How Green Finance Drives the Synergy of Pollution Reduction and Carbon Mitigation: Evidence from Chinese A-Share Firms

Abstract

As a pivotal instrument for integrating environmental governance with a low-carbon transition, green finance plays a critical role in achieving China’s dual-carbon goals. This study draws on a panel dataset covering 2008–2023, combining city-level indices of green finance development with firm-level emissions data from Chinese A-share listed companies. It investigates how green finance influences firms’ ability to reduce pollution and carbon emissions in a coordinated way, as well as the mechanisms and boundary conditions of this relationship. The results reveal that green finance significantly enhances firms’ synergistic performance in pollution and carbon abatement. The effect operates mainly through two channels: reallocating resources more efficiently and strengthening ESG performance. The benefits are particularly evident among firms with a stronger green innovation capacity, higher levels of carbon market participation, and more advanced environmental management systems. Green finance also helps deter corporate greenwashing. In addition, financial technology and environmental information disclosure amplify its positive impact. These findings highlight the need to deepen the integration of ESG evaluation with capital allocation and to design green financial instruments suited to firms at different stages of transition. They also point to the importance of harnessing the complementarities of fintech and environmental transparency to further enhance firms’ sustainable performance.

1. Introduction

Globally, governments and institutions are intensifying efforts to address environmental challenges arising from escalating pollution and carbon emissions [1,2]. According to the World Meteorological Organization, the global average atmospheric CO₂ concentration reached 420.0 ppm in 2023. This is the highest level since the systematic observations began. To confront the persistent rise in emissions, the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: 2019 Refinement emphasizes the strategic importance of jointly constructing national inventories for greenhouse gases and air pollutants [3]. In response, China has introduced the implementation plan for the synergistic enhancement of pollution and carbon reduction, actively exploring integrated pathways to mitigate both carbon emissions and conventional pollutants [4]. Nevertheless, progress at the subnational level remains uneven. The Assessment Report on Synergistic Management of Urban CO₂ and Air Pollution in China Cities (2020) reveals that only about one-third of Chinese cities achieved simultaneous reductions in CO₂ and major air pollutants between 2015 and 2019 [5]. Consequently, enhancing the synergy between pollution abatement and carbon reduction has become a critical policy priority for advancing China’s sustainable development agenda.

The concept of synergy effects was introduced as early as 2001, underscoring that greenhouse gas mitigation measures can simultaneously generate co-benefits in air pollution control [6]. Since then, studies have developed evaluation frameworks for synergistic pollution and carbon reduction performance (SPC), providing quantitative assessments at the provincial, urban, and industrial levels [7,8,9]. However, enterprise-level analysis remains limited. This gap constrains the precision of environmental policy design aimed at optimizing synergies between carbon and pollution abatement. Enterprises are among the primary sources of emissions [10]. Their potential for the integrated control of pollutants and carbon emissions has not been fully leveraged, despite their crucial role in advancing systemic environmental governance and sustainable development [11]. Accordingly, investigating the mechanisms and pathways of SPC enhancement from a micro-level corporate perspective is not only urgent but also indispensable for informing more effective policy interventions.

In recent years, green finance has emerged as a market-based instrument for environmental governance and has been widely applied to address pollution and climate change. Compared with administrative measures, market-oriented tools often demonstrate greater policy effectiveness [12]. In 2016, China issued the Guidelines for Establishing a Green Financial System, which explicitly defined green finance as “economic activities that support environmental improvement, climate change mitigation, and resource conservation and efficiency” [13]. This includes green loans, green bonds, green stock indices and related products, green development funds, green insurance, and carbon finance. At the macro level, empirical evidence has shown that green finance contributes positively to enhancing national ESG performance [14], facilitating the transition to sustainable energy [15], improving green economic efficiency [16], optimizing environmental quality [17], reducing PM_2.5 pollution [18], and curbing CO₂ emissions [19]. For instance, green finance policies have significantly strengthened urban environmental governance by promoting technological innovation, expanding the wastewater treatment capacity, and advancing waste management [20]. Nevertheless, its implementation continues to face challenges, including definitional inconsistencies, inadequate policy coordination, and weak enforcement continuity [21]. Ren et al. [22] further argue that the environmental impacts of green finance policies tend to lack persistence. These concerns raise a critical question: can green finance systematically advance the synergistic objectives of pollution mitigation and carbon reduction in China? If so, through what mechanisms? Answering this question requires shifting the analytical lens from the macro to the micro level, where the heterogeneity of corporate behaviors and capabilities may fundamentally shape the effectiveness of green finance.

At the firm level, the existing research has primarily examined the impact of green finance on corporate risk [23], financial performance [24], environmental performance [25], green technological innovation [26], and green total factor productivity [27]. For example, China’s pilot programs for green finance reform and innovation have been shown to enhance corporate ESG performance by alleviating financing constraints and strengthening social responsibility awareness [28]. However, they may also induce ESG “greenwashing” behaviors due to managerial short-sightedness and the relaxation of financing conditions [29]. In addition, some studies suggest that green finance can foster synergistic effects between pollution control and carbon reduction [30,31]. Yet, evidence at the firm level remains limited, and the underlying mechanisms have not been systematically uncovered. This gap underscores the necessity of investigating how green finance influences corporate SPC and through which channels this effect may materialize.

To bridge the aforementioned research gap, this study employs the China Urban Green Finance Development Index in conjunction with the carbon and pollutant emissions data of A-share listed firms. We empirically examine the impact of green finance on corporate SPC and explore its underlying mechanisms. Moreover, we further investigate the heterogeneity of these effects across firms with different characteristics, test whether green finance can mitigate greenwashing, and examine the moderating roles of fintech development and environmental information disclosure.

This study makes several contributions to the literature. First, while prior research has focused on either carbon or single pollutants, or assessed synergy only at the macro level, we provide a systematic corporate-level analysis of SPC. By constructing an integrated performance metric based on the concurrent reduction rates of pollutants and carbon emissions, we elucidate the intrinsic mechanisms underlying synergistic gains, thereby enriching the micro-level synergy literature.

Second, drawing upon established theoretical frameworks including the information asymmetry theory, the resource-based view, and the signaling theory, we develop a dual-core analytical framework comprising resource allocation optimization and ESG performance enhancement. It explicates how green finance shapes firms’ synergistic abatement behaviors. We also identify the heterogeneous effects. These arise from differences in the corporate green innovation capacity, carbon market participation, and environmental management system implementation. This provides theoretical foundations for differentiated policy formulation.

Third, we advance the understanding of the relationship between green finance and corporate greenwashing. We also demonstrate how fintech development and environmental information disclosure mechanisms enhance the efficacy of green finance policies. These findings provide novel empirical evidence for strengthening China’s green financial governance infrastructure.

The remainder of this paper proceeds as follows: Section 2 develops the theoretical framework and research hypotheses. Section 3 presents the empirical strategy, variable construction, and data sources. Section 4 reports baseline results, robustness tests, and mechanism analyses. Section 5 explores the heterogeneous effects. Section 6 provides additional discussions. Section 7 concludes with the policy implications.

2. Theoretical Analysis and Research Hypothesis

2.1. Direct Mechanism Analysis

As a market-based instrument that integrates environmental considerations with traditional financial functions, green finance has emerged as a vital force driving high-quality economic development. It is also expected to exert a profound influence on firms’ synergistic performance in pollution and carbon reduction [32].

From the perspective of information asymmetry theory, traditional financial models often underestimate the environmental externalities due to the asymmetric information between lenders and borrowers [33]. This leads to a chronic underinvestment in green projects. By embedding environmental assessments into credit decision-making, green finance helps internalize these externalities and corrects market failures. As a result, capital is channeled toward environmentally friendly and low-emission projects.

The resource-based view emphasizes that firms must acquire valuable, rare, inimitable, and non-substitutable resources to secure a sustained competitive advantage. Green finance, which encompasses green credit, green bonds, and sustainable funds, provides such strategic resources [34]. By lowering financing barriers through preferential interest rates, dedicated credit lines, and green bond issuance, it alleviates capital constraints faced by technology-driven and green-oriented enterprises [35]. This process not only fosters the development and application of energy-saving and carbon-reducing technologies but also strengthens firms’ pollution control and carbon mitigation capabilities at the source [36].

From the perspective of the principal–agent theory, financial institutions provide external supervision that helps restrain managerial opportunism. They employ risk management systems and environmental auditing mechanisms to monitor firms’ capital use and environmental performance. For instance, the Industrial Bank of China has established an environmental credit risk monitoring framework that integrates environmental risk assessment into the entire loan cycle [37]. These measures strengthen the compliance with environmental regulations. In addition, the stakeholder theory highlights how capital providers hold firms accountable for environmental commitments [38].

According to the signal transmission theory, green finance policies convey the government’s credible commitment to sustainable development. They send a strong signal to the market [39]. This signaling effect reinforces the legitimacy of green development. It also rewards environmentally responsible firms while constraining high-pollution enterprises. For example, green credit policies transmit regulatory signals to both capital markets and supply-chain financing. Green firms gain preferential access to financial resources, which incentivizes them to expand energy-saving and environmentally friendly projects [40]. In contrast, heavily polluting firms face tighter restrictions in debt, equity, and trade financing [41]. This dynamic accelerates the reallocation of resources toward sustainable enterprises.

Overall, green finance lowers the costs associated with corporate green transition and addresses the financing challenges of long-cycle, high-risk green projects. For instance, green credit policies provide favorable interest rates and sufficient capital to green firms [42]. Corporate green bonds also mitigate financing frictions and stimulate green investment, thereby facilitating enterprises’ green transformation [43]. Accordingly, we propose the following hypothesis:

H1: 

Green finance enhances firms’ synergistic performance in pollution and carbon reduction.

2.2. Indirect Mechanism Analysis

Green finance, through policy guidance and institutional agglomeration effects, effectively alleviates this problem of environmental information asymmetry. By internalizing environmental externalities, it corrects the resource misallocation in traditional finance. In this way, it channels environmental capital toward green technologies, and accelerates the upgrading of industrial structures [44]. On one hand, the expansion and diversification of green financial products and services provide firms with a broader access to external financing. Financial institutions offer differentiated credit conditions and innovative financial instruments that direct capital flows toward low-carbon, eco-friendly projects and enterprises. This reduces financing costs and improves capital efficiency. It also supports the growth of green industries and indirectly enhances firms’ coordinated performance in pollution and carbon reduction. On the other hand, drawing on the theory of resource reallocation, green finance influences firms’ entry and exit decisions. It mitigates the issue of financial resource misallocation [45]. Green financial policies steer capital toward high-productivity firms, especially in heavily polluting sectors. This improves the intra-industry resource allocation efficiency. As a result, more efficient firms gain market share, leading to improved environmental outcomes [46]. Accordingly, we propose the following hypothesis:

H2: 

Green finance positively influences firms’ synergistic pollution and carbon reduction performance by optimizing resource allocation.

Green finance responds to the sustainability demands of multiple stakeholders, including governments, investors, and communities. By transmitting market signals, it incentivizes firms to strengthen their environmental and social responsibilities. This, in turn, drives improvements in environmental, social, and governance (ESG) performance and enhances the synergistic effects of pollution and carbon reduction [47]. Specifically, on the environmental dimension, green finance incentivizes firms to increase their investment in environmental protection. It promotes the adoption of clean technologies and the upgrading to end-of-pipe controls. These measures enable comprehensive pollution management. On the social dimension, green financial institutions support clean investments while restricting financing for pollution-intensive activities. These actions lower firms’ environmental and social risks. They also reduce energy consumption and pollution intensity by improving social performance [48]. On the governance dimension, financial institutions participate in corporate governance practices. They guide firms to integrate environmental governance into strategic planning and operational management. This process enhances the structure and effectiveness of environmental oversight systems. These pathways collectively drive firms to intensify ESG investments and improve resource allocation efficiency. Ultimately, they enable synergistic reductions in both pollutants and carbon emissions. Thus, we propose the following hypothesis:

H3: 

Green finance positively influences firms’ synergistic pollution and carbon reduction performance by enhancing ESG performance.

The theoretical framework summarizing the proposed hypotheses is illustrated in Figure 1.

3. Research Design

3.1. Variable Definitions

3.1.1. Dependent Variable

Following the approach of Shi et al. [49], this study adopts the calculation method from the Assessment Report on Synergistic Management of Urban CO₂ and Air Pollution in China Cities (2020) to construct a ranking-based indicator (Rank) that measures firms’ synergistic performance [5]. Specifically, the indicator is derived from the combined rankings of each firm’s CO₂ emission reduction rate and SO₂ reduction rate. A higher rank reflects a stronger synergistic performance in pollution and carbon mitigation.

3.1.2. Key Independent Variable

The key explanatory variable is the green finance index (GRFI), which reflects the level of regional green finance development. Drawing on Fan et al. [50] and Wang et al. [51], the GRFI is constructed based on seven dimensions: green credit, green investment, green insurance, green bonds, green support policies, green funds, and green equity instruments. The index weights are determined using the entropy method applied to panel data, ensuring objective weighting across dimensions. Although GRFI primarily measures the development of green finance, it is also closely correlated with the regional economic performance [52], as regions with more advanced green finance systems typically demonstrate a stronger innovation capacity and higher-quality growth. To mitigate potential endogeneity concerns, we conduct three robustness checks.

3.1.3. Control Variables

To control for other factors that may influence firms’ synergistic performance, we include both firm-level and city-level control variables. At the firm level, following Li et al. [27] and Fan et al. [50], we select the following control variables. The firm size (Scale) is measured as the natural logarithm of total assets at year-end, reflecting firms’ resource endowment and scale effects. The growth capacity (Growth) is defined as the annual growth rate of the operating revenue, capturing the market expansion potential and dynamic competitiveness. The financial leverage (Lev) is measured by the ratio of total liabilities to total assets, indicating the capital structure and financial risk exposure. The profitability (Profit) is represented by the ratio of total profit to total revenue, reflecting the operational efficiency. The CEO duality (Dual) is a dummy variable equal to 1 if the CEO simultaneously serves as the board chair and 0 otherwise, capturing the concentration of managerial power. The return on assets (Roa) is calculated as net profit divided by total assets, measuring the efficiency of asset utilization. The financing constraints (Fr) are proxied by a dummy variable equal to 1 if the firm’s SA index is below the sample median, and 0 otherwise. The cash flow capacity (Cash) is defined as the operating cash flow divided by total revenue, reflecting the internal liquidity position of firms.

At the city level, following Ran and Zhang [53], we control the environmental regulation intensity (Er), measured by a weighted linear combination of the SO₂ removal rate and industrial dust removal rate, capturing the stringency of local environmental policies. We also include the economic development level (Pgdp), measured as the natural logarithm of the city-level GDP per capita, representing the regional economic development.

3.2. Data Sources and Processing

Firm-level data are primarily sourced from the CSMAR (China Stock Market & Accounting Research) database, which provides comprehensive financial and governance information for Chinese listed firms. City-level data, including green finance indicators, are obtained from authoritative national sources such as the China Financial Yearbook, China Science and Technology Statistical Yearbook, and China Urban Statistical Yearbook.

To ensure data reliability and comparability, we adopt the following data-cleaning procedures: (1) exclude samples with missing main explanatory variables; (2) exclude financial industry samples; (3) exclude firms classified as ST and PT during the sample period; (4) exclude firms with age less than one year; and (5) winsorize the top and bottom 1% of continuous variables to mitigate the influence of outliers.

After data cleaning, the final sample consists of 23,851 firm-year observations for 2075 A-share listed companies in China over the period 2008–2023. Definitions and descriptive statistics of the main variables are reported in

Table 1. Variable definitions and descriptive statistics.

Variables	Definition	Mean	SD	Min	Max
Rank	Combined ranking of CO2 emission reduction rate and SO2 reduction rate	11,926	6886	1	23,852
GRFI	Green credit, green investment, green insurance, and other seven dimensions to build a comprehensive index	0.644	0.230	0.017	1.267
Scale	Natural logarithm of total assets at year-end	22.390	1.417	14.110	28.640
Growth	Annual growth rate of operating revenue	0.204	0.480	−0.512	1.883
Lev	Total liabilities divided by total assets	0.474	0.637	0.007	3.080
Profit	Total profit divided by total revenue	0.238	0.530	−0.860	0.734
Dual	A dummy variable equal to 1 if the CEO also serves as board chair, and 0 otherwise	0.238	0.426	0.000	1.000
Roa	Net profit divided by total assets	0.056	0.0518	−0.0382	0.203
Fr	A dummy variable equal to 1 if the firm’s SA index is below the sample median, and 0 otherwise	0.569	0.495	0.000	1.000
Cash	Operating cash flow divided by total revenue	0.071	2.362	−0.463	0.499
Er	A weighted linear combination of SO2 removal rate and industrial dust removal rate	2.012	6.546	0.549	11.497
Pgdp	Natural logarithm of city-level GDP per capita	11.088	0.625	9.348	12.378

.

3.3. Model Specification

To examine the impact of green finance on firms’ synergistic performance in pollution and carbon reduction, we construct the following baseline regression model based on the theoretical framework discussed above:

$${\mathrm{Rank}}_{it}=\alpha +\beta {\mathrm{GRFI}}_{jt}+\gamma {\mathrm{Controls}}_{ijt}+\mathrm{FE}+{\epsilon }_{ijt}$$

(1)

where i, j, and t denote the firm, city, and year, respectively. ${\mathrm{Rank}}_{it}$ represents the synergistic pollution and carbon reduction performance of firm i in year t. ${\mathrm{GRFI}}_{jt}$ measures the level of green finance development in city j during year t. ${\mathrm{Controls}}_{ijt}$ denotes a set of firm-level and city-level control variables that may influence firms’ environmental performance. $\mathrm{FE}$ represents fixed effects at the firm, year, industry, and province levels to control for unobserved heterogeneity. ${\epsilon }_{it}$ is the error term. The primary coefficient of interest is $\beta$. A significantly positive estimate of $\beta$ would indicate that green finance significantly enhances firms’ synergistic pollution and carbon reduction performance, thereby supporting Hypothesis H1.

4. Empirical Results and Analysis

4.1. Baseline Regression Analysis

The baseline regression results examining the impact of green finance on firms’ synergistic pollution and carbon reduction performance are presented in

Table 2. Results of baseline regression.

Variables	(1)	(2)	(3)	(4)
	Rank	Rank	Rank	Rank
GRFI	−2342.377 ***	−1078.441 ***	−723.984 ***	−460.798 ***
	(352.201)	(400.909)	(173.200)	(154.212)
Scale		−308.653 ***		−91.470 ***
		(84.969)		(32.976)
Growth		−0.476 **		−0.341
		(0.233)		(0.217)
Lev		2970.229 ***		2155.420 ***
		(510.978)		(266.521)
Profit		−189.286		−214.630
		(329.266)		(337.094)
Dual		−95.760		137.927
		(155.575)		(94.421)
Roa		14,494.117 ***		14,353.199 ***
		(1429.671)		(1102.032)
Fr		−540.931 ***		−559.532 ***
		(122.245)		(79.325)
Cash		34.884		31.395
		(43.258)		(43.576)
Er		−52.349 **		−53.538 **
		(20.342)		(21.628)
Pgdp		34.565 **		36.649 **
		(15.282)		(16.049)
Constant	13,434.705 ***	18,061.980 ***	12,392.523 ***	13,120.815 ***
	(226.774)	(1724.563)	(119.824)	(661.698)
Firm FE	NO	NO	YES	YES
Province FE	NO	NO	YES	YES
Industry FE	NO	NO	YES	YES
Year FE	NO	NO	YES	YES
Observations	23,851	23,851	23,851	23,851
R2	0.402	0.458	0.468	0.524

Note: Robust standard errors clustered at the firm level are reported in parentheses. ** and *** denote statistical significance at the 5% and 1% levels, respectively.

. Based on the results of the Hausman test and the joint significance test (LR test), we adopt a fixed effects model for estimation.

The results in Table 2 show that the coefficient of green finance (GRFI) is significantly negative at the 1% level, regardless of whether control variables are included. Specifically, in the fully controlled model (column 4), a one-unit increase in GRFI is associated with a 460.798-rank improvement in SPC. Considering that the mean rank of SPC is 11,926, this corresponds to an improvement of approximately 3.9% in firms’ coordinated environmental performance. This indicates that higher levels of green finance development are associated with better rankings in synergistic pollution and carbon reduction performance—confirming Hypothesis H1. In other words, green finance exerts a significant positive effect on firms’ synergistic environmental performance. This finding is consistent with the theoretical mechanisms discussed earlier and can be attributed to two primary channels. First, green finance provides both capital support and technical guidance, encouraging firms to adopt cleaner technologies and improved environmental management practices. This financial support helps alleviate capital constraints, enabling firms to invest in pollution control and carbon reduction initiatives, which, in turn, enhances overall performance. Second, the implementation of green finance programs reinforces corporate attention to environmental performance and strengthens their sense of social responsibility. Consequently, firms are incentivized not only by economic returns but also by social and regulatory pressures to adopt green strategies, further improving their synergistic performance.

Regarding firm-level control variables, firm size (Scale) and financing constraints (Fr) both exhibit significantly negative coefficients at the 1% level. This suggests that larger firms and those facing tighter financing constraints tend to achieve a better synergistic environmental performance. Larger firms typically possess greater resources for environmental investment, while constrained firms may be more efficient in capital allocation, favoring environmentally responsible practices to maximize utility. In contrast, financial leverage (Lev) and return on assets (Roa) show significantly positive coefficients at the 1% level, indicating that a higher leverage and profitability are associated with lower synergistic performance. Heavily leveraged firms may reduce the investment in long-term environmental initiatives due to debt-servicing pressures, while more profitable firms may prioritize short-term returns over long-term environmental outcomes. Other firm-level control variables are statistically insignificant and do not exert a significant influence on synergistic performance.

At the city level, environmental regulation (Er) has a significantly negative coefficient, implying that stricter environmental policies effectively enhance firms’ pollution and carbon reduction performance. Stronger regulatory enforcement likely imposes external pressure on firms, compelling them to adopt greener technologies and allocate more resources to environmental governance, thereby facilitating coordinated emission reductions and industrial upgrading. In contrast, economic development level (Pgdp) exhibits a significantly positive coefficient, suggesting that firms in more developed regions tend to perform worse in synergistic environmental metrics. This may be due to a higher concentration of energy-intensive and pollution-heavy industries in these regions, which constrains marginal improvements in emissions performance. Moreover, the emphasis on economic growth in such areas may weaken firms’ incentives to invest in long-term environmental strategies.

4.2. Robustness Checks

4.2.1. Alternative Measures of the Dependent Variable

To verify the robustness of our baseline results, we re-estimate the model by constructing alternative measures of firms’ synergistic performance in pollution and carbon reduction. Specifically, we replace the original ranking based on the joint reduction rates of carbon dioxide and sulfur dioxide with four alternative composite rankings: carbon dioxide and particulate matter (Rank_Smoke), carbon dioxide and nitrogen oxides (Rank_NO), carbon dioxide and ammonia nitrogen (Rank_AO), and carbon dioxide and total nitrogen (Rank_TN). The regression results based on these alternative dependent variables are presented in

Table 3. Robustness check: alternative measures of the dependent variable.

Variables	(1)	(2)	(3)	(4)
	Rank_Smoke	Rank_NO	Rank_AO	Rank_TN
GRFI	−815.223 ***	−869.384 ***	−769.712 ***	−725.514 ***
	(170.479)	(174.735)	(167.621)	(196.358)
Constant	15,783.211 ***	14,072.303 ***	14,550.931 ***	14,279.983 ***
	(732.377)	(583.568)	(593.228)	(747.502)
Controls	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	23,851	23,851	23,851	23,851
R2	0.415	0.415	0.412	0.412

Note: Robust standard errors clustered at the firm level are reported in parentheses. *** denotes statistical significance at the 1% level.

. Across all specifications, the coefficients of green finance remain statistically significant and consistent in sign and magnitude with the baseline findings. This confirms the robustness and reliability of our core results.

4.2.2. Removing the Influence of Outliers

To address the potential biases caused by outliers, two robustness strategies were employed. First, from the perspective of cross-sectional heterogeneity, we applied a 1% winsorization to the explanatory variables. The results are presented in Columns (1) and (2) of

Table 4. Robustness check: removing the influence of outliers.

Variables	(1)	(2)	(3)	(4)
	Rank	Rank	Rank	Rank
GRFI	−727.630 ***	−463.283 ***	−703.348 ***	−405.276 ***
	(173.954)	(144.942)	(191.259)	(131.300)
Constant	12,394.966 ***	13,121.540 ***	12,375.958 ***	12,997.765 ***
	(120.182)	(661.682)	(128.678)	(822.020)
Controls	NO	YES	NO	YES
Firm FE	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	23,651	23,651	21,776	21,776
R2	0.403	0.424	0.403	0.423

Note: Robust standard errors clustered at the firm level are reported in parentheses. *** denotes statistical significance at the 1% level.

. Second, from the temporal dimension, to mitigate the impact of the COVID-19 pandemic on the estimation results, observations from the year 2020 were excluded. The corresponding results are reported in Columns (3) and (4) of Table 4. Across all specifications, the coefficients of green finance remain consistent in sign and significance with the baseline findings, confirming the robustness of the main results.

4.2.3. Alternative Estimation Methods

To further test the robustness of our findings, we categorize firms’ pollution and carbon reduction performance into three groups based on their joint rankings in CO₂ emission reduction and SO₂ reduction: Class I (the CO₂ reduction rate and SO₂ reduction rate are both positive and ranked as optimal); Class II (the CO₂ reduction rate and SO₂ reduction rate are one positive and one negative, and ranked in the middle); and Class III (the CO₂ reduction rate and SO₂ reduction rate are both negative).

We then employ a multinomial Logit model, a generalized ordered Logit model, and a multinomial Probit model to re-estimate the effect of green finance on the probability of firms being classified into each performance category. The regression results are reported in

Table 5. Robustness check: alternative estimation methods.

Variables	(1)	(2)	(3)
	Multinomial Logit Model	Generalized Ordered Logit Model	Multinomial Probit Model
GRFI	−0.158 ***	−0.158 ***	−0.102 **
	(0.054)	(0.054)	(0.042)
Constant			−1.467 ***
			(−6.26)
Controls	YES	YES	YES
Observations	23,851	23,851	23,851
chi2	28.37 ***	22.21 ***	96.07 ***

Note: Robust standard errors clustered at the firm level are reported in parentheses. ** and *** denote statistical significance at the 5% and 1% levels, respectively.

.

As shown, the signs and significance levels of the green finance coefficients remain consistent across different model specifications, aligning with the baseline results. These findings reinforce the robustness and reliability of our main conclusion.

4.3. Endogeneity Test

4.3.1. Instrumental Variables and Two-Stage Least Squares (IV-2SLS)

To mitigate the issues of omitted variable bias and reverse causality, we construct an interaction term between the logarithmic shortest distance from each city to the nearest coastal port and the lagged national total volume of loans issued by financial institutions. This interaction term is used as an instrumental variable for the green finance index. We then estimate the model using two-stage least squares, and the results are reported in Column (1) of

Table 6. Results of endogeneity analysis.

Variables	(1)	(2)	(3)
	IV-2SLS	DML	GMM
GRFI	−675.919 **		−787.156 ***
	(271.879)		(253.351)
Treat × Post		−832.494 ***
		(212.779)
Constant	13,698.72 ***	7062.276 ***	13,726.139 ***
	(844.071)	(2031.281)	(844.113)
Controls	YES	YES	YES
Firm FE	YES	YES	YES
Province FE	YES	YES	YES
Industry FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	23,851	23,851	23,851
R2	0.520	0.519	0.521

Note: Robust standard errors clustered at the firm level are reported in parentheses. ** and *** denote statistical significance at the 5% and 1% levels, respectively.

.

As shown, the coefficient of GRFI remains significantly negative at the 5% level, consistent with the baseline regression results. This confirms that green finance significantly enhances firms’ pollution and carbon reduction performance, and the conclusion holds under IV estimation, suggesting a strong robustness to endogeneity concerns.

4.3.2. Double Machine Learning (DML) Under Policy Shock

To overcome the “curse of dimensionality” and multicollinearity issues often encountered in high-dimensional datasets, we adopt a double machine learning (DML) approach, following the methodology of Chernozhukov et al. [54]. Specifically, we exploit the exogenous policy shock of the establishment of Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment and implement the following DML model for causal inference:

$$\begin{array}{l}{\mathrm{Rank}}_{it}=\theta \left({\mathrm{Treat}}_{ij}\times {\mathrm{Post}}_{jt}\right)+g\left(\mathrm{X}\right)+u_{it}\\ {\mathrm{Treat}}_{ij}\times {\mathrm{Post}}_{jt}=f\left(\mathrm{X}\right)+v_{it}\end{array}$$

(2)

Here, ${\mathrm{Treat}}_j$ and ${\mathrm{Post}}_t$ are policy and time dummy variables, respectively. u and v are error terms, both with conditional means of 0. $g\left(\mathrm{X}\right)$ and $f\left(\mathrm{X}\right)$ are unknown high-dimensional functions of control variables, approximated using machine-learning algorithms. $\theta$ measures the causal impact of green finance on firms’ pollution–carbon reduction synergy performance. The estimation follows the orthogonalization principle of Robinson [55], ensuring consistency via bias-corrected moment conditions.

The results in Column (2) of Table 6 show that the estimated treatment effect (Treat × Post) remains significantly negative at the 1% level, reaffirming the baseline conclusion. Even after accounting for the potential model misspecification and algorithmic bias through DML, the finding that green finance policies significantly improve firms’ pollution–carbon coordination performance in the short term remains strongly supported.

4.3.3. Dynamic Panel Generalized Method of Moments (GMM)

To further address the unobserved individual heterogeneity and dynamic endogeneity, we apply the System GMM estimator to our panel data model, using the one-period lag of the green finance index (GRFI) as an internal instrument. The corresponding estimation results are presented in Column (3) of Table 6.

As shown, the coefficient of GRFI remains significantly negative at the 1% level, indicating that green finance continues to exert a statistically significant and positive effect on firms’ pollution–carbon reduction synergy performance. This result is highly consistent with our baseline regression and demonstrates that the conclusion remains robust even after accounting for the dynamic panel structure and endogeneity.

4.4. Mechanism Analysis

To avoid the potential interference from multiple mechanisms, we focus exclusively on verifying the relationship between the core explanatory variable and the proposed mediating variables. This approach helps to clarify the underlying mechanisms through which green finance influences firms’ coordinated performance in pollution and carbon reduction. As previously discussed, green finance is expected to promote the synergistic environmental performance primarily through two channels: resource allocation optimization and enhanced ESG performance.

4.4.1. Resource Allocation Optimization

Resource allocation optimization serves as a key channel through which green finance enhances firms’ pollution–carbon reduction synergy. By implementing policy tools such as differentiated credit pricing and mandatory environmental information disclosure, green finance prompts markets to re-evaluate firms’ environmental risks and long-term sustainability. This, in turn, forces highly polluting sectors to phase out inefficient capacities while facilitating access to low-cost funding for green technologies and clean investment projects. To empirically test this mechanism, we employ capital allocation efficiency (Source_K) and labor allocation efficiency (Source_L) as proxies for resource allocation optimization. The estimation results are presented in Columns (1) and (2) of

Table 7. Results of mechanism test.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Source_K	Source_L	Score_E	Score_S	Score_G	ESG
GRFI	0.604 ***	1.576 ***	0.610 ***	3.042 ***	1.454 ***	0.569 ***
	(0.027)	(0.031)	(0.202)	(0.272)	(0.197)	(0.146)
Constant	−1.824 ***	−0.501 ***	16.395 ***	27.759 ***	62.534 ***	44.748 ***
	(0.112)	(0.130)	(0.857)	(1.157)	(0.837)	(0.594)
Controls	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	23,851	23,851	23,851	23,851	23,851	23,851
R2	0.585	0.540	0.519	0.596	0.540	0.527

Note: Robust standard errors clustered at the firm level are reported in parentheses. *** denotes statistical significance at the 1% level.

.

As shown, the coefficients on the green finance index (GRFI) are significantly positive at the 1% level across both specifications. This indicates that green finance significantly contributes to optimizing both capital and labor allocation. It channels resources toward more efficient and environmentally friendly projects and technologies, consistent with Hypothesis 2. Such reallocation not only improves the overall resource utilization efficiency and reduces mismatches and waste, but also enhances firms’ competitiveness while encouraging greater investment in pollution and carbon reduction efforts—achieving a synergy between economic and environmental performance.

4.4.2. ESG Performance Enhancement

Enhancing ESG performance represents another key mechanism through which green finance drives firms’ coordinated pollution and carbon reduction performance. Green finance reshapes corporate valuation frameworks by embedding environmental risk pricing and signaling ESG investment preferences. This compels corporate managers to internalize environmental externalities and incorporate social welfare and governance efficiency into their long-term strategic decision-making. To empirically examine this mechanism, we adopt the Huazheng ESG Rating System, which evaluates firms’ ESG performance across three dimensions, Environmental (Score_E), Social (Score_S), and Governance (Score_G), as well as the overall ESG composite score. The corresponding estimation results are reported in Columns (3) to (6) of Table 7.

The results indicate that the coefficient of green finance (GRFI) is significantly positive at the 1% level in all models, including those based on the environmental, social, governance, and overall ESG scores. This provides strong evidence that green finance enhances firms’ ESG performance, thereby driving improvements in environmental accountability, social value creation, and internal governance practices—thus confirming Hypothesis 3. Through this mechanism, firms are more likely to incorporate pollution and carbon reduction targets into their long-term strategic agenda. They tend to upgrade production technologies, improve CSR disclosures, and optimize managerial processes. Collectively, these improvements systematically reduce pollutant and carbon emission intensities, ultimately enhancing firms’ capacity for sustainable development.

5. Heterogeneity Analysis

5.1. Firms’ Green Innovation Capacity

The green innovation capacity reflects a firm’s technological reserves and its ability to convert environmental technologies into practical applications. Firms with stronger innovation capabilities can lower the marginal cost of environmental compliance through technological breakthroughs, thus translating green finance support into substantial emission-reduction benefits. In contrast, firms with weaker innovation capabilities may struggle to overcome technological bottlenecks, resulting in delayed policy responses [56].

To capture this, we measure the green innovation capacity using the ratio of green patents to total patents (Greenp), and divide the sample based on the mean value of this variable for group regressions. The results are presented in Columns (1) and (2) of

Table 8. Results of heterogeneity analysis.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	Green Innovation Capacity		Carbon Market Participation		Environmental Management System Maturity
	High	Low	High	Low	High	Low
GRFI	−636.837 ***	−109.921	−423.530 **	234.158	−547.004 **	−290.896
	(237.574)	(246.585)	(165.860)	(1048.965)	(222.668)	(235.344)
Constant	11,370.52 ***	14,152.98 ***	13,164.6 ***	9717.876 ***	11,872.8 ***	13,152.07 ***
	(1206.664)	(1009.004)	(673.228)	(1922.768)	(985.003)	(1076.354)
Controls	YES	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES	YES
Province FE	YES	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	14,254	8936	22,008	1188	10,271	12,921
R2	0.585	0.027	0.024	0.038	0.033	0.029
Chow Test p-value	2.44 0.012		2.18 0.016		12.87 0.000

Note: Robust standard errors clustered at the firm level are reported in parentheses. ** and *** denote statistical significance at the 5% and 1% levels, respectively.

. Empirical findings reveal that the coefficient of green finance (GRFI) is significantly negative at the 1% level for firms with a high green innovation capacity, while the coefficient is statistically insignificant for firms with a lower innovation capacity. This suggests that green technologies accelerate innovation cycles and reduce the unit cost of emission reduction, thereby improving coordinated pollution and carbon reduction performance.

5.2. Green Innovation Capacity

Carbon markets internalize external environmental costs through price signals, creating a synergistic policy effect with green finance. Firms participating in carbon trading are directly constrained by emission costs and are more responsive to carbon pricing and related policy instruments. In contrast, non-participating firms lack economic incentives for emission reduction, weakening the transmission of policy effects [57].

To assess this heterogeneity, we divide the sample based on whether a firm is located in a pilot region of China’s Emissions Trading Scheme (ETS). The results are shown in Columns (3) and (4) of Table 8. For firms with a high participation in carbon markets, the coefficient of GRFI is significantly negative at the 5% level, while, for those with low participation, the coefficient is positive but not statistically significant. This indicates that ETS-participating firms can leverage mechanisms such as carbon credit trading and green credit to incentivize coordinated emission reductions. In contrast, non-participating firms may face increased compliance costs without sufficient carbon cost internalization, potentially leading to short-term efficiency losses.

5.3. Environmental Management System Maturity

Firms certified under the ISO 14001 Environmental Management System typically possess a structured and institutionalized approach to environmental governance [58,59]. These firms are better equipped to translate policy requirements into operational strategies and improve their environmental performance. Conversely, non-certified firms may lack robust internal control, resulting in a delayed or ineffective policy implementation [60].

To assess this dimension, we classify firms based on whether they have obtained ISO 14001 certification. The results, presented in Columns (5) and (6) of Table 8, show that the coefficient of GRFI is significantly negative at the 5% level for firms with mature environmental management systems, while it remains insignificant for those without certification. This finding suggests that ISO-certified firms can utilize green finance more effectively by streamlining environmental management processes, accurately assessing the net present value of emission-reduction projects, and reducing the execution costs of green investments. In contrast, firms with underdeveloped environmental management systems face inefficiencies in resource allocation and struggle to achieve synergistic emission-reduction outcomes.

6. Further Discussion

6.1. Corporate Greenwashing Behavior

Some enterprises engage in greenwashing by issuing vague or misleading environmental claims in an attempt to inappropriately gain environmental reputation premiums, financial resources, and public support under the green finance policy framework. The existing academic literature holds divergent views on the impact of green finance on greenwashing behavior. On one hand, green finance—through financing cost penalty mechanisms and mandatory information disclosure requirements—can effectively curb greenwashing behavior, particularly among state-owned and pollution-intensive enterprises [61]. On the other hand, the implementation of green finance pilot policies has been associated with a decline in actual ESG performance and no significant improvement in disclosure quality, which may instead exacerbate greenwashing tendencies, especially among highly leveraged firms or those facing the expiration of environmental subsidies [62].

To investigate whether green finance suppresses or exacerbates greenwashing behavior, this study constructs two proxy measures. First, drawing on the method proposed by Hu et al. [63], we define GW_1 as the interaction between whether the number of environmental-related keywords disclosed exceeds the industry median and whether the firm received environmental penalties in the same year. Second, following Zhang [64], we compute GW_2 as the gap between standardized ESG disclosure scores and ESG performance scores to capture the degree of greenwashing.

As shown in

Table 9. Results of corporate greenwashing behavior analysis.

Variables	(1)	(2)	(3)	(4)
	GW_1	GW_1	GW_2	GW_2
GRFI	−0.170 ***	−0.108 ***	−10.708 ***	−5.995 ***
	(0.034)	(0.029)	(1.699)	(1.202)
Constant	0.946 ***	3.099 ***	76.836 ***	177.007 ***
	(0.017)	(0.111)	(1.149)	(4.586)
Controls	NO	YES	NO	YES
Firm FE	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	23,851	23,851	8555	8457
R2	0.060	0.161	0.1081	0.370

Note: Robust standard errors clustered at the firm level are reported in parentheses. *** denotes statistical significance at the 1% level.

, the regression coefficients of green finance (GRFI) on both greenwashing measures are significantly negative at the 1% level, indicating that green finance effectively restrains corporate greenwashing behavior. This can be attributed to several mechanisms. First, green finance policies require companies to truthfully disclose environmental performance when applying for green loans or issuing green bonds, thereby reducing the scope for false claims. Second, financial resources are increasingly allocated to firms with a strong environmental performance, and greenwashing companies are more likely to face restricted access to financing or higher capital costs. Third, the development of green rating and auditing systems enhances market supervision and increases both the detection probability and costs of greenwashing. These three effects work together to pressure firms into undertaking genuine green transformations.

6.2. Fintech

Fintech, as a core tool of digital and intelligent transformation, possesses powerful capabilities in capital aggregation and information-driven operations [65]. First, by aggregating fragmented funds from long-tail investors into scalable capital, fintech facilitates the construction of a digital green finance resource management ecosystem, enhancing both the supply scale and precision of green capital deployment. Second, through the application of big data mining and analytics, fintech bridges the information gap between financial institutions and enterprises, significantly improving the targeting accuracy and allocation efficiency of green credit and investment. Third, by empowering green technological innovation and promoting the low-carbon transformation of industrial structures, fintech compels firms to prioritize green capital for clean technology R&D, thereby systematically suppressing the carbon emission intensity.

To examine the moderating effect of fintech on the relationship between green finance and firms’ pollution–carbon reduction synergy performance, this study uses web-scraping techniques to extract the frequency of 48 fintech-related keywords—covering terms such as Internet finance, business intelligence, and mobile payments—to construct a measure of the fintech development level (Ftech). An interaction term between green finance and fintech (GRFI × Ftech) is introduced into the regression model, with the results reported in Columns (1) and (2) of

Table 10. Results of moderating effects.

Variables	(1)	(2)	(3)	(4)
	Rank	Rank	Rank	Rank
GRFI × Ftech	−108.554 **	−69.800 **
	(52.304)	(33.745)
GRFI × EXP			−19.309 ***	−24.895 ***
			(5.3075)	(5.909)
Constant	12,763.337 ***	12,068.768 ***	12,091.1 ***	11,729.12 ***
	(143.804)	(633.655)	(58.397)	(647.814)
Controls	NO	YES	NO	YES
Firm FE	YES	YES	YES	YES
Province FE	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	23,851	23,851	23,851	23,851
R2	0.506	0.525	0.003	0.024

Note: Robust standard errors clustered at the firm level are reported in parentheses. ** and *** denote statistical significance at the 5% and 1% levels, respectively.

.

The regression results show that the coefficient of the interaction term (GRFI × Ftech) is significantly negative at the 5% level, indicating that fintech plays a positive moderating role in the impact of green finance on firms’ pollution and carbon reduction synergy performance. This finding suggests that fintech enhances the transparency and efficiency of green finance projects. By leveraging technologies such as big data and blockchain, fintech reduces information asymmetry and enables capital to be more accurately directed toward projects with genuine environmental benefits. Moreover, fintech simplifies financial service procedures and reduces corporate financing costs, thereby encouraging more firms to participate in pollution reduction and carbon mitigation efforts.

6.3. Environmental Information Disclosure

As a vital component of the green finance market infrastructure, corporate environmental information disclosure plays a crucial institutional role in enhancing the efficiency of financial resource allocation by mitigating the information asymmetry between investors and investees [66]. On one hand, environmentally leading firms can proactively disclose information to gain a “green certification premium,” thereby alleviating both internal and external information asymmetries and creating a virtuous cycle between emission reduction investment and financing access [67]. On the other hand, when firms face legitimacy crises due to high carbon emissions, they may adopt ambiguous disclosure strategies to obscure the actual performance and maintain their legitimacy.

To test the moderating effect of environmental information disclosure, this study divides disclosure into monetized and non-monetized components and assigns a composite disclosure score. The logarithmic value of the total score is used to measure the degree of environmental information disclosure (EXP). An interaction term between green finance and the disclosure level (GRFI × EXP) is introduced into the regression model. The results in Columns (3) and (4) of Table 10 show that the interaction term (GRFI × EXP) is significantly negative at the 1% level.

This indicates that environmental information disclosure significantly strengthens the pollution–carbon reduction effects of green finance policies. On the one hand, quantifiable and traceable environmental data provide reliable references for financial institutions to assess the environmental performance of projects, thereby lowering the evaluation costs. On the other hand, mandatory disclosure regulations create implicit reputational pressures that incentivize firms to allocate green capital toward technological innovation rather than conventional end-of-pipe governance measures, improving the marginal effectiveness of emission reduction investments.

7. Conclusions and Policy Implications

Using data from Chinese listed companies from 2008 to 2023, this paper investigates the impact of green finance on the synergistic performance of pollution reduction and carbon mitigation at the firm level.

The results show that green finance demonstrates a significantly positive effect on firms’ pollution–carbon synergy performance, a result that remains robust after addressing endogeneity concerns and conducting comprehensive robustness tests. The mechanism analysis further reveals that green finance enhances corporate environmental performance through dual channels: optimizing resource allocation efficiency and strengthening ESG performance. The policy impact is particularly pronounced among firms with a strong green innovation capacity, active carbon market participation, and mature environmental management systems. In addition, green finance serves as an effective deterrent against corporate greenwashing behaviors. Finally, fintech adoption and environmental information disclosure emerge as positive moderators, amplifying the environmental benefits of green finance.

Recent research has underscored the role of internal organizational mechanisms—such as green product innovation and transformational leadership—in enhancing firms’ sustainable performance [68]. This study complements that line of inquiry by shifting the analytical focus from internal organizational practices to external financial mechanisms. Specifically, we demonstrate that green finance can optimize resource allocation and strengthen ESG performance. As a result, it promotes synergistic reductions in pollution and carbon emissions. Furthermore, our findings extend classical theoretical frameworks, including the information asymmetry theory, the resource-based view, the principal–agent theory, and the signaling theory. They also provide an empirical validation of the mechanisms underlying these theories. Taken together, these contributions offer both valuable theoretical insights and practical guidance. They are particularly relevant for achieving China’s dual-carbon targets and fostering high-quality economic development. Based on our empirical evidence, we propose the following policy recommendations:

First, enhancing the integration between ESG performance evaluation and capital allocation mechanisms. Regulatory authorities should establish sector-specific mandatory environmental disclosure standards. These standards should require the quantitative reporting of critical metrics, particularly carbon emission data. The development of a comprehensive ESG assessment framework directly linked to financing terms would facilitate more accurate environmental risk pricing by financial institutions and capital markets. Additionally, targeted policy instruments, including green refinancing facilities and carbon-neutral bond programs, should be systematically deployed. This would redirect capital flows toward environmentally sustainable sectors and promote efficient resource reallocation.

Second, developing differentiated policy frameworks aligned with firms’ environmental capabilities. Financial institutions should design heterogeneous green financial instruments tailored to firms’ varying stages of environmental transition, such as green loans, green bonds, green insurance, green funds, and carbon finance. For example, sustainability-linked bonds and loans have been developed for traditional industries committed to a more sustainable future. These instruments directly link financing costs to firms’ environmental performance [59], providing stronger financial support for environmentally responsible enterprises.

Third, leveraging the complementarities between financial technology and environmental information systems. Advanced technological solutions can substantially enhance the efficiency of green finance allocation and monitoring mechanisms. For example, artificial intelligence algorithms can assess environmental risk, and distributed ledger technologies can verify emissions. Simultaneously, comprehensive capacity-building programs and professional certification systems should be established. These initiatives will strengthen the operational effectiveness of green finance policy implementation.