and the follow-up research can use the microdata of enterprises for specific research. According to the characteristics of each sub-industry, the influence mechanism of GF on regional CGPRCR can be discussed respectively. To exert the effect of GF effectively and improve the degree of regional CGPRCR, more applicable guidance should be put forward. Second, this paper explores the nonlinear effect of GF on regional CGPRCR. Subsequent studies may try to include intermediary variables to further reveal the mechanism “black box” of GF on CGPRCR. Third, this paper selects data from 30 provinces in China from 2013 to 2022 as research samples, and the data coverage is relatively limited. In future research, we will broaden the international perspective, no longer limited to the data of a single country, and actively obtain global data as research samples, hoping to analyze the global practice of GF and CGPRCR more comprehensively and provide useful references for international cooperation and exchange.