How Does Green Finance Affect CO2 Emissions? Heterogeneous and Mediation Effects Analysis

The original intention of green finance advocacy is to provide financing support for energy conservation and emission-reduction activities. In this context, the carbon dioxide (CO₂) emission-reduction effect is worth further discussion. To this end, by gauging the green finance index, we apply the econometric method to evaluate the impact of green finance on CO₂ emissions. We also discuss geographical heterogeneity and the impact mechanism. The main findings imply that: 1) China’s implementation of green finance is an effective measure to mitigate greenhouse gas emissions; in other words, green finance in China can effectively reduce CO₂ emissions; 2) both green finance and CO₂ emissions show significant geographical heterogeneity and asymmetry; only in the eastern and central regions, can green finance help alleviate the greenhouse effect; and 3) besides the total effect, green finance can affect the greenhouse effect by promoting the rapid growth of the provincial economy, restraining the improvement of energy efficiency, and accelerating the optimization of the current industrial structure. Following the above three findings, we propose some policy suggestions related to green finance evolution and CO₂ emissions reduction.

Highlights

➢ We assess the impact of China’s green finance on CO₂ emissions.

➢ Both geographic heterogeneity and CO₂ emission asymmetry are explored.

➢ This study evaluates how green finance affects CO₂ emissions.

➢ Green finance is an effective strategy for solving the greenhouse effect.

➢ Green finance in China reduces CO₂ mainly by promoting industrial optimization.

1 Introduction

With the rise of global temperatures, the frequent occurrence of natural disasters, and global environmental pollution, the issue of climate change has increasingly attracted the attention of scholars around the world (Hulme et al., 2018; Wang et al., 2021a; Zhao et al., 2022a; Ren et al., 2022c; Yan et al., 2022). If left unchecked, carbon dioxide (CO₂) emissions will cause an additional 1–3.7°C increase in global average temperatures by the end of this century, leading to irreversible changes (Dusenge et al., 2019; IPCC, 2022). Accordingly, the United Nations has held several climate change conferences aimed at cooperating with countries around the world to alleviate the problems caused by climate change and CO₂ emissions. For example, at the COP 26 UN Climate Change Conference, governments committed to phasing out coal-fired power generation, providing a credible guarantee for mitigating global CO₂ emissions (Arora and Mishra, 2021). As the world’s largest emission producer, China is also actively seeking ways to reduce CO₂ emissions. China’s 14th Five-Year Plan points out that by 2025, energy intensity should be reduced by 13.5%, and non-fossil energy should account for one fifth of primary energy consumption by 2030 (He, 2015; Pan and Dong, 2022), so as to gradually achieve China’s worldwide commitment to a zero-carbon target by 2060 (Zhao et al., 2022b).

In order to achieve climate goals, the world needs to transition to a greener, more climate-resilient economy (Ren et al., 2022a), in which green finance will accelerate the construction of greener infrastructure and innovation. In 2012, the China Banking Regulatory Commission released the “Green Credit Guidelines,” which established the core framework of the green credit system, and more and more funds are used in terms of low-carbon environmental protection (Yu et al., 2021). In 2015, China became the first country to establish a sound green financial policy system around the world (Lv et al., 2021). The scale of China’s green finance market continues to expand (Ren et al., 2022b). In 2018, China issued more than 280 billion yuan of green bonds, and the stock of green bonds is worth close to 600 billion yuan, ranking among the top in the world (PBC, 2019). Therefore, green finance has become the main way for China to deal with climate change and reduce the greenhouse effect. On the one hand, green finance provides financial services for green innovation industries and offers more advanced environmental-protection technologies (Khan et al., 2021). On the other hand, green finance can help mitigate the greenhouse effect by underwriting environmental regulations and environmental directives (Nouira et al., 2016). Statistics published by the International Energy Agency (IEA, 2017) show that green finance will reduce fossil fuel combustion by 26%. However, large-scale inefficient investments caused by green finance should also be taken into account, with the negative effects of climate change (Nawaz et al., 2021). Figure 1 also presents the average levels of green finance and CO₂ emissions in China, both of which show an increasing trend. Therefore, we urgently need to clarify the role of green finance in addressing the problem of climate change and mitigating CO₂ emissions.

FIGURE 1

FIGURE 1. Time trend chart of the average values of CO₂ emissions and green finance.

At the same time, there are significant differences in CO₂ emission levels across provinces in China, as shown in Figure 2, which has led to different carbon-mitigation policies and the different influences of green finance. Although a few scholars have (explored the nexus between green finance and CO₂ emissions Saeed Meo and Karim, 2022; Xiong and Sun, 2022), there is still insufficient research on the heterogeneity among Chinese provinces. Furthermore, the path through which green finance affects CO₂ emissions is still unclear. Therefore, we put propose to address the following questions: 1) Does green finance help mitigate CO₂ emissions in China? 2) Is green finance affecting CO₂ emissions heterogeneously? 3) What is the impact mechanism between green finance and CO₂ emissions? Based on the panel data of 30 provinces in China from 2004 to 2018, we construct a comprehensive indicator of green finance and assess the impact of green finance on CO₂ emissions in China. We also explore their heterogeneous and mediating effects.

FIGURE 2

FIGURE 2. Spatial distribution of CO₂ emissions in selected years.

Accordingly, this paper makes the following research contributions. First, we explore the relationship between China’s green finance development and CO₂ emissions, which clarifies the direction of China’s green finance development and will make the government more confident in investing in green industries. Second, we conduct heterogeneity analyses by geographic position and CO₂ emissions level, which provide more valuable references for regional governments. Third, we innovatively study the mediating effect between green finance and CO₂ emissions, which can help governments formulate clearer emission-reduction paths.

The remainder of this paper is organized as follows. The next section is the literature review; Section 3 represents the econometric methodology and data; Section 4 discusses the nexus between green finance and CO₂ emissions; in Section 5, we analyze the heterogeneity and mediating effect, followed by the conclusion and policy implications in Section 6.

2 Literature Review

2.1 An Overview of Green Finance

In 2010, in an attempt to provide financial support for developing countries dealing with climate change, 194 countries established the Green Fund, which is a prototype of the concept of “green finance” (Zhang et al., 2019). Generally speaking, green finance refers to a financial activity that promotes environmental development, improves resource utilization, and responds to climate change (Cui et al., 2020). The green financial market will use various green products as trading platforms, including mainly green credit, green funds, and green derivatives (Taghizadeh-Hesary and Yoshino, 2019). With the severe situation of global climate change in the international community, various governments and scholars have focused on the role of green finance, and carried out large-scale research on this subject.

Constructing and evaluating financial risk indicators accurately are the first concerns of scholars. Some scholars use a single indicator, such as green bonds, as a proxy variable for green finance (Saeed Meo and Karim, 2022). However, a completely green financial system should not only contain a single indicator, but should also be a comprehensive indicator that includes economic reform, economic transformation, and the combination of environmental benefits (Lv et al., 2021). Therefore, Wang et al. (2021b) use the improved fuzzy comprehensive evaluation method combined with the relevant statistical indicators of China’s green credit to evaluate the development level of China’s green finance. Yang et al. (2021) measure green finance by integrating green credit, green investment, green insurance, green securities, and carbon finance.

Based on the establishment of comprehensive indicators, many scholars have conducted in-depth explorations on the influence of green finance on the global economy and environment (Wang and Zhi, 2016; Li and Gan, 2021; Zhang et al., 2021; Dong et al., 2022). For example, Zhou et al. (2020) point out that green finance can significantly balance the relationship between economic development and environmental quality, and achieve a win-win situation between economic development and the environment. Some scholars have also explored the nexus between green finance and energy security (Sachs et al., 2019a; Sachs et al., 2019b), green finance and non-fossil energy use (Ren et al., 2020), and green finance and energy efficiency (Jin et al., 2021). All of the above studies have shown that green finance supports green and sustainable development and contributes to the progress of the energy system towards a cleaner, more efficient trajectory.

2.2 Studies on the Green Finance-CO₂ Emissions Nexus

Green finance aims to finance green industries, help improve environmental quality, and promote technological innovation in green industries (La Rovere et al., 2018). Therefore, it is not difficult to guess that green finance will have a significant impact on CO₂ emissions. In fact, many scholars have proved the above hypothesis. Globally, Saeed Meo and Karim (2022) study the relationship between green finance and greenhouse gases in major economies that support green finance. Their results show that green finance negatively affects CO₂ emissions. Moreover, from the perspective of a specific country, the nexus between green finance and CO₂ emissions is also a popular subject of research (Xiong and Sun, 2022). For example, Tran (2021) uses multivariate time series analysis to investigate the relationship between green finance and CO₂ emissions in Vietnam. Their results show that there is a one-way causal negative relationship between green investment and CO₂ emissions. In addition, some scholars have explored the sensitivity of CO₂ emissions in a certain industry to green finance. For example, Guo et al. (2022) measure the relationship between green finance and agricultural CO₂ emissions. Their results show that green finance can negatively affect agricultural CO₂ emissions. Gholipour et al. (2022) explore the relationship between green property finance and CO₂ emissions in the construction industry. Their results indicate an obvious negative correlation between the above two factors.

2.3 Literature Gaps

The discussion on green finance has gradually matured in the international community, and research on the green finance-CO₂ emissions nexus has recently emerged. However, we still find knowledge gaps in the existing literature: First, there is no unified standard for measuring green finance, which leads to significant differences in the conclusions of relevant researches on green finance. Second, scholars rarely discuss the nexus between green finance and CO₂ emissions in China. Third, the influence mechanism of green finance on CO₂ emissions is still unclear, and most scholars have failed to show a clearer path for mechanism analysis.

3 Empirical Model and Data Sources

3.1 Empirical Model

To quantitatively interpret the CO₂ emission reduction effect of green finance development in China, we build an econometric model in the following framework:

$C{O}_{2it}=f(GF{I}_{it},Pgd{p}_{it},E{E}_{it},IS{U}_{it},Tr{a}_{it},Ed{u}_{it},Wag{e}_{it},Ga{p}_{it})(1)$

where i denotes Chinese 30 provinces, and t refers to the time (2004–2018). CO₂ means the total amount of CO₂ emissions, and GFI indicates green finance. Pgdp, EE, ISU, Tra, Edu, Wage, and Gap represent economic growth, energy efficiency, industrial structure upgrading, trade openness, education level, wage level, and income inequality, respectively. $f(·)$ is a function.

To effectively address the problems of dimensional inconsistency of the variables and data fluctuation, we apply the natural logarithm of Eq. 1, as follows:

$\ln C{O}_{2it}={\alpha }_0+{\alpha }_1\ln GF{I}_{it}+{\displaystyle \sum _{k=2}^8{\alpha }_k\ln Ctr{l}_{it}}+{\eta }_t+{\nu }_i+{\epsilon }_{it}(2)$

where ${\alpha }_0$ represents the constant term, ${\eta }_t$ refers to the time-specific effect, ${\nu }_i$ denotes the province-specific effect, and ${\epsilon }_{it}$ indicates the error term. ${\alpha }_1-{\alpha }_8$ are the coefficients that need to be gauged. Ctrl refers to the control variables (i.e., Pgdp, EE, ISU, Tra, Edu, Wage, and Gap). It is worth noting that the implementation of green finance policies aims to guide financial capital from polluting enterprises or projects to enterprises or projects that actively manage the environment; thus, we expect the coefficient of green finance (i.e., ${\alpha }_1$) to be negative.

To address the impact mechanism between green finance and CO₂ emissions, based on the conventional and commonly used theory of three effects of CO₂ emissions proposed by Copeland and Taylor (1994), we choose economic effect, technical effect, and structural effect as mediating variables to further explore whether green finance influences CO₂ emissions by affecting regional economy, technology, and industrial structure. The specific equations of the mediation effect model are constructed as follows:

$\ln M_{it}={\phi }_0+{\phi }_1\ln GF{I}_{it}+{\displaystyle \sum _{k=2}^5{\phi }_k\ln Ctr{l}_{it}}+{\eta }_t+{\nu }_i+{\epsilon }_{it}(3)$

$\ln C{O}_{2it}={\xi }_0+{\xi }_1\ln GF{I}_{it}+{\xi }_2\ln M_{it}+{\displaystyle \sum _{k=3}^6{\xi }_k\ln Ctr{l}_{it}}+{\eta }_t+{\nu }_i+{\epsilon }_{it}(4)$

where the parameters in front of the variables are estimated coefficients. M in these equations denotes economic effect, technical effect, and structural effect, respectively, which are gauged by economic growth, energy efficiency, and industrial structure upgrading, respectively. In these equations, ${\xi }_1$ refers to the direct effect, the product of ${\phi }_1$ and ${\xi }_2$ indicates the mediation effect, and ${\alpha }_1$ represents the total effect.

Furthermore, to investigate the asymmetric effect between green finance and CO₂ emissions, we construct the quantile regression model following the estimation framework of Coad and Rao (2006) and Kang et al. (2021):

$Q_{\tau }\left(\ln C{O}_{2it}\right)={\varphi }_{\text{0}\tau }+{\varphi }_{1\tau }\ln GF{I}_{it}+{\displaystyle \sum _{k=2}^8{\varphi }_{k\tau }Ctr{l}_{it}+{\epsilon }_{it}}(5)$

where the dependent variable $Q_{\tau }\left(\ln C{O}_{2it}\right)$ is the ${\tau }^{th}$ distribution quantile of CO₂ emissions; ${\varphi }_{0\tau }\cdots {\varphi }_{8\tau }$ are the estimated coefficients at quantile $\tau$.

3.2 Variables Setting

3.2.1 Explained Variable

CO₂ emissions (denoted as CO₂). China’s official statistics bureau or yearbook currently does not directly publish research data on CO₂ emissions. Generally speaking, most scholars use data on CO₂ emissions caused by fossil energy consumption, which can more accurately assess the greenhouse effect caused by CO₂ emissions (Wang et al., 2021a; Wang et al., 2021b).

3.2.2 Explanatory Variables

Since China put forward the concept of green finance in 2016, many scholars have launched a series of discussions and measures on green finance; however, there is no unified standard for gauging green finance at present. Thus, to effectively assess green finance in China, we refer to the indictor system of Jiang et al. (2020), which consists of three dimensions (i.e., economy, finance, and environment); the specific second-level indicators are listed in Supplementary Appendix Table S1 in the Supplementary Appendix S1. By using the improved entropy method, we gauge the provincial composite index of green finance for the period 2004–2018; the specific calculation steps can refer to the work of Zhao et al. (2021).

On this basis, we further conduct a spatio-temporal analysis on China’s green finance. In Figure 1, we display the time trend chart of average values of green finance from 2004 to 2018; obviously, before 2011, green finance showed a substantial upward trend and reached its peak in 2011. Thereafter, it gradually declined and then rose slowly between 2016 and 2018. In addition, Figure 3 presents the spatial pattern of China’s green finance in 2004, 2009, 2014, and 2018. These charts imply that green finance shows a wide range of heterogeneous distribution. More specifically, the phenomenon of green finance in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, Pearl River Delta, and inland western provinces is obviously better than that in the central region. A possible reason is that the relatively prosperous coastal provinces such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta usually have a sound and complete financial system and relatively frequent financial investment activities. The inner western provinces have a favorable ecological environment despite their relatively backward financial development.

FIGURE 3

FIGURE 3. Spatial distribution of the green finance index in selected years.

3.2.3 Control Variables

In addition to CO₂ emissions and green finance, we also introduce some control variables which influence the CO₂ emissions. Referring to Bano et al. (2018), Wang and Zhang (2021), and Zhao et al. (2021), we introduce economic growth, energy efficiency, industrial structure upgrading, trade openness, education level, wage level, and income inequality as the determinants of CO₂ emissions.

3.2.4 Mediator Variables

We select three mediator variables, namely economic effect, technical effect, and structural effect. Among them, referring to Zhang et al. (2020), we use the per capita GDP as the proxy variable of economic effect. Referring to Wang et al. (2022), we use the energy efficiency as the proxy variable of technical effect. And referring to Luan et al. (2021), we use the ratio of output value of tertiary industry to GDP as the proxy variable of structural effect. And their definition and units are listed in Table 1.

TABLE 1

TABLE 1. Definitions and descriptive statistics of the selected variables.

3.3 Data

The explained variable (CO₂ emissions) is obtained from the China Emission Accounts and Datasets (CEADs, 2019). The key explanatory variable (green finance) is calculated by three dimensions—economy, finance, and environment—and the relevant data have been collected from the China Statistical Yearbook (CSY, 2021), the China Regional Financial Operation Report, and the China Environment Statistical Yearbook. Furthermore, CSY (2021) and the China Energy Statistical Yearbook provide the data on the control variables and mediator variables. The specific measures and descriptive statistics of variables used are presented in Table 1.

4 Empirical Findings

4.1 Correlation Check

Before examining the estimated results between green finance and CO₂ emissions, we first check the correlations of all the variables, and present their results in Table 2. Except for lnTra, the correlation tests between the other variables and the dependent variable (lnCO₂) are all significant at the 5% significance level, which indicates that the variables selected in this paper are reliable. Moreover, lnISU and lnGap are negatively correlated with the dependent variables, which indicates that industrial upgrading and income inequality may negatively affect CO₂ emissions, but more accurate results require further estimation. In addition, most of the correlation test results between the variables are less than 0.8, which indicates that there is no serious multicollinearity issue between the variables.

TABLE 2

TABLE 2. Results of the correlation check.

4.2 Benchmark Estimates

Next, we examine the correlation between green finance and CO₂ emissions, and show the results in Table 3. We apply the feasible generalized least squares (FGLS) method to estimate. FGLS generates estimates that are dependent on the disturbance covariance matrix estimations as well as any estimated autocorrelation parameters (Gulnaz and Manglani, 2022). When the exact form of data heteroskedasticity is known, FGLS is the most suitable model, which is resistant to any type of heteroscedasticity. Also, we set three tests for the FGLS model—the Wald test, the Wooldridge test, and the CD test. Their results show that the null hypotheses are all rejected at the 1% level, indicating that the panel data have groupwise heteroskedasticity, first-order autocorrelation, and cross-sectional dependence (Shahzad et al., 2018). Therefore, the FGLS method can solve the above problems and obtain unbiased and consistent estimators.

TABLE 3

TABLE 3. Estimated results of the impact of green finance on CO₂ emissions.

The results of the FGLS method show that the coefficient of green finance is always negative, regardless of whether control variables are added to the model. In other words, green finance negatively affects CO₂ emissions in China. Guo et al. (2022) and Saeed Meo and Karim (2022) also draw the same conclusion by examining the agricultural sector and the top ten economies. Green finance focuses on investment in green-related industries and encourages companies to develop innovative technologies that are conducive to low-carbon energy, such as renewable energy technologies (Saeed Meo and Karim, 2022). China has basically established an overall green financial framework such as green bonds, green insurance, green credit, and so on (Guo et al., 2022). This framework will guide consumers and enterprises to establish the concept of green consumption and reduce the generation of carbon footprint by supporting domestic green-friendly projects (Yang et al., 2021). At the same time, local governments have accelerated the construction of the green financial industry, including establishing green financial reform and innovation pilot zones, accelerating the green transformation of local economies, and providing subsidies for the transformation of high-polluting enterprises, thereby reducing fossil energy consumption and CO₂ emissions (PBC, 2019).

As for the control variables, economic growth effectively promotes CO₂ emissions, which can be attributed to industrial expansion and energy consumption brought about by economic growth. Energy efficiency significantly reduces CO₂ emissions, a fact confirmed in Liu et al. (2019) and He et al. (2021). This is because improved energy efficiency represents advanced technology, which reduces the energy consumption per unit of economic output, thus contributing to the reduction of CO₂ emissions. Industrial structure upgrading has also significantly reduced CO₂ emissions. By guiding the upgrading of strategic emerging industries, China has shifted the country’s industrial focus from secondary industry to tertiary industry. The expansion of service industries and high-tech industries has effectively alleviated the large consumption of energy and reduced CO₂ emissions (Wu et al., 2021). Finally, education level and wage level are significant; the former positively promotes CO₂ emissions, while the latter negatively affects CO₂ emissions.

4.3 Robustness Tests

4.3.1 Alternative Estimated Method

In the case of small sample sizes, an econometric model may produce inconsistent estimates; and due to the high correlation among macroeconomic variables, independent variables are correlated with random error terms. Selecting appropriate instrumental variables (IV) can solve the above issues. In general, the IV needs to satisfy the assumption that it should correlate highly with endogenous dependent variables and not correlate with random error terms. Lewbel (2012) constructs IV using heteroscedasticity, and the estimated results are represented in Table 3. The test of Hansen J rejects the null hypothesis, and the IV selected in this method is suitable. The coefficient of green finance is significantly negative, and the benchmark result is robust.

4.3.2 Alternative Explained Variables

Further, we replace the dependent variables with sulfur dioxide (SO₂) emissions and chemical oxygen demand (COD) emissions. Similar to CO₂ emissions, these two pollutants are also the targets of key emission reductions in China (Liu and Wang, 2017); their estimated results are listed in Table 4. Green finance negatively affects SO₂ and COD emissions, which is consistent with the results of the benchmark regression. This further confirms the robustness of the benchmark regression results.

TABLE 4

TABLE 4. Robust results of alternative explained variables.

5 Further Discussion

5.1 Heterogeneous Analysis

5.1.1 Geographic Heterogeneity

In the last section, we conducted the benchmark regression on the CO₂ emission reduction effect of China’s green finance. Notably, the spatial patterns of China’s CO₂ emissions and green finance imply significant geographic heterogeneity across different provinces. To this end, in this section, we proceed to investigate the regional heterogeneous effects of green finance on CO₂ emissions by dividing the whole sample into three subsamples—the eastern, central, and western regions. The specific provinces of these three regions are listed in Supplementary Appendix Table S2.

Also using the FGLS technique, we evaluate the CO₂ emission effect of green finance in these three regions, and present the corresponding results in Table 5. More specifically, in the eastern region, each 1% increase in green finance will result in a 0.619% reduction in CO₂ emissions. This finding verifies the effective role of green finance popularization in the eastern coastal provinces of China in alleviating the greenhouse effect. The superior geographical location and convenient transportation of our eastern provinces have contributed to the rapid economic growth and basic perfection of financial institutions in this region. In response to the governments’ call for environmental protection and the achievement of the provinces’ own green and sustainable development, financial institutions have begun to invest their capital in low-carbon technological innovation projects, which is conducive to alleviating the deteriorating ecological environment.

TABLE 5

TABLE 5. Estimated results of geographic heterogeneous analysis.

Consistent with the eastern region, the evolution of green finance and CO₂ emissions also shows a significant negative correlation. Table 5 shows that an increase of green finance by 1% can reduce CO₂ emissions by 0.574%. The CO₂ emission-reduction effect of green finance in the eastern region is significantly better than that in the central region. On the contrary, the widespread advocacy of green finance policies in the western region is not a powerful weapon for mitigating greenhouse gas emissions. The results in Table 5 illustrate that a 1% increase of green finance can increase CO₂ emissions by 0.205%. As we all know, the rugged geographical location and harsh climate in the western provinces limit population agglomeration and economic evolution, and the scattered pollution and backward economic system hinder the extensive distribution of financial institutions and the continuous perfection of the financial system, which is not conducive to alleviating the greenhouse effect.

5.1.2 Heterogeneity of CO₂ Emissions

In addition to geographical heterogeneity, we further examine the differential causal linkage between green finance and the greenhouse effect under different quantiles of CO₂ emissions. The corresponding results are listed in Table 6. Furthermore, this study displays the variation characteristics of green finance and control variables under different quantiles (Figure 4).

TABLE 6

TABLE 6. Estimated results of the heterogeneity of CO₂ emissions.

FIGURE 4

FIGURE 4. Change in panel quantile regression coefficients. Notes: The x-axis represents the conditional quantiles of CO₂ emissions and y-axis denotes the coefficient values of various variables. The red line indicates the coefficient values of panel data model with fixed effect.

As this table shows, although under different quantiles, green finance contributes to the mitigation of greenhouse gas emissions, the magnitude of its impact varies. At the 10th quantile of CO₂ emissions, green finance plays the largest role in promoting carbon reduction, followed by the effect in the 25th quantile. This suggests that the carbon reduction effect of green finance is particularly pronounced in regions with relatively low CO₂ emissions. Furthermore, at the 90th and 75th quantiles, we find that the coefficients of green finance are −1.050 and −1.007, respectively, while at the 50th quantile, an increase of green finance by 1% can reduce CO₂ emissions by 0.809%. The above analysis emphasizes that both green finance and CO₂ emissions at the 10th, 25th, 50th, 75th, and 90th quantiles exhibit a U-shaped feature. Actively guiding the green investment of financial institutions is an effective measure to manage the increasingly deteriorating ecological environment.

5.2 Mediating Analysis

In Section 4, we perform a systematic analysis on the specific and heterogeneous impacts of green finance on China’s greenhouse effect; thus, an interesting question ignites our consideration—through what channels does green finance reduce CO₂ emissions?

By employing the Sobel test and bootstrap sampling method simultaneously, we estimate the above three equations, and present the estimated results in Table 7. In terms of the economic effect, the value of the Sobel test is 0.153, which is significant at the 1% level. This test underscores the effective mediating role of economic growth. Moreover, from the first three columns of Table 7, the coefficients of green finance from column (1) to column (3) are −1.225, 0.095, and −1.377. This implies that while the development of green finance policies in China directly hinders the intensification of the greenhouse effect, it will increase greenhouse gas emissions by facilitating economic growth, and the proportion of total effect that is mediated by economic growth is 12.5%. Green finance policies, while promoting the transfer of investment to environmentally friendly enterprises, also facilitate the corresponding facility construction and productivity improvement, thus boosting economic growth; however, economic growth is highly dependent on fossil fuel burning, thus exacerbating the greenhouse effect. Furthermore, in the bootstrap test, _bs_1 and _bs_2 refer to the indirect and direct effects, respectively. And the confidence intervals do not contain 0, which further verifies the reliability of the mediation effect of economic growth on the green finance-CO₂ emissions nexus.

TABLE 7

TABLE 7. Estimated results of the mediation effects.

Regarding the technical effect, the value of the Sobel test is 0.157, and is significant at the 1% level; this finding confirms the effectiveness of energy efficiency in affecting the green finance-CO₂ emissions nexus. Specifically, the coefficients of green finance in column (4) and energy efficiency in column (5) are −0.335 and −0.467, respectively. This suggests that the development of green finance cannot enhance energy efficiency and thus reduce greenhouse gas emissions, and the proportion of total effect that is mediated is 12.8%, which is contrary to the findings of Rasoulinezhad and Taghizadeh-Hesary (2022) and Yu et al. (2022). This may be because the implementation of green finance aims to apply economic leverage to guide capital to gradually withdraw from high-energy-consuming enterprises and integrate into energy-saving projects, so as to facilitate the low-carbon allocation of financial resources. However, the ambiguity of credit standards among financial institutions in the early stage of implementation of the policy leads to the phenomenon of “bad money driving out good money” in unfair competition, resulting in an unreasonable layout of green finance and producing the opposite effect on energy efficiency. In addition, a check of the bootstrap test confirms the mediating role of energy efficiency.

The Sobel test of the structural effect implies the validity of the mediating role of industrial structure upgrading. To be specific, the coefficients of green finance in column (6) and industrial structure upgrading in column (7) are 0.553 and −0.997, respectively, which suggests that green finance can effectively facilitate the optimization and upgrading of the existing industrial structure, thereby alleviating the greenhouse effect. Moreover, the proportion of total effect that is mediated is 45%. This ratio suggests that industrial structure upgrading has the most obvious mediation effect between green finance and CO₂ emissions. In summary, we can conclude that China’s green finance not only directly mitigates CO₂ emissions, but also affects the greenhouse effect by promoting economic growth, restraining energy efficiency improvement, and facilitating industrial structure upgrading. We also draw a mechanism diagram to illustrate the above relationships (Figure 5).

FIGURE 5

FIGURE 5. The impact mechanism between green finance and CO₂ emissions.

6 Conclusion and Policy Implications

6.1 Conclusion

Based on China’s provincial panel dataset from 2004 to 2018, this paper estimates the impact of green finance on CO₂ emissions using the FGLS method, and further explores their heterogeneity and mediating effect. Accordingly, we have drawn the following main conclusion:

1) China’s green finance negatively affects CO₂ emissions, which indicates that China’s green finance investment is conducive to the country’s carbon mitigation process. Moreover, the benchmark regression results pass the robustness test of the alternative estimation method and dependent variables.

2) The results of the heterogeneous analysis suggest that green finance has a heterogeneity impact on China’s CO₂ emissions. Specifically, green finance negatively affects CO₂ emissions in eastern and central China; in addition, green finance has a stronger negative impact on CO₂ emissions in regions with a lower level of emissions.

3) The estimated results of the Sobel test and bootstrap test indicate that China’s green finance not only helps mitigate the greenhouse effect directly, but also can affect CO₂ emissions by promoting economic growth, inhibiting energy efficiency improvement, and accelerating the optimization and transfer of the current industrial structure.

6.2 Policy Implications

Based on the above three findings, we propose the following policy implications. First, given the negative effect of green finance on the greenhouse effect, it is imperative to strengthen the development of green finance. At present, China’s green finance is still in an exploratory stage, so promoting the sustainable and stable development of green finance is crucial. To be more specific, first, local governments should pay attention to comprehensive and specific planning for the overall development of green finance, and establish and improve legal guarantees for green finance. Enterprises that pay attention to environmental protection will be provided with preferential credit or tax policies, and capital will be guided into green financial investment through cooperation between the government and private capital. Second, in addition to government control and guidance, relevant departments should strengthen the supervision of green bonds and improve enterprises’ social responsibility in the field of green finance. In addition, environmental protection departments can use advanced technologies such as cloud computing or big data to disclose corporate environmental information and promote timely access and the effective transmission of relevant information by financial institutions. Credit investigation departments should establish an enterprise credit investigation system, and financial institutions should provide timely feedback on green finance-related information to environmental protection departments. Third, comprehensive financial human capital is key to promoting the effective implementation and stable development of green finance. Thus, China should focus on training financial talents, accelerate the transfer and upgrading of intermediate business models, establish scientific talent-training plans, and help employees in the financial industry to carry out financial knowledge training in colleges and universities.

Second, the empirical results of geographic heterogeneity and CO₂ emissions heterogeneity show that provincial governments should introduce or formulate policies and regulations related to green finance or CO₂ emission reduction according to actual local conditions. To be more specific, in the prosperous eastern coastal provinces, especially in specific economic agglomeration areas such as the Beijing-Tianjin-Hebei area, the Yangtze River Delta, and the Pearl River Delta, financial institutions and the policy system are relatively sound. On the basis of the continuous promotion of green finance, the eastern provinces should focus on the agglomeration of technology, human capital, material capital, and public resources, establish financial centers, and give play to their spatial diffusion and radiation functions to drive the rapid evolution of financial institutions in the central and western regions. Provinces in the central region should capitalize on the advantages of the regional economy, actively implement green finance policies, and fully tap the supporting role of green finance in reducing CO₂ emissions. In the economically backward western provinces, the excellent ecological environment in this area should be used to actively exploit the potential role of green finance to overcome obstacles. On the basis of drawing on the advanced experience of green finance in the eastern and central regions, the green credit process and a scheme suitable for local development are being formulated in combination with the actual features of the local region, and the rapid evolution of green finance in the western region will be actively promoted.

Third, the mediation effects of economic, technical, and structural effects imply that the industrial structure optimization effect of green finance has been significantly verified, but its promotion effect on energy efficiency has not been fully explored. Thus, it is vital to further guide and strengthen green finance for investment in tertiary industries with high added value and low pollution. Furthermore, local government should comprehensively determine credit standards to avoid deviations caused by vague and inconsistent investment standards. In addition, financial institutions should strengthen financial investment in enterprises actively engaged in the research and development of green, low-carbon technologies, and provide financial support for environmental protection projects.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author Contributions

JW: validation, visualization, writing—original draft, investigation, resources, and data curation. YM: conceptualization, methodology, software, writing—review and editing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.931086/full#supplementary-material

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