The Impact of Regional Carbon Emission Reduction on Corporate ESG Performance in China

Abstract

The integrated planning of central and local emission reduction tasks is crucial for achieving sustainable economic development, and corporate ESG performance aligns with the principles of sustainable development, having become a prominent topic in academic research. This paper empirically investigates the impact of regional carbon emission reductions on the ESG performance of local enterprises from 2009 to 2021 using provincial carbon emission data from China. The findings indicate that regional carbon emission reductions significantly enhance the ESG performance of local firms. The underlying mechanism is that regional carbon emission reductions facilitate local enterprises obtaining green credit, attracting media coverage and green investors and thus improving ESG performance. Second, heterogeneity tests reveal that regional carbon emission reductions enhance the ESG performance of local firms more significantly in regions with stricter environmental regulations, within heavily polluted industries, and among less digitized enterprises. Finally, further analysis demonstrates that regional residents’ carbon emission reductions can enhance the ESG performance of local enterprises, with regional carbon emission reductions exerting a dual effect after improving ESG performance. The findings of this study provide valuable insights into the low-carbon development of various economic entities and the collaborative promotion of economic green transformation.

1. Introduction

As risks to the climate and environment, public health, and social justice have intensified globally, the concept of sustainable development has garnered widespread public attention and significant interest from nations worldwide [1]. From the perspective of Chinese practice, its rapid economic growth has made it the second largest economy in the world. However, this rapid development has led to increased energy consumption, rising carbon emissions, and significant threats to the climate, biodiversity, and the natural environment. In response, China has gradually elevated the construction of an ecological civilization and sustainable economic development to a national strategy. In September 2020, China proposed a “dual-carbon” goal. As a critical strategic decision, the “dual-carbon” target is a complex systematic project with significant implications for China’s future regional economic development. Governments across China have actively responded to the central government’s call by exploring new ideas and development modes. Considering their local characteristics and strengths, they have issued a series of policy documents and implemented various measures to promote regional carbon emission reduction. Thus, regional carbon emission reduction is a crucial step for China in practicing the concept of sustainable development.

Besides carbon emission reduction, an effective green financial support system is essential for China’s ecological civilization and sustainable development. The importance of the ESG (Environmental, Social, and Governance) paradigm to finance and the economy is rapidly growing [2]. ESG criteria, often linked with ethical or socially responsible investing, have become crucial measures of corporate stewardship, risk management, and non-financial performance [3]. On 30 September 2018, the China Securities Regulatory Commission (CSRC) established the basic ESG information disclosure framework. Initially, Chinese ESG information disclosure for listed companies was based on encouragement and voluntary disclosure. However, numerous studies have shown that companies voluntarily disclosing ESG information are intrinsically motivated, as active disclosure can lead to superior returns [4]. On 8 February 2024, under the unified deployment of the CSRC, the three major stock exchanges in China released the “Self-Regulatory Guidelines for Listed Companies—Sustainability Report (Trial) (Draft for Public Comments).” This report represents a significant institutional arrangement for the three exchanges to implement sustainable development concepts, marking a milestone in the ESG information disclosure of Chinese A-share listed companies. This indicates that China’s green financial system aims to promote sustainable economic, social, and environmental development by enhancing sustainability information disclosure and guiding various factors to focus on sustainable development. In summary, regional carbon emission reduction and implementing ESG concepts are crucial measures for China to address ecological issues and promote sustainable development.

ESG principles have been actively practiced in developed countries, like the United States, since their formal introduction in 2004. In recent years, even in emerging markets like China, companies are increasingly participating in ESG activities [5]. Regarding ESG investment, the Beijing Stock Exchange established its first ESG-themed fund in March 2024, guiding investors to enter the securities market with an ESG perspective and enhancing the enthusiasm for and quality of corporate ESG disclosure. Regarding ESG disclosure, as of May 2024, 2094 A-share companies in China have released ESG-related reports, up from only 872 in 2018. In the context of Chinese practice, carbon emission reduction is highly compatible with corporate ESG performance. On the one hand, companies focusing on ESG performance pay more attention to environmental governance, minimizing carbon emissions, and reducing damage to the natural environment [6]. On the other hand, focusing on stakeholders and improving the corporate impact on society and the environment can enhance ESG performance [7].

Our research examines the impact of regional carbon reduction on corporate ESG performance for two primary reasons. Firstly, compared to developed nations, Chinese ESG practices commenced later and remain nascent [3]. Specifically, constrained by institutional barriers, insufficient funding, a lack of qualified professionals, and various practical challenges, the development of ESG (Environmental, Social, and Governance) still faces issues such as uneven disclosure quality and high implementation costs [8]. The construction of China’s ESG framework remains a formidable challenge, and corporate ESG performance requires further enhancement [9]. Therefore, addressing how to effectively remove barriers to improving corporate ESG performance and enhance the driving force behind ESG performance improvements has become an urgent critical issue requiring resolution [10]. Secondly, the carbon reduction initiatives initially driven by governmental bodies to protect the ecological environment were not initially corporate objectives. As sustainable development theories gained public awareness, corporate voluntary emission reductions may have been driven by reputational pressures or by meeting the interests of external stakeholders such as governments [11]. Our research aims to demonstrate that carbon reduction actions, in addition to meeting PR goals or satisfying external stakeholders’ demands, also enhance a company’s non-financial performance. This encourages companies to effectively integrate their interests with governmental and societal objectives, transitioning from passive emission reductions to active green development initiatives. Ultimately, this promotes a positive trend of collaborative, sustainable development among regional economic entities—companies, governments, and residents.

Recently, many scholars have studied the economic consequences of ESG performance. Existing research has focused on the impact of ESG performance on corporate operational efficiency [6], level of innovation [12], risk-taking [13], and firm value [14]. Recognizing the significant positive impact of ESG performance on firms, subsequent studies have examined the internal and external drivers of corporate ESG performance. In terms of the external factors, scholars have focused on the impact of environmental regulations and tax incentives on corporate ESG performance [15,16]. In terms of internal factors, some scholars have explored the impact of digitalization [5], corporate culture [17], and executive traits on corporate ESG performance [3]. However, companies interact with the local environment, and the characteristics of the regional environment affect the operations, financial performance, and market performance of local enterprises [18,19,20]. Although existing literature has studied the factors affecting ESG performance at the internal and external levels, the impact of regional carbon emission reductions on the ESG performance of local firms has not yet been thoroughly examined. There is no clear evidence of whether regional carbon emission reduction, a key aspect of achieving sustainable development in China and globally, affects the ESG performance of local firms.

Based on this premise, this paper empirically examines the impact of regional carbon emission reductions on the ESG performance of local firms from 2009 to 2021 using unbalanced panel data on provincial carbon emissions in China. The findings show that regional carbon emission reduction significantly improves the ESG performance of local enterprises. The mechanism is that regional carbon emission reduction can secure more green credits for local firms and attract more attention from the media and green investors, thus contributing to enhancing ESG performance. Second, this paper examines the heterogeneity of the impact of regional carbon emission reduction on corporate ESG performance from three perspectives: region, industry, and enterprise. Specifically, carbon emission reduction in regions with stricter environmental regulations has a more significant effect on enhancing the ESG performance of local enterprises. Additionally, regional carbon emission reduction significantly affects heavily polluted enterprises and those with a lower degree of digitization. Third, regional residents’ carbon emission reduction has a spillover effect. Regional residents’ energy saving, emission reduction, and low-carbon lifestyles can enhance local enterprises’ ESG performance. Fourth, regional carbon emission reduction can enhance the ESG performance of enterprises, producing a dual effect of value enhancement and risk control. This specifically manifests as an increase in enterprise value and a reduction in stock price crash risk.

Our study contributes to the literature in three ways. First, in terms of its research perspectives, this paper enriches the literature on the impact of the regional characteristics on local firms from the perspective of regional carbon emission reduction. Existing research focuses on the impact of the regional cultural, religious, and climatic characteristics on local firms and capital markets [18,19,20]. Little literature has discussed the economic effects of regional carbon emission reductions on local firms directly in the context of regional environmental governance. Therefore, this paper is useful within the literature on regional characteristics affecting local firms. Second, in terms of the research content, this paper extends the study of the drivers of corporate ESG performance based on the carbon emission reduction behavior of economic agents in various regions. In addition, many studies have examined the economic consequences of carbon emissions at the firm level [21,22]. This paper expands the research by examining regional carbon emission reductions as a whole and further analyzing the residential sector. Third, in terms of its practical implications, the findings of this paper show that regional carbon emission reduction can create a win-win situation for cities and local enterprises, providing a valuable reference for economic entities worldwide to actively take measures to save energy, reduce emissions, and achieve sustainable development. Additionally, there are relatively few studies on carbon emission reduction in the residential sector, which is also significant for environmental protection. This paper analyzes the spillover effect of regional residents’ carbon emission reduction, enriching research related to spillover effects and encouraging global residents to take active measures to reduce carbon emissions.

2. Institutional Background

2.1. Chinese Carbon Emission Reduction History

Since becoming a party to the United Nations Framework Convention on Climate Change, China has actively promoted energy conservation and emission reduction. For a long time, Chinese carbon emission reduction policies have aimed to reduce energy waste and pollutant emissions. Their purpose has gradually evolved from the initial “energy saving and emission reduction” to “low-carbon” development and has now transitioned to a “dual-carbon” goal. Overall, the evolution of China’s carbon emission reduction policy can be divided into four stages: initial exploration, development and change, deepening reform, and strategic breakthrough.

First, the initial exploration stage (1988–1997): During this period, there was significant controversy between developing and developed countries regarding carbon emissions reduction, primarily concerning the responsibilities and obligations regarding carbon emissions or reductions. China ratified the United Nations Framework Convention on Climate Change (UNFCCC) in 1992, which was enacted in 1994. The convention acknowledged that developed countries historically hold the greatest responsibility for current emissions and have been the largest contributors to global greenhouse gas emissions. In 1997, the United Nations Climate Conference held in Tokyo adopted the Kyoto Protocol, the first international treaty to restrict global greenhouse gas emissions. Due to rapid economic growth, greenhouse gas emissions from emerging developing countries have constituted an increasingly larger share of global emissions. Developed countries emphasize using current emissions as the benchmark, seeking to enhance global emission reduction efficiency and reduce costs [23]. On the other hand, developing countries stress the historical responsibilities of developed countries [23]. They also highlight the relatively low per capita emissions in developing countries and advocate for respecting and protecting their right to development. Particularly, developing countries still must address poverty, which affects a significant portion of their population. Therefore, during this phase, China believed that developed countries should proactively take the lead in emissions reduction efforts.

Second, the development and change stage (1998–2010): After 1998, compared to the first stage, China began actively promoting carbon reduction initiatives, and it actively participated in the Clean Development Mechanism (CDM) negotiations. China signed the Kyoto Protocol in 1998 and ratified it in 2002. In December 2008, China released the Green Carbon Fund Offsetting Label, the first domestic carbon market program initiated by the Chinese government. In 2009, at the Copenhagen Climate Conference, China set a target to reduce carbon dioxide emissions per unit of GDP by 40–45% by 2020 compared to 2005. As shown in Figure 1, China’s carbon emission intensity in 2005 was about 2.8819 (tons/one hundred yuan). Compared with the first phase, China’s carbon emission reduction actions had begun to bear fruit. Additionally, China’s carbon dioxide emissions per unit of GDP in 2020 were about 0.9748 (tons/one hundred yuan), representing a decrease of 66.04% compared to 2005.

Third, the phase of deepening reforms (2011–2019) was marked by China’s proactive efforts to construct a unified carbon trading market and further solidify its determination to reduce emissions. Regarding the objective of establishing a unified carbon trading market, in March 2011, China approved seven provinces and municipalities, including Beijing, conducting pilot carbon emission trading from 2013 to 2015, marking the initial stage of the carbon trading market’s development. In 2014, China initiated the top-level design of a national carbon market. By 2016, China proposed establishing a national carbon emission trading system and introduced policies mandating the implementation of a dual-level management system at the national and local levels, enhancing departmental coordination mechanisms, and enforcing a carbon emission quota management system. In 2017, China mandated the construction of a nationally unified carbon emission trading market. Demonstrating firm commitment to emissions reduction, China entered into joint climate change statements with other parties, such as India, Pakistan, the European Union, and France. In September 2015, the “China-U.S. Joint Statement on Climate Change” reiterated China’s pledge to reduce carbon dioxide emissions per unit of GDP by 60–65% from 2005 levels by 2030. As illustrated in Figure 1, by the end of 2020, China had reduced its carbon dioxide emissions per unit of GDP within the targeted range compared to 2005, indicating China’s resolute determination, substantial efforts, and commendable outcomes in carbon emission reduction endeavors.

Fourth, the phase of strategic breakthrough (2020–present) began on 22 September 2020, when China formally introduced the “dual-carbon” objective at the 75th United Nations General Assembly. Correspondingly, China’s emissions reduction policies have begun to elevate the concept of “carbon reduction” to new strategic heights. As illustrated in Figure 1, since 2013, China’s carbon emission intensity has steadily decreased yearly, while its overall carbon emissions have gradually stabilized. However, the challenge of achieving green and low-carbon development remains daunting in the long term. In July 2021, China’s carbon emissions trading market began trading operations, with the electricity generation industry being the first to integrate into the national carbon market.

In summary, the Chinese carbon emission reduction policy system has undergone several years of development and achieved significant results, providing a solid real-world context for this study. As issues like climate change and global warming become increasingly pressing, low-carbon development remains inevitable for China and other countries. This necessitates collaborative efforts from various economic entities, including central and local governments, businesses, and residents. Given that enterprises are the fundamental units of the economy and their performance in ESG factors forms the basis for achieving sustainable economic development [2], does regional carbon reduction impact the ESG performance of local firms? This question offers an opportunity and direction for this study.

2.2. Analysis of the Relationship between Carbon Emission Reduction and ESG Performance of Local Firms in Chinese Provinces

Building upon Figure 1, Figure 2 and Figure 3 provide detailed insights into the carbon emission intensity and reduction levels across various provinces in China and their relationship with the ESG performance of local listed companies. Specifically, we calculate the average carbon emission intensity, level of carbon emission reduction, and ESG performance of listed companies across different provinces from 2009 to 2021. In essence, Figure 2 and Figure 3 depict the average levels of carbon emissions and local corporate ESG performance over the sample period. Compared to random selection of a single year for observation, averaging across all years provides a more effective means to mitigate the impact of specific events, thereby facilitating a more objective assessment of the research sample.

Figure 2 illustrates the average carbon intensity and the average ESG performance of local companies in each province from 2009 to 2021. The statistical results show that the average carbon intensity of coastal provinces in eastern China during the sample period is relatively low, highlighting the differences in carbon intensity among China’s provinces. At the same time, we can observe an inverse relationship between regional carbon emission intensity and the ESG performance of local companies, indicating that companies in areas with a lower carbon intensity may have superior ESG performance.

Following the methodology used in Figure 2, the statistical findings from Figure 3 reveal a synchronous trend between the average carbon reduction levels across 30 provinces of China (excluding Tibet, Hong Kong, Macau, and Taiwan due to data absence) and the ESG performance of local listed companies from 2009 to 2021. This result offers a clearer opportunity and direction for the research. Based on these findings, it can be preliminarily inferred that regional carbon reduction may positively influence the ESG performance of local enterprises. However, this assertion requires further theoretical analysis and empirical validation to draw more precise and reliable conclusions.

2.3. The Impact of the COVID-19 Pandemic on Carbon Emissions in China

In our study sample, two particular years of interest are 2020 and 2021. During these years, the COVID-19 pandemic erupted in China, significantly impacting carbon emissions across many provinces. Therefore, in this section, we examine through a literature review and data analysis whether the COVID-19 pandemic influenced our study.

The global COVID-19 pandemic has resulted in significant loss of life worldwide. The widespread transmissibility of the virus halted economic activities and industrial production in 2020 across many countries, leading to a sharp decline in carbon emissions [24]. China, as the largest emitter of carbon dioxide among developing economies (alongside India), garnered significant scholarly attention regarding the impact of the pandemic on its carbon emissions. Furthermore, while China’s total emissions are substantial, this is largely attributed to its large population base; per capita carbon emissions in China have shown significant reductions. Han et al. (2021) estimated real-time changes in China’s carbon dioxide emissions based on GDP fluctuations, revealing a 11.0% reduction in carbon emissions during the most severe period of the pandemic (the first quarter of 2020) compared to the first quarter of 2019 [25]. However, China’s carbon emissions witnessed a substantial decline only in the early stages of the pandemic, rapidly rebounding from March 2020 onwards, linked to the stringency of pandemic control measures [26]. Furthermore, since the onset of the COVID-19 pandemic, governments and industry associations in some countries have called for postponing the implementation of “green policies,” relaxing vehicle emission standards, and suspending research on clean energy deployment and supply issues [27]. Therefore, the impact of the COVID-19 pandemic on carbon dioxide emissions is temporary [27]. Similarly, the impact of the 2008–2009 global financial crisis on carbon emissions was transient, with global carbon dioxide emissions rebounding swiftly in 2010 [28]. This rebound was driven by rapid declines in energy prices, substantial government investments in stimulating economic recovery, and rapid economic growth in developing countries [28]. In summary, the COVID-19 pandemic is expected to have a transient impact on our 2020 study sample. However, the affected period constitutes a minor proportion of the entire study interval.

Under the strict control measures implemented by the Chinese government in response to the COVID-19 pandemic, sectors such as transportation, accommodation, and food services were most directly affected. Due to the difficulty of obtaining data for the accommodation and food services sector and considering that railway transportation is a significant source of carbon dioxide emissions, a tangible understanding of the impact of the COVID-19 pandemic on China’s carbon emissions can be gained through railway transportation data. Figure 4 illustrates the number of railway passengers transported in China from 2017 to 2021. It shows a gradual increase in railway passenger traffic prior to the outbreak of the COVID-19 pandemic. Following the onset of the COVID-19 pandemic in 2020, passenger volumes sharply decreased, with a partial recovery observed in 2021. These data support evidence from the literature indicating that the COVID-19 pandemic initially led to a significant reduction in carbon emissions, followed by a subsequent recovery, influenced by various factors.

Based on the annual data presented in Figure 4, Figure 5 presents monthly data on the number of railway passengers transported in China. As observed in the figure, following the outbreak of the COVID-19 pandemic, railway passenger traffic sharply declined in February 2020, followed by a gradual recovery. Starting in September 2020, passenger volumes gradually returned to pre-pandemic levels. Therefore, all the evidence points to the same conclusion: the pandemic had an impact on carbon emissions in China, but this impact was transient and represented only a small fraction of our study period. Furthermore, even though the COVID-19 pandemic had a slight impact on carbon emissions reductions, in other words, regional carbon reductions during the pandemic are not only the result of collective efforts by economic entities within the region but also include the impacts of external events. This does not affect the original intent of our study, which is to confirm the benefits of carbon reductions in incentivizing companies to engage in emission reduction actions and enhance ESG performance proactively. Despite the pandemic contributing to carbon emissions reductions in the short term, it does not alter our encouragement of companies effectively integrating their interests with governmental and societal objectives, ultimately forming the research goal of coordinated, sustainable development among regional economic entities. To ensure the robustness of our conclusions, robustness tests will be conducted in the empirical analysis excluding samples from 2020 and 2021 to mitigate any bias caused by the pandemic.

3. Literature Review, Theoretical Analysis, and Hypothesis Development

3.1. Regional Carbon Emission Reduction and ESG Performance of Local Enterprises

Climate change caused by carbon dioxide emissions has become a global challenge, prompting governments worldwide to implement stringent control policies and action plans to reduce carbon dioxide emissions and achieve low-carbon transitions. Consequently, a growing body of scholars is conducting in-depth research on carbon emissions and carbon reduction. Stakeholder theory posits that actively assuming social responsibility contributes to harmonizing stakeholder relations, thereby reducing transactional friction, mitigating potential operational risks, and fostering a favorable business environment for enterprises [29]. Therefore, enterprises with high carbon emissions are likely to face increased operational risks and transaction costs. Building on this theoretical framework, the existing literature indicates that increased carbon emissions lead to greater difficulty in securing bank loans for companies [21], lowered corporate credit ratings [22], and decreased corporate bond prices [30]. Consequently, most stakeholders evidently prefer environmentally friendly companies [31], indicating that carbon reduction can yield potential benefits for enterprises. In this study, we seek to extend the literature on the economic consequences of carbon emissions by examining the overall regional carbon reduction scenario, as regional environments can influence local firms’ strategic decisions [18,19,20].

The ESG concept integrates social benefits into the corporate value system, which represents a greener development approach, a more responsible corporate image, and more effective corporate governance, aligning closely with the requirements of corporate sustainability [32]. Encouraging greater ESG participation poses a challenge for many researchers and practitioners [5]. Therefore, scholars have begun to continuously explore the internal and external factors influencing corporate ESG performance. In terms of the internal factors, scholars have investigated the impact of corporate culture [17], board racial diversity [33], and CEO characteristics on corporate ESG performance [34]. In terms of external factors, scholars have examined the effects of environmental regulations and tax incentives on corporate ESG performance [15,16]. Additionally, media coverage [35] and institutional investor site visits effectively provide supervision and enhance corporate ESG performance [36]. However, the formulation and implementation of formal institutions are not sufficiently robust, as they rely solely on external “mandatory” norms and requirements, which may lead to the frequent occurrence of the “adverse selection” phenomenon [17]. On the one hand, regional carbon emission reduction is a consequence of formal institutions, compelling local enterprises to enhance their ESG performance under environmental regulatory pressures, albeit with limited efficacy. On the other hand, regional carbon emission reduction can be seen as a form of informal institutional practice at the regional level. Although lacking mandatory, binding force, it can exert a subtle but significant influence on the sustainable development of local enterprises, specifically as follows:

Firstly, regional carbon emission reduction can lower the operational costs of local enterprises, thus strengthening the economic foundation for improving corporate ESG performance. Most stakeholders prefer environmentally friendly companies [31]. Companies in high-carbon-emission regions often face stricter environmental regulations and higher compliance costs, necessitating higher carbon management and operational expenses [37]. This may lead them to adopt passive strategies to comply with regulations and maintain operations, thereby neglecting the negative externalities on the environment to save costs. Furthermore, high-carbon-emission regions typically rely on carbon-intensive industries [38]. Due to the increased costs associated with carbon emissions, carbon-intensive enterprises usually exhibit lower profitability [39], heightened cash flow uncertainty, and higher profit distribution risks [21]. Regional carbon emission reduction can improve the resource utilization efficiency of local enterprises. Maintaining the necessary cash flow for operations and ensuring profit distribution to shareholders and stakeholders are essential for enterprises to support and improve their ESG performance.

Second, regional carbon reduction efforts partially establish a novel institutional environment, serving as an environmental safeguard for enterprises to enhance their ESG performance. On the one hand, environmental regulations influence industrial activities, reducing pollution emissions and significantly impacting regional environments [40]. On the other hand, corporate behavior is also influenced by environmental policies [15]. According to institutional theory, corporate structures and behaviors tend to become similar in order to acquire legitimacy, even if this uniformity does not improve economic efficiency [41]. The achievement of carbon reduction at the regional level is heavily influenced by government policies. Local government emission reduction regulations and standards partially establish a new institutional environment, wherein government supervision and public scrutiny may lead carbon-intensive enterprises to incur additional compliance costs [42]. Punitive local government policies against corporate externalities can rectify firms’ external environmental behavior, albeit with high monitoring costs [17]. Conversely, adopting incentive-based policies towards positive externalities can more effectively steer firms towards low-carbon development and green transformation, with the monitoring costs typically being negligible. Therefore, whether driven by legitimacy pressures or incentivized pursuits of low-carbon development, regional carbon reduction initiatives can encourage local enterprises to actively enhance their ESG performance, resulting in benefits for both regional environments and businesses.

Third, regional carbon reduction initiatives encourage local enterprises to adopt similar operational and financial decisions, driving low-carbon green transformations and stimulating improvements in ESG performance. On the one hand, according to social norm theory, individual behaviors tend to align with the norms of their respective communities; deviating from these norms may result in punishment or even a loss of legitimacy as community members [43]. Regional carbon reduction efforts often stem from concerted efforts among regional economic entities, cultivating a low-carbon atmosphere that encourages local enterprises to actively enhance environmental governance. On the other hand, the distance between economic entities affects information costs and asymmetries [44]. Soft information such as that on regional environment, government, and corporate relations is intangible and harder to acquire, disseminate, and understand yet crucial for assessing company risks and making investment decisions [45]. Additionally, corporate behavior is susceptible to peer effects [46]. Therefore, signals for regional carbon reduction are easily picked up on by local enterprises because strategic imitation learning can enhance enterprise value and mitigate potential failure risks [46]. Consequently, in regions with significant carbon reduction efforts, enterprises gradually assimilate into this low-carbon environment, spontaneously engaging in environmental governance to enhance ESG performance. Therefore, we propose the hypothesis as follows:

H1. 

Regional carbon reduction significantly enhances the ESG performance of local enterprises.

3.2. The Channel Mechanisms of Regional Carbon Emission Reduction in the ESG Performance of Local Enterprises: Green Credit

Green credit can steer credit resources away from highly polluting and energy-intensive industries through stringent credit management, thereby promoting energy conservation and emissions reduction [47]. Green credit requires commercial banks to fully consider the environmental risks associated with loan projects. Traditionally, information asymmetry leads to agency problems, causing external investors to demand risk premiums to protect their interests. Consequently, the extent of a firm’s financing constraints largely depends on the level of information asymmetry it faces [48]. Due to the existence of information asymmetry, commercial banks are unable to gain deep insights into the specific operational conditions of each enterprise. Regional carbon emissions reductions improve the favorability of local enterprises with fund suppliers, resulting in more green credit support. Long-term stable funding support forms the foundation for enterprises to enhance their ESG performance [49]. Regional carbon emissions reduction efforts alleviate local enterprises’ financing constraints by securing more green credit, thus enhancing ESG performance. Furthermore, enterprises require substantial capital investment for green technology innovation, often facing significant risks [50]. Green credit obtained through regional carbon emissions reductions can stimulate enterprise green innovation, enhancing ESG performance. A higher level of regional carbon emissions reduction does not mean the absence of high-carbon-emitting enterprises. Fund suppliers consider carbon emissions status in loan decisions, charging higher interest rates to high-carbon emitters and offering lower rates to environmentally responsible companies [51]. Green credit encourages environmentally friendly practices and pressurizes high-emission, high-consumption enterprises [49]. Green credit from regional carbon emissions reductions deepens the financing constraints for local high-emission enterprises, accelerating their green transformation and enhancing ESG performance. Therefore, we propose the hypothesis as follows:

H2. 

Regional carbon emissions reduction can help local enterprises secure more green credit, thereby enhancing the ESG performance of local enterprises.

3.3. The Channel Mechanisms of Regional Carbon Emission Reduction in the ESG Performance of Local Enterprises: Media Coverage

As an external factor, the media can directly influence ESG performance by enhancing corporate social responsibility through its coverage [52]. The ultimate control of Chinese media rests in the hands of the central and local governments, with news content subject to review by the party’s propaganda departments [35]. Thus, regional carbon emissions reduction, which responds to national calls and promotes well-being, attracts media attention to local businesses. Simultaneously, media coverage enables enterprises to harness the governance utility of external oversight [20,52], thereby fostering enhancements in ESG performance [35]. Firstly, media coverage enhances information transparency [53], reducing supervision and governance costs for external stakeholders. This compels enterprises to improve ESG performance to meet stakeholder demands, enhance their reputation, and maintain sustainable development [54]. Secondly, increased media coverage constrains management’s ability to manipulate information [20], alleviating information asymmetry [44]. Under media scrutiny, enterprises will adhere to social responsibility guidelines, enhancing ESG performance. Thirdly, media influence through reputational mechanisms impacts corporate governance [35]. Under media scrutiny, managers must pay more attention to environmental and social responsibilities in order to meet stakeholder expectations [35]. In summary, regional carbon emissions reduction attracts media coverage for local businesses, and under media scrutiny, enterprises will improve their ESG performance. Therefore, we propose the hypothesis as follows:

H3. 

Regional carbon emissions reduction can attract more media coverage for local enterprises, thereby enhancing the ESG performance of local enterprises.

3.4. The Channel Mechanisms of Regional Carbon Emission Reduction in the ESG Performance of Local Enterprises: Green Investors

Although green investments have increased in the professional investment arena and some fund companies have incorporated green investments into their corporate strategies, they still constitute only a small fraction of the financial industry in terms of the funds managed and assets under management [55]. Moreover, the additional costs incurred by enterprises in considering environmental issues during production processes often prevent them from obtaining short-term economic benefits. If they fail to ensure maximized shareholder interests, corporate managers face the risk of dismissal, potentially resulting in their reluctance to address environmental issues and hindering improvements in corporate ESG performance. Companies lack intrinsic motivation to actively pursue green investments from investors. Thus, reductions in environmental pollution by enterprises cannot be achieved without the driving force of external stakeholders [56]. Green investors, as critical stakeholders of companies, assess economic, social, and environmental performance in the investment process [57]. They play a crucial role in supervision and governance, thereby achieving economic benefits and environmental protection [57]. When selecting investment targets, green investors consider compliance with environmental standards, pollution control, and ecological protection crucial for pre-governance [56]. According to the theory of information asymmetry, external stakeholders find it difficult to fully comprehend the true operational status of companies. How can green investors identify attractive investment targets? In situations with limited information, green signals from regional carbon reductions can attract more green investors to invest in local enterprises. From the perspective of green investors, corporate involvement in green governance contributes to improving relationships among stakeholders, creating shared value, and achieving sustainable investment goals [58]. Under this value orientation, green investors express their suggestions to companies on improving environmental conditions and ESG performance through various means, including enhancing private communication and negotiation with management [59]. If communication with management does not yield satisfactory results, green investors exert pressure on corporate management by selling off stocks or exiting the company [60]. In conclusion, regional carbon reductions attract more green investors to invest in local enterprises. Under robust external oversight, companies provide higher-quality environmental information and performance, thereby enhancing their ESG performance. Therefore, we propose the hypothesis as follows:

H4. 

Regional carbon emissions reduction can attract more green investors to local enterprises, thereby enhancing the ESG performance of local enterprises.

Combining the above analyses, we construct the theoretical model of this study (Figure 6).

4. Research Design

4.1. Sample and Data

Our sample comprises all Chinese A-share listed firms for the period from 2009 to 2021. The sample period starts in 2009 due to the availability of detailed ESG rating information from the Huazheng Index for Chinese A-share listed companies. The period extends to 2021, and it is aligned with the detailed carbon emission information provided by the China Emission Accounts and Datasets for various provinces in China. The samples were selected according to the following criteria: (1) excluding financial and insurance companies; (2) excluding ST, (*)ST, and PT companies; and (3) excluding companies missing the data. We applied Winsorization to all continuous variables at the 1% and 99% levels to mitigate the effects of outliers. Additionally, media data were primarily sourced from the China Research Data Service Platform (CNRDS) database. Other financial data were obtained from the China Securities Market and Accounting Research (CSMAR) database. Provincial-level data were manually collected from the China National Bureau of Statistics. Our final sample includes 30,349 firm-year observations.

4.2. Variable Definitions

4.2.1. Carbon Emission Reduction

Following Xu et al. [61], data on the total carbon emissions of each province (excluding Hong Kong, Macau, Taiwan, and Tibet due to missing data) were acquired from the China Emission Accounts and Datasets. This database employs a sectoral approach following the Intergovernmental Panel on Climate Change (IPCC, 2006), wherein various economic sectors serve as the accounting units. It calculates the carbon emissions of each sector by multiplying the consumption of different fuel types over a specific period by three parameters: the lower heating value of the fuel, the carbon content per unit of heat, and the oxidation rate (considered collectively as the energy carbon emission factor). These emissions are then aggregated to obtain the total carbon emissions generated by energy utilization in economic activities. Using the carbon emission data of various provinces in China, we construct Model (1) to estimate the annual carbon reduction levels for the 30 provinces (excluding Hong Kong, Macau, Taiwan, and Tibet):

$${CER}_{j,t}=\frac{{TEC}_{j,t-1}-{TEC}_{j,t}}{{GDP}_{j,t}}$$

(1)

where ${TEC}_{j,t-1}$ and ${TEC}_{j,t}$ represent the total carbon emissions of province j in year (t − 1) and year t, respectively. If the difference between ${TEC}_{j,t-1}$ and ${TEC}_{j,t}$ is positive, this indicates that the province achieved carbon reductions in year t; otherwise, it denotes an increase in carbon emissions. ${GDP}_{j,t}$ represents the gross domestic product of province j in year t, and ${CER}_{j,t}$ reflects the level of carbon reduction per unit of regional economic output. ${CER}_{j,t}$ demonstrates the contribution of regional economic development to carbon reduction and indirectly reflects the rationality of the economic structure and the level of scientific and technological advancement in economic development.

Furthermore, considering that Model (1) can effectively measure the “absolute decline” in regional carbon emissions, it is possible that although the total carbon emissions increase, the per capita carbon emissions relative to GDP (i.e., carbon intensity) may decrease. To ensure the robustness of the conclusions, we further construct dummy variables based on Model (2) to estimate the annual carbon reduction levels of 30 provinces in China (excluding Hong Kong, Macau, Taiwan, and Tibet), which are used for robustness testing.

$${CER}_{j,t}=\frac{{CEI}_{j,t-1}}{{GDP}_{j,t-1}}-\frac{{CEI}_{j,t}}{{GDP}_{j,t}}$$

(2)

where ${CEI}_{j,t-1}$ and ${CEI}_{j,t}$ represent the total carbon emissions of province j in year (t − 1) and year t, respectively. The ratio of these emissions to the current year’s gross domestic product (${GDP}_{j,t-1}$ and ${GDP}_{j,t}$) yields the carbon intensity of the region for that year. If the difference between $\frac{{CEI}_{j,t-1}}{{GDP}_{j,t-1}}$ and $\frac{{CEI}_{j,t}}{{GDP}_{j,t}}$ is positive, this indicates that the province achieved relative carbon reduction (a decrease in carbon intensity) in year t, and the dummy variable ${CER\_VIR}_{j,t}$ is assigned a value of 1; otherwise, it is assigned a value of 0.

4.2.2. Corporate ESG Performance

We refer to prior studies and use the Hua Zheng ESG Rating System to measure corporate ESG performance [3,17]. Among the existing ESG evaluation systems for Chinese listed companies, the Hua Zheng ESG Rating System excels in both the breadth and depth of its evaluation [3]. Compared with the ESG rating of other third-party institutions, the advantages of the Hua Zheng ESG Rating System include a long evaluation backtracking period, evaluations covering all A-share listed companies, and an evaluation system that conforms to China’s national conditions [17]. This index has been widely recognized by industries and academia; the explained variable ($ESG$) is assigned based on the following allocation approach: the nine grades, from C to AAA, are given numerical values ranging between 1 and 9 in ascending order [3,17]. The Hua Zheng ESG Rating System is based on a quarterly cycle. In this study, we have selected enterprises’ average ESG performance score over four quarters (the third and fourth quarters of year t and the first and second quarters of year t + 1) as our dependent variable. For example, we define firm i’s ESG performance at the end of 2021 as the average of its ESG performance in the third and fourth quarters of 2021 and the first and second quarters of 2022. The advantage of this definition is that it mitigates the effects of singular events on ESG performance and reduces potential issues related to reverse causality.

4.2.3. Control Variables

Following prior studies, we control for several factors that influence corporate ESG performance [3,4,17]. These control variables include corporate size $\left(Size\right)$, corporate age $\left(Age\right)$, company performance $\left(Roe\right)$, operating revenue growth rate ($Growth$), board size $\left(Board\right)$, equity checks and balances $\left(Balance\right)$, proportion of independent directors $\left(Indep\right)$, management expense ratio $\left(Mfee\right)$, dual positions $\left(Dual\right)$, institutional investor shareholding ratio $\left(Inst\right)$, and property rights nature $\left(Soe\right)$. Furthermore, in addressing the research question of this study, we further select control variables at the provincial level, including human capital $\left(HC\right)$, transportation infrastructure level $\left(Trans\right)$, energy structure $\left(Energy\right)$, and level of informatization $\left(Inform\right)$. Finally, two dummy variables, $Year$ and $Industry$, are also set to control for the effects of macroeconomic fluctuations and industry differences. The details of the variables are provided in

Table 1. Variable definition.

Type	Variable	Symbol	Definition
Explained variable	Corporate ESG performance	$ESG$	According to the Hua Zheng ESG rating, the ESG index has nine levels: the lowest level is 1, and the highest level is 9
Explanatory variables	Regional carbon emission reduction	$CER$	Carbon emission reduction level per unit regional gross domestic product
		$CER\_VIR$	Dummy variable, assigned a value of 1 if carbon emission intensity decreases and 0 otherwise (this variable is used for robustness testing)
Province-level control variables	Human capital	$HC$	Enrollment in higher education institutions/total population
	Transportation infrastructure level	$Trans$	The natural logarithm of freight volume
	Energy structure	$Energy$	Regional electricity consumption/national electricity consumption
	Level of informatization	$Inform$	Total postal and telecommunications services volume/regional gross domestic product
Firm-level control variables	Corporate size	$Size$	The natural logarithm of the total number of assets
	Corporate age	$Age$	The natural logarithm of years that the company has been in existence
	Company performance	$Roe$	Net profit/operating income
	Operating revenue growth rate	$Growth$	(Current operating income − previous operating income)/previous operating income
	Board size	$Board$	The natural logarithm of the number of directors
	Equity checks and balances	$Balance$	The combined shareholdings of the second to fifth largest shareholders/the shareholding of the largest shareholder
	Proportion of independent directors	$Indep$	Number of independent directors/total number of board of directors
	Management expense ratio	$Mfee$	Operating expenses/operating revenue
	Dual positions	$Dual$	Dummy variable, which equals 1 if the CEO also serves as chairman and 0 otherwise
	Institutional investor shareholding ratio	$Inst$	The total number of shares held by institutional investors/the total outstanding shares of the company
	Property rights nature	$SOE$	Assign a value of 1 to state-owned enterprises and a value of 0 to non-state-owned enterprises based on the nature of the actual controlling entity
	Year fixed effects	$Year$	Year dummy variables
	Industry fixed effects	$Industry$	Industry dummy variables

.

4.3. Empirical Models

To investigate the impact of regional carbon emissions reduction on the local firms’ ESG performance, we estimate the following model:

$${ESG}_{i,t}={\beta }_0+{\beta }_1{CER}_{j,t}+{\beta }_2{Controls}_{i,t}+{\beta }_3{Controls}_{j,t}+\sum Year+\sum Industry+{\epsilon }_{i,t}$$

(3)

where i represents the firm, t represents the year, and j represents the province. ${ESG}_{i,t}$ represents the ESG performance of firm i in year t. ${CER}_{j,t}$ represents the level of carbon emissions reduction in province j in year t. ${Controls}_{i,t}$ and ${Controls}_{j,t}$ represent control variables at the firm and provincial levels, respectively, as defined in Table 1. All regressions in this study control for year and industry fixed effects, and we cluster the standard errors by firm.

5. Empirical Results and Analysis

5.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variables	N	Mean	SD	Min	Median	Max
$ESG$	30,349	5.758	0.874	3.250	5.500	7.750
$CER$	390	−0.090	0.181	−1.140	−0.042	0.173
$CER\_VIR$	390	0.877	0.329	0.000	1.000	1.000
$HC$	30,349	0.020	0.005	0.012	0.020	0.036
$Trans$	30,349	11.940	0.834	9.907	12.170	12.940
$Energy$	30,349	0.051	0.030	0.011	0.038	0.097
$Inform$	30,349	0.064	0.046	0.017	0.046	0.195
$Size$	30,349	22.220	1.284	19.940	22.040	26.210
$Age$	30,349	2.880	0.336	1.792	2.944	3.497
$Roe$	30,349	0.067	0.121	−0.550	0.072	0.358
$Growth$	30,349	0.178	0.399	−0.540	0.114	2.486
$Board$	30,349	2.132	0.198	1.609	2.197	2.708
$Balance$	30,349	0.348	0.284	0.009	0.263	0.994
$Indep$	30,349	0.375	0.053	0.333	0.357	0.571
$Mfee$	30,349	0.086	0.066	0.008	0.070	0.395
$Dual$	30,349	0.266	0.442	0.000	0.000	1.000
$Inst$	30,349	0.398	0.234	0.002	0.407	0.883
$SOE$	30,349	0.394	0.489	0.000	0.000	1.000

presents the descriptive statistics of the main variables used in this study. The results indicate that the mean firm ESG performance ($ESG$) is 5.758, with a standard deviation of 0.874, suggesting that the majority of the firms exhibit good ESG performance, mainly concentrated in the BBB and BB ratings. However, there are notable differences in the ESG performance among firms, consistent with previous research. Combining the financial data of companies with regional carbon emissions data based on the location of the companies, the year–province data before merging shows that the mean regional carbon emissions reduction ($CER$) is −0.090, with a median of −0.042. The maximum value is 0.173, and the minimum value is −1.140, indicating that most provinces in China have carbon emissions reduction levels above the mean but with significant differences among provinces. Additionally, although the mean regional carbon emissions reduction is negative, this only signifies that some provinces did not experience an “absolute decrease” in carbon emissions in certain years, as the carbon emissions per capita of regional GDP (i.e., carbon intensity) may still decrease. The mean of the virtual variable for regional carbon emissions reduction ($CER\_VIR$) is 0.877, indicating that the majority of regions achieved a “relative decrease” in carbon emissions. The other control variables align with existing research findings.

5.2. Baseline Regression Analysis

Table 3. Regional carbon emissions reduction and corporate ESG performance.

Variables	(1) ESG	(2) ESG	(3) ESG
$CER$	0.238 **	0.247 ***	0.297 ***
	(2.503)	(2.823)	(3.475)
$HC$	−2.391		−8.062 ***
	(−0.752)		(−2.980)
$Trans$	−0.076 ***		−0.067 ***
	(−3.423)		(−3.579)
$Energy$	−0.227		0.111
	(−0.424)		(0.243)
$Inform$	−0.861 **		−1.044 ***
	(−2.080)		(−2.872)
$Size$		0.187 ***	0.184 ***
		(20.923)	(20.526)
$Age$		−0.040	−0.034
		(−1.250)	(−1.077)
$Roe$		1.150 ***	1.161 ***
		(19.751)	(20.112)
$Growth$		−0.092 ***	−0.093 ***
		(−8.803)	(−8.847)
$Board$		0.077	0.078
		(1.397)	(1.409)
$Balance$		0.001	0.005
		(0.042)	(0.167)
$Indep$		0.335 *	0.316 *
		(1.919)	(1.817)
$Mfee$		−0.021	−0.073
		(−0.146)	(−0.516)
$Dual$		−0.004	−0.003
		(−0.231)	(−0.183)
$Inst$		0.176 ***	0.177 ***
		(4.745)	(4.781)
$SOE$		0.212 ***	0.205 ***
		(8.455)	(8.127)
$Constant$	6.363 ***	0.964 ***	1.970 ***
	(19.879)	(3.132)	(5.030)
$Year$	YES	YES	YES
$Industry$	YES	YES	YES
N	30,349	30,349	30,349
adj. R2	0.247	0.372	0.374

Notes: This table presents the estimates of regressions of regional carbon emissions reduction for corporate ESG performance. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

reports the regression results of the impact of regional carbon emissions reduction on firm ESG performance. In column (1), with only provincial-level control variables added, the coefficient of carbon emissions reduction ($CER$) is 0.238, and it is significant at the 5% level. In column (2), with only firm-level control variables added, the coefficient of carbon emissions reduction ($CER$) is 0.247, significant at the 1% level. In column (3), further controlling for provincial- and firm-level control variables, carbon emissions reduction ($CER$) is significantly positively correlated with corporate ESG performance ($ESG$) at the 1% level, with a regression coefficient of 0.297. These results indicate that regional carbon emissions reduction significantly enhances local firms’ ESG performance, validating hypothesis H1. Economically, for every 1% increase in the standard deviation of the regional carbon emissions reduction levels, local firms’ ESG performance relative to their mean increases by 0.444% (0.297 × 0.086 ÷ 5.758 × 100%). The empirical findings reflect the effectiveness of the energy-saving and emission reduction actions taken by various regions in China. Moreover, regional energy-saving and emission reduction actions not only improve the living environment for Chinese residents but also effectively promote the sustainable development of local firms, achieving a win-win situation.

Additionally, the regression results for the provincial-level control variables indicate that regional levels of human capital, transportation infrastructure, and information technology are negatively correlated with local firm ESG performance, suggesting that demographic factors and the development of freight and telecommunications industries have certain environmental implications. At the firm level, the regression results for the control variables show that firm size, return on net assets, proportion of independent directors, percentage of institutional investor ownership, and property rights ownership positively impact firm ESG performance. Conversely, the growth rate of operating income is negatively associated with ESG performance, indicating that firms striving for rapid revenue growth may potentially overlook environmental, social, and governance performance.

5.3. Robustness Check

5.3.1. Instrumental Variable Approach

The analysis in the previous sections identifies a significantly positive association between regional carbon emissions reduction and the ESG performance of local enterprises. Although we employed an averaging approach to assess corporate ESG performance to mitigate potential reverse causality, endogeneity concerns remain. To address the endogeneity concerns, we conduct a two-stage least square (2SLS) estimation. We identify two plausible instruments for our 2SLS estimation. Following Ding et al. [21], the first instrument variable is the mean level of carbon emissions reduction in provinces other than the province where the firm is located in the same year ($IVCER$). On the one hand, companies interact with their local environment [18,19,20], making it difficult for carbon emissions reductions outside an enterprise’s location to influence the enterprise. On the other hand, measurement errors and unobservable factors affect carbon emissions in each province, thus establishing a correlation between carbon emissions in other provinces and those in the province where the enterprise is located. The second instrumental variable is the mean annual rate of change in the number of births in provinces other than the province where the enterprise is located ($BRC$). The specific calculation method is as follows:

$${BRC}_{j,t}=\sum \frac{{Birthrate}_{j,t}-{Birthrate}_{j,t-1}}{{Birthrate}_{j,t-1}}/Num$$

(4)

where ${Birthrate}_{j,t}$ and ${Birthrate}_{j,t-1}$ represent the population birth rates in province j (excluding the province where the enterprise is located) in years t and (t − 1), respectively. $Num$ denotes the number of provinces (excluding the province where the enterprise is located). ${BRC}_{j,t}$ reflects the mean change in population birth rates in provinces other than the province where the enterprise is located for the current year. A positive ${BRC}_{j,t}$ indicates an increase in the number of births in other provinces compared to the previous year, while a negative value indicates a decrease. On the one hand, changes in the number of newborns outside the region where the enterprise is located are unlikely to influence the characteristics of that enterprise. On the other hand, as the population increases, the demand for food, space, and energy increases annually, accompanied by energy consumption and vegetation destruction. One direct consequence is an increase in carbon dioxide. Therefore, the change in the number of births in other provinces is correlated with the carbon emissions of the province where the enterprise is located. Therefore, our instrumental variables satisfy both the relevance and exclusion conditions.

The results are presented in

Table 4. Instrumental variable regression.

Variables	(1) 1st Stage CER	(2) 2nd Stage ESG
$CER$		0.293 ***
		(3.058)
$IVCER$	−19.774 ***
	(−20.167)
$BRC$	0.021 ***
	(5.326)
$Constant$	−3.818 ***	1.969 ***
	(−25.163)	(5.048)
Control variables	YES	YES
$Year$	YES	YES
$Industry$	YES	YES
N	30,349	30,349
adj. R2	0.855	0.377

Notes: This table presents the two-stage least square (2SLS) tests for regional carbon emissions reduction and corporate ESG performance. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

. To save space, all the regression coefficients for the controlling variables and fixed effects are omitted in the subsequent tables. Column (1) reports the first stage. In the first-stage regression results, the coefficient for the instrumental variable $IVCER$ is −19.774, and the coefficient for the instrumental variable $BRC$ is 0.021, both significant at the 1% level. Further, the F-statistic from a Kleibergen–Paap weak identification test is 448.762, significantly higher than the Stock and Yogo (2005) critical value of 19.93 for a 10% maximal bias of the instrumental variable estimator relative to OLS, which rejects the null hypothesis that the $IVCER$ and $BRC$ are weak instruments. Hansen’s J-statistics for overidentifying restrictions fail to reject the null hypothesis that our instruments are uncorrelated with the error term, further giving us confidence that our instruments are appropriate. Column (2) reports our second-stage regression results. In the second-stage regression results, the coefficient for regional carbon emissions reduction ($CER$) is 0.293, significant at the 1% level and positively significant. The results indicate that even after controlling for potential endogeneity, regional carbon emissions reduction remains positively correlated with the ESG performance of local enterprises.

5.3.2. Propensity Score Matching

Considering the potential systematic differences between companies in regions with high and low carbon emissions, we employ the propensity score matching (PSM) method to control for such systematic differences. The research sample is divided into high and low groups based on the median regional carbon emissions reduction, and these are treated as the experimental and control groups, respectively. Using the control variables in the baseline regression as covariates, a 1:1 matching with a caliper (set at 0.05) without replacement is conducted [20]. The PSM method matching results indicate that the bias of all variables is less than 10, passing the balance test. Finally, based on the sample selected through propensity score matching, we re-regress Model (3), and the results, as shown in column (1) of Table 5, still demonstrate a significantly positive coefficient for regional carbon emissions reduction, consistent with the baseline regression results.

5.3.3. Excluding the Three Biggest Provinces in Terms of Population

To mitigate potential biases caused by population size, we exclude companies located in the three provinces with the highest populations (Guangdong, Shandong, and Henan). The regression results, as shown in column (2) of Table 5, confirm the consistency of the research conclusions.

5.3.4. Alternative Measures of Corporate ESG Performance

As third-party rating agencies’ assessments of corporate ESG performance may introduce estimation bias, we utilize ESG ratings provided by the Wind database as an alternative variable for the dependent variable, further examining the impact of regional carbon emissions reduction on corporate ESG performance. As the Wind database’s ESG rating data start from 2018, the sample period spans from 2018 to 2021. The regression results, as shown in column (3) of Table 5, still reveal a significantly positive coefficient for regional carbon emissions reduction, further confirming the robustness of the baseline regression results.

5.3.5. Alternative Measures of Regional Carbon Emissions Reduction

Considering the possibility of achieving “relative carbon emissions reduction” by region, we utilize the previously defined dummy variable as an alternative indicator for carbon emissions reduction. The regression results, as presented in column (4) of Table 5, still show a significantly positive coefficient for regional carbon emissions reduction, further verifying the robustness of the baseline regression findings.

5.3.6. Lagging the Explanatory Variable

In the baseline regression, to mitigate potential issues of reverse causality, we adopted a mean approach to ESG. Considering the possibility of lagged effects of regional carbon emissions reduction on local corporate ESG performance, we conduct a one-period lagged regression on regional carbon emissions reduction. The regression results, as displayed in column (5) of Table 5, show no significant deviation from the baseline regression findings.

5.3.7. Excluding the Impact of the COVID-19 Pandemic

As discussed in the institutional context section, the COVID-19 pandemic may have temporarily affected carbon emissions. Therefore, we excluded samples from the years 2020 and 2021, which may have been affected by the pandemic, and re-estimated the model. As shown in column (6) of Table 5, our findings remain unchanged despite the potential impact of the COVID-19 pandemic.

Table 5. Robustness test results.

Variables	(1) ESG	(2) ESG	(3) WindESG	(4) ESG	(5) ESG	(6) ESG
$CER$	0.360 ***	0.261 ***	0.693 ***		0.294 ***	0.270 ***
	(3.813)	(2.760)	(5.666)		(3.272)	(3.225)
$CER\_VIR$				0.055 **
				(2.112)
$Constant$	2.072 ***	2.136 ***	−0.516	1.855 ***	1.563 ***	1.977 ***
	(4.902)	(4.923)	(−1.248)	(4.748)	(3.684)	(4.573)
Control variables	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES
N	21,680	23,057	12,852	30,349	26,238	23,560
adj. R2	0.360	0.367	0.203	0.374	0.390	0.262

Notes: This table presents the results of robustness tests of our main findings. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

Table 5. Robustness test results.

Variables	(1) ESG	(2) ESG	(3) WindESG	(4) ESG	(5) ESG	(6) ESG
$CER$	0.360 ***	0.261 ***	0.693 ***		0.294 ***	0.270 ***
	(3.813)	(2.760)	(5.666)		(3.272)	(3.225)
$CER\_VIR$				0.055 **
				(2.112)
$Constant$	2.072 ***	2.136 ***	−0.516	1.855 ***	1.563 ***	1.977 ***
	(4.902)	(4.923)	(−1.248)	(4.748)	(3.684)	(4.573)
Control variables	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES
N	21,680	23,057	12,852	30,349	26,238	23,560
adj. R2	0.360	0.367	0.203	0.374	0.390	0.262

Notes: This table presents the results of robustness tests of our main findings. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

5.4. Mechanism Analysis

5.4.1. The Green Credit Mechanism

Following Hu and Zheng [62], we use the ratio of the interest expenditure of six high-energy-consuming industries to the total interest expenditure of industrial industries to measure the development of green credit in each province. Specifically, the level of regional green credit ($GC$) = (1 − interest expenditure of six high-energy-consuming industries/total interest expenditure of industrial industries) [62]. The larger the $GC$, the higher the level of green credit in the province; conversely, the lower the $GC$, the lower the level. Column (1) of

Table 6. Results of green credit mechanism test.

Variables	Entire Sample	Financing Costs		Credit Availability		Implementation of the “Green Credit Guidelines”
	(1) ESG	(2) Low ESG	(3) High ESG	(4) Low ESG	(5) High ESG	(6) Pre- ESG	(7) Post- ESG
$CER$	0.283 ***	0.158	0.400 ***	0.143	0.371 ***	0.122	0.302 ***
	(20.202)	(1.559)	(3.565)	(1.284)	(3.188)	(1.277)	(2.705)
$Constant$	0.522 ***	1.764 ***	1.786 ***	1.424 ***	1.445 **	2.043 ***	1.825 ***
	(7.488)	(3.499)	(3.581)	(2.818)	(2.472)	(3.216)	(4.273)
Control variables	YES	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES	YES
N	30,349	15,164	15,164	11,005	11,004	6129	24,220
adj. R2	0.323	0.402	0.370	0.353	0.368	0.220	0.401

Notes: This table presents the results of green credit mechanism test. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

reports the regression results of regional carbon emissions reduction for green credit. The results indicate that the regression coefficient of regional carbon emissions reduction ($CER$) is significantly positive at the 1% level, suggesting that regional carbon emissions reduction can facilitate local enterprises in securing more green credit.

The theoretical analysis in the preceding text suggests that green credit can provide external financing channels for enterprises, alleviate internal financing constraints, and thereby promote the enhancement of corporate ESG performance. If regional carbon emissions reduction can secure more green credit for local enterprises, it could provide more assistance to enterprises with higher financing costs and stronger credit availability in terms of their ESG performance. Additionally, the China Banking Regulatory Commission issued and implemented the “Green Credit Guidelines” in 2012, which clearly stipulate the requirements for financial institutions to effectively engage in green credit. If regional carbon emissions reduction can help local enterprises secure more green credit, it could play a greater role in promoting the ESG performance of local enterprises after the implementation of the guidelines. Based on this logic, we measure the financing cost of enterprises by the ratio of financial expenses to total liabilities and the credit availability of enterprises by the ratio of long-term and short-term borrowings to total assets. We then divide the research sample into groups based on high and low financing costs, high and low credit availability, and pre-and post-implementation of the “Green Credit Guidelines” (before and after 2013) for group testing. Columns (2) to (7) of Table 6 show that only in groups with high financing costs, strong credit availability, and post-implementation of the “Green Credit Guidelines” does regional carbon emissions reduction significantly affect corporate ESG performance. The results indicate that regional carbon emissions reduction can help local enterprises secure more green credit, alleviate financial pressure, and enhance ESG performance, thereby validating hypothesis H2. Additionally, the research findings of this study can provide theoretical support for the further development of green credit and its allocation towards sustainable development.

5.4.2. The Media Coverage Mechanism

Following He et al. [35], we use the total number of reports about a company from online and print media divided by 1000 to measure the company’s media coverage ($Media$). The higher the value of $Media$, the greater the extent to which the company is covered by the media. Column (1) of

Table 7. Results of media coverage mechanism test.

Variables	Entire Sample	Number of Securities Analysts
	(1) ESG	(2) Low ESG	(3) High ESG
$CER$	0.223 ***	0.345 ***	0.160
	(3.778)	(3.307)	(0.996)
$Constant$	−4.922 ***	2.562 ***	0.503
	(−10.538)	(5.156)	(0.801)
Control variables	YES	YES	YES
$Year$	YES	YES	YES
$Industry$	YES	YES	YES
N	21,422	14,083	7559
adj. R2	0.337	0.328	0.339

Notes: This table presents the results of media coverage mechanism test. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

reports the regression results of regional carbon emissions reduction for media coverage. The results indicate that the regression coefficient of regional carbon emissions reduction ($CER$) is significantly positive at the 1% level, suggesting that regional carbon emissions reduction can attract more media attention to local companies.

Media coverage can enhance the external monitoring and governance effectiveness of enterprises [20]. If regional carbon reduction has a more significant impact on improving the ESG performance of enterprises with insufficient external monitoring and governance, it can effectively verify the mechanism of media coverage. Following Ji et al. [20], we measure the degree of external supervision of enterprises by the number of securities analysts tracking them and divide the research samples into two groups, those with a high number of securities analysts tracking them and those with a low number, for group testing. Columns (2) and (3) of Table 7 show that only in the group with a low number of securities analysts tracking them does regional carbon reduction have a significant impact on the ESG performance of enterprises. The results indicate that the media, with its dissemination function, has become an effective regulatory force for the external governance of companies. Regional carbon reduction can attract more media attention to local enterprises and promote enterprises to take active measures to improve their ESG performance, thus verifying hypothesis H3.

5.4.3. The Green Investor Mechanism

Following Zhang et al. [56], we queried the fund details table for investments in listed companies to determine the presence of green investors in the companies. Based on this information, we measure the entry of green investors into a company by taking the natural logarithm of the number of green investors the company includes each year ($GI$). A higher value of $GI$ indicates that more green investors have entered the enterprise. Column (1) of

Table 8. Results of green investor mechanism test.

Variables	Entire Sample	Executives with Environmental Employment Backgrounds
	(1) ESG	(2) Without ESG	(3) Have ESG
$CER$	0.163 **	0.291 ***	0.218
	(2.297)	(2.635)	(1.132)
$Constant$	−5.257 ***	1.562 ***	1.888 ***
	(−22.588)	(3.023)	(2.704)
Control variables	YES	YES	YES
$Year$	YES	YES	YES
$Industry$	YES	YES	YES
N	25,742	18,999	6743
adj. R2	0.346	0.394	0.391

Notes: This table presents the results of green investor mechanism test. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

displays that the regression coefficient of regional carbon emission reduction is significantly positive at the 5% level, indicating that regional carbon emission reduction has attracted more green investors to enter local enterprises.

The appointment of executives with backgrounds in environmental protection is advantageous in attracting green investors. As the number of executives with environmental backgrounds increases and their autonomy in management grows, their attractiveness to green investors gradually strengthens. If regional carbon emission reduction can attract green investors to local enterprises, this can better assist companies lacking executives with environmental backgrounds in improving their ESG performance. Therefore, we divide the research sample into groups with executives with environmental employment backgrounds and those without for subgroup analysis. Columns (2) and (3) of Table 8 indicate that only in companies without executives with environmental employment backgrounds does regional carbon emission reduction significantly impact corporate ESG performance. The results suggest that when companies lack attention from green investors, regional carbon emission reduction can enhance the attractiveness of local enterprises and guide more green investors to promote an improvement in local enterprise ESG performance, thus validating hypothesis H4.

5.5. Heterogeneity Analysis

5.5.1. Impact of Environmental Regulation

The empirical results indicate that regional carbon emission reduction significantly enhances the ESG performance of local enterprises. However, ecological and environmental resources, as public goods with unclear property rights, entail issues that cannot be solely addressed through market mechanisms, requiring government intervention and control. Thus, the impact of regional carbon emission reduction on corporate ESG performance may exhibit heterogeneity due to variations in regional environmental regulation. On the one hand, environmental regulations can notably promote carbon emission reduction, with stricter environmental constraints exerting a greater effect on carbon emission reduction [40]. On the other hand, environmental regulations can also influence corporate ESG performance [15]. Therefore, we use the ratio of a completed investment in industrial pollution control to the industrial value added in 30 provinces in China as a measure of regional environmental regulation and separate the sample into two groups based on the median level of environmental regulation—with lenient and stringent regulations. The regression results, as shown in columns (1) and (2) of Table 9, indicate that in regions with strict environmental regulation, the regression coefficient of the carbon emission reduction level ($CER$) is significantly positive at the 1% level, whereas the regression coefficient of the carbon emission reduction level ($CER$) in regions with lenient environmental regulation is not significant. This suggests that stringent environmental regulations may lead local high-carbon-emitting enterprises to incur additional compliance costs, necessitating green transformation and enhancing the ESG performance of local enterprises.

5.5.2. Impact of Industry Nature

Environmentally friendly enterprises have embraced the proactive notion of environmental protection, capable of managing their production and operation activities within the limits sustainable for natural resources and the ecological environment, thus achieving green development. Enterprises that do not generate negative externalities on the ecological environment typically exhibit commendable ESG performance. Hence, the benefits brought by regional carbon emission reduction to such enterprises may be limited. We anticipate a more significant enhancement in the ESG performance of local heavily polluting enterprises due to regional carbon emission reduction. Our samples are categorized into heavy-polluting industry groups and non-heavy-polluting industry groups according to the “Catalogue for the Management of Industry Categories for Environmental Protection Verification of Listed Companies in China.” The group regression results, as shown in columns (3) and (4) of Table 9, indicate that in the heavy-polluting industry group, the regression coefficient of regional carbon emission reduction ($CER$) is significantly positive at the 1% level, whereas in the non-heavy-polluting industry group, although the regression coefficient of regional carbon emission reduction ($CER$) is positive, it is not significant. The research findings suggest that heavily polluting enterprises should seize upon regional carbon emission reduction as a green signal; actively engage in energy conservation, emission reduction, and green development; establish a favorable corporate image; and improve the environment while benefiting society in achieving sustainable development.

5.5.3. Impact of Digitization

Digital transformation has emerged as a crucial means of revitalizing traditional dynamics and fostering the development of new ones. Specifically, enterprise digitization has the potential to enhance ESG performance [5]. Therefore, drawing upon the work of Fang et al. [5], we establish a digital dictionary based on the semantic expressions of national policies related to the digital economy. Leveraging text analysis, we construct an index of the degree of enterprise digitalization and divide companies into high and low groups based on the median. The results of group regression, as indicated in columns (5) and (6) of Table 9, reveal that in companies with lower levels of digitalization, the regression coefficient of regional carbon emission reduction ($CER$) is significantly positive at the 1% level, whereas in companies with higher levels of digitalization, the regression coefficient of regional carbon emission reduction ($CER$) is not significant. These findings suggest that digital transformation can effectively promote the sustainable development of enterprises, and regional carbon emission reduction can, to some extent, compensate for the deficiencies of enterprises with lower levels of digitalization within their localities.

Table 9. Heterogeneity analysis results.

Variables	Environmental Regulation		Industry Nature		Digitization
	(1) Lenient ESG	(2) Stringent ESG	(3) Non-Heavy-Polluting ESG	(4) Heavy-Polluting ESG	(5) Low ESG	(6) High ESG
$CER$	0.079	0.314 ***	0.167	0.476 ***	0.335 ***	0.128
	(0.545)	(3.226)	(1.492)	(3.715)	(3.720)	(0.902)
$Constant$	3.033 ***	1.309 ***	1.709 **	2.675 ***	2.634 ***	1.137 **
	(6.273)	(2.700)	(3.845)	(3.961)	(5.281)	(2.111)
Control variables	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES
N	15,264	15,085	21,401	8945	14,824	14,824
adj. R2	0.444	0.271	0.410	0.290	0.322	0.414

Notes: This table presents the results of the heterogeneity test. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

Table 9. Heterogeneity analysis results.

Variables	Environmental Regulation		Industry Nature		Digitization
	(1) Lenient ESG	(2) Stringent ESG	(3) Non-Heavy-Polluting ESG	(4) Heavy-Polluting ESG	(5) Low ESG	(6) High ESG
$CER$	0.079	0.314 ***	0.167	0.476 ***	0.335 ***	0.128
	(0.545)	(3.226)	(1.492)	(3.715)	(3.720)	(0.902)
$Constant$	3.033 ***	1.309 ***	1.709 **	2.675 ***	2.634 ***	1.137 **
	(6.273)	(2.700)	(3.845)	(3.961)	(5.281)	(2.111)
Control variables	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES
N	15,264	15,085	21,401	8945	14,824	14,824
adj. R2	0.444	0.271	0.410	0.290	0.322	0.414

Notes: This table presents the results of the heterogeneity test. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

6. Further Analysis

6.1. The Spillover Effects of Regional Residents’ Carbon Emission Reduction

The empirical results indicate that regional carbon emission reduction significantly enhances the ESG performance of local enterprises. On the one hand, the improvement in ESG performance is a result of the energy-saving and emission reduction efforts of local enterprises themselves. On the other hand, ESG performance may be a spillover effect of carbon emission reduction actions undertaken by other enterprises within the region and even by other economic entities. A research report by BloombergNEF (BNEF) suggests that direct and indirect carbon emissions from household consumption account for 65% of the global total carbon emissions, making household consumption a significant contributor to carbon emissions. To address this, this study utilizes carbon emission data from the residential sectors (including urban and rural residents) of 30 Chinese provinces. Following the same approach as Model (1), we construct a regional residential carbon emission reduction indicator ($RESCER$). The other variables remain consistent with the baseline regression.

Table 10. Regional residents’ carbon emission reduction and corporate ESG performance.

Variables	(1) ESG
$RESCER$	0.016 ***
	(3.041)
$Constant$	1.884 ***
	(4.827)
Control variables	YES
$Year$	YES
$Industry$	YES
$\mathrm{N}$	30,349
$\mathrm{adj}. {\mathrm{R}}^2$	0.374

Notes: This table presents the estimates of regressions of regional residents’ carbon emissions reduction on corporate ESG performance. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

presents the regression results of the impact of regional residential carbon emission reduction on local enterprise ESG performance. The regression coefficient of regional residential carbon emission reduction ($RESCER$) is significantly positive at the 1% level, with a coefficient of 0.016. Economically, for every 1% increase in the standard deviation of the regional residential carbon emission reduction levels, the ESG performance of local enterprises relative to the mean increases by 0.204% (0.016 × 0.758 ÷ 5.758 × 100%). These results indicate the presence of spillover effects from regional residential carbon emission reduction, whereby energy-saving and emission reduction efforts by regional residents promote the enhancement of local enterprise ESG performance. Economic entities within the same region share common economic environments and fortunes. Furthermore, this finding reaffirms the absence of bias in the previous analyses due to potential endogeneity issues such as reverse causality. Lastly, residential carbon emission reduction constitutes an essential component of comprehensive sustainable development. Given China’s substantial population base, expediting a transition in household lifestyles has become an inevitable choice for mitigating climate change. The research conclusions of this study hold theoretical significance in raising global public awareness of carbon reduction.

6.2. Economic Consequence Check

The value effect and risk control are two crucial aspects of the operation of enterprises. Enterprise value is linked to the healthy development and long-term interests of an enterprise, while risk control aims to safeguard the assets of the enterprise and enhance its competitiveness. This study reveals the impact of regional carbon emission reduction on enterprise ESG performance, but whether it can bring dual benefits of the value effect and risk control to companies in the future requires further investigation.

Therefore, following prior research [4,20], we use Tobin’s Q value at time t + 1 ($TobinQ$) as a proxy variable for future enterprise value and the skewness of stock negative returns at time t + 2 ($NCSKEW$) and the ratio of upper to lower volatility ($DUVOL$) as proxy variables for future stock price crash risk. We divide the sample into high and low groups based on the median level of regional carbon emission reduction and examine whether local enterprise ESG performance can create value and reduce risk for enterprises in the future.

Table 11. Results of economic consequence check.

Variables	Regional Carbon Emission Reduction
	(1) High TobinQ	(2) Low TobinQ	(3) High NCSKEW	(4) Low NCSKEW	(5) High DUVOL	(6) Low DUVOL
$ESG$	0.033 ***	0.020	−0.023 ***	−0.009	−0.014 ***	−0.001
	$\left(2.627\right)$	(1.486)	(−2.926)	(−1.227)	(−2.776)	(−0.100)
$Constant$	10.211 ***	9.878 ***	−0.261	0.361	0.222	0.468 **
	(10.106)	(14.952)	(−0.905)	(1.280)	(1.133)	(2.470)
Control variables	YES	YES	YES	YES	YES	YES
$Year$	YES	YES	YES	YES	YES	YES
$Industry$	YES	YES	YES	YES	YES	YES
$\mathrm{N}$	$13,247$	12,525	12,683	10,953	12,683	10,953
$\mathrm{adj}. {\mathrm{R}}^2$	$0.394$	0.379	0.053	0.057	0.063	0.068

Notes: This table presents the results of the economic consequence check. The t-statistics reported in parentheses are based on the standard errors clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. To save space, all regression coefficients for control variables and fixed effects are omitted.

reports the results of the grouped regressions, showing a significant positive correlation between the ESG performance of enterprises in regions with high carbon emission reduction levels and future enterprise value and a significant negative correlation with future stock price crash risk, revealing the dual effects of enhancing enterprise ESG performance through regional carbon emission reduction.

7. Discussion

Increasing pressure on resources and the environment remains a significant global challenge facing economic development. Carbon emission reduction and ESG principles are important measures to promote sustainable development, attracting attention from both the business community as a practical issue and the academic realm as a hot research topic. In this regard, this study uses Chinese A-share listed companies from 2009 to 2021 as the research sample to investigate the impact of regional carbon emission reduction on local enterprise ESG performance. The research findings are as follows. Firstly, regional carbon emission reduction significantly enhances the ESG performance of local enterprises. Even after subjecting this conclusion to a battery of robustness tests, including instrumental variable methods, propensity score matching, and demographic considerations, its validity persists. The mechanism tests indicate that regional carbon emission reduction can enable local enterprises to secure more green credits, which can alleviate internal financing constraints and attract increased media coverage and green investors, which can enhance external oversight of enterprises, thus enhancing corporate ESG performance. Secondly, this study delves into the heterogeneity of the impact of regional carbon emission reduction on enterprise ESG performance from the perspectives of region, industry, and company. At the regional level, it is observed that regions with stringent environmental regulations experience a more pronounced positive effect on the ESG performance of local enterprises. At the industry level, the impact of regional carbon emission reduction on the enhancement of ESG performance is more significant for local enterprises with heavy pollution. At the company level, the effect of regional carbon emission reduction on improving ESG performance is more notable for companies with lower levels of digitalization. Thirdly, there exists a spillover effect of regional residential carbon emission reduction, wherein energy-saving and low-carbon lifestyles of local residents promote the enhancement of local enterprise ESG performance. Lastly, following the enhancement of enterprise ESG performance, regional carbon emission reduction can yield dual effects of value creation and risk mitigation in the future, specifically by increasing enterprise value and reducing the risk of stock price collapse. In conclusion, this study integrates regional carbon emission reduction, ESG principles, and China’s green financial system to discuss the necessity and intrinsic value of various economic entities, such as enterprises and residents, implementing sustainable concepts. The research findings provide micro theoretical support for China’s carbon peak and carbon neutrality strategies and offer valuable insights for economic entities worldwide into proactively adopting energy-saving and emission reduction measures, enhancing the ecological environment, and achieving sustainable development.

This paper has two main limitations. Firstly, constrained by data availability, this study utilizes provincial-level carbon emission data from China to examine the impact of regional carbon emission reduction on local enterprise ESG performance. Future research could extend the analysis by employing carbon emission data at the prefecture-level city level or even district and county levels to further investigate the economic benefits of regional carbon emission reduction. Secondly, household consumption is also a significant contributor to global carbon emissions. This study explores the spillover effects of regional residents’ carbon emission reduction. However, further investigation is needed to elucidate the mechanisms behind the impact of regional residents’ carbon emission reduction on local enterprise ESG performance. Future research could start from the resident sector, examining the economic consequences and mechanisms of residents’ consumption carbon emission reduction, thereby inspiring the general public to join the ranks of sustainable development.

8. Conclusions

In light of the research findings of this study, we propose corresponding policy recommendations tailored to different economic entities. Firstly, the government should strengthen carbon emission management in different regions, enhance the implementation of relevant policies, and promote regional carbon reduction. The research findings of this study indicate that regional carbon reduction not only improves the ecological environment but also fosters the sustainable development of local enterprises. Therefore, local governments can provide market incentives for local enterprises to reduce carbon emissions, thereby lowering the capital threshold for enterprises to transition to low-carbon practices. Furthermore, the government can offer incentives for green consumption among residents to encourage the general public to adopt low-carbon lifestyles. Secondly, enterprises should take proactive measures to improve their ESG performance. They can invest in renewable energy projects and clean technologies to enhance production processes and reduce their environmental footprint. Moreover, enterprises can enhance the transparency of their social responsibilities by publishing comprehensive ESG reports, demonstrating their commitment to continuous improvement to stakeholders. Thirdly, residents should enhance their awareness of climate change and carbon reduction. The research presented in this paper demonstrates that regional residents’ efforts in carbon reduction can positively impact the sustainable development of local enterprises. Therefore, residents should conscientiously engage in waste sorting, reduce waste generation, and adopt low-carbon lifestyles. In conclusion, achieving sustainable development is not solely a matter of individual actions taken by major economic entities but rather a question of coordinating efforts from all parties involved. Only through collaborative cooperation and a holistic awareness among major societal and economic entities can the efforts towards carbon reduction be advanced collectively, thereby realizing regional and even global sustainable development goals.