Can Green Funds Improve Corporate Carbon Performance? Firm-Level Evidence from China

Abstract

Intensifying challenges posed by global warming have elevated the urgency of improving corporate carbon performance and curbing carbon emissions. Green financial instruments serve a vital function in advancing corporate transitions toward environmentally responsible and low-carbon operational models. This research explores the influence of green funds on carbon performance at the firm level, aiming to clarify the micro-level mechanisms through which green financial instruments promote low-carbon development. The study utilizes data from Chinese listed companies spanning 2012 to 2021 and employs a TWFE regression model to empirically assess the effects. The findings indicate that green funds contribute to improved carbon performance. Furthermore, this effect is positively moderated by executive green awareness and financial background, indicating that managerial cognition and experience play a vital role in amplifying the benefits of green finance. Notably, green funds exert a stronger positive effect in highly polluting industries, suggesting that green financial resources should be directed not only to low-emission sectors but also to high-emission ones to improve their carbon efficiency. These findings extend existing literature by offering firm-level evidence on the effectiveness of green financial instruments and underscore the importance of targeted policy support to encourage green upgrading across all industry types.

1. Introduction

The escalating challenges posed by global warming have made reducing greenhouse gas emissions, particularly carbon dioxide, a global priority. Since the pre-industrial era, atmospheric CO₂ concentrations have risen sharply, resulting in serious ecological and environmental consequences [1]. Industrialization has further intensified these impacts by accelerating resource consumption and environmental degradation. As a result, the global consensus on achieving carbon neutrality has strengthened, necessitating collaborative responses grounded in diverse environmental and economic strategies [2]. In particular, corporate action aimed at sustainable development and the creation of carbon-neutral business ecosystems has become essential [3,4].

As a major global emitter and economic power, China plays a crucial role in addressing climate change [5]. In recent years, China has shifted its focus from high-speed growth to high-quality, green, and low-carbon development. This transition requires the reconstruction of traditional economic drivers and industrial systems. To align with this strategic direction, the Chinese government introduced the “dual carbon” goals in 2020, aiming to peak carbon emissions before 2030 and achieve carbon neutrality by 2060. These targets are supported by a series of national initiatives to optimize the industrial structure, promote renewable energy, and strengthen green financial systems [6]. Green finance, in this context, is positioned as a key pillar of China’s strategy to support sustainable transformation and reduce carbon emissions [7].

Green finance refers to a suite of financial services and instruments that support environmentally friendly investments and carbon reduction efforts. As an evolution of traditional finance, green finance integrates environmental goals into financial decision-making through tools such as green funds, green bonds, and green credit [8,9]. It acts as a bridge between economic growth and ecological sustainability, facilitating investments in clean energy, sustainable infrastructure, and climate mitigation. A mature green financial system can effectively incentivize companies to adopt low-carbon technologies and drive industrial upgrading in polluting sectors [10,11]. Green funds are gaining prominence as specialized investment vehicles channeling capital toward firms with strong environmental performance. As the number and scale of green funds continue to expand, they have become a powerful force in promoting corporate green transformation [12]. Institutional investors, who dominate this market, evaluate firms based on both financial returns and ESG criteria, and play a significant role in shaping firm behavior and disclosure practices [13,14]. Operating in the secondary market, green funds combine liquidity with an environmental focus by investing in companies committed to sustainable operations [15,16]. By aligning financial goals with environmental values, they create market mechanisms that reward low-carbon business strategies.

Enterprises are both key drivers of economic development and major contributors to environmental degradation. In China, more than 80% of pollution originates from corporate production and operations [17]. Highly polluting industries are not only significant sources of emissions but also face the greatest challenges in transitioning toward carbon neutrality [18]. Promoting the green transformation of enterprises—particularly those in high-emission sectors—is therefore critical for national carbon goals [9]. Within organizations, top executives play a central role in environmental strategy through their influence on corporate planning, resource allocation, and investment decisions [19]. Executive decisions often reflect personal values and expertise. For instance, those with strong green awareness may prioritize environmental goals more explicitly, while executives with financial backgrounds may be better equipped to leverage green finance tools and adapt to evolving green policies. Although previous research has examined the influence of executive traits on environmental performance [20,21], limited attention has been given to how green awareness and financial expertise among top management specifically moderate the effect of green finance on corporate carbon performance.

Although existing research on green finance and carbon performance has made significant progress, encompassing green credit [22,23], green bonds [24,25,26], composite green finance indices [27,28,29,30], and green finance policy [31,32,33,34,35], two important limitations remain. First, most studies are conducted at the macro (national or provincial) or meso (industry) levels, while firm-level micro-level empirical evidence is still relatively scarce [36,37,38]. Second, the literature has primarily focused on green credit and green bonds, with limited attention paid to green funds. Existing studies on green funds have largely concentrated on their financial performance [39], neglecting a systematic examination of their environmental impact. In response to these gaps, this study investigates the influence of green funds on corporate carbon performance from a micro perspective and further explores the moderating roles of executive characteristics and industry attributes. This contributes to a more nuanced understanding of how specific green financial instruments shape firm-level environmental outcomes.

This study contributes to the literature in three key ways. First, by concentrating on green funds as a distinct instrument, it provides the firm-level analysis of how green funds influence corporate carbon performance, whereas most prior work has relied on aggregate proxies or policy shocks at macro or meso scales. Second, it incorporates managerial characteristics—specifically executive green awareness and financial background —as moderators, thereby extending upper echelons and imprinting theories into the green finance domain; this highlights how decision-makers’ backgrounds can amplify or attenuate the efficacy of green funds. Third, by examining heterogeneity across pollution-intensive versus cleaner industries, the study uncovers that channeling funds toward high-emission firms yields disproportionately larger carbon-performance gains. Together, these innovations fill important gaps in existing studies on green finance and corporate sustainability.

2. Theoretical Background and Hypotheses

2.1. Green Funds and Corporate Carbon Performance

Considerable research has examined how financial development influences environmental quality, with evidence indicating that advancements in financial systems and increased trade openness contribute to lower carbon emissions and improved environmental outcomes [40]. Empirical studies focusing on APEC member economies highlight that financial development, coupled with the adoption of renewable energy, positively impacts ecological quality [41]. Emerging financial mechanisms, such as green finance, are recognized as critical tools in the global transition toward a low-carbon energy system [42]. Studies on the BRICS countries indicate that green finance negatively affects carbon emissions, affirming that it is an optimal financial strategy for emission reduction [43]. Based on a sample of 141 Chinese renewable energy firms, He et al. [44] concluded that the development of green finance enhances energy efficiency and facilitates the restructuring of energy consumption patterns, thereby improving carbon performance. Xu and Li [45] noted that green finance supports environmentally friendly industries by channeling investments at reduced financing costs while ensuring higher returns. Hu et al. [46] observed that green credit significantly influences industrial structure transformation via corporate financing mechanisms.

Wan et al. [36] explored how green financing influences carbon emissions. Their results revealed a substantial inverse association between green financing and carbon emissions within the construction industry, with this relationship being more pronounced in emerging countries. Zhang et al. [47] found that the gains in carbon emission efficiency linked to green finance largely arise from its stimulation of technological innovation and its assistance in upgrading industrial structures. In a related study, Saeed Meo and Karim [26] employed green bonds as a representative indicator of green finance and explored their impact on carbon emissions among the ten leading economies advancing green finance. Their findings validated that green finance constitutes an effective financial approach for mitigating carbon emissions. Additionally, Zhou et al. [35] observed that green financial policies in China lead to a substantial decline in industrial gas emissions.

As a crucial instrument of green finance, green funds are expected to facilitate reductions in corporate carbon emissions and enhance carbon performance. Schumacher et al. [48] highlighted the pivotal function of equity financing in facilitating the growth of low-carbon initiatives within advanced economies. Similarly, Thomä et al. [49] found that integrating climate risks into investment portfolio management can help achieve sustainable development goals. Drawing on the preceding analysis, this research proposes the following research hypothesis:

H1: 

Green funds positively affect corporate carbon performance.

2.2. Moderating Effect of Executive Characteristics: Executive Green Awareness and Financial Background

According to upper echelons theory, top management is instrumental in shaping and executing strategic decisions, which in turn influence a firm’s outcomes and development trajectory [50,51]. This theoretical framework posits that both the cognitive tendencies and observable traits of top management are critical drivers of a firm’s strategic orientation and performance metrics [52]. Managers with different background characteristics, such as green awareness and financial background, may make different behavioral choices, thereby affecting organizational outcomes, particularly in terms of environmental performance [19]. Executive green awareness refers to executives’ understanding of environmental practices and their evaluation of the associated risks and opportunities [20]. Therefore, executives with heightened green awareness are more likely to prioritize ecological considerations, which may enhance a firm′s commitment to sustainability and amplify the impact of green funds on carbon performance.

The implementation of environmental management strategies within firms is highly dependent on the involvement and backing of senior leadership [53]. Zhang et al. [54] suggested that top management′s commitment to environment significantly impacts a firm′s strategic environmental initiatives. Rahman Belal and Owen [55] pointed out that managers′ concerns about threats to environmental legitimacy and the need to enhance corporate image drive environmental management practices. Martin et al. [56] found that the more attention top executives pay to environmental issues, the more likely firms are to adopt environmentally friendly technologies and equipment for production activities. Hence, executive green awareness can influence corporate green behavior. Zhu et al. [57] noted that executive green awareness is a crucial cognitive factor driving green production and environmental performance within enterprises. Chen et al. [58] discovered that increased green awareness positively affects environmental quality using an empirical analysis of panel data covering over 60 countries; notably, higher levels of green awareness encourage more environmental investments, thereby improving environmental quality. Tu et al. [59] indicated that executive green awareness can influence the environmental performance of firms. Prior studies suggest that green-oriented managers are more proactive in adopting energy-efficient technologies, setting emissions reduction targets, and disclosing environmental performance indicators. Therefore, executives with high green awareness are more likely to recognize the strategic value of environmental sustainability and align corporate behavior with long-term ecological goals. When such executives are present, the pressure or guidance from green funds may be more readily internalized into concrete decarbonization actions, thus strengthening the effectiveness of green fund investment. In light of these findings, the following hypothesis is proposed:

H2: 

Executive green awareness positively moderates the relationship between green funds and corporate carbon performance.

According to the imprinting theory, work experiences during an individual’s career formation period leave imprints on their psyche and have a lasting impact on their career [60]. The finance industry is a high-risk sector [61] with unique operating models and significant work pressure. Work experience in the finance sector often leaves profound “imprints” on individuals and continues to influence their subsequent behavioral decisions. Consistent with the high-tier theory perspective, executives’ work experience in the finance sector and the unique cognition formed during this period influence their strategic decision-making choices.

Li and Kong [62] found that when commercial bankers join the board of directors as independent directors, companies can obtain more loans. Guo and Zhao [63] pointed out that independent directors with financial backgrounds can enhance company value when serving on audit committees. Wei et al. [64] argued that companies with CEOs with financial backgrounds are financially more mature than those without such CEOs; this is because they manage financial policies more flexibly and respond more sensitively to external regulatory changes.

Executives with financial backgrounds tend to exhibit more sophisticated and specialized insights into financial decision-making processes within the financial sector [65]. Xu and Shi [66] suggested that chief executive officers with financial backgrounds are more likely to comprehend why stakeholders and investors assign higher value to firms that demonstrate strong environmental performance. Additionally, CEOs with financial backgrounds can quickly understand and access the latest green finance policies through their social networks. Such individuals are often the first to learn about new green finance policies, incentives and regulatory changes, enabling the firm to tap into those resources or comply more readily. Their ability to bridge financial and sustainability objectives may enhance the firm’s capability to channel green investment into effective carbon reduction strategies, thereby reinforcing the impact of green fund investment. Thus, the research proposes the following hypothesis:

H3: 

Executive financial background positively moderates the relationship between green funds and corporate carbon performance.

2.3. Moderating Effect of Highly Polluting Industries

Companies’ environmental behavior is influenced by internal and external pressures, including those from government regulatory agencies, shareholders, executives, and other internal and external stakeholders [67]. Different industries have varying likelihoods and consequences of environmental issues and are subject to different pressures from government regulations. This may lead to differences in companies’ sensitivity to the environment. Pache and Santos [68] found that companies of different natures respond differently to external institutional pressures, with some companies showing characteristics that make them more susceptible to external pressures. In particular, companies in highly polluting industries exhibit different characteristics from companies in other industries, with different average pollution control costs and developmental stages. Thus, the impact of green funds on them may differ from that of other companies. González-Benito and González-Benito [69] pointed out that companies in industries with significant environmental impacts face greater environmental pressures. Siedschlag and Yan [70] found that energy-intensive firms are more likely to allocate resources toward pollution abatement technologies. Wen et al. [71] examined the influence of the Green Credit Guidelines. They found that credit allocation efficiency within energy-intensive sectors experienced a substantial decline after the policy was implemented. Green financial instruments can constrain the expansion of environmentally detrimental projects and simultaneously encourage a transition toward lower-carbon energy consumption structures, ultimately enhancing firms’ carbon performance [18]. Li and Chen [72] as well as Yan and Gong [23] further demonstrated that green credit policies are pivotal in driving structural transformation within pollution-intensive industries. These credit constraints can motivate carbon-intensive firms to shift toward greener business domains. Therefore, the following hypothesis is proposed:

H4: 

The impact of green funds on corporate carbon performance is more significant in highly polluting industries.

3. Research Methodology

3.1. Sample and Data

This research utilized data from Chinese A-share listed firms spanning the period from 2012 to 2021 as the primary sample for empirical analysis. Industry energy consumption data were obtained from the “China Energy Statistical Yearbook.” The coefficients of energy carbon emission were sourced from the “2006 IPCC Guidelines for National Greenhouse Gas Inventories.” Executive green awareness metrics were extracted from the firms’ annual reports, whereas other firm-level variables were sourced from the CSMAR database. Calculating carbon performance at the firm level requires a substantial amount of key data, many of which, such as industrial energy consumption, came from the statistical yearbooks published by the National Bureau of Statistics of China. However, these yearbooks are often published several years later, with the latest data available for 2021. Therefore, the sample period was set from 2012 to 2021.

The following procedures were followed to process the sample: (1) firms operating within the financial sector were omitted; (2) companies exhibiting abnormal operating conditions, including those labeled ST or ST*, were excluded; (3) observations with incomplete data were removed; and (4) firms displaying significant irregularities in financial information were also excluded. Our final sample consisted of 2543 companies from 2012 to 2021, comprising 16,556 sample observations. To mitigate the influence of outliers on the estimation outcomes, primary continuous variables were subjected to a logarithmic transformation and winsorized at the 1st and 99th percentile thresholds. Data processing and analysis were conducted using STATA 18.0 and Python 3.12.

3.2. Variable Definition and Measurement

3.2.1. Dependent Variable: Corporate Carbon Performance (CEP)

The main variable of interest is corporate carbon performance. Carbon-emission indicators have become popular topics in academia. Generally, a reduction in carbon emissions reflects improved carbon performance at the firm level. Due to the absence of mandatory carbon dioxide emission disclosure requirements in China and the fact that there is limited direct access to corporate carbon emissions data, this study adopted an indirect approach to calculate corporate carbon emissions following Liu et al. [5]; Ren et al. [73]; Shu and Tan [74]; Wang et al. [75]; and Zhou et al. [35]. The formula is as follows:

$${\mathrm{C}\mathrm{E}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{O}\mathrm{C}}_{\mathrm{i}}}{{\mathrm{O}\mathrm{C}}_{\mathrm{i}\mathrm{n}\mathrm{d}}}}\times {\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{n}\mathrm{d}}$$

(1)

where CE_i denotes the carbon emissions of company i; OC_i and OC_ind denote the operating costs of company i and its respective industry, respectively; and CE_ind refers to the total carbon emissions for the entire industry. Data on industrial energy consumption were sourced from the “China Energy Statistical Yearbook.” Subsequently, the aggregate industry carbon emissions (CE_ind) were computed using emission coefficients for different energy types.

Corporate carbon performance (CEP) refers to the amount of revenue obtained for each unit of carbon dioxide emitted. A higher CEP value reflects improved carbon efficiency and superior carbon performance at the firm level. The formula is as follows:

$${\mathrm{C}\mathrm{E}\mathrm{P}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{I}\mathrm{N}\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}\mathrm{E}}_{\mathrm{i}}}}$$

(2)

where CEP_i represents the carbon performance of the ith company, and INC_i and CE_i denote its revenue and carbon emissions, respectively.

3.2.2. Independent Variable: Green Funds (GF)

Green funds refer to funds specifically invested in enterprises engaged in sustainable, eco-friendly, and low-emission activities [6,13]. The independent variable GF in this study is the number of green funds holding a company’s shares. We collected data on corporate green funds as follows. First, we obtained relevant data on the fund market from the CSMAR database and matched fund entities with their stock investment details to obtain details of funds investing in listed companies. We then filtered the funds’ investment objectives and investment scope using keywords. If the fund’s investment targets included areas such as “green, sustainable, clean energy, new energy development, ecology, energy-saving, low carbon, and environmental protection,” it was classified as green funds. Finally, we counted the number of such investment funds at the company level to obtain the GF.

3.2.3. Moderating Variables

Executive Green Awareness (EGA)

Executive green awareness is defined as senior executives’ cognitive understanding of environmental issues, encompassing their perception and knowledge structure concerning environmental resources, as well as their psychological experiences in fulfilling obligations associated with sustainability and environmental stewardship. It includes elements such as recognition of green-oriented strategic benefits, sensitivity to corporate social responsibility, and responsiveness to institutional and regulatory pressures. Specifically, the recognition of green competitive advantage reflects executives’ acknowledgment of the strategic gains achievable through the implementation of environmentally sustainable practices. Social responsibility awareness refers to senior executives’ proactive assumptions of responsibilities for resource conservation and environmental protection, as well as their conscientious contributions and characteristics. The perception of external pressures refers to senior executives’ feelings and intuitions regarding the current market’s preference for green consumption and their understanding of government environmental regulations.

Textual analysis has been validated as a reliable method for capturing executive cognitive attributes and is applicable to longitudinal data research [76]. Building on the methodology of Tu et al. [59] and Wang et al. [77], the research utilized textual analysis of listed companies’ annual reports to quantify the level of green awareness among senior executives. A set of keywords was identified based on three primary dimensions: awareness of green competitive advantage, commitment to CSR, and sensitivity to external environmental pressures. Executive green awareness was quantified by calculating the frequency with which the selected keywords appeared in the annual reports of listed firms.

Executive Financial Background (EFB)

Drawing on Li and Kong [62] and Xu and Shi [66], the executive financial background was assessed by calculating the proportion of those with financial backgrounds relative to the overall size of the executive team. An executive was considered to possess a financial background if they had previously worked in organizations such as securities companies, insurance firms, policy or commercial banks, fund management entities, trust and asset management companies, stock exchanges, financial regulatory agencies, or other financial institutions.

Highly Polluting Industry (Pollute)

Following Cui et al. [18] and adhering to the industry classification standards updated by the China Securities Regulatory Commission in 2012, this study defined the following sectors as highly polluting industries: coal, steel production, chemical and petrochemical industries, thermal power generation, cement manufacturing, electrolytic aluminum, metallurgical processes, construction materials, paper production, brewing, pharmaceutical manufacturing, fermentation, textile and leather industries, and mineral extraction. Specifically, this dummy variable equaled 1 if the company was classified within polluting industries, and 0 otherwise.

3.2.4. Control Variables

To account for other characteristics that may affect CEP, we adopted variables from previous research [6,37,75], including firm size (Size), leverage (Lev), profitability (ROA), Tobin’s Q (TobinQ), board size (Board), proportion of independent directors (Indep), concentration of equity ownership (TOP1), and firm age (FirmAge). Additionally, to mitigate potential estimation biases caused by industry characteristics and temporal fluctuations, the analysis includes controls for both year and industry fixed effects. Comprehensive definitions of all variables are provided in

Table 1. Definition and Measurement of Variables.

Variable Type	Variable Symbol	Variable Name	Variable Definition
Dependent Variable	CEP	Corporate Carbon Performance	Firm’s revenue/CO2 emissions
Independent Variable	GF	Green Funds	Natural logarithm of the number of green funds plus 1
Moderating Variables	EGA	Executive Green Awareness	Natural logarithm of the frequency of selected EGA keywords in the firm’s annual reports plus 1
	EFB	Executive Financial Background	Proportion of those with financial background relative to the overall size of the executive team
	Pollute	Highly Polluting Industry	The dummy variable is equal to 1 if the company was classified within polluting industries, and 0 otherwise
Control Variables	Size	Firm Size	Natural logarithm of the firm’s total assets at year-end
	Lev	Leverage	Ratio of total liabilities to total assets
	ROA	Return on Assets	Net income divided by total assets
	TobinQ	Tobin’s Q	Market value of the firm divided by its total assets
	Board	Board Size	Natural logarithm of the number of board directors plus 1
	Indep	Proportion of Independent Directors	Proportion of independent directors relative to the total number of directors
	TOP1	Ownership Concentration	Shareholding percentage of the firm’s largest shareholder
	FirmAge	Firm Age	Natural logarithm of firm age plus 1
	IND	Industry	Industry fixed effect
	YEAR	Year	Year fixed effect

.

3.3. Research Model

3.3.1. Baseline Regression Model

To test the hypothesis H1, a fixed effects regression model was developed to investigate the influence of GF on CEP. The model is specified as follows:

$${CEP}_{i,t}={\beta }_0+{\beta }_1{GF}_{i,t}+\gamma {Control}_{i,t}+{\lambda }_{ind}+{\mu }_t+{\epsilon }_{i,t}$$

(3)

where i and t identify the firm and the year, respectively. ${CEP}_{i,t}$ denotes the carbon performance of firm i in year t, while ${GF}_{i,t}$ denotes the number of green funds for firm i during year t. ${Control}_{i,t}$ represents the vector of control variables. ${\lambda }_{ind}$ accounts for the industry fixed effects, and ${\mu }_t$ accounts for the year fixed effects. ${\epsilon }_{i,t}$ represents the error term.

This study employed a TWFE panel model to account for unobserved heterogeneity across both time and industry dimensions, thereby controlling for external influences that may vary throughout the sample period. Robust standard errors were used to address potential heteroskedasticity issues. The Hausman test results also confirmed the suitability of the fixed-effects model.

3.3.2. Moderation Effect Regression Models

Building on Model (3), we added interaction terms of green funds with executive green awareness, executive financial background, and polluting industries, constructing moderation effect Models (4), (5), and (6), respectively. These models are employed to test hypotheses H2, H3, and H4. The specific models are as follows:

$${CEP}_{i,t}={\beta }_0+{\beta }_1{GF}_{i,t}+{\beta }_2{EGA}_{i,t}+{\beta }_3{GF}_{i,t}\times {EGA}_{i,t}+\gamma {Control}_{i,t}+{\lambda }_{ind}+{\mu }_t+{\epsilon }_{i,t}$$

(4)

$${CEP}_{i,t}={\beta }_0+{\beta }_1{GF}_{i,t}+{\beta }_2{EFB}_{i,t}+{\beta }_3{GF}_{i,t}\times {EFB}_{i,t}+\gamma {Control}_{i,t}+{\lambda }_{ind}+{\mu }_t+{\epsilon }_{i,t}$$

(5)

$${CEP}_{i,t}={\beta }_0+{\beta }_1{GF}_{i,t}+{\beta }_2{\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{e}}_{i,t}+{\beta }_3{GF}_{i,t}\times {\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{e}}_{i,t}+\gamma {Control}_{i,t}+{\lambda }_{ind}+{\mu }_t+{\epsilon }_{i,t}$$

(6)

where ${\mathrm{E}\mathrm{G}\mathrm{A}}_{\mathrm{i},\mathrm{t}}$ represents executive green awareness, ${\mathrm{E}\mathrm{F}\mathrm{B}}_{\mathrm{i},\mathrm{t}}$ denotes executive financial background, ${\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{e}}_{\mathrm{i},\mathrm{t}}$ is a binary indicator representing whether the enterprise belongs to a highly polluting industry, and the other terms in Models (4)–(6) were the same as those in Model (3). The coefficients of the interaction terms were used to examine the presence of moderation effects.

The inclusion of “Pollute” as a moderating variable is grounded in relevant literature support. Firms in pollution-intensive sectors often face higher environmental regulatory pressure and public scrutiny. Investments aimed at environmental improvements in heavily polluting sectors may yield more pronounced marginal effects because these industries start from a lower baseline of environmental performance and thus have greater potential for improvement. Cui et al. [18] find that companies in pollution-intensive industries often encounter more severe financing constraints, prompting them to invest in green projects to enhance their financing opportunities. Li and Chen [72] demonstrate that the implementation of green credit policies significantly increases the propensity of heavily polluting firms to diversify, thereby facilitating the transformation and upgrading of pollution-intensive industries.

4. Results and Analysis

4.1. Descriptive Statistics

Descriptive statistics for the main variables within the sample listed companies are displayed in

Table 2. Descriptive Statistics.

Variable	N	Mean	SD	Min	p50	Max	Skewness	Kurtosis
CEP	16556	5.365	1.943	0.994	5.629	8.384	−0.367	2.050
GF	16556	0.469	0.669	0	0	2.833	1.274	3.664
EGA	16556	0.850	0.841	0	0.693	3.091	0.614	2.377
EFB	16556	0.0530	0.106	0	0	0.500	2.108	7.063
Pollute	16556	0.226	0.419	0	0	1	1.308	2.710
Size	16556	22.11	1.197	19.92	21.95	26.09	0.837	3.691
Lev	16556	0.408	0.198	0.0510	0.400	0.881	0.245	2.269
ROA	16556	0.0410	0.0600	−0.192	0.0380	0.226	−0.575	6.058
TobinQ	16556	2.026	1.209	0.868	1.639	8.195	2.433	10.35
Board	16556	2.113	0.191	1.609	2.197	2.708	−0.482	3.784
Indep	16556	37.67	5.404	33.33	36.36	57.14	1.263	4.440
TOP1	16556	34.23	14.35	9.124	32.14	74.18	0.493	2.646
FirmAge	16556	2.888	0.316	1.609	2.944	3.497	−0.770	3.915

. The dependent variable CEP has an average value of 5.365, accompanied by a standard deviation of 1.943, while its median stands at 5.629. The mean of CEP is 5.365, with a standard deviation of 1.943 and a median of 5.629. The range is from 0.994 to 8.384, indicating significant variations in carbon performance among Chinese A-share listed firms. The mean of GF is 0.469, with a standard deviation of 0.669. Its median is 0, with values ranging from 0 to 2.833. This indicates that over 50% of the sampled companies did not receive investment from green funds. This preliminarily shows that green funds focus on promoting green, low-carbon, and sustainable development rather than investing in every company indiscriminately. EGA has an average value of 0.850, a standard deviation of 0.841, and a median of 0.693. The values span from 0 to a maximum of 3.091, reflecting considerable variation in executive green awareness across the listed companies. The mean value of EFB is 0.053, with a standard deviation of 0.106 and a median of 0. The variable ranges from 0 to a maximum of 0.5, suggesting that there are few executives with financial backgrounds in the sample listed companies. Additionally, 22.6% of the sample companies belong to polluting industries.

4.2. Correlation Analysis

Table 3. Correlation Matrix.

	CEP	GF	EGA	EFB	Pollute	Size	Lev	ROA	TobinQ	Board	Indep	TOP1	FirmAge
CEP	1
GF	0.192 ***	1
EGA	−0.317 ***	0.013 *	1
EFB	0.040 ***	−0.035 ***	−0.035 ***	1
Pollute	−0.657 ***	−0.135 ***	0.342 ***	−0.043 ***	1
Size	−0.109 ***	0.347 ***	0.128 ***	−0.00600	0.090 ***	1
Lev	−0.078 ***	0.032 ***	0.054 ***	0.0110	0.064 ***	0.500 ***	1
ROA	0.061 ***	0.307 ***	0.00400	−0.044 ***	−0.045 ***	−0.018 **	−0.382 ***	1
TobinQ	0.149 ***	0.187 ***	−0.162 ***	0.035 ***	−0.116 ***	−0.383 ***	−0.283 ***	0.185 ***	1
Board	−0.174 ***	0.063 ***	0.083 ***	−0.045 ***	0.092 ***	0.263 ***	0.134 ***	0.00900	−0.133 ***	1
Indep	0.080 ***	0.0130	−0.040 ***	0.023 ***	−0.047 ***	−0.0100	0	−0.032 ***	0.038 ***	−0.577 ***	1
TOP1	−0.117 ***	0.042 ***	0.041 ***	−0.068 ***	0.022 ***	0.186 ***	0.014 *	0.145 ***	−0.082 ***	0.013 *	0.036 ***	1
FirmAge	0.024 ***	−0.063 ***	−0.020 ***	0.044 ***	0.027 ***	0.111 ***	0.105 ***	−0.058 ***	0.0110	0.026 ***	−0.00700	−0.120 ***	1

Note: Robust t-statistics are shown in parentheses; * indicates p < 0.1, ** represents p < 0.05, and *** denotes p < 0.01.

displays the Pearson correlation coefficients among the main variables for the full sample period. The correlation between GF and CEP is 0.192, significant at the 1% level. This positive and statistically meaningful association provides preliminary support for hypothesis H1. Almost all coefficients are less than 0.7, suggesting that all variables appear to be mutually independent and multicollinearity is not a major issue. We additionally calculate variance inflation factors (VIF) to evaluate whether multicollinearity might induce estimation issues in the model. The VIF values for these variables range from 1.01 to 2.15 and are all below 3, indicating no multicollinearity issues.

4.3. Regression Analysis

4.3.1. Baseline Regression Analysis

We employ a TWFE panel regression to assess the effect of GF on CEP.

Table 4. Regression Analysis Results: Baseline and Moderation Effects.

	Model (1)	Model (2)	Model (3)	Model (4)
VARIABLE	CEP	CEP	CEP	CEP
GF	0.0243 *** (3.983)	0.0154 * (1.872)	0.0189 *** (2.882)	0.0204 *** (3.166)
lnEGA		−0.0610 *** (−11.48)
EGA*GF		0.0139 ** (2.337)
EFB			0.0360 (0.892)
EFB*GF			0.116 ** (2.404)
Pollute				0.0594 ** (2.252)
Pollute*GF				0.0409 *** (3.314)
Size	−0.0129 *** (−2.987)	−0.00963 ** (−2.228)	−0.0132 *** (−3.067)	−0.0143 *** (−3.271)
Lev	−0.162 *** (−7.303)	−0.159 *** (−7.203)	−0.162 *** (−7.330)	−0.159 *** (−7.191)
ROA	1.142 *** (16.67)	1.151 *** (16.87)	1.143 *** (16.68)	1.140 *** (16.65)
TobinQ	0.0262 *** (6.909)	0.0238 *** (6.304)	0.0258 *** (6.796)	0.0262 *** (6.884)
Board	−0.0478 ** (−2.169)	−0.0470 ** (−2.132)	−0.0447 ** (−2.028)	−0.0507 ** (−2.298)
Indep	−1.11 × 10−5 (−0.0150)	−0.000100 (−0.137)	1.05 × 10−6 (0.00143)	−3.55 × 10−5 (−0.0482)
TOP1	−0.000903 *** (−3.703)	−0.000929 *** (−3.825)	−0.000848 *** (−3.479)	−0.000963 *** (−3.929)
FirmAge	−0.0586 *** (−4.991)	−0.0580 *** (−4.962)	−0.0577 *** (−4.909)	−0.0600 *** (−5.105)
Constant	5.907 *** (54.77)	5.889 *** (54.57)	5.902 *** (54.76)	5.935 *** (54.70)
Observations	16,556	16,556	16,556	16,556
R-squared	0.0591	0.0670	0.0599	0.0598
IND FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES

Note: Robust t-statistics are shown in parentheses; * indicates p < 0.1, ** represents p < 0.05, and *** denotes p < 0.01. Model (1) tests the baseline relationship between GF and CEP (H1). Model (2) includes the interaction term between GF and EGA to test the moderating effect of EGA (H2). Model (3) tests the moderating effect of EFB (H3). Model (4) assesses the moderating effect of Pollute (H4).

presents the corresponding estimates. The coefficient of GF captures the magnitude of this impact. In Column (1), which controls for all covariates and includes both year and industry fixed effects, the GF coefficient is 0.0243 and is significant at the 1% level. The result indicates a positive impact of green funds on corporate carbon performance, as expected. Thus, H1 was supported.

4.3.2. Moderation Effects

To assess the moderation effect of EGA, we introduced the interaction term EGA*GF into Model (4). Column (2) in Table 4 reports these results, where the interaction coefficient is 0.0139 (p < 0.05). The main effect of GF remains positive and statistically significant. This suggests that firms with high green-aware executives are more capable of translating green fund investments into substantive carbon performance improvements. Executives with higher green awareness are likely to possess stronger environmental values and a more strategic long-term vision for sustainability. Their heightened sensitivity to environmental concerns may lead them to channel green financial resources toward green technology or cleaner production processes. These findings provide support for hypothesis H2.

According to Model (5), we further investigated the moderating effect of EFB, where EFB*GF represents the interaction between green funds and executive financial background. In Column (3), the coefficient of EFB*GF is significantly positive (β = 0.116, p < 0.05), while the main effect of GF remains both positive and significant. This indicates that executive financial backgrounds have a positive moderation effect on the relationship. Executives with financial backgrounds may be better equipped to interpret and respond to green finance policies and market signals. Their familiarity with financial instruments enables them to optimize the use of green capital. As such, these executives may serve as a conduit for aligning financial decision-making with low-carbon transition goals, thereby validating hypothesis H3.

An interaction term between GF and Pollute was incorporated into Model (6) to assess differential effects between highly polluting and non-highly polluting industries. From Column (4), the interaction coefficient is 0.0409 (p < 0.01), and the main effect of GF remains positive and significant. These results imply that green funds exert a more pronounced enhancement of corporate carbon performance in highly polluting industries. This may be attributed to both higher marginal abatement potential and stronger policy or market pressures faced by such firms. The results support hypothesis H4.

Overall, all principal variables in the model exhibit statistically significant coefficients. These findings demonstrate that green funds effectively incentivize firms to lower carbon emissions and enhance carbon performance. Executive green awareness, executive financial background, and polluting industries positively moderate the relationship between green funds and corporate carbon performance, supporting all proposed hypotheses.

4.4. Robustness Test

4.4.1. Endogeneity Test

Although the fixed-effects model can alleviate endogeneity to some extent, concerns such as omitted variable bias and sample selection issues may still exist, potentially leading to endogeneity problems. To further enhance the robustness of the findings and address these limitations, this study adopts a 2SLS approach, following the methodologies of Bansal et al. [78] and He et al. [38].

Building on this study’s empirical framework, we constructed the mean number of green funds of peer firms (excluding the observed firm) over the entire sample period (GFN) as the instrumental variable for GF. The instrument is expected to satisfy the exclusion restriction, given that the average number of green funds among peer firms is unlikely to exert a direct influence on an individual firm’s carbon performance.

Table 5. Robustness Test Results.

	(1)	(2)
	First	Second
VARIABLE	GF	CEP
GFN	−42.88 *** (−37.77)
GF		0.110 *** (5.104)
Size	0.288 *** (61.28)	−0.0398 *** (−5.131)
Lev	−0.0530 ** (−1.978)	−0.157 *** (−7.176)
ROA	2.435 *** (31.89)	0.911 *** (10.71)
TobinQ	0.154 *** (38.34)	0.0117 ** (2.382)
Board	−0.0402 (−1.476)	−0.0448 ** (−2.012)
Indep	−0.000744 (−0.818)	2.44 × 10−5 (0.0328)
TOP1	−0.00254 *** (−8.690)	−0.000687 *** (−2.804)
FirmAge	−0.146 *** (−10.24)	−0.0450 *** (−3.697)
Observations	16,556	16,556
R-squared	0.435	0.048
IND FE	YES	YES
Year FE	YES	YES

Note: Robust t-statistics are shown in parentheses; ** represents p < 0.05, and *** denotes p < 0.01.

presents the results of 2SLS. Column (1) indicates a significant association between the instrumental variable (GFN) and GF. Column (2) shows the second-stage regression outcomes incorporating the instrumental variable, where the coefficient of GF is 0.110 (p < 0.01). Notably, the coefficient becomes larger after correcting for potential endogeneity, reinforcing the robustness of the primary findings. Additionally, the Lagrange Multiplier (LM) statistic is 1317.43 (p < 0.01), indicating the strong relevance of the instrumental variable. And the C-D Wald F statistic is 1426.75, which surpasses the Stock–Yogo critical threshold at the 10% level, thereby ruling out the presence of weak instrument bias. Thus, the selected instrumental variable is identifiable. Results from the endogeneity test confirm that the explanatory variable GF is indeed endogenous, and the chosen instrument is valid. In sum, addressing endogeneity through instrumental variable estimation does not alter the study’s conclusions; rather, it enhances the robustness and credibility of the empirical findings.

4.4.2. Alternative Measurement of Green Funds

To address potential concerns regarding the measurement error of green funds, we conduct a robustness check using a binary proxy for GF. Specifically, we construct a dummy variable that equals 1 if the company has received any green fund investment in a given observation year, and 0 otherwise. The regression results using this alternative measurement are reported in Column (1) of

Table 6. Results of Alternative Measurement and Regional Heterogeneity.

	(1)	(2)	(3)
	Alternative GF Dummy	East Region	Central and Western Regions
VARIABLES	CEP	CEP	CEP
GF	0.0169 **	0.0271 ***	0.0203
	(2.201)	(3.840)	(1.642)
Controls	YES	YES	YES
IND FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	16,556	11,599	4956
R-squared	0.0584	0.0583	0.0649

Note: Robust t-statistics are shown in parentheses; ** represents p < 0.05, and *** denotes p < 0.01.

. The coefficient of GF remains significantly positive, indicating that even when measured through a binary indicator, the presence of green fund investment is still associated with improved corporate carbon performance. This confirms the robustness of our main findings.

4.5. Regional Heterogeneity Analysis

Given China’s regional differences in economic development, environmental policy, and financial infrastructure, it is plausible that the effect of green funds on carbon performance may vary geographically. In particular, firms located in the eastern region typically operate in more mature capital markets and benefit from stronger environmental regulatory frameworks and policy support. In contrast, firms in the central and western regions may face relatively weaker financial ecosystems and policy enforcement. To test for this regional heterogeneity, we divide the sample into two subgroups: firms located in eastern China and those in central and western China. We then estimate the baseline model separately for each subgroup to compare the magnitude of the green fund effect. Columns (2) and (3) of Table 5 report the regression results for firms located in the eastern region and central–western regions of China, respectively. The results suggest regional heterogeneity in the effect of green funds on corporate carbon performance.

In Column (2), representing firms in eastern China, the coefficient of GF is 0.0271 and is statistically significant at the 1% level (p < 0.01). This indicates that green fund investment has a stronger and more robust positive impact on the carbon performance of firms located in the eastern region. This result may be attributed to the more developed financial systems, greater environmental policy enforcement, and higher levels of green awareness in eastern China, which together create more favorable conditions for green funds to operate effectively.

In contrast, Column (3) presents the results for firms located in the central and western regions, where the coefficient of green funds (GF) is 0.0203 but statistically insignificant (p > 0.1). This suggests that the impact of green fund investment on carbon emission performance (CEP) in these areas is relatively weak or lacks consistency. Several factors may account for this outcome, including the relatively underdeveloped state of capital markets, weaker enforcement of environmental regulations, and limited awareness or access to green financial instruments.

5. Discussion

This research centers on two widely recognized and globally significant themes: green finance and carbon neutrality. Increasingly severe global climate issues require innovative and sustainable financial strategies to mitigate their impact and promote sustainable low-carbon development. Although research in these two areas has made some progress and their importance has been increasing, a significant research gap exists in how they synergistically drive the shift towards a green economy. Existing literature has primarily focused on the macro and meso levels (national, regional, or industrial) [9,10,27,28,30,35,36]. In contrast, micro-level literature has largely focused on the effects of green financial policies on corporate carbon emissions [5,23,32,34,37], while the specific impact of individual green financial instruments on corporate carbon outcomes has received limited scholarly attention.

In response to this research gap, the present study concentrates on the micro-level effects of green financial instruments, specifically investigating for the first time how GF influence CEP. By addressing this underexplored area, the study contributes valuable insights. The findings reveal a significant positive impact of GF on CEP, thereby enhancing our comprehension of how emerging green financial tools can facilitate the shift toward a low-carbon economy. Drawing on the perspectives of upper echelons and imprinting theories, the research highlights the pivotal role of managerial characteristics in molding firms’ environmental strategies. The results suggest that EGA and EFB can positively moderate the impact of GF on CEP, supporting the upper echelons and imprinting theories.

In light of the ongoing academic discourse surrounding the efficacy of green finance in pollution-intensive sectors [18,37,72], the research investigates the heterogeneity of green funds’ impact within these industries. The results reveal that GF exert a more pronounced positive effect on CEP in highly polluting industries, offering important empirical support for differentiated regulatory approaches and targeted policy design. However, He et al. [38] report conflicting evidence. Their study finds that green finance negatively affects corporate environmental responsibility performance (CER) in highly polluting industries. They point out that green finance can increase financing constraints, reduce environmental investment, and undermine technological innovation, leading to poorer environmental responsibility performance. The divergence may stem from differences in measurement (CEP versus CER), or sample period. By acknowledging such contradictory evidence, we underscore the need for further investigation into the contextual conditions under which green finance either improves or worsens carbon performance in highly polluting industries.

Overall, this study provides suggestions for policymakers and valuable references for green corporate practices. It also indicates new research directions for further academic exploration, positively contributing to the global shift towards a green and sustainable future.

6. Conclusions

Green financial instruments are crucial for achieving the dual carbon goals. This research concentrates on Chinese listed companies to investigate the influence of GF on CEP. Furthermore, it examines the moderation effects of EGA, EFB, and Pollute on this relationship. The main findings are presented below: (1) GF have a significant positive effect on firms’ carbon performance. The result holds after testing with the IV-2SLS method. This emphasizes the importance of green funds as a green financial instrument, positively incentivizing corporate carbon performance. (2) EGA positively moderates their relationship. It highlights the crucial role of EGA in green decision-making. (3) Executive financial background is another positive moderator between GF and CEP. (4) The positive impact of GF on CEP is more evident for companies within highly polluting industries. This indicates that current green finance policies should not be limited to directing funds solely to low-carbon environmental industries. Instead, firms in highly polluting industries need more green finance support to realize their green transformation. Relevant policies should encourage and guide financing towards green upgrading and transformation projects for firms in highly polluting industries.

This study has important theoretical and practical contributions. First, the study provides strong theoretical evidence for the positive impact of green financial instruments on corporate carbon performance, filling a research gap in green finance and carbon-neutrality literature. Second, the research investigates the influence of GF on CEP at the firm level, providing a fresh perspective for the study of green finance. Additionally, the research highlights the pivotal influence of executives in environmental decision-making. Executive awareness of environmental issues and their financial expertise significantly enhance firms’ capacity to effectively leverage green financial instruments, thereby improving corporate carbon performance. These results validate the upper echelons and imprinting theories. Finally, the research explores the impact of GF on CEP in highly polluting industries, providing substantial evidence for the promotion of green transformation in highly polluting industries using green financial instruments.

Practically, leveraging green financial instruments is crucial in the green transition process. Companies can use green financial instruments to supplement the funding needs of environmental investments, alleviate financial pressure, and promote sustainable low-carbon development. Based on our findings, we put forward the following recommendations:

First, to amplify the effectiveness of GF in improving CEP, it is essential to enhance executives’ green awareness. Policymakers can promote this by incentivizing enterprises to establish formal environmental education and training programs for senior managers. Regulatory bodies may also consider integrating green leadership capacity into ESG evaluation frameworks, encouraging firms to internalize green values at the top decision-making level.

Second, given that executives with financial backgrounds demonstrate a stronger capacity to translate green funds into tangible carbon reduction outcomes, firms are encouraged to prioritize candidates with such expertise in key leadership roles. Government talent policies can further support this by launching training certifications in “green financial leadership,” thereby fostering a pipeline of executives equipped to align financial strategies with sustainability objectives.

Third, to address the funding gap in highly polluting industries which often face stricter credit scrutiny and higher financing costs, governments should introduce differentiated green finance policies. For example, establishing risk-sharing mechanisms such as green credit guarantees or subsidies for emissions-reduction projects in these sectors would lower barriers to access. In parallel, tax incentives or regulatory relief could be offered to green funds that allocate capital toward qualified high-emission firms undergoing certified low-carbon transformations.

Finally, financial institutions should continue innovating green financial products and services, expanding the toolkit available to firms for energy-efficiency upgrades and emissions reduction. This includes developing sector-specific instruments (e.g., transition bonds for steel or cement industries), bundling financial and technical support, and engaging in closer cooperation with local regulators to identify and fund projects with significant carbon abatement potential. In doing so, financial institutions can play a dual role as both financiers and green strategic enablers.

The research has certain limitations. First, as we focus on Chinese companies, our results may not be more generalizable to other contexts. Cultural differences between countries may significantly affect green awareness and behavior, leading to uncertainty regarding the impact of GF on CEP. Extensive studies are needed on companies from different countries and regions. Second, as only some companies voluntarily disclose carbon emissions data, this study can only obtain approximate data through indirect calculations. These indirect measures may deviate from the actual data. As corporate green awareness continues to strengthen, more companies will set carbon neutrality as a strategic goal and will be more willing to disclose their carbon emissions data. Future research could use carbon emissions data disclosed by companies or third-party institutions to conduct related studies and improve the accuracy of the research. Third, owing to data availability, the research only focuses on green funds among green financial instruments without considering other instruments that might affect CEP. To address this issue, future research could measure green bonds at the firm level using issuance volume and coupon spreads to examine the relationship between green bonds and corporate carbon performance. Similarly, scholars could utilize firm-level lending data—such as increases in loan volumes or concessional interest rates—to quantify green credit and examine its potential impact on corporate carbon reduction. Beyond these instruments, the roles of green insurance and ESG-linked derivatives can be investigated to uncover additional mechanisms by which financial innovation enhances corporate carbon performance. By broadening the scope to include multiple green finance models, future studies can construct a more comprehensive framework to understand how different financial products collectively accelerate the transition to carbon neutrality.