Influence analysis of green finance development impact on carbon emissions: an exploratory study based on fsQCA.

Increasing environmental degradation has forced policymakers to include sustainability in the economic growth agenda. Green finance has attracted the attention of policymakers and the industry, but the impact of green finance on social and environmental sustainability has not been confirmed. This study uses the panel data of 34 Chinese provinces to investigate the relationship between green finance and environmental degradation. The fuzzy set qualitative comparative analysis (fsQCA) method is utilized to analyze the mixed effect of green finance on CO2 emissions. These factors include green innovation, green insurance, green investment, and industrial structure. The results show that exogenous demand factors, including green insurance and industrial structure, have auxiliary effects when endogenous demand factors, including green investment and green innovation, exist as the core antecedent conditions among green finance and environmental degradation. Finally, the policymakers should encourage financial technology to actively participate in environmental protection initiatives that promote green consumption while minimizing the systemic risks caused by financial technology.

Introduction

For the past few years, the deepening of green financing and a low-carbon economy has greatly impacted on each other. They are very necessary for the betterment of the environment. Low carbon is no longer a problem of the society itself because of green finance but has become an important part of the environment (Aleksandrov et al. 2013). Green finance and the low-carbon economy are closely related concepts. Its essence in the financial sector is to make the environmental protection as a basic policy, considering the potential environmental influence when we make the investment and financing decision (Lee 2020). Green finance is a convergence between harmless to the ecosystem conduct and the monetary and business world. As it may, not many investigations have connected finance with the environment. The relationship between monetary organizations’ appearance and their social responsibility was examined. They are reasoned that natural finance/supportable finance is the best method to decrease ecological debasement. Green finance and low-carbon economies are an important part of the national environment (Guild (2020), Mohsin et al. (2021), and Mohsin et al. (2018a).

Since the mechanical unrest, the monetary area has been an incredible mainstay of human development. The essential job of the worldwide monetary area is to utilize the worldwide reserve funds (Li et al. (2021), Chien et al. (2021), and Iqbal et al. (2021)). Appropriate utilization of speculation empowers improvement in individuals’ satisfaction (Criscuolo and Menon 2015). Notwithstanding, due to the breakdown of the monetary framework, individuals have put their investment funds in land bubbles and earth-harming projects, including those that worsen human-initiated environmental change (Mohsin et al. 2018b). The monetary area already overlooked the biological system, which empowered the rise or deterioration of ecological issues, like environment and normal asset exhaustion, environmental change, and contamination (Zhang et al. 2019). Green finance supports interest in new advances and developments, including environmentally friendly power (Zhang et al. (2021), Hsu et al. (2021), and Ehsanullah et al. (2021)). Consequently, we are spurred to inspect the powerful effect of green finance on the carbon dioxide (CO2) outflows that help green finance.

Until this time, hardly any investigations have connected finance to the environment. Iqbal et al. (2019) recommends that can accomplish natural manageability through creating financing for sunlight-based energy. A comparative report by Iqbal et al. (2020) additionally infers that ecological finance/economic finance is the best method to diminish natural corruption. Maintainable finance/green finance empowers interest in new advances and developments, including environmentally friendly power (Wang et al., 2021b). Be that as it may, past investigations disregarded the connection between green bonds (an intermediary for green finance) and CO2 discharges (Sun et al. (2020b) and Sun et al. (2020a)). Green bonds are long-haul monetary instruments in which the returns from Sun et al. (2020e), Sun et al. (2020c), and Sun et al. (2020d). Green bonds are utilized exclusively to fund harmless projects to the ecosystem or decrease contamination in the climate. For instance, green bond incomes are utilized to help sun-powered energy, clean water, and clean vehicle projects (Chandio et al. (2020) and Sun et al. (2020c)). What is the reason why green finance has a great impact on low carbon, especially in COVID-19?

Researchers have worked on many interference factors affecting the low-carbon disclosure process (Agyekum et al. (2021) and Zhang et al. (2021)). These factors include green finance, green investment, and green innovation. However, the existing research focuses on the “net effect” of single influencing factors. It neither comprehensively identifies the factors influencing the carbon emission behavior nor explores the “joint effect” of multiple factors. Especially, the emission behavior of carbon is a complex situation nowadays and as well as in the future, which the traditional single-factor net effect analysis cannot completely explain:

In the first attempt, we identify the critical criteria underpinning medical equipment replacement in the studied hospitals and provide a coherent program to plan the process and minimize the adverse impacts of inattention on this crucial stage in the fuzzy-TOPSIS model. The present study tries to take advantage of the fuzzy-TOPSIS technique and combine it with a fuzzy approach to tackle the lack of precise and comprehensive input information. The study proposed a hybrid approach as a programming tool to select replacement strategies for medical equipment.

This study makes three contributions to the current writing, in the first place, contrasted with earlier investigations, which for the most part pressure the job of monetary turn of events, rather than just the impact of green finance on natural factors; this investigation presents a spearheading assessment of green finance and CO2 outflows. Besides, this investigation utilizes the QCA approach that catches the heterogeneous and deviated connection between green finance and low-carbon economies.

This is one of the principal studies to consider 34 provinces in China in which green finance has been utilized fundamentally. The exact discoveries on the effect of green finance on their comparing CO2 emanations go about as benchmarks for different nations. At last, our observational examination gives new experiences into the lopsided reaction of CO2 discharges to green finance use at various QCA. Our contribution also includes applying the fuzzy analytical hierarchy process to influence the analysis of green finance development impact on carbon emissions based on fsQCA. Moreover, the interaction among various factors may replace or complement one another. Therefore, which factors affect carbon emission behavior? This is exactly the problem that this paper will explore.

The rest of the paper is organized as follows: the second section explains the literature review and model construction, the third section discusses the design of research, the fourth section does empirical analysis and explains the results, and the fifth section concludes the study.

Literature review and model construction

Theoretical basis

According to the classical synergetic, “synergy” signifies the cooperation between multiple subjects based on common goals, and carbon emission synergy is a typical synergy theory in environmental science. Finance assumes an essential part in the anthropogenic (i.e., human effect on the climate), yet very little has been done to fuse natural issues into finance. In the recent years, the monetary area has focused on green ventures, progressing manageable development. As indicated by Chen et al. (2021), green monetary instruments can accomplish a green climate. Meanwhile, monetary delegates and markets have developed green securities, greenhouse loans, green advances for company structures, and natural home value programs. Similarly, Australia launched its first natural store drive, which includes medium- to long-term financial instruments that finance non-harmful environmental undertakings and business exercises and directly support feasible events and environment-related ventures. Important components of a collaborative environment include green finance, green innovation, and green investment which was the major cause of low-carbon economies (Nawaz et al. 2021). Ren et al. (2020) studied green finance and the carbon loss in return-on-investment model after being hacked. The authors concluded that green finance significantly affects environmental decision-making under a given potential loss level.. With the high level of green financing, Jin et al. (2021) pointed out that enterprises tended to be compatible with affiliated enterprises.

Model construction

In traditional financial activities, two factors are mainly considered when studying the impact mechanism of carbon emissions. First, there is a link between enterprises’ credit financing activities and their level of financial development, i.e., the higher the level of financial development, the more convenient the credit financing of enterprises; second, after obtaining more credit funds, financial development encourages enterprises to increase R&D investment and improve their innovation ability. In other words, the higher the degree of development of green finance, the more enterprises are willing to promote green transformation and upgrading enterprises through green financial tools. Therefore, the inhibition mechanism of green finance on carbon emission can be expressed as follows:

Green finance gives priority to supporting a low-carbon economy.

Enterprises rely on green funds for low-carbon innovation.

Low-carbon technology will reduce carbon emissions.

Green financing can be divided into green innovation, green investment, and green bond. In addition, carbon emissions are also affected by many factors, such as openness level, industrial structure, and urbanization which are introduced.

In this paper, the main factors of endogenous demand include green investment and green innovation. The main factors of exogenous demand that are considered include green insurance and industry type. Both internal and external demands reflect the degree of green finance. To sum up, this paper puts forward the following conceptual model as shown in Fig. 1.

Fig. 1

Conceptual model

Design of research

Selection of research methods

The qualitative comparative analysis (QCA) method adopted in this paper is a case-oriented method instead of a variable-oriented research method (Marks et al. (2018), Pappas and Woodside (2021), Wang et al. (2016), and Wang et al. (2021a)). QCA has been applied comprehensively in organization and management research at the technical analysis and research method levels, and the fuzzy set qualitative comparative analysis (fsQCA) method (Li 2019) has superior performance for studying “joint effect” and “interactive relationship” (Elliott 2013). Therefore, this paper uses the fsQCA method to analyze the “joint effect” of various factors on carbon emission behavior and the “interactive relationship” among various factors to identify the single factors influencing the carbon economies’ and green finance (Casady (2021), Gabriel et al. (2018), Skarmeas et al. (2014), and Maier et al. (2020)). The analysis results in a summary of the combination of factors that affect green finance in low-carbon economies (Pappas and Woodside 2021).

Data, selection, and measurement of variables

TOPSIS is a technique for moving closer to a positive ideal solution (i.e., minimizing the distance between criteria) and away from a negative ideal solution (maximizing the gap in each criterion). This approach is especially well suited to solving the group decision-making problem in a fuzzy setting (Prakash and Barua 2016). The combination of fuzzy mathematics with TOPSIS produces FTOPSIS, which is used to handle decision criteria issues in a fuzzy situation with uncertainty, immeasurable information, and incomplete knowledge. The key steps for multi-person multi-criteria decision-making with fuzzy are as follows: TOPSIS for addressing supplier selection.

According to the above analysis, the choice of low-carbon emission or high-carbon emission is made due to the green finance effect in the model construction, i.e., the dependent variable. Green finance, green innovations, green insurance, and industrial structure are considered independent variables that reflect the internal and external carbon emission factors. This study uses the panel data for 34 Chinese provinces from 2003 to 2017. All the data were collected from the National Bureau of Statistics of China. Table 1 shows the description of each variable:

Table 1

Description of variables

Name of variables	Description of variables
Carbon emission	Ratio of carbon dioxide emissions of each region to its GDP
Green investment	Green investment has a direct impact on carbon economies. If the green investment is high, the ratio of the carbon in the environment is low, and if the green investment is low, then the ratio high
Green innovation	Green innovation has a direct impact on carbon economies. If the green innovation is high, the ratio of the carbon in the environment is low, and if the green innovation is low, then the ratio of carbon in the environment is high
Green insurance	Green insurance, also known as environmental pollution liability insurance, is the most representative environmental pollution liability insurance in which insurance companies compensate pollution victims. According to law, it is based on the damage caused by pollution accidents to a third party and the liability for compensation
Industrial structure	Ratio of industrial added value of each region to its GDP

The major problem is choosing and measuring the suitable site weights needed to meet the experts’ criteria requirements. The weights evaluated and assigned by individuals are usually controversial and uncertain. Generally, academia’s research forecasters, policymakers, professors, executives, and stakeholders are engaged to examine the weights score for each indicator. To achieve the objective of the current research, we have consulted with ten professionals from a university background, researchers, government institutes professionals, and associated stakeholders. These professionals have experience in assigning the weights to different variables for multiple case studies, and they have a piece of full knowledge about the country’s current situation and environment. The random consistency index and the consistency index provided by Ho and Ma (2018) were used to verify the 10 experts’ perspectives and findings. The software YAAHP (V. 10.5) has been used to get weights of the study’s proposed criteria. Table 2 shows the variables for fuzzy numbers.

Table 2

Fuzzy numbers factors Sr. number linguistic variables TFN

No	Linguistic variable	TFN
1	Very bad (VB)	(0, 0.05, 0.15)
2	Bad (B)	(0.1, 0.2, 0.3)
3	Fairly bad (FB)	(0.2, 0.35, 0.5)
4	Fairly (F)	(0.3, 0.5, 0.7)
5	Fairly good (FG)	(0.5, 0.65, 0.8)
6	Good (G)	(0.7, 0.8, 0.9)
7	Very good (VG)	(0.85, 0.95, 1)

Stage one

Select the relevant linguistic variables for the value weight of selection criteria and supplier linguistic scores. Result shows the scoring methods used to score linguistic variables (Rouyendegh et al. (2020), Rajak and Shaw (2019), and Dhiman and Deb (2020)).

Stage two

Construct the fuzzy decision matrix. Let be a TFNs for : where and

Stage three

Normalize the fuzzy decision matrix: where (benefit criteria) normalization for cost (negative) measures (Unvan (2020), Dang et al. (2019), and Lima Junior et al. (2014):

(cost criteria)

Stage four

Determine the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) (Salih et al. (2019) and Sirisawat and Kiatcharoenpol (2018)):

(10)where and

Stage five

Calculate the distance of each supplier from FPIS () and FNIS (), respectively: where where

Here, the distance between two TFNs and can be as

(13)

Stage six

Calculate the coefficients and iteratively refine alternatives for achieving satisfaction for each criterion (Papapostolou et al. 2020): where , and are the distances from FPIS and FNIS, respectively.

Results and discussion

Descriptive statistics

A descriptive statistical analysis of antecedent and outcome variables involved in the study is carried out, and the basic results are provided in Table 2. It can be noted from Table 3 that the correlation among green investment, green innovation, green insurance, and industrial structure is not very strong among carbon emissions. But without considering other factors, these four antecedents have a positively correlate with carbon emission. The paper analyzes the data further based on this information.

Table 3

Correlation analysis

	Average value	Standard deviation	Green insurance	Industrial structure	Green innovation	Green investment	Carbon emission
Green insurance	188.36	86.07	1
Industrial structure	0.813	0.297	0.05	1
Green innovation	5.711	1.574	0.106**	0.273***	1
Green investment	179.8	79.01	0.041	0.035	0.019	1
Carbon emission	0.932	0.339	0.154***	0.141*	0.3061**	0.088*	1

*** means p < 0.001; ** means p < 0.01; * means p < 0.05.

Calibration of variables

The key difference between fuzzy sets and conventional variables lies in how they are conceptualized and labeled. Before the QCA can be run, the condition and outcome data must be calibrated. It is necessary to specify a target set to calibrate it as a fuzzy set, which constitutes the calibration of the set and provides a direct connection between the theoretical discourse and empirical analysis.

In this study, the fsQCA was adopted, and the related antecedents and results were calibrated as fuzzy set membership scores using the method of direct calibration. Set membership does not have to be binary (0/1). Rather, in fsQCA, the aim is to calibrate set membership so that the levels of membership represent meaningful groupings. Out of the scores, the intersection point value has the greatest fuzziness, which determines whether most cases belong to or not belong to the target set on the value of the fixed-distance scale variable. Among the antecedent variables related to green finance and CO2 emissions chosen in this paper, the mean is the calibration standard for the intersection of green investment, green innovation, green insurance, and industrial structure. Other calibration standards are “mean – standard deviation” and “mean + standard deviation.” This choice is because the mean reflects the average level of carbon emission, while the standard deviation reflects the difference of carbon emission in a certain index. Table 4 shows the final calibration results.

Table 4

Calibration threshold of each variable

Variables	Nonmembership anchor point	Intermediate anchor point	Complete membership anchor point
Green investment	100.79	179.8	258.81
Green innovation	4.137	5.711	7.285
Green insurance	102.29	188.36	274.43
Industrial structure	0.516	0.813	1.11

Result analysis

Single-factor necessity analysis

Based on the general steps of the fsQCA, this paper first checks whether a single factor and its non-set constitute a necessary condition for the results, i.e., high-carbon emission and low-carbon emission among the green finance and CO2 emissions. This indicates that the result set is checked to see whether it is a subset of the single factor and its non-set, as determined by consistency. It is determined that the single factor or non-set is the necessary condition for the result set when the consistency level is higher than 0.9. Table 5 shows the results of fsQCA. It can be observed that all antecedents cannot constitute the necessary conditions for realizing the realization of specific results. The necessity of all single antecedents and their non-sets affecting low-carbon emission does not exceed 0.8, and the necessity of influencing high-carbon emission does not exceed 0.6. Therefore, all single antecedents do not constitute necessary conditions for low-carbon emission or high-carbon emission.

Table 5

Test of adequacy and necessity of antecedents

	Low-carbon emission		High-carbon emission
	Consistency	Coverage	Consistency	Coverage
Green insurance	0.448251	0.735248	0.481222	0.28638
~ Green insurance	0.551749	0.723511	0.518778	0.216394
Green innovation	0.452361	0.701241	0.552362	0.290216
~ Green innovation	0.547639	0.784562	0.447638	0.213542
Green investment	0.421561	0.745123	0.463251	0.263512
~ Green investment	0.578439	0.763581	0.536749	0.2521684
Industrial structure	0.621534	0.700236	0.583962	0.2723651
~ Industrial structure	0.378466	0.762821	0.416038	0.285135

Analysis of the adequacy of antecedent configuration

Antecedent configuration analysis reveals the sufficiency of the outcome caused by different configurations composed of multiple antecedent conditions. The fsQCA 3.0 program was used to process data for the truth table in this study:

Case frequency threshold. Rihoux et al. (2009) proposed choosing the frequency threshold such that the number of retained cases is higher than or equal to 75% of the total number of cases. In this paper, the total number of cases is 956. Four antecedent conditions will generate 16 configurations in the truth table. So the frequency threshold of the effective antecedent condition combination is set to 50.

Raw consistency threshold. Consistency measures “how closely a perfect subset relation [between a configuration and an outcome] is approximated”; in the simple case of crisp sets, consistency is the proportion of cases exhibiting the configuration that exhibits the outcome. In this paper, the outcome is carbon emission. It is good practice to establish different consistency thresholds for necessity and sufficiency analyses and not to interpret subset relations that do not meet these thresholds. The antecedent condition configuration where the raw consistency value is higher than the threshold is a subset of the outcome, and the outcome is assigned 1; otherwise, it is 0. In this paper, the minimum threshold of raw consistency is set to 0.75.

After analyzing the truth table, three kinds of solutions are obtained, complex solution, concise solution, and optimized solution. The complex solution does not include any logical remainder. The intermediate solution only includes the logical remainder in line with the theoretical direction and empirical evidence. And the simplified solution includes all the logical remainder without evaluating its rationality. So, intermediate solution is considered the first choice for reporting and interpretation in QCA results. Based on previous research, this work describes the intermediate solution, with the simpler solution serving as an auxiliary. Table 6 shows the configuration results of green finance conditions before carbon emission, where “●” and “ ⊗ ” indicate the existence and absence of a core antecedent condition, respectively, and “●” and “ ⊗ ” indicate the existence and absence of an auxiliary antecedent condition, respectively. Blanks can indicate either existence or absence of antecedent conditions.

Table 6

Precondition configuration for carbon emission

Type	Low-carbon emission			High-carbon emission
	L1	L2	L3	H1	H2	H3
Green investment	●	●		⊗	⊗
Green innovation	⊗	⊗	●		⊗	⊗
Industrial structure	●		●	⊗	●	●
Green insurance		●	⊗	●		⊗
Coverage rate	0.143	0.235	0.273	0.171	0.219	0.199
Net coverage rate	0.143	0.103	0.078	0.077	0.077	0.136
Consistency	0.789	0.801	0.774	0.768	0. 801	0.757
Total coverage	0.508	0.496
Total consistency	0.769	0.771

Table 6 shows that the consistency of each configuration and the total consistency are higher than the minimum acceptable standard of 0.75 in both models. The total coverage rates of low-carbon emission and high-carbon emission are 0.508 and 0.497, respectively, equal to those obtained using QCA research in the fields of organization and management. From the results, it can be gathered that the fsQCA effectively identifies six antecedent configurations. The identifications can indicate whether the existence or absence of antecedent factors has a positive or negative impact on high or low-carbon emission.

In low-carbon emission implementation configurations L1 (industry structure + green investment +  ~ green innovation) and L2 (green insurance + green investment +  ~ green innovation), green investment exists as the core precondition, and the lack of green innovation plays an auxiliary role. In the former configuration, the industrial structure plays an auxiliary role. In contrast, in the latter configuration, green insurance plays a core role when the industrial structure either exists or is absent. The industrial structure and green innovation play a core role, while the lack of green insurance plays an auxiliary role in L3 (industrial structure +  ~ green insurance + green innovation).

Green investment and the lack of industrial structure are the core antecedents. Green insurance plays a key role in configuration H1 (~ industrial structure + green insurance +  ~ green investment), which causes high-carbon emissions. In H2 (industrial structure +  ~ green investment +  ~ green innovation), green investment and the lack of green innovation are core antecedents, and industrial structure exists as auxiliary antecedents. In H3 (industrial structure +  ~ green insurance +  ~ green investment), green innovation and the lack of green insurance are core antecedents, and industrial structure exists as core antecedents.

The total consistency of the configurations in this study is 0.769, which indicates that the interpretation degree of the six configurations concerning the carbon emission behavior of enterprises is 76.9%. The total coverage rate is 0.508, which indicates that the research results can cover 50.8% of cases. It is necessary to simultaneously analyze the consistency and coverage of all configurations during qualitative comparative analysis. The consistency of the six configurations is about 0.79, which proves that there is a good subset relationship between the six configurations and high- or low-carbon emission, signifying a high explanatory capability of carbon emission behavior. It can be concluded based on the results that the fsQCA can effectively identify six antecedent configurations, which show how the existence or absence of each element in different antecedent configurations affects carbon emission behavior.

Configuration effect as a robustness test

This paper adjusts the consistency threshold and reprocesses the sample data based on well-known research results. The original minimum consistency threshold is adjusted from 0.75 to 0.76. The antecedent configuration obtained under the consistency threshold of 0.76 is the same as that obtained 0.75, which is consistent with the conclusion as mentioned earlier. Therefore, this paper obtains robust research conclusions such as the sensitivity analysis is carried out to assess the robustness of the findings obtained, for example, to investigate how the ranking of alternatives evolves as the weight of the criterion changes. As a result, the impact of weight factor weights on the prioritization order of the strategies (i.e., alternatives) has been explored during the sensitivity analysis phase. In this sense, ten cases were created and evaluated by adjusting the weights of the weight factors to determine the outcome/priority of the strategies. Table 7 shows the various weights of weight factors in these ten cases. The importance of the factors is given in column 2 of Table 7, followed by ten other patients evaluated using the sensitivity analysis. The factor weights have remained constant in the vast majority of cases. Finally, Table 7 shows the ranking of the methods based on ten issues of sensitivity analysis. In these tests, it is discovered that the ranking order of strategy weight factors has changed in cases 1, 5, and 6, while the priority order of the strategy has remained constant in the remaining cases.

Table 7 

Weight factor weights for real and different cases

Carbon emission	Green investment	Green innovation	Green insurance	Industrial structure
Case 1	0.15	0.26	0.28	0.27
Case 2	0.21	0.21	0.22	0.22
Case 3	0.23	0.31	0.33	0.33
Case 4	0.37	0.42	0.44	0.44
Case 5	0.11	0.10	0.11	0.11
Case 6	0.09	0.21	0.22	0.22
Case 7	0.23	0.31	0.33	0.33
Case 8	0.37	0.36	0.39	0.38
Case 9	0.10	0.16	0.17	0.16
Case 10	0.12	0.36	0.39	0.38

Following a sub-factor analysis using the AHP technique, this section presents the prioritizing order of eight health strategies/alternatives from Table 7 using the FTOPSIS approach. The research performed by the study’s expert group aided in developing of a fuzzy evaluation matrix into TFNs using linguistic variables. As a result, the assessment matrix concerning the alternatives was calculated in this analysis. This came after developing of a fuzzy decision matrix, a fuzzy normalized decision matrix, and a weighted normalized fuzzy decision matrix for each element of this analysis (see Supplementary section). The ranking of emission reduction factors and sub-factors was then established. Finally, Table 8 shows the prioritized order of the eight health planning strategies.

Table 8 

Final ranking of carbon emission strategies

Factors	di +	di −	CCi	Rank
Carbon emission	13.56	0.62	0.15	3
Green Investment	13.52	0.67	0.13	5
Green innovation	13.54	0.64	0.16	1
Green Insurance	13.55	0.65	0.10	8
Industrial structure	13.54	0.63	0.12	7

Source: Research findings

Column five of Table 9 provides the ranking of this study’s energy strategies, briefly discussed concerning their rank.

Table 9

Inner matrix factor

Carbon emission	Green Investment	Green innovation	Green Insurance	Industrial structure
Carbon emission	(1, 1, 1)
Green Investment	(2, 3, 4)	(1, 1, 1)
Green innovation	(5, 6, 7)	(2, 3, 4)	(1, 1, 1)
Green Insurance			(4,5,6)	(1,1,1)
Industrial structure				(7,8,9)
Importance weights	(0.364, 0.453, 0.552)	(0.154, 0.282, 0.287)	(0.042, 0.093, 0.142)

Table 9 shows the weight factor dependency matrices, in which one factor is regulated in each case. The carbon emission, for example, was managed to assess the relationship between green investment, green innovation, and green insurance. In Table 9, the fuzzy value weights of factors are shown. Table 9 shows that the expert’s answers are all consistent. Pairwise comparisons and studies of the effect of the factors on one another were used to assess the inner dependence of the weight factors. As previously mentioned, it is not always possible to conclude that all weight variables are independent. The use of both the weight analysis and the AHP method simultaneously could yield more acceptable and practical results. The dependency of the weight factors shown schematically in results can be calculated by studying the internal and external environments of the studied hospitals.

Theoretical analysis of configuration effect

This paper puts forward the following three research propositions, based on the antecedent configuration of carbon emission and the theoretical analysis behind it and comparison with the antecedent configuration of carbon emission:

The green investment is the core prerequisite for low-carbon emission under the condition of the environment. The comparative analysis of green investment shows that high green investment leads to a relatively high proportion of low-carbon emission sample cases (L1, L2). The coverage rates for these two cases are 0.143and 0.235, respectively, and the consistency rates are 0.789 and 0.801, respectively, which are higher than the total consistency of low-carbon emission sample cases of 0.769. However, the proportion of sample cases with low green investment (H1, H2) that cause high-carbon emission is also high, with coverage rates of 0.171 and 0.219 and consistency rates of 0.768 and 0.801, respectively. Zhang et al. (2020) proved through empirical analysis that high green investment has a high impact on low-carbon emission behavior. These results theoretically demonstrate that the green investment is one of the core elements in low-carbon emission.

However, only a single core condition of a high green investment level is insufficient to force low-carbon emissions. In L1, the industrial structure exists as an auxiliary condition, i.e., it enlarges the possibility of emission. In L2, green insurance exists as another core condition. Higher green insurance causes a lower loss caused by high-carbon emission. One of the necessary conditions for enterprises reducing carbon emissions is green investment of endogenous demand factors in low-carbon emission. However, it is not a sufficient condition. Industrial structure and green insurance of exogenous demand factors jointly affect the decision of enterprises carbon emission.

When compared to configurations H2 and H3, the coverage rate of configuration L3 is the highest among all configurations, at 0.273. If the antecedent of a high energy consumption industrial structure exists and if the antecedent of green innovation does not exist, it is very likely to lead to the high-carbon emission behavior of enterprises, regardless of whether green insurance and green investment are missing or pending, e.g., the cover rate of configuration L3 is the highest among all configurations at 0.273.

The industry has made outstanding contributions to economic development. But behind this contribution is the cost of environmental sacrifice. The annual increase in carbon dioxide emissions has greatly damaged the living environment and affected climate improvement. Green finance should be guided by promoting industrial structure adjustment, guiding and supporting enterprises to carry out technological innovation, and reducing carbon emissions. Enterprises use green funds to invest in new energy R&D, give policy preference to industries that pay attention to energy conservation, emission reduction, and environmental protection, and prioritize their green investment and financing activities. The development of green finance is to guide enterprises to achieve energy conservation and emission reduction through green funds, build a low-carbon economy, and achieve sustainable economic development.

During the process of carbon emission management, the green investment should coexist as a core condition of green insurance is a core precondition. This increases the possibility of enterprises to decreasing carbon emissions. If green insurance is present as a core condition, green investment is absent. The industrial structure is absent as an auxiliary antecedent condition; businesses’ willingness to reduce carbon emissions falls regardless of the extent of green innovation, i.e., H1 in the table is configured with a coverage rate of 0.171 and a consistency of 0.768.. In the process of transformation from a high-carbon economy to a low-carbon economy, enterprises are full of uncertainty in strategic transformation and green technology development. Insurance institutions can design targeted and innovative green insurance products and services for businesses, assisting them in effectively dealing with innovation risks in green technology R&D and allowing businesses to have more “trial and error space” in the practice of green transformation as an important financial institution for climate change-related risk management.. Green insurance can promote enterprises to decrease carbon emissions.

Conclusion and policy implication

This article employed fsQCA to gather sample case data on green financing and carbon emissions to cope with the present high-carbon emissions, which have a clear influence on climate change. It discussed the “joint effect” of green investment, green innovation, green insurance, and industrial structure on carbon emissions to explore the factor configuration under different circumstances. It was observed that the antecedents were neither a necessary nor a sufficient condition for carbon emission. Enterprises’ CO2 emissions behavior resulted from multiple antecedents, characterized by “multiple concurrency.” This paper analyzed the antecedent configuration of high-carbon and low-carbon emissions and obtained three main paths to promote enterprises to decrease carbon emission. Each path was made up of many antecedent elements. This conclusion shows that, unlike previous studies that focused on a single factor, such as urban degree, emission cost, and benefit, enterprises’ carbon emission behavior should be examined from an overall perspective. Based on the research as mentioned earlier conclusions, it is suggested that attention should be paid to the following factors during the process of carbon emission government:

Gradually improve the standards of the green financial system, optimize the green financial structure, and improve the enthusiasm of financial institutions and enterprises to participate in green financial activities. Further, deepen the innovation of green financial products, research and develop green securities and carbon financial products, and cultivate green financial markets. Encourage private institutions and investors to participate in green financial activities; establish a green financial cooperation mechanism integrating banks, guarantees, insurance, and securities institutions; and compensate the risks borne by investors employing loss reserves and green insurance subsidies to attract private capital to participate in green financial activities actively.

Constructing green communication mechanism and credit evaluation system in financial activities. The information communication mechanism between investors and investees is related to the interests of both sides. The accuracy of sharing financial institutions’ information is the basis of a successful follow-up. A standardized information receiving, processing, and publishing process should be established to ensure the authenticity and integrity of information.

At present, in the development of green finance in China, the government and financial institutions do not grasp the environmental protection information and low-carbon behavior of enterprises, which leads to investors’ inability to make investment decisions accurately or easily leads to decision-making risks. A green information communication system must be established to address the practical issue of erroneous information disclosure, allowing financial institutions and businesses to communicate green environmental protection information and allow actual green enterprises to benefit from green funds. Simultaneously, a green credit assessment system should be developed, a negative list of green companies should be established, and credit punishment for fake green firms and unlawful green investment should be implemented.

Promote the green transformation of industrial structure. Green finance should be guided by promoting industrial structure adjustment, supporting enterprises to carry out green innovation, and reducing carbon emissions. For enterprises that use green funds to invest in new energy R&D, give policy preference to industries that pay attention to energy conservation, carbon emission reduction, and environmental protection and give priority support to their green investment, green insurance, and financing activities. The development of green finance is to guide enterprises to achieve energy conservation and emission reduction through green funds, build a low-carbon economy, and achieve sustainable economic development.

This study still has a few shortcomings: (1) identifying factors influencing carbon emission behavior is not comprehensive enough, and (2) the research model may not include all antecedents. For example, competition factors among enterprises, carbon cost, and responsibility sharing of loss may affect carbon emission behavior. More comprehensive data will be collected for further exploration to deal with this issue.

The local governments achieve green governance learning. The green production efficiency, the degree of renewable energy consumption, and the purchase of domestic technology funding and social patent transfer income reflect each local government’s participation in innovation and green energy. Internal expenditure on R&D funding characterizes the extent to which it helps transform towards green energy, which is supported internally in each phase.