Effect of Appointed Directors on Corporate Carbon Emission Intensity: Evidence from Mixed-Ownership Reform in Chinese Private Industrial Enterprises

Abstract

The growing prominence of global warming has led to a worldwide consensus on the need to reduce carbon emissions. Employing a sample of private industrial enterprises listed on the Chinese stock market from 2008 to 2021, this study explores the effect of directors appointed by non-controlling state shareholders (appointed directors), which is a growing type of mixed-ownership reform, on corporate carbon emission intensity. The results show that appointed directors significantly reduce corporate carbon emission intensity. Mechanism tests suggest that this reduction is achieved through developing environmental strategies and increasing executive compensation incentives. Heterogeneity analyses reveal that the effect of appointed directors is more pronounced for firms with lax regional environmental regulation, in non-heavily polluting industries, with low analyst coverage, and with poor green innovation abilities. Our findings shed light on the effectiveness of mixed-ownership reform from the perspective of appointed directors and offer new implications and evidence for environmental protection and the sustainable development of enterprises in emerging markets.

1. Introduction

In recent years, the growing prominence of global climate change has led to a worldwide consensus that economic development at the expense of the environment is unacceptable and environmental protection is urgent. Of the greenhouse gases resulting in global warming, carbon dioxide (CO₂) constitutes a significant portion and carbon emissions continue to increase year on year. According to “CO₂ Emissions in 2023”, published by the International Environmental Agency (IEA), although clean energy growth has limited the rise in carbon emissions, the total global emissions still grew by 1.1% in 2023, reaching a new record high of 37.4 billion tonnes. Therefore, the most urgent action to curb climate change is to reduce carbon emissions.

As the world’s largest developing country and the second largest economy, China is confronted with severe environmental pollution in the process of its leapfrog economic development. The IEA reveals that in 2023, carbon emissions in China grew by around 565 million tonnes, presenting the largest increase globally, and per capita emissions were 15% higher than those in advanced economies. While enterprises are the main producers of carbon emissions [1,2], private industrial enterprises bear more responsibilities. Based on the “Carbon Emission Ranking of Top 100 Listed Firms in China (2023)”, the total amount of carbon emissions from the top 100 listed firms equated to nearly half of China’s annual carbon emissions in 2022, and firms operating in the electricity, cement, and steel industries contributed 75.48% of the total emissions on the list. Among these industrial firms, private industrial enterprises make up the majority. Consequently, effectively reducing the carbon footprint of private industrial enterprises is the true challenge in achieving environmentally sustainable development.

However, carbon reduction is a complex project [3,4,5,6], which requires continuous efforts from multiple parties [7,8] and cannot generate immediate economic gains. Reducing carbon emissions without decreasing revenue is more arduous. Firms, especially private firms, tend to prioritize profits and growth over environmental responsibilities [2,9,10], leading to problematic development producing extensive environmental pollution and carbon emissions. Therefore, strong and sustained support is needed to make enterprises aware of the importance of harmonious coexistence between economic development and environmental protection. In this context, directors appointed by non-controlling state shareholders (hereby appointed directors) may provide an effective solution.

The appearance of appointed directors represents a further development of mixed-ownership reform (MOR) in private enterprises, which is regarded as a growing type of MOR at the governance level. In general, MOR is defined as the mutual integration between state-owned capital and private-owned capital [11,12,13]. The reorganization of the equity structure is the direct result of MOR. This reform also provides new shareholders with the right to nominate external directors, which boosts a strengthened reconstruction of the firm’s control and management structure. Therefore, MOR in private enterprises describes the introduction of state shareholders, and the growing presence of MOR in private enterprises refers to the existence of appointed directors. While existing studies focus on the shareholding ratio of state shareholders in private enterprises [12,13,14,15], our study investigates the engagement of appointed directors to reflect the effect of MOR from a novel perspective. The reasons behind this are twofold.

On the one hand, while the purpose of MOR is to facilitate the integration of diverse capitals and optimize their respective strengths, there is a deadlock of “only mix, no reform” for MOR in private enterprises, which refers to the phenomenon that state shareholders do not act as active shareholders but only participate in shares. Despite the importance of the ownership structure [16,17], the strict control over state-owned capital determines that state shareholders in private enterprises are usually minority shareholders [13,14,15] whose effect is elusive since holding shares does not necessarily lead to corresponding control rights [18]. Therefore, state shareholders may be unable to influence the private enterprises they invest in. However, prior research has mainly studied the shareholding ratio of state shareholders, thereby drawing contradictory conclusions. For example, Hu et al. [15] and Xu et al. [13] indicate that state shareholders can promote green innovation, whereas Li et al. [14] argue that state shareholders inhibit private enterprises from conducting innovative investments. These contradictory findings confirm that to fully explore the effect of MOR in private enterprises, it is inchoate to simply examine the shareholding ratio of state shareholders.

On the other hand, compared with holding shares, having seats on the board of directors can better reflect whether and how MOR works in private enterprises. Based on the principal–agent theory, the board of directors is an agency responsible to the shareholders [19] and a key organization significantly and directly affecting corporate management and control [20,21], acting as a vital connection between the shareholders and lower-tier management. Thus, the appointed directors represent the interests of state shareholders and the “voice” of state shareholders in the corporate decision-making process, thereby providing a fertile ground for state shareholders to actually participate in corporate governance and actively exercise shareholder rights, breaking the deadlock of “only mix, no reform”. Kim et al. [17] demonstrate the positive relationship between minority shareholder rights and board composition. Manzaneque et al. [22] indicate that shareholders have a more significant effect when they can appoint directors. Since the appointed directors enable state shareholders to actually and actively govern the firm, studying the effect of appointed directors can better unveil the role of MOR in private enterprises.

Practically, many state shareholders have seats on the board of directors of private enterprises. For instance, as disclosed in the 2021 annual report of Xizang Development Co., Ltd. (Stock code: 000752), its state shareholder, Xizang State-owned Capital Investment and Operation Co., Ltd., while holding 7.3% of the shares, ranking as the third largest shareholder, appointed a director and a supervisor to directly attend corporate management and control. Nevertheless, the effect of appointed directors has not yet been fully explored, with Xu et al. [13] suggesting that state shareholders can appoint directors to implement supervision. Consequently, analyzing the effect of appointed directors is academically valuable and empirically feasible.

We argue that the appointed directors can drive a significant reduction in corporate carbon emissions without undercutting corporate profits due to their dual functions: to lessen carbon footprints and safeguard economic viability. The principal–agent theory determines that the appointed directors are responsible to the state shareholders, who are ultimately controlled by the government. Since the Chinese government attaches great importance to environmental protection [23,24,25], the appointed directors, affected by the will of the controller, will actively pursue social benefits, thereby exerting efforts to mitigate corporate pollution. Meanwhile, the appointed directors, as members of the board, are associated with duties to maximize corporate business efficiency and guarantee economic gains [26]. Therefore, it is less likely that the appointed directors will stubbornly reduce carbon emissions without considering the firm’s economic goals.

Specifically, the appointed directors can effectively reduce corporate carbon emission intensity through two channels. First, they can participate in setting the firm’s strategic direction. According to the principal–agent theory, the shareholder’s preference directly affects the behavior of the director [19,21,27]. Since the state shareholders place great emphasis on environmental protection [13], the appointed directors will positively stimulate the establishment of the environmental strategy. By prioritizing sustainability in strategic planning, the top-down influence helps promote carbon reduction more effectively [28]. Second, the appointed directors can provide sufficient compensation incentives to encourage proactive efforts in reducing corporate carbon emission intensity. The modern corporate system entrusts executives to manage daily corporate operations [19]. As stated by the compensation incentive theory, executives diligently fulfill their professional obligations based on satisfying compensation [29,30,31]. Since the board of directors is responsible for the compensation arrangement [32], the appointed directors are associated with the right to design an attractive compensation scheme to motivate executives and ensure the effective implementation of corporate green plans.

However, the efficacy of appointed directors may be questioned. The controlling shareholder of private enterprises often owns a great number of shares and has absolute control over the board of directors [33], while the state shareholders are usually minority shareholders [13,14,15], thus having limited seats on the board, making it difficult for the appointed directors to supervise the corporate carbon footprint. In addition, by adding state shareholders, MOR helps private enterprises build political connections [13,34]. Private enterprises may lobby the local government to evade emission regulations and penalties. This example of potential regulatory capture may lead to selective regulation enforcement by the regulator [35] and block the efficacy of appointed directors. Therefore, the appointed directors may have no significant impact on corporate carbon emission intensity.

Focusing on private industrial enterprises listed in the Chinese A-share market from 2008 to 2021, we analyze the impact of appointed directors, a growing type of MOR at the governance level, on corporate carbon emission intensity. The findings suggest that appointed directors significantly attenuate corporate carbon emission intensity, regardless of whether there are appointed supervisors and executives simultaneously. This conclusion remains valid after a series of endogeneity tests and robustness tests. Specifically, the efficacy of appointed directors works through promoting the formulation of environmental strategies and increasing executive compensation incentives, particularly cash-based compensation incentives. In addition, cross-sectional analyses are conducted from different aspects. The results show that the reducing effect of appointed directors on corporate carbon emission intensity is more salient in firms registered in regions with weak environmental regulation, operating in non-heavily polluting industries, with a low level of analyst coverage, and with a poor ability to conduct green innovation.

Our study makes several contributions to the growing literature. First, it provides new insights into the determinants of corporate carbon emission intensity. Previous studies have primarily concentrated on macro-level factors affecting carbon emissions [1,23,24,25,36,37]. With the appearance of convincing measures to calculate firm-level carbon emissions, scholars have begun to investigate how micro-level factors impact carbon emissions, such as corporate innovation capacity [38], fund sponsors [39], and digital transformation [40]. While Shi et al. [41] and Yu et al. [2] prove that MOR in SOEs reduces corporate carbon emissions, the effect of MOR on private enterprises’ carbon reduction is still unknown. Moreover, instead of focusing on the overall carbon footprint of corporate entities [2,38,39], which may raise significant questions about the balance between corporate environmental responsibility and economic performance, we have looked at a limited number of studies investigating corporate carbon emission intensity, which represents carbon emissions per unit of income [40,41,42], thus introducing empirical support for efficient carbon reduction without jeopardizing economic growth.

Second, our research extends the literature on the economic consequences of mixed-ownership reform in private enterprises. The prevailing literature focuses on MOR in SOEs and states that the introduction of private shareholders has a significantly positive impact on the governance and performance of SOEs [2,43,44,45,46,47,48]. However, there is little research on MOR in private enterprises [12,13,14,15]. This study pays attention to the carbon performance of private industrial enterprises, which is the foreground of carbon reduction and sustainable development in China, thus providing additional evidence for the positive effect of mixed-ownership reform in private enterprises.

Third, our study conducts an in-depth analysis of the effect of appointed directors. Scholars mainly focus on the single dimension of MOR by examining the shareholding ratio of state shareholders [12,13,14,15] and have not effectively distinguished the effect of appointed directors from the role of state shareholders. In fact, research about the significance of appointed directors is scant. However, the board of directors is playing an increasingly important role in modern enterprises. The appointed directors realize shareholder rights at the governance level, thus reflecting the real effect of MOR, which deserves further analysis. Our findings theoretically and empirically prove that appointed directors play a significant role in corporate management and sustainable development, providing novel evidence of the effect of MOR from the governance level, emphasizing the importance of direct governance, and underlining the necessity to promote strengthened mixed-ownership reform in private enterprises.

Furthermore, our study has important policy implications for developing countries. With the primary mission of economic growth, developing countries are encountering a dilemma between economic development and environmental protection [49,50]. Our study suggests that the appointed directors should be a viable reference for firms to integrate the requirement of the government in promoting sustainable development and the objective of profit-oriented firms to achieve economic growth.

The rest of this paper is organized as follows. Section 2 introduces the institutional background and research hypotheses. Section 3 states the sample and the construction of variables. Section 4 provides the empirical results, endogeneity tests, and robustness tests. Section 5 explores possible mechanisms underlying the observed relationship. Section 6 presents the heterogeneity tests. Section 7 presents the conclusions and implications.

2. Institutional Background and Research Hypotheses

2.1. Institutional Background

2.1.1. Carbon Reduction Measures and Achievements

In recent years, China has made significant strides in reducing carbon emissions. At the macro level, in the “Outline of the 14th Five-Year Plan”, the government plans to reduce carbon intensity by 18%, reduce energy intensity by 13.5%, and increase the share of non-fossil fuels in primary energy consumption to about 20%. In September 2020, China officially and internationally promised to peak carbon emissions before 2030 and to achieve carbon neutrality before 2060 at the 75th session of the United Nations General Assembly, which is referred as the “two carbon goals”. In October 2021, the Central Committee of the Communist Party of China (CPC) and the State Council issued the “Opinions on Fully Implementing the New Development Concept and Achieving Carbon Peaking and Carbon Neutrality” and the “Plan for Carbon Peaking before 2030”.

At the meso level, the adjustment of energy structures is being phased. In 2010, the National Development and Reform Commission (NDRC) proclaimed the “Notice on Piloting Low-Carbon Provinces and Cities”. The low-carbon city pilot policy significantly eases pollution [25] and reduces corporate carbon emissions [1,37]. In October 2011, the NDRC released the “Notice on the Pilot Work of Trading the Carbon Emission Rights”, which permitted seven provinces and cities, including Beijing, Tianjin, Shanghai, Guangdong, Shenzhen, Hubei, and Chongqing, to build the carbon emission trading market for key emission enterprises to trade carbon emission rights. The implementation of the carbon emission trading policy significantly decreases the carbon emissions in the pilots [23,24]. On 20 October 2023, the NDRC issued the “Plan for the Construction of Carbon Peak Pilot”, selecting 100 representative cities and regions to realize carbon peak first, which is believed to benefit the achievement of the “two carbon goals”.

At the micro level, in 2007, the Ministry of Environmental Protection, China Banking Regulatory Commission, and People’s Bank of China jointly promulgated the “Opinions on Implementing Environmental Protection Policies and Regulations to Prevent Credit Risks”, requiring banks to stop providing loans to heavy-pollution firms and increase credits to clean projects. Lee et al. [42] prove that this green credit policy has remarkably reduced the carbon emission intensity of heavily polluting firms. In addition, external supervision of corporate environmental performance is continuously improving. On 11 December 2021, the Ministry of Ecology and Environment issued the “Management Measures for Legal Disclosure of Enterprise Environmental Information”, which required certain polluting firms to disclose environmental information (including carbon emissions) since 8 February 2022. On 31 December 2021, the Ministry of Ecology and Environment issued the “Guidelines for the Legal Format of Enterprise Environmental Information Disclosure”, which officially standardized the format of firms’ environmental information disclosure. These documents, for the first time, propose official regulations requiring firms to disclose carbon information.

Overall, although developing countries always face the dilemma between economic growth and environmental protection [49,50], China has made great efforts to control carbon emissions while achieving stable economic growth [51]. These efforts reflect that the Chinese government not only proactively responds to global climate change, but also has an evolving understanding of the sustainable development of the economy.

2.1.2. Mixed-Ownership Reform

In China, although the concept of “mixed-ownership reform” has appeared relatively late, the mixed-ownership structure has a rather long history, with the core goal of integrating state-owned capital and private-owned capital and optimizing their respective advantages.

In 1978, the initial stage of reform and opening, the Third Plenary Session of the 11th CPC Central Committee formulated the policy of taking public ownership as the mainstay and promoting the joint development of diverse ownership. Since then, the Chinese government has been working on facilitating the integration of diverse ownership. In 2013, the Third Plenary Session of the 18th CPC Central Committee highlighted the necessity to proactively develop a mixed-ownership economy, which was considered as the official beginning of mixed-ownership reform (MOR) at the micro level. The session put forward that encouraging private-owned capital to participate in state-owned enterprises (SOEs) was the key to overcoming the problems and shortcomings of the SOEs and establishing a more efficient modern company system. In 2015, documents supporting the MOR in SOEs, known as “1 + N”, were addressed by the CPC and the State Council. These policies stated that the SOEs could cooperate with private enterprises in various ways, such as equity integration, strategic cooperation, and resource integration. Subsequently, MOR became one of the central issues in the fields of SOE reform and corporate governance.

The existing literature focusing on the economic consequences of MOR in SOEs indicates that the unique vitality of private-owned capital plays a crucial role in promoting the participation of SOEs in the market and reducing the dependence of SOEs on the government [43,46]. In addition, studies have shown that MOR is beneficial to the sustainable development of the national economy. For example, Zhang et al. [48] report that MOR improves SOEs’ innovation. Liu et al. [45] state that MOR significantly improves the ESG performance of SOEs. Shi et al. [41] and Yu et al. [2] find that the adoption of MOR in China’s SOEs has led to a notable reduction in carbon emissions.

The success of MOR in SOEs confirms that state-owned capital and private-owned capital have different merits and that this reform promotes the complementation of different capitals, generating an impressively positive effect. Therefore, besides continuously promoting MOR in SOEs, the government began to consider whether MOR in private enterprises could be feasible and effective.

On 23 September 2015, the State Council released the “Opinions on the Development of Mixed Ownership Economy in State Owned Enterprises”, officially encouraging state-owned capital to invest in private enterprises. With the political guidance, an increasing number of private enterprises have reconstructed their equity structure by introducing state-owned capital to hold shares. Studies refer to the engagement of state shareholders as “reverse mixed-ownership reform” [13], “participation of state-owned equity” [14], or just as “mixed-ownership reform” [12], a term that we use in our study. Notably, MOR in private enterprises is not nationalization, because state shareholders do not become controlling shareholders after participating in private enterprises.

As expected, MOR positively influences private enterprises. State shareholders have a resource effect [13,52], thereby providing government support and financial resources to private enterprises [14,15], moderating financial constraints [12], and easing access to high-barrier industries [53]. MOR also has a governance effect [13], which strengthens external supervision [12] and reduces default risks [11]. Hu et al. [15] elucidate that state shareholders facilitate green innovations by bringing talent to private enterprises. Xu et al. [13] discovered that state shareholders raise awareness of the social responsibilities of private enterprises, thus boosting their green innovations.

However, some studies have found MOR in private enterprises to be inefficient. Unlike MOR in SOEs, where non-state shareholders hold a pretty large number of the shares [45,46], state shareholders in private enterprises are usually minority shareholders [13,14], limiting the effect of state-owned capital. In addition, the state shareholders are ultimately controlled by the central or the local government. Government intervention affects the corporate efficiency [54], exposes private enterprises to supervision pressures [12,14,55], aggregates agency problems [44,56], increases unnecessary operating costs [57], and reduces market vigor [46].

Therefore, further efforts are made to guarantee the positive effect and alleviate the negative effect of MOR in private enterprises. The strengthening MOR at the governance level is now considered. In 2023, the State-owned Assets Supervision and Administration Commission of the State Council issued the “Interim Measures for the Management of SOE’s Participation in Enterprises”, which required state shareholders to appoint representatives, directors, supervisors, or important executives according to the articles of association of the private enterprises, thereby actively playing the role of shareholders. By appointing directors, supervisors, and executives, the MOR in private enterprises progressed into a new stage: a greater mixture of the governance structure, which combines the duties of the government and the responsibilities of the board of directors, safeguards state shareholders’ rights to participate in corporate management and control, and helps to realize the purpose of MOR in private enterprises. However, the literature on the effect of strengthened MOR is relatively rare. Therefore, in this study, we go further to investigate the impact of appointed directors, thus contributing to the growing literature on mixed-ownership reform.

2.2. Hypothesis Development

Ecological economists and policymakers have emphasized the significance of sustainability and made great efforts to redirect the economies toward sustainability [58,59]. Enterprises, as the major economic organizations at the micro level, should respect their responsibilities to the environment so as to foster the “harmony” between economic growth and environmental protection. Therefore, carbon reduction is the most pressing matter at this stage. However, carbon reduction is a complex and systemic project [3,4], including reorganizing corporate strategy, adjusting organizational structures, introducing talent and experts, promoting green technologies, eliminating energy-consuming equipment, etc. It is also a costly and long-term task [5,6] that involves various uncertainties, such as short-term losses, reduced economic performance, and investment failure [60,61,62]. Enterprises, especially private enterprises, are generally profit-oriented [2,9], risk-averse [14,63], and lack environmental awareness [10], thereby having insufficient motivations to pursue low-carbon development. Therefore, strong and determined support is needed to stimulate these firms’ initiatives in reducing carbon emissions without jeopardizing corporate profit.

The appointed directors may provide such support since they undertake dual missions that encompass reducing carbon emissions and ensuring revenue growth. The modern corporate system enables the board of directors to play a pivotal role in corporate management and control [21]. The board is responsive to shareholder’s claims [64] and concurrently supervises and advises the implementation of corporate strategies and plans [65], serving as a critical link between the shareholders and the lower-level management. It could be argued that the actions and decisions of directors directly shape the enterprise’s operating outcomes. Since state shareholders attach great importance to environmental protection [13], the appointed directors, accountable to state shareholders, may wield significant influence in steering private industrial enterprises toward environmentally sustainable practices. Despite their environmental stewardship, the appointed directors are also entrusted with fundamental duties to uphold corporate efficiency and drive economic prosperity [26]. Many scholars have emphasized the relationship between directors and corporate performance, especially financial performance [66,67]. The appointed directors, as members of the board, inevitably are responsible for boosting corporate economic growth. Therefore, with a nuanced understanding of the intricate interplay between environmental sustainability and financial outcomes, appointed directors are less inclined to impulsively reduce carbon emissions at the expense of corporate financial health, but tend to strike a balance between environmental goals and business objectives.

There are two channels through which appointed directors can reduce carbon emission intensity. On the one hand, the appointed directors can have significant influence over the strategic direction and decision-making processes of private industrial enterprises, and therefore, the formation of environmental strategies. The principal–agent theory determines that the board of directors is an important part of the corporate governance structure [68,69], which represents the interests of shareholders [19,21] and significantly impacts the corporate decision-making process [20,21]. The existing literature states that appointed directors directly play the role of shareholders at the governance level [70] and provide firm support to the shareholders [45]. As state shareholders attach great importance to environmental sustainability [13], the appointed directors may support and propel the establishment of sustainability-prioritized strategies within the firm. Such a top-down influence can directly shape the environmental attitude of the enterprise [71,72], thus improving corporate carbon performance [28], promoting a reduction in carbon emission intensity, and facilitating the achievement of sustainable development. In addition, the appointed directors are labeled as “official” directors because they are ultimately controlled by the central or the local government. Scholars have demonstrated that, in China, authorities with an official background can draw widespread attention from external supervisors such as regulators [73], thereby having a deterrent effect that notably affects corporate decisions [74,75]. The official background of appointed directors may lead to a more agile and adaptive organizational environment that can more readily comply with the green concept of appointed directors, which helps the development of environmental strategies.

On the other hand, the appointed directors can motivate executives to effectively implement low-carbon strategies and plans by providing appropriate compensation incentives. While the board of directors is responsible for the overall management at the governance level, executives play a pivotal role in corporate daily operations [19]. Therefore, to guarantee the efficiency of the environmental strategies established by the directors and ultimately promote the reduction in carbon emission intensity, it is of great significance to encourage executives to fulfill their obligations. The compensation incentive theory indicates that the executive’s behaviors largely depend on the compensation and risk level at which they are involved [31]. Scholars have illustrated that reasonable compensation can incentivize executives to perform their duties and take on valuable projects [29,30]. Conyon [76] suggests that more pay is required to compensate the agent for increased risk. Haque [77] states that compensation policy is positively associated with carbon reduction initiatives. As carbon reduction is accompanied by risks and uncertainties [60,61,62] and cannot bring immediate benefits, increased compensation is needed to offset the extra risks associated with carbon reduction projects and improve the executive’s enthusiasm in reducing carbon emission intensity. The appointed directors play an irreplaceable role in this context. Based on the modern corporate system, the executives’ compensation is set by the board of directors and the subordinate compensation committee [32]. The appointed directors, by participating in private industrial enterprise’s activities, are more likely to discover the conflicts between uncertainties and risks associated with carbon reduction projects and unbalanced executive compensation incentives. Therefore, they may exercise their rights to rationalize the compensation arrangement, thus avoiding short-sighted and opportunistic behaviors of the executives and inspiring them to actively implement carbon reduction plans.

Furthermore, the catering theory maintains that enterprises will consider investors’ expectations while making decisions [78]. Investors take corporate carbon performance into consideration and demand compensation for their exposure to carbon emission risks [79]. Consequently, to build a green reputation and satisfy investors’ expectations, private industrial enterprises may be willing to follow the directions of appointed directors, resulting in a multiplier effect on reducing carbon emission intensity.

Overall, the appointed directors play a significant role in promoting the reduction in carbon emission intensity. Therefore, we propose the following hypothesis:

H1a. 

Appointed directors can reduce the carbon emission intensity of private industrial enterprises.

However, the efficacy of appointed directors may be questioned. First, the effect of appointed directors may be too weak to influence the carbon decisions of private industrial enterprises. One noticeable feature in Chinese private enterprises is the highly concentrated ownership [80,81]. The controlling shareholder has a large shareholding ratio and has absolute control over the board of directors by over-appointing directors [33] and interfering in the nomination and election of independent directors [82]. Nevertheless, the state shareholders in private firms are usually minority shareholders [13,14,15]. Owing to the small shareholding ratio, the number of directors appointed is negligible. Although there are exceptions in that the private enterprises actively offer additional seats to minority state shareholders, empowering them with governance rights not limited by the shareholding ratios, state shareholders, in most cases, have only one or two seats on the board. The disproportionate board composition ensures the controlling shareholder’s significant effect on the corporate decision-making process and operation [83] and makes the voice of appointed directors unheard [11]. Therefore, it may be tough for the appointed directors to effectively regulate and supervise corporate carbon behaviors.

Second, potential regulatory capture may block the efficacy of appointed directors. Prior studies have summarized that there is a phenomenon called regulatory capture, which deflects the public regulator’s behavior from its mandated mission [35,84]. For example, firms with political connections can obtain the selective enforcement of regulations. Correia [85] indicates that politically connected firms are less likely to be involved in SEC enforcement actions. Maung et al. [34] find that firms with state ownership pay lower environmental levies. Heitz et al. [86] prove that politically connected firms experience less regulatory enforcement and lower penalties for the enforcement of the Clean Air Act. Since state shareholders are regarded as the representatives of the government, MOR helps private industrial enterprises build political connections with the government [13,34], which increases the possibility for enterprises to lobby the government through their political ties. Because the local regulators are captured, private enterprises may evade emission regulations and penalties. Affected by the attitude of the government, the state shareholders and their appointed directors are less likely to endeavor to reduce carbon emission intensity.

In addition, behavior theory regards directors as bounded rational humans, who may be unable to maximize the interest of the shareholders due to constraints on their capabilities [87]. Appointed directors are also bounded rational humans; thus, they may not perfectly actualize carbon reduction without undercutting revenue.

Based on the above discussion, the appointed directors may not have a substantive impact on corporate carbon emission intensity, so we have developed the following hypothesis:

H1b. 

Appointed directors have no impact on the carbon emission intensity of private industrial enterprises.

3. Research Design

3.1. Sample Selection and Data Source

Given that the private industrial sector bears the primary responsibility for carbon emissions, we concentrate on A-share private industrial enterprises listed in the Shanghai and Shenzhen stock exchanges over the period from 2008 to 2021. The implementation of new Chinese Accounting Standards in 2007 prompted the initiation of our sample period in 2008, thereby avoiding any potential influence from the transition from the old to the new accounting standards on our findings.

The sample data were rigorously processed as follows: (1) firms with a state-owned shareholding ratio equal to 0 or greater than 50% were removed because they are not mixed-ownership enterprises and therefore will not have directors appointed by non-controlling state shareholders; (2) firms listed in the stock exchanges for less than one year were excluded; (3) firms with special treatment status (labeled ST, *ST, and PT) were eliminated; (4) cross-listed firms were excluded; (5) insolvent firms (those with total liabilities surpassing total assets) were removed; (6) firms with missing data for variables were eliminated. Our final sample encompasses 6969 firm-year observations.

The data were sourced from the China Stock Market & Accounting Research (CSMAR) database, the Chinese Research Data Services (CNRDS) database, the China Energy Statistical Yearbook, and the China Industrial Economic Statistical Yearbook. For firms whose ownership type was unclear, we manually collected the data from the annual report. To avoid the influence of outliers, we winsorized all continuous variables at the 1% and 99% levels. Details regarding the sample selection process are presented in

Table 1. Sample selection.

Sample Selection Criteria	Firm-Year Observations
Private Industrial Enterprises listed in the Shanghai and Shenzhen Stock Exchanges from 2008 to 2021	17,355
Less:
Observations with state-owned shareholding ratio equal to 0 or greater than 50%	(8461)
Observations listed in the stock exchanges for less than 1 year	(990)
Observations with special treatment status	(425)
Observations that are cross-listed	(146)
Observations that are insolvent	(12)
Observations with missing data for variables	(352)
Final sample	6969

.

3.2. Empirical Model

To examine the relationship between appointed directors and carbon emission intensity, we develop our empirical model as follows:

CO2_i,t = α + β₁AD_i,t + ∑Controls + δ_i + γ_t + μ_j + ε_i,t

(1)

where i and t denote firm and year, respectively. CO2 is the dependent variable measuring corporate carbon emission intensity. AD is the independent variable measuring the proportion of appointed directors. Controls are control variables. We also include firm fixed effects (δ_i), year fixed effects (γ_t), and industry fixed effects (μ_j). ε_i,t is the error term

3.3. Variable Constructions

3.3.1. Dependent Variable: Carbon Emission Intensity

In China, the number of listed firms that voluntarily disclose carbon emission information is very limited, and standardized principles mandating listed firms to disclose carbon emission information were not introduced until the end of 2021. Consequently, research on carbon emissions at the firm level is nascent, and there is a dearth of a uniform methodology for computing firms’ carbon emission intensity.

Drawing inspiration from recent studies [2,40,41,42,88], we operationalize the carbon emission intensity (CO2) as the ratio of the total carbon emissions to total revenue. The variable is constructed as follows:

Carbon Emission_i,t = Industrial Energy Consumption_j,t × Carbon Dioxide Conversion Factor × Operating Cost_i,t/Industrial Operating Cost_j,t

(2)

CO2_i,t = Carbon Emission_i,t/Operating Income_i,t

(3)

where i, j, and t refer to the firm, industry, and year, respectively. To measure the carbon emission intensity (CO2), we first calculate corporate carbon emissions in a given year (Carbon Emission) using Model (2), where Industrial Energy Consumption is the total energy consumption of a given industry in a given year, including coal, petroleum, natural gas, hydropower, nuclear power, etc.; Carbon Dioxide Conversion Factor is a factor designed to convert per energy consumption to equivalent carbon emissions; Operating Cost is the total operating costs of a firm in a given year; Industrial Operating Cost is the total operating costs of a given industry in a given year. We then calculate carbon emission intensity (CO2) using Model (3), which equals the ratio of a firm’s carbon emission in a given year (Carbon Emission) to its operating income, thus measuring the corporate carbon emission efficiency. Data for Industrial Energy Consumption come from the China Energy Statistical Yearbook; data for Industrial Operating Cost are from the China Industrial Economic Statistical Yearbook; data for Operating Cost and Operating Income come from the CSMAR database; Carbon Dioxide Conversion Factor equals 2.493, which is designated by the Xiamen Energy Conservation Center, an official energy institution.

3.3.2. Independent Variable: Proportion of Appointed Directors

The state shareholders may only appoint directors or appoint directors, supervisors, and executives simultaneously. As additional helpers, the supervisors and executives may assist appointed directors in corporate environmental management. Therefore, we develop two independent variables to investigate the effect of appointed directors.

The first variable, AD1, measures the proportion of appointed directors. Referring to Wang et al. [44] and Yan et al. [46], AD1 equals the ratio of the number of directors appointed by state shareholders among the top ten shareholders to the total number of all directors of the private industrial firm.

The second variable, AD2, measures the proportion of appointed directors, supervisors, and executives. Following Wang et al. [43,44], AD2 equals the ratio of the number of directors, supervisors, and executives appointed by state shareholders among the top ten shareholders to the total number of directors, supervisors, and executives of the private industrial firm.

To construct the two independent variables, we first collect data on shareholder types of the top ten shareholders from the CSMAR database. If the shareholder’s nature is unclear, we manually trace this back to the ultimate controller of the shareholder by searching on platforms such as “TianYanCha” (An officially registered institution, which is governed by the National Minority Enterprise Development Fund). If the ultimate controller is the central government, any local government, or SOEs, we classify the shareholder as a state shareholder [13]. Finally, we collect detailed information about the directors, supervisors, and executives from the CSMAR database and through hand-collection, and select those appointed by state shareholders.

3.3.3. Control Variables

We first control the state-owned shareholding ratio (State), which equals the sum of the shareholding ratio of state shareholders among the top ten shareholders [13]. In addition, drawing from established literature [2,40,41,42], we incorporate the following control variables: firm size (Size); financial leverage (Lev); profitability (Roa); growth ability (Growth); operating cash flow (Cfo); environmental protection input (Epe); book to market ratio (BM); board size (Director); proportion of independent directors (Idr); duality (Dual); ownership concentration (Top1); equity balance (Gqz); firm age (Age); regional development (GDPgrowth). All data are derived from the CSMAR database.

Table 2. Variable definitions.

Variable Name	Variable Symbol	Definition
Carbon emission intensity	CO2	Carbon emission divided by operating income, where carbon emission = (industrial energy consumption × carbon dioxide conversion factor × operating cost)/industrial operating cost
Proportion of appointed directors	AD1	The number of directors appointed by state shareholders among the top ten shareholders divided by the total number of directors
	AD2	The number of directors, supervisors, and executives appointed by state shareholders among the top ten shareholders divided by the total number of directors, supervisors, and executives
State-owned shareholding ratio	State	The sum of the shareholding ratio of state shareholders among the top ten shareholders
Firm size	Size	Natural logarithm of total assets
Financial leverage	Lev	Total liabilities divided by total assets
Profitability	ROA	Net profit divided by total assets
Growth ability	Growth	(Operating income in year t − operating income in year t − 1)/operating income in year t − 1
Operating cash flow	Cfo	Net operating cash flow divided by total assets
Environmental protection input	Epe	Environment-related expenses divided by the operating costs
Book to market ratio	BM	Book value divided by market value
Board size	Director	Natural logarithm of the number of directors plus 1
Proportion of independent directors	Idr	The number of independent directors divided by the number of directors
Duality	Dual	A dummy variable that equals 1 if the CEO and the chairperson are the same individual and 0 otherwise
Ownership concentration	Top1	The shareholding ratio of the largest shareholder
Equity balance	Gqz	The sum of the shareholding ratio from the second to the fifth largest shareholders divided by the shareholding ratio of the largest shareholder
Firm age	Age	Natural logarithm of the number of years since the firm is listed plus 1
Regional development	GDPgrowth	(Gross regional product in year t − gross regional product in year t − 1)/gross regional product in year t − 1

provides detailed definitions of the variables.

4. Empirical Results

4.1. Descriptive Statistics

Table 3. Descriptive statistics.

Variables	N	Mean	SD	Min	P25	P50	P75	Max
CO2	6969	0.515	0.632	0.076	0.121	0.221	0.522	2.625
AD1	6969	0.022	0.057	0.000	0.000	0.000	0.000	0.286
AD2	6969	0.018	0.047	0.000	0.000	0.000	0.000	0.231
State	6969	5.195	6.271	0.220	1.280	2.800	6.290	31.490
Size	6969	21.952	1.006	19.903	21.213	21.875	22.600	24.683
Lev	6969	0.381	0.179	0.049	0.237	0.381	0.511	0.809
Roa	6969	0.044	0.064	−0.250	0.018	0.043	0.076	0.211
Growth	6969	0.193	0.368	−0.518	0.001	0.140	0.305	2.100
Cfo	6969	0.051	0.068	−0.145	0.012	0.049	0.090	0.247
Loss	6969	0.092	0.289	0.000	0.000	0.000	0.000	1.000
Epe	6969	0.026	0.104	0.000	0.000	0.000	0.000	0.741
BM	6969	0.560	0.225	0.113	0.388	0.558	0.728	1.082
Director	6969	2.216	0.159	1.792	2.079	2.303	2.303	2.565
Idr	6969	0.374	0.051	0.333	0.333	0.333	0.429	0.556
Dual	6969	0.378	0.485	0.000	0.000	0.000	1.000	1.000
Top1	6969	31.194	12.962	8.630	21.370	29.690	39.520	67.730
Gqz	6969	0.810	0.583	0.061	0.365	0.670	1.110	2.800
Age	6969	1.970	0.699	0.693	1.386	1.946	2.485	3.258
GDPgrowth	6969	0.093	0.051	−0.082	0.068	0.091	0.117	0.233

presents the descriptive statistics for the main variables. The mean value of CO2 is 0.515, implying that, on average, for every unit of revenue, the firm concurrently produces 0.515 units of carbon emissions. The standard deviation of CO2 is 0.632, underscoring the substantial variation in carbon emission intensity among private industrial enterprises. The mean value of AD1 (AD2) is 0.022 (0.018), indicating that appointed directors have not yet occupied the “right of discourse”. The mean value of State is 5.195, that is, the state-owned shareholding ratio is about 5.2%. The low shareholding ratio implies that state shareholders are still minority shareholders and promoting MOR is a long-term and arduous task. Descriptive statistics for other variables exhibit no pronounced deviations.

4.2. Baseline Results

Model (1) is used to explore the relationship between appointed directors and corporate carbon emission intensity, and the results are reported in

Table 4. Effect of appointed directors on carbon emission intensity.

Variables	(1)	(2)	(3)
	CO2	CO2	CO2
AD1		−0.306 **
		(−2.20)
AD2			−0.485 ***
			(−2.74)
State	−0.003	−0.002	−0.002
	(−1.60)	(−1.07)	(−0.99)
Size	−0.012	−0.013	−0.012
	(−0.59)	(−0.64)	(−0.63)
Lev	0.044	0.047	0.045
	(0.58)	(0.62)	(0.60)
Roa	−0.647 ***	−0.652 ***	−0.654 ***
	(−4.88)	(−4.92)	(−4.94)
Growth	−0.037 ***	−0.038 ***	−0.038 ***
	(−2.91)	(−2.97)	(−3.01)
Cfo	−0.023	−0.022	−0.022
	(−0.30)	(−0.29)	(−0.29)
Loss	0.009	0.008	0.008
	(0.42)	(0.39)	(0.39)
Epe	0.032	0.033	0.032
	(0.35)	(0.35)	(0.34)
BM	0.115 ***	0.111 ***	0.110 ***
	(2.95)	(2.84)	(2.83)
Director	−0.139 *	−0.139 *	−0.139 *
	(−1.68)	(−1.70)	(−1.70)
Idr	−0.385 **	−0.398 **	−0.405 **
	(−2.21)	(−2.28)	(−2.34)
Dual	−0.019	−0.018	−0.017
	(−1.23)	(−1.15)	(−1.11)
Top1	0.002	0.002	0.002
	(0.98)	(1.01)	(1.03)
Gqz	−0.005	−0.004	−0.004
	(−0.23)	(−0.19)	(−0.19)
Age	0.016	0.017	0.017
	(0.53)	(0.56)	(0.55)
GDPgrowth	−0.007	−0.012	−0.014
	(−0.08)	(−0.14)	(−0.17)
Constant	1.399 **	1.437 **	1.440 **
	(2.23)	(2.29)	(2.30)
Firm	Yes	Yes	Yes
Year	Yes	Yes	Yes
Ind	Yes	Yes	Yes
Obs.	6969	6969	6969
Adj. R2	0.135	0.137	0.138

Note: Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

. Column (1) exclusively incorporates the control variables. The coefficient of the state-owned shareholding ratio (State) is negative but not significant, suggesting that the injection of state shareholders cannot significantly reduce carbon emission intensity; therefore, promoting strengthened MOR at the governance level is essential. Columns (2) and (3) examine the impact of appointed directors. The coefficients of AD1 and AD2 are negative and statistically significant at the 5% and 1% level, respectively (β = −0.306, t = −2.20; β = −0.485, t = −2.74), implying that appointed directors exert a negative influence on the carbon emission intensity of private industrial firms, which is highly in line with our hypothesis H1a. Specifically, the coefficient of AD1 is smaller than that of AD2, which shows that the appointed supervisors and executives work together with the appointed directors at the governance level, and therefore stimulate a more remarkable reduction in carbon emission intensity. Moreover, the observed effect is economically significant. A one-standard-deviation rise in AD1 and AD2 (0.057 and 0.047) is linked to a 1.74- and 2.28-basis-point reduction (−0.306 × 0.057 and −0.485 × 0.047) in CO2, which is approximately 3.38% and 4.43% of the sample mean value (0.515). Consequently, there is a statistically and economically significant inverse association between appointed directors and carbon emission intensity.

In terms of the control variables, carbon emission intensity (CO2) exhibits a significantly positive correlation with the book to market ratio (BM). In contrast, it displays a significant negative association with the firm profitability (Roa), growth ability (Growth), board size (Director), and proportion of independent directors (Idr). The coefficients for the remaining variables are statistically insignificant.

4.3. Endogeneity Tests

4.3.1. Multi-Period DID Approach

Due to variations in the timing of the appointment of directors, we follow Beck et al. [89] and Wang et al. [11,43], and establish a multi-period difference-in-difference (DID) model to address the causal effect of the appointed directors on corporate carbon emission intensity:

CO2_i,t = α + β₁Treat_i × Post_t + ∑Controls + δ_i + γ_t + μ_j + ε_i,t

(4)

where i and t refer to the firm and year, respectively. Controls are the same set of control variables defined in Model (1). δ_i, γ_t, and μ_j are the firm, year, and industry fixed effects, respectively. ε_i,t is the error term. Treat is a dummy variable that equals 1 for the treatment group, and 0 for the control group. Given that the term of office for directors and supervisors is three years and state shareholders may appoint another director or supervisor to replace the current one upon the expiration of the three-year term of office or just give up the seat, we take the year when the firm first introduces a full-term appointed director as the current period (T0) and take three years before (T − 3, T − 2, T − 1) and two years after (T + 1, T + 2) the engagement as our observation period. In this way, our sample for the multi-period DID test comprises observations that have not appointed directors during the entire six years (the control group) and observations that do not have appointed directors during the former three years (T − 3, T − 2, T − 1) while having at least one appointed director during the latter three years (T0, T + 1, T + 2) (the treatment group). We define the former group as the control group, where Treat equals 0, and employ the latter group as the treatment group, where Treat equals 1. Post is also a dummy variable that equals 1 since the year the appointed directors take office, and 0 otherwise. We are interested in the coefficient of Treat × Post, which should be significantly negative if our main results are robust.

An important premise of the DID approach is that the control group and the treatment group meet the parallel trend assumption. Therefore, we refer to Beck et al. [89] and conduct the parallel trend test. Figure 1 shows the results of the parallel trend test. The coefficients of the years T − 3, T − 2, and T − 1 are not significant, while the coefficients of the years T0, T + 1, and T + 2 are mostly significant, suggesting that there is no remarkable difference between the treatment group and the control group before the introduction of appointed directors, and the parallel trend assumption is satisfied. Overall, the parallel trend analysis confirms the significant role of appointed directors in reducing carbon emission intensity and demonstrates the validity of the DID design.

The results of the multi-period DID analysis are reported in

Table 5. Multi-period DID approach.

Variables	(1)	(1)
	CO2	CO2
Treat × Post	−0.037 *	−0.084 *
	(−1.79)	(−1.67)
State	−0.002 **	0.001
	(−2.11)	(0.16)
Size	−0.012	−0.021
	(−1.29)	(−0.86)
Lev	0.039	0.092
	(1.26)	(0.91)
Roa	−0.315 ***	−1.071 ***
	(−5.25)	(−4.44)
Growth	−0.012 **	−0.103 ***
	(−2.50)	(−3.08)
Cfo	0.007	−0.128
	(0.25)	(−0.97)
Loss	−0.002	0.027
	(−0.17)	(0.80)
Epe	−0.036	−0.059
	(−1.57)	(−0.29)
BM	0.019	0.107
	(1.45)	(1.59)
Director	0.008	−0.136
	(0.23)	(−0.95)
Idr	0.011	−0.603
	(0.15)	(−1.26)
Dual	−0.003	−0.016
	(−0.56)	(−0.66)
Top1	0.000	0.006 **
	(0.13)	(2.09)
Gqz	0.006	0.000
	(0.37)	(0.00)
Age	−0.004	0.058
	(−0.32)	(1.38)
GDPgrowth	0.007	0.093
	(0.15)	(0.55)
Constant	0.973 ***	1.377 **
	(3.92)	(2.13)
Firm	Yes	Yes
Year	Yes	Yes
Ind	Yes	Yes
Obs.	5802	4848
Adj. R2	0.175	0.248

Note: Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

. The coefficients of Treat × Post are significantly negative at the 10% level (β = −0.037, t = −1.79; β = −0.084, t = −1.67), suggesting that appointed directors can significantly reduce private industrial firms’ carbon emission intensity. Therefore, our main results are robust.

4.3.2. Instrumental Variable Approach

To alleviate the endogeneity resulting from the two-way causality problem, we employ the instrumental variable approach. Inspired by Wang et al. [44], we apply the provincial average proportion of appointed directors except the given firm as the instrumental variables (IV_AD1 and IV_AD2). The reasons for this are as follows: First, in terms of the correlation requirement, the average proportion of appointed directors in a province indicates the activism of MOR within a specific region, thus affecting whether firms in the same province will engage in MOR or not. Second, in terms of the exogeneity requirement, there is no direct evidence of an immediate relationship between appointed directors in other firms and the carbon performance of the given firm. The results are reported in

Table 6. Instrumental variable approach.

Variables	(1)	(2)	(3)	(4)
	AD1	CO2	AD2	CO2
AD1		−1.313 *
		(−1.85)
IV_AD1	8.183 ***
	(5.92)
AD2				−0.864 **
				(−2.03)
IV_AD2			10.830 ***
			(5.07)
State	0.003 ***	0.004	0.002 ***	0.003 **
	(13.40)	(1.60)	(12.96)	(2.46)
Size	−0.003 *	−0.016 *	−0.005 ***	−0.053 ***
	(−1.75)	(−1.77)	(−3.68)	(−6.12)
Lev	0.012 *	0.064 *	0.010 *	0.162 ***
	(1.74)	(1.85)	(1.80)	(4.71)
Roa	−0.013	−0.527 ***	−0.008	−0.363 ***
	(−0.78)	(−5.42)	(−0.59)	(−3.68)
Growth	−0.003 *	−0.035 ***	−0.003 **	−0.047 ***
	(−1.79)	(−4.06)	(−2.32)	(−5.12)
Cfo	0.000	−0.107 **	0.006	−0.021
	(0.05)	(−2.10)	(0.79)	(−0.40)
Loss	−0.001	0.016	−0.001	0.031 **
	(−0.51)	(1.13)	(−0.49)	(2.14)
Epe	0.001	−0.078	−0.000	−0.138 **
	(0.21)	(−1.42)	(−0.06)	(−2.29)
BM	−0.011 ***	0.073 ***	−0.003	0.194 ***
	(−2.62)	(3.38)	(−1.40)	(12.62)
Director	−0.000	−0.098 **	0.003	−0.044
	(−0.03)	(−2.33)	(0.50)	(−1.05)
Idr	−0.039 *	−0.349 ***	−0.028 *	−0.252 **
	(−1.71)	(−3.19)	(−1.71)	(−2.41)
Dual	0.005 **	−0.013	0.003 **	−0.019 **
	(2.49)	(−1.51)	(2.34)	(−2.32)
Top1	0.000	0.001	0.000 *	0.001
	(1.03)	(1.16)	(1.75)	(0.79)
Gqz	0.004 *	−0.014	0.004 *	−0.007
	(1.67)	(−1.02)	(1.76)	(−0.52)
Age	0.004	0.026	−0.005 ***	0.052 ***
	(1.18)	(1.52)	(−2.85)	(3.80)
GDPgrowth	−0.002	0.026	−0.005	−0.050
	(−0.13)	(0.37)	(−0.68)	(−0.93)
Constant	0.288 ***	1.415 **	0.256 ***	1.457 **
	(3.70)	(2.08)	(3.63)	(2.33)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	6614	6614	6614	6614
R2	0.438	0.298	0.401	0.254
Weak identification test	35.082 > 16.38		25.694 > 16.38
Under-identification test	36.829 *** (p = 0.000)		67.386 *** (p = 0.000)
Weak-instrument-robust inference	3.63 * (p = 0.056)		4.69 ** (p = 0.030)
Over-identification test	equation exactly identified		equation exactly identified

Note: The results of weak identification tests are shown as Kleibergen–Paap Wald rk F statistics, which are greater than the critical values of 10%. The results of underidentification tests are shown as Kleibergen–Paap rk LM statistics. The results of weak-instrument robust-inference tests are presented by Anderson–Rubin Wald tests. Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

. Columns (1) and (3) present the regression results of the first-stage procedures; the coefficients of both IV_AD1 and IV_AD2 are positively significant at the 1% level (β = 8.183, t = 5.92; β = 10.830, t = 5.07), which indicates high correlations between the instrumental and endogenous variables. Columns (2) and (4) display the regression results of the second-stage procedures. The coefficients of AD1 and AD2 are negatively significant at least at the 10% level (β = −1.313, t = −1.85; β = −0.864, t = −2.03), which confirms the negative relationship between appointed directors and corporate carbon emission intensity. We also conduct the under-identification test, weak identification test, and weak-instrument robust-inference test to rationalize the instrumental variable approach. In summary, our main findings are unchanged.

4.3.3. Independent Variables Lag by One Period

To further mitigate endogeneity concerns, we take the independent variables one year behind. The results are shown in

Table 7. Independent variables lagged by one period.

Variables	(1)	(2)
	CO2	CO2
L.AD1	−0.145 *
	(−1.72)
L.AD2		−0.177 *
		(−1.68)
State	−0.002	−0.002
	(−1.25)	(−1.26)
Size	−0.008	−0.008
	(−0.61)	(−0.63)
Lev	0.037	0.036
	(0.71)	(0.70)
Roa	−0.398 ***	−0.397 ***
	(−4.30)	(−4.28)
Growth	−0.016 *	−0.016 *
	(−1.80)	(−1.81)
Cfo	−0.004	−0.004
	(−0.10)	(−0.12)
Loss	−0.008	−0.008
	(−0.54)	(−0.53)
Epe	0.051	0.051
	(1.14)	(1.13)
BM	0.044 *	0.044 *
	(1.72)	(1.73)
Director	−0.015	−0.014
	(−0.30)	(−0.28)
Idr	−0.006	−0.003
	(−0.06)	(−0.03)
Dual	−0.008	−0.008
	(−1.03)	(−1.02)
Top1	−0.001	−0.001
	(−0.69)	(−0.69)
Gqz	−0.011	−0.011
	(−0.75)	(−0.77)
Age	−0.005	−0.006
	(−0.19)	(−0.20)
GDPgrowth	−0.054	−0.055
	(−1.02)	(−1.03)
Constant	0.795	0.801
	(1.60)	(1.61)
Firm	Yes	Yes
Year	Yes	Yes
Ind	Yes	Yes
Obs.	4650	4650
Adj. R2	0.102	0.102

Note: Reported in parentheses are t-values based on robust standard errors. * and *** represent statistical significance at the 10% and 1% level, respectively.

. The coefficients of L.AD1 and L.AD2 are significantly negative at the 10% level (β = −0.145, t = −1.72; β = −0.177, t = −1.68), indicating the robustness of our main results.

4.4. Robustness Tests

4.4.1. Alternative Measurement of the Dependent Variable

Following Shang et al. [40], we replace the dependent variable with the natural logarithm of carbon emission intensity (lnCO2). As shown in Columns (1) and (2) of

Table 8. Alternative measurement of variables.

Variables	Alternative Measurement of Dependent Variable		Alternative Measurement of Independent Variable
	(1)	(2)	(3)	(4)
	lnCO2	lnCO2	CO2	CO2
AD1	−0.390 **
	(−2.13)
AD2		−0.545 **
		(−2.26)
dumAD1			−0.043 **
			(−2.24)
dumAD2				−0.036 **
				(−2.31)
State	−0.003	−0.002	−0.002	−0.002
	(−0.98)	(−0.96)	(−1.17)	(−1.25)
Size	−0.026	−0.026	−0.013	−0.012
	(−0.89)	(−0.89)	(−0.64)	(−0.61)
Lev	0.088	0.086	0.044	0.043
	(0.87)	(0.85)	(0.59)	(0.57)
Roa	−1.369 ***	−1.370 ***	−0.653 ***	−0.652 ***
	(−7.90)	(−7.90)	(−4.93)	(−4.92)
Growth	−0.059 ***	−0.060 ***	−0.038 ***	−0.038 ***
	(−3.57)	(−3.59)	(−2.96)	(−2.96)
Cfo	0.008	0.007	−0.023	−0.022
	(0.08)	(0.08)	(−0.31)	(−0.30)
Loss	0.011	0.011	0.008	0.008
	(0.41)	(0.40)	(0.39)	(0.39)
Epe	0.148	0.147	0.034	0.033
	(1.33)	(1.33)	(0.37)	(0.35)
BM	0.094 **	0.094 **	0.112 ***	0.112 ***
	(2.07)	(2.05)	(2.88)	(2.88)
Director	−0.129	−0.128	−0.128	−0.130
	(−1.20)	(−1.20)	(−1.59)	(−1.59)
Idr	−0.144	−0.149	−0.383 **	−0.385 **
	(−0.69)	(−0.72)	(−2.21)	(−2.21)
Dual	−0.020	−0.020	−0.018	−0.018
	(−1.07)	(−1.04)	(−1.17)	(−1.16)
Top1	0.001	0.001	0.002	0.002
	(0.39)	(0.40)	(1.02)	(1.01)
Gqz	0.005	0.005	−0.004	−0.005
	(0.19)	(0.18)	(−0.19)	(−0.24)
Age	0.032	0.032	0.016	0.016
	(0.81)	(0.79)	(0.54)	(0.53)
GDPgrowth	−0.027	−0.029	−0.014	−0.014
	(−0.21)	(−0.23)	(−0.16)	(−0.16)
Constant	0.425	0.422	1.411 **	1.403 **
	(0.50)	(0.50)	(2.25)	(2.24)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	6969	6969	6969	6969
Adj. R2	0.255	0.255	0.137	0.136

Note: Columns (1) and (2) report regression results with lnCO2 as the dependent variable. Columns (3) and (4) report regression results with dumAD1 and dumAD2 as the independent variables. Reported in parentheses are t-values based on robust standard errors. ** and *** represent statistical significance at the 5% and 1% level, respectively.

, the coefficients of AD1 and AD2 are negative and statistically significant at the 5% level (β = −0.390, t = −2.13; β = −0.545, t = −2.26), implying the negative relationship between appointed directors and carbon emission intensity. Our results remain solid.

4.4.2. Alternative Measurement of Independent Variables

To ensure the robustness of our findings, we replace the independent variables with dummy variables. Specifically, if there is at least one appointed director in a given firm, the variable dumAD1 equals 1, and 0 otherwise. If there is at least one appointed director, supervisor, or executive in a given firm, the variable dumAD2 equals 1, and 0 otherwise. Columns (3) and (4) of Table 8 present the regression results. The coefficients of dumAD1 and dumAD2 are significantly negative at the 5% level (β = −0.043, t = −2.24; β = −0.036, t = −2.31), implying that our findings are unaltered.

4.4.3. Considering Observations without State Shareholders

In the baseline test, we exclude private industrial enterprises with the state-owned shareholding ratio equal to 0, which may underestimate the effect of state shareholders but overestimate the influence of appointed directors. Therefore, we further include observations without state shareholders. The results are reported in Columns (1) and (2) of

Table 9. Adjusting samples based on state-owned shareholding ratio.

Variables	Including Observations without State Shareholders		Removing Observations with State-Owned Shareholding Ratio Less than 3%
	(1)	(2)	(3)	(4)
	CO2	CO2	CO2	CO2
AD1	−0.417 ***		−0.336 **
	(−2.95)		(−2.23)
AD2		−0.590 ***		−0.453 ***
		(−3.23)		(−2.74)
State	−0.001	−0.001	−0.002	−0.002
	(−0.90)	(−0.81)	(−0.79)	(−0.85)
Size	−0.028 *	−0.028 *	−0.010	−0.010
	(−1.90)	(−1.89)	(−0.36)	(−0.37)
Lev	0.111 **	0.110 **	0.050	0.045
	(2.03)	(2.01)	(0.42)	(0.38)
Roa	−0.560 ***	−0.560 ***	−0.320 *	−0.324 *
	(−5.50)	(−5.51)	(−1.74)	(−1.76)
Growth	−0.022 **	−0.022 **	−0.032 *	−0.032 *
	(−2.28)	(−2.28)	(−1.77)	(−1.79)
Cfo	−0.036	−0.036	−0.001	−0.002
	(−0.65)	(−0.65)	(−0.01)	(−0.03)
Loss	0.017	0.017	0.012	0.012
	(1.23)	(1.23)	(0.44)	(0.43)
Epe	0.072	0.072	−0.105	−0.106
	(1.02)	(1.01)	(−0.83)	(−0.85)
BM	0.075 ***	0.074 ***	0.085	0.085
	(2.62)	(2.60)	(1.61)	(1.62)
Director	−0.078	−0.078	−0.008	−0.009
	(−1.43)	(−1.43)	(−0.09)	(−0.11)
Idr	−0.217	−0.220	−0.115	−0.119
	(−1.57)	(−1.59)	(−0.51)	(−0.53)
Dual	−0.006	−0.006	−0.013	−0.013
	(−0.53)	(−0.51)	(−0.61)	(−0.64)
Top1	−0.000	−0.000	0.001	0.001
	(−0.42)	(−0.41)	(0.26)	(0.25)
Gqz	−0.019	−0.020	−0.001	−0.002
	(−0.96)	(−0.97)	(−0.04)	(−0.07)
Age	0.008	0.008	0.035	0.033
	(0.37)	(0.37)	(0.89)	(0.85)
GDPgrowth	−0.037	−0.037	−0.165	−0.166
	(−0.47)	(−0.48)	(−1.24)	(−1.25)
Constant	1.763 ***	1.763 ***	1.747 ***	1.751 ***
	(2.86)	(2.86)	(2.73)	(2.76)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	13,391	13,391	3336	3336
Adj. R2	0.119	0.120	0.098	0.098

Note: Columns (1) and (2) include observations without state shareholders. Columns (3) and (4) exclude observations with the state-owned shareholding ratio less than 3%. Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

. The coefficients of AD1 and AD2 are negative and significant at the 1% level (β = −0.417, t = −2.95; β = −0.590, t = −3.23), and the state-owned shareholding ratio (State) is negative but still not significant, suggesting that the effect of appointed directors is not overestimated and our findings are robust.

4.4.4. Removing Observations with State-Owned Shareholding Ratio Less than 3%

According to corporate law, shareholders who individually or collectively hold more than 3% of the shares of a listed firm have the right to nominate directors. However, some of our observations have appointed directors with a state-owned shareholding ratio less than 3%. These exceptions may have a much more prominent impact on our results. Therefore, we exclude observations with State less than 3%. The results are reported in Columns (3) and (4) of Table 9. The coefficients of AD1 and AD2 are significantly negative at the 5% and 10% level, respectively (β = −0.336, t = −2.23; β = −0.453, t = −2.74). Consequently, the observed relationship between appointed directors and carbon emission intensity is robust.

5. Mechanism Analysis

As proposed in the theoretical analysis of Section 2, we expect appointed directors to reduce carbon emission intensity by promoting the development of environmental strategies and increasing executive compensation incentives. In this section, we empirically test the mediation effects of these two channels. Following Baron and Kenny [90] and Yan et al. [46], we employ the following models to conduct the mechanism test:

M_i,t = α + β₁AD_i,t + ∑Controls + δ_i + γ_t + μ_j + ε_i,t

(5)

CO2_i,t = α + β₁AD_i,t + β₂M_i,t + ∑Controls + δ_i + γ_t + μ_j + ε_i,t

(6)

where i and t are the firm and year, respectively. M is the mediator, including the variable measuring the establishment of environmental strategies and variables measuring executive compensation incentives. Controls are the same set of control variables defined in Model (1). δ_i, γ_t, and μ_j are the firm, year, and industry fixed effects, respectively. ε_i,t is the error term. Model (5) is used to explore the association of appointed directors and the mediators. Model (6) is used to verify the existence of the mediation effects.

5.1. Mediation Effect of Environmental Strategy

A systematic environmental management system reflects a firm’s approach to environmental strategies [91], thereby mitigating its impact on the natural environment [92]. Therefore, we measure the formulation of corporate environmental strategies (Evis) according to the maturity level of the firm’s environmental management system. It is evaluated based on eight items: environmental concept, environmental goal, environmental management structure, environmental education and training, environmental special acts, environmental emergency management, environmental honors and rewards, and simultaneity of environmental management (all construction, renovation, expansion of projects, and research projects must be designed, constructed, and put into operation simultaneously with the treatment of exhaust gas, wastewater, and waste residue). The firm receives one point for each item; thus, the minimum and maximum values of Evis are 0 and 8, respectively. The data are from the CSMAR database.

Table 10. Mediation effect of environmental strategy.

Variables	(1)	(2)	(3)	(4)
	Evis	CO2	Evis	CO2
AD1	0.270 *	−0.139 **
	(1.78)	(−2.14)
AD2			0.397 *	−0.014
			(1.81)	(−0.17)
Evis		−0.012 **		−0.012 **
		(−2.08)		(−2.13)
State	−0.002	−0.001	−0.002	−0.001
	(−1.39)	(−0.83)	(−1.36)	(−1.18)
Size	0.100 ***	−0.008	0.099 ***	−0.007
	(5.24)	(−0.86)	(5.19)	(−0.77)
Lev	−0.217 ***	0.031	−0.216 ***	0.031
	(−4.52)	(1.16)	(−4.52)	(1.13)
Roa	0.848 ***	−0.241 ***	0.843 ***	−0.244 ***
	(3.84)	(−2.90)	(3.82)	(−2.95)
Growth	−0.022 **	−0.015 ***	−0.022 **	−0.015 ***
	(−2.40)	(−3.27)	(−2.41)	(−3.23)
Cfo	−0.139 **	−0.018	−0.139 **	−0.017
	(−2.24)	(−0.75)	(−2.24)	(−0.73)
Loss	0.000	0.028 ***	0.000	0.028 ***
	(0.00)	(4.45)	(0.02)	(4.45)
Epe	−0.072	0.034	−0.069	0.033
	(−1.15)	(0.97)	(−1.11)	(0.95)
BM	0.017	0.029 **	0.018	0.030 **
	(0.52)	(2.34)	(0.55)	(2.39)
Director	0.028	−0.025	0.030	−0.028
	(0.44)	(−0.86)	(0.47)	(−0.94)
Idr	0.382 **	0.003	0.385 **	0.004
	(2.33)	(0.06)	(2.35)	(0.07)
Dual	0.004	−0.003	0.004	−0.003
	(0.26)	(−0.57)	(0.28)	(−0.65)
Top1	−0.002	−0.000	−0.002	−0.000
	(−1.46)	(−0.49)	(−1.45)	(−0.52)
Gqz	−0.033 *	−0.003	−0.032 *	−0.004
	(−1.86)	(−0.41)	(−1.82)	(−0.44)
Age	−0.014	0.004	−0.014	0.004
	(−0.54)	(0.38)	(−0.54)	(0.35)
GDPgrowth	−0.036	0.001	−0.036	0.003
	(−0.38)	(0.02)	(−0.38)	(0.09)
Constant	−1.939 ***	0.800 ***	−1.930 ***	0.786 ***
	(−4.19)	(2.99)	(−4.16)	(2.91)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind.	Yes	Yes	Yes	Yes
Obs.	6969	6969	6969	6969
Adj. R2	0.956	0.160	0.956	0.158
Sobel–Goodman	Z = −1.664, p = 0.096 *		Z = −1.717, p = 0.086 *

Note: Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

reports the mediation effect of environmental strategies. In Columns (1) and (3), the coefficients of AD1 and AD2 are significantly positive at the 10% level (β = 0.270, t = 1.78; β = 0.397, t = 1.81), suggesting the appointed directors positively influence the construction of firms’ environmental management system and promote the adoption of environmental strategies. In Columns (2) and (4), the coefficients of Evis are negative and significant at the 5% level (β = −0.012, t = −2.08; β = −0.012, t = −2.13), indicating that the establishment of environmental strategies mediates the relationship between appointed directors and corporate carbon emission intensity. In addition, the Sobel–Goodman tests demonstrate that the mediation effect is significant at the 10% level (Z = −1.664, p = 0.096; Z = −1.717, p = 0.086), which further verifies the mediation effect. In addition, the coefficient of AD1 in Column (2) is still significant (β = −0.139 t = −2.14), suggesting that the adoption of environmental strategies is a partial mediator.

5.2. Mediation Effect of Executive Compensation Incentive

The executive compensation package generally encompasses two distinctive parts: cash-based compensation and stock-based compensation, which have different impacts [93]. Scholars argue that cash-based compensation works better in inspiring the top management team [94], but the change from cash-based incentives to stock-based compensation is negatively related to firm performance [95]. Zhou et al. [93] also state that the executive’s cash-based salary can effectively increase firms’ innovation input, while equity compensation fails to do so. However, some studies demonstrate that stock-based compensation may be more useful. For example, stock compensation is positively related to the CEO’s reputation [96] and helps increase corporate productivity [97]. Therefore, to clarify the different effects of executive compensation incentives, we employ two mediators: cash-based compensation (Pay), which is measured by the average salary of executives, and stock-based compensation (Mshr), which is expressed by the average shareholding ratio of executives.

5.2.1. Cash-Based Compensation Incentive

Table 11. Mediation effect of cash-based compensation incentives.

Variables	(1)	(2)	(3)	(4)
	Pay	CO2	Pay	CO2
AD1	0.198 *	−0.297 **
	(1.68)	(−2.16)
AD2			0.372 **	−0.468 ***
			(2.50)	(−2.67)
Pay		−0.045 ***		−0.044 ***
		(−2.93)		(−2.88)
State	−0.001	−0.002	−0.001	−0.002
	(−0.64)	(−1.11)	(−0.80)	(−1.04)
Size	0.198 ***	−0.004	0.199 ***	−0.004
	(8.40)	(−0.18)	(8.42)	(−0.18)
Lev	−0.079	0.043	−0.078	0.042
	(−1.26)	(0.57)	(−1.25)	(0.56)
Roa	0.752 ***	−0.618 ***	0.754 ***	−0.621 ***
	(4.86)	(−4.63)	(4.87)	(−4.66)
Growth	−0.055 ***	−0.040 ***	−0.055 ***	−0.041 ***
	(−4.16)	(−3.12)	(−4.13)	(−3.15)
Cfo	0.144 *	−0.015	0.144 *	−0.015
	(1.69)	(−0.20)	(1.69)	(−0.21)
Loss	0.055 **	0.011	0.056 **	0.011
	(2.54)	(0.51)	(2.55)	(0.51)
Epe	−0.066	0.030	−0.065	0.029
	(−1.07)	(0.32)	(−1.06)	(0.31)
BM	−0.312 ***	0.097 **	−0.310 ***	0.096 **
	(−6.09)	(2.44)	(−6.07)	(2.43)
Director	−0.005	−0.140 *	−0.005	−0.139 *
	(−0.05)	(−1.70)	(−0.05)	(−1.70)
Idr	0.168	−0.391 **	0.175	−0.397 **
	(0.90)	(−2.24)	(0.94)	(−2.29)
Dual	−0.020	−0.019	−0.021	−0.018
	(−1.27)	(−1.21)	(−1.30)	(−1.17)
Top1	−0.001	0.002	−0.001	0.002
	(−0.55)	(0.99)	(−0.57)	(1.01)
Gqz	−0.003	−0.004	−0.004	−0.004
	(−0.14)	(−0.20)	(−0.14)	(−0.20)
Age	−0.159 ***	0.010	−0.159 ***	0.010
	(−4.56)	(0.31)	(−4.56)	(0.31)
GDPgrowth	0.009	−0.011	0.011	−0.014
	(0.07)	(−0.13)	(0.09)	(−0.16)
Constant	−3.470 ***	1.281 **	−3.476 ***	1.286 **
	(−5.99)	(2.01)	(−6.00)	(2.03)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	6969	6969	6969	6969
Adj. R2	0.366	0.140	0.366	0.141
Sobel–Goodman	Z = −1.739, p = 0.082 *		Z = −2.305, p = 0.021 **

Note: Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

presents the mediation effect of cash-based compensation incentives. In Columns (1) and (3), the coefficients of AD1 and AD2 are positive and significant at the 10% and 5% level, respectively (β = 0.198, t = 1.68; β = 0.372, t = 2.50), showing that the appointed directors positively impact the executive’s cash-based compensation. In Columns (2) and (4), the coefficients of Pay are significantly negative at the 1% level (β = −0.045, t = −2.93; β = −0.044, t = −2.88), demonstrating that cash-based compensation plays a pronounced mediating role. In addition, Sobel–Goodman tests indicate that the mediation effect is significant at the 10% and 5% level, respectively (Z = −1.739, p = 0.082; Z = −2.305, p = 0.021), proving the effectiveness of the mediator. Moreover, the coefficients of AD1 and AD2 in Columns (2) and (4) are still statistically significant (β = −0.297, t = −2.16; β = −0.468, t = −2.67), so the cash-based compensation incentive partially mediates the relationship between appointed directors and corporate carbon emission intensity.

5.2.2. Stock-Based Compensation Incentive

Table 12. Mediation effect of stock-based compensation incentives.

Variables	(1)	(2)	(3)	(4)
	Mshr	CO2	Mshr	CO2
AD1	0.008	−0.304 **
	(1.36)	(−2.17)
AD2			0.018 **	−0.480 ***
			(2.19)	(−2.70)
Mshr		−0.275		−0.255
		(−0.97)		(−0.90)
State	−0.000	−0.002	−0.000	−0.002
	(−0.86)	(−1.08)	(−1.11)	(−1.01)
Size	0.001	−0.012	0.001	−0.012
	(0.66)	(−0.63)	(0.67)	(−0.62)
Lev	0.002	0.047	0.002	0.046
	(0.71)	(0.63)	(0.71)	(0.61)
Roa	0.005	−0.651 ***	0.005	−0.653 ***
	(0.89)	(−4.92)	(0.91)	(−4.94)
Growth	−0.001 *	−0.038 ***	−0.001	−0.039 ***
	(−1.67)	(−2.99)	(−1.62)	(−3.03)
Cfo	0.001	−0.021	0.001	−0.022
	(0.31)	(−0.29)	(0.31)	(−0.29)
Loss	−0.001	0.008	−0.001	0.008
	(−1.15)	(0.38)	(−1.14)	(0.38)
Epe	−0.002	0.032	−0.002	0.031
	(−0.65)	(0.35)	(−0.65)	(0.34)
BM	−0.000	0.111 ***	−0.000	0.110 ***
	(−0.07)	(2.84)	(−0.03)	(2.82)
Director	0.005	−0.138 *	0.005	−0.138 *
	(1.15)	(−1.69)	(1.15)	(−1.69)
Idr	0.035 ***	−0.388 **	0.036 ***	−0.396 **
	(2.79)	(−2.26)	(2.82)	(−2.32)
Dual	0.013 ***	−0.014	0.013 ***	−0.014
	(9.78)	(−0.92)	(9.78)	(−0.90)
Top1	0.000	0.002	0.000	0.002
	(0.42)	(1.02)	(0.39)	(1.04)
Gqz	0.001	−0.004	0.001	−0.004
	(0.55)	(−0.18)	(0.54)	(−0.18)
Age	−0.012 ***	0.014	−0.012 ***	0.014
	(−7.91)	(0.46)	(−7.92)	(0.45)
GDPgrowth	0.006	−0.010	0.006	−0.013
	(1.18)	(−0.12)	(1.21)	(−0.15)
Constant	−0.012	1.434 **	−0.012	1.437 **
	(−0.46)	(2.29)	(−0.48)	(2.30)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	6969	6969	6969	6969
Adj. R2	0.169	0.137	0.170	0.138
Sobel–Goodman	Z = −0.993, p = 0.321		Z = −1.080, p = 0.280

Note: Reported in parentheses are t-values based on robust standard errors. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

presents the mediation effect of stock-based compensation incentives. In Columns (1) and (3), the coefficients of AD1 and AD2 are positive (β = 0.008, t = 1.36; β = 0.018, t = 2.19). In Columns (2) and (4), the coefficients of Mshr are negative but insignificant (β = −0.275, t = −0.97; β = −0.255, t = −0.90). The Sobel–Goodman tests indicate that the mediation effect is insignificant (Z = −0.993, p = 0.321; Z = −1.080, p = 0.280). The results in Table 12 suggest that a stock-based compensation incentive is not an effective mediator. A possible explanation is that Chinese executives prefer cash-based compensation as it is a “bird in the hand”. Overall, compared with stock-based compensation incentives, cash-based compensation incentives have a much more notable mediation effect in the relationship between appointed directors and corporate carbon emission intensity.

6. Heterogeneity Analysis

This section delves into the heterogeneous effects of appointed directors. We first explore this issue from the perspective of the regional and industrial levels, regional environmental regulation and the industrial pollution level, and then study the heterogeneity from the firm level, in terms of analyst coverage and green innovation ability.

6.1. Regional Environmental Regulation

Previous research has convincingly demonstrated that as an external force, regional environmental regulation wields considerable influence in shaping the environmental practices of enterprises. The Porter hypothesis [98,99] states that appropriately structured environmental regulations can trigger an “innovation compensation effect”, stimulating innovation in green technologies and driving sustainable development. Scholars also underscore the substantially positive impact of government environmental regulations on corporate green patents [100] and environmental responsibilities [101]. When the government pays more attention to sustainable development, enterprises are more inclined to respond to government environmental regulations so as to obtain government support and reduce the cost of violating regulations, thereby improving environmental protection and decreasing carbon emission intensity.

Consistent with the established literature [102], we employ the ratio of provincial environmental pollution treatment investment to the provincial GDP (EPI) as a proxy for regional environmental regulation. A higher EPI signifies more stringent environmental regulation. We match the EPI to our sample firms based on their registration provinces and partition observations into high-EPI and low-EPI groups according to the annual average of the EPI. The results of the grouped tests are displayed in

Table 13. Heterogeneous effects of regional environmental regulation.

Variables	(1)	(2)	(3)	(4)
	High	Low	High	Low
	CO2	CO2	CO2	CO2
AD1	−0.193	−0.484 **
	(−1.34)	(−2.38)
AD2			−0.335	−0.703 ***
			(−1.61)	(−2.67)
State	−0.004 **	−0.001	−0.004 **	−0.000
	(−2.36)	(−0.21)	(−2.26)	(−0.13)
Size	0.033	−0.013	0.034	−0.012
	(1.17)	(−0.47)	(1.18)	(−0.44)
Lev	−0.105	0.088	−0.106	0.084
	(−1.05)	(0.81)	(−1.06)	(0.78)
Roa	−0.565 ***	−0.838 ***	−0.565 ***	−0.846 ***
	(−2.68)	(−4.75)	(−2.67)	(−4.80)
Growth	−0.043 **	−0.036 *	−0.043 **	−0.037 *
	(−2.36)	(−1.71)	(−2.37)	(−1.75)
Cfo	−0.156	0.076	−0.157	0.076
	(−1.38)	(0.80)	(−1.39)	(0.81)
Loss	0.051 *	−0.009	0.051 *	−0.010
	(1.70)	(−0.29)	(1.70)	(−0.33)
Epe	−0.134	0.163 *	−0.137	0.161 *
	(−0.75)	(1.74)	(−0.77)	(1.71)
BM	0.135 **	0.075	0.134 **	0.074
	(2.08)	(1.64)	(2.08)	(1.62)
Director	−0.161	−0.080	−0.158	−0.083
	(−1.51)	(−0.76)	(−1.49)	(−0.78)
Idr	−0.733 **	−0.161	−0.739 **	−0.167
	(−2.39)	(−0.78)	(−2.42)	(−0.81)
Dual	0.010	−0.040 *	0.011	−0.039 *
	(0.55)	(−1.86)	(0.57)	(−1.82)
Top1	0.001	0.002	0.001	0.002
	(0.39)	(0.78)	(0.39)	(0.80)
Gqz	0.005	−0.027	0.005	−0.027
	(0.17)	(−0.96)	(0.15)	(−0.94)
Age	0.067 *	−0.009	0.067 *	−0.010
	(1.74)	(−0.21)	(1.75)	(−0.25)
GDPgrowth	0.009	0.026	0.008	0.021
	(0.10)	(0.14)	(0.08)	(0.11)
Constant	0.932	1.214	0.934	1.203
	(1.29)	(1.44)	(1.29)	(1.43)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	3033	3936	3033	3936
Adj. R2	0.178	0.137	0.179	0.139
Empirical p-value	0.045 **		0.030 **

Note: We use Fisher’s permutation test for the coefficient difference testing and conduct self-sampling 1000 times to obtain the corresponding empirical p-value through the Bootstrap method. Reported in parentheses are t-values based on robust standard errors. *, **, *** represent statistically significantly different from zero at the 10%, 5%, and 1% level, respectively.

.

In Table 13, Columns (1) and (3) provide the regression outcomes for the high-EPI group, where the coefficients of AD1 and AD2 are insignificantly negative. Columns (2) and (4) present the results for the low-EPI group, where the coefficients of AD1 and AD2 are significantly negative at least at the 5% level (β = −0.484, t = −2.38; β = −0.703, t = −2.67). Fisher’s permutation tests affirm that the coefficients between the two groups are statistically significantly different at the 5% level (p = 0.045; p = 0.030), with the coefficient of AD1 and AD2 in the low-EPI group being more pronounced.

Overall, our findings reveal the heterogeneity in the relationship between appointed directors and carbon emission intensity concerning regional environmental regulation, with a more pronounced association observed in firms located in provinces with loose environmental regulation. A possible explanation is that firms in regions with stringent environmental regulation face greater external pressures to reduce carbon emission intensity, thus limiting the additional incentives provided by appointed directors. In contrast, firms in regions with loose regulation might find appointed directors more effective due to the lack of external pressures to curb carbon emissions.

6.2. Industrial Pollution Level

Cannikin’s law states that the amount of water a barrel can hold is determined by its shortest stave. Firms in heavily polluting industries consume a great amount of energy, inherently resulting in elevated carbon emissions. Therefore, despite great efforts having been made in environmental protection, the carbon performance of firms in heavily polluting industries severely restrict the effect of measures aiming at carbon reduction [103]. However, since these firms predominantly engage in highly polluting activities that shape their environmental performance and, consequently, their capacity to curtail carbon emission intensity, it is difficult to relieve the environmental problems of these firms. Hence, for firms operating in heavily polluting industries, the efficacy of appointed directors may be constrained by their heavily polluting nature.

In 2010, the Ministry of Environmental Protection issued the “Guidelines for Environmental Information Disclosure of Listed Companies (Draft for Comment)”, which required firms in sixteen heavily polluting industries (the sixteen heavily polluting industries cover thermal power, steel, cement, electrolytic aluminum, coal, metallurgy, chemical, petrochemical, building materials, papermaking, brewing, pharmaceuticals, fermentation, textiles, leather, and mining industries) to disclose the required environmental information. Following Cai et al. [100], if the private industrial firm is operating in one of the sixteen heavy-polluting industries, it is classified into the heavily polluting group (HP). Naturally, the remaining firms belong to the non-heavily polluting group (non-HP).

Table 14. Heterogeneous effects of industrial pollution level.

Variables	(1)	(2)	(3)	(4)
	HP	Non-HP	HP	Non-HP
	CO2	CO2	CO2	CO2
AD1	0.072	−0.220 *
	(0.48)	(−1.73)
AD2			−0.004	−0.172 *
			(−0.02)	(−1.77)
State	−0.002	0.001	−0.002	0.000
	(−0.83)	(0.87)	(−0.70)	(0.52)
Size	−0.075 **	0.004	−0.075 **	0.004
	(−2.06)	(0.28)	(−2.05)	(0.33)
Lev	0.092	0.052	0.091	0.050
	(0.82)	(0.88)	(0.82)	(0.85)
Roa	−0.932 ***	−0.355 ***	−0.933 ***	−0.352 ***
	(−4.41)	(−2.88)	(−4.41)	(−2.85)
Growth	−0.020	−0.040 ***	−0.020	−0.039 ***
	(−1.02)	(−2.62)	(−1.02)	(−2.61)
Cfo	−0.070	−0.030	−0.070	−0.029
	(−0.55)	(−0.65)	(−0.55)	(−0.62)
Loss	0.063 *	0.007	0.063 *	0.007
	(1.78)	(0.49)	(1.78)	(0.53)
Epe	−0.037	0.037	−0.038	0.037
	(−0.36)	(0.54)	(−0.37)	(0.54)
BM	0.210 ***	−0.031	0.208 ***	−0.030
	(3.41)	(−1.19)	(3.38)	(−1.18)
Director	−0.186	−0.125	−0.188	−0.128
	(−1.25)	(−1.55)	(−1.27)	(−1.56)
Idr	−1.047 ***	0.003	−1.056 ***	−0.004
	(−2.91)	(0.03)	(−2.96)	(−0.03)
Dual	−0.019	−0.017	−0.018	−0.017
	(−0.56)	(−1.61)	(−0.55)	(−1.59)
Top1	0.005	0.000	0.005	0.000
	(1.54)	(0.42)	(1.54)	(0.42)
Gqz	0.036	0.004	0.037	0.003
	(0.77)	(0.39)	(0.78)	(0.35)
Age	0.095 *	−0.018	0.095 *	−0.018
	(1.76)	(−0.62)	(1.78)	(−0.61)
GDPgrowth	−0.067	0.076	−0.069	0.073
	(−0.51)	(1.10)	(−0.53)	(1.06)
Constant	3.187 ***	0.804 ***	3.195 ***	0.796 ***
	(3.36)	(2.78)	(3.37)	(2.72)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	2844	4125	2844	4125
Adj. R2	0.298	0.123	0.298	0.120
Empirical p-value	0.060 *		0.000 ***

Note: We use Fisher’s permutation test for the coefficient difference testing and conduct self-sampling 1000 times to obtain the corresponding empirical p-value through the Bootstrap method. Reported in parentheses are t-values based on robust standard errors. *, **, *** represent statistically significantly different from zero at the 10%, 5%, and 1% level, respectively.

presents the results of the grouped tests.

Columns (1) and (3) of Table 14 report the results for the heavily polluting group (HP), and Columns (2) and (4) present the results for the non-heavily polluting group (non-HP). The coefficients of AD1 and AD2 of the HP group are not significant, while those of the non-HP group are significantly negative at the 10% level (β = −0.220, t = −1.73; β = −0.172, t = −1.77). Fisher’s permutation test reveals that the coefficients are statistically significantly different at the 10% and 1% level, respectively (p = 0.060; p = 0.000), with the coefficients of AD1 and AD2 in the non-HP group exhibiting greater significance.

In summary, our findings underscore the heterogeneity effects of appointed directors in the context of the industrial pollution level. The association between appointed directors and carbon emission intensity is more noticeable for firms operating in the non-heavily polluting industries. These results suggest that firms in non-heavily polluting industries are more amenable to reducing carbon emission intensity incentivized by appointed directors, while firms in heavily polluting industries face greater challenges in mitigating carbon emission intensity due to their reliance on high-carbon-emission activities. Therefore, more efforts should be devoted to improving the carbon performance of firms in heavily polluting industries.

6.3. Analyst Coverage

Analysts are important intermediaries and external supervisors in the capital market, playing a crucial role in alleviating information asymmetry [104], increasing external pressures [105], affecting the corporate market value [106,107], and thereby standardizing corporate behaviors [105]. The literature exploring firms’ ethical behaviors has illustrated that a greater analyst coverage exposes firms’ ethical behaviors to the public and promotes firms to actively take on social responsibilities [107,108]. Therefore, private industrial enterprises become more transparent to the public when they are followed by more analysts. Since the public and the government both attach great importance to environmental protection [24,79], these firms may actively engage in activities reducing carbon emission intensity to build a good reputation and cater to potential investors, which may weaken the additional effect of appointed directors in carbon emission reduction.

Drawing from Jo and Harjoto et al. [107], we employ the number of analysts following a firm in a given year to measure the analyst coverage (Ana). The observations are partitioned into high-Ana and low-Ana groups according to the annual average Ana.

Table 15. Heterogeneous effects of analyst coverage.

Variables	(1)	(2)	(3)	(4)
	High	Low	High	Low
	CO2	CO2	CO2	CO2
AD1	−0.071	−0.291 *
	(−0.58)	(−1.79)
AD2			−0.081	−0.422 ***
			(−0.57)	(−2.69)
State	−0.001	−0.000	−0.001	−0.000
	(−0.80)	(−0.23)	(−0.82)	(−0.16)
Size	−0.064 **	−0.014	−0.063 **	−0.013
	(−2.02)	(−0.51)	(−2.01)	(−0.49)
Lev	0.079	0.113	0.079	0.112
	(1.22)	(1.13)	(1.22)	(1.11)
Roa	−0.762 ***	−0.481 ***	−0.763 ***	−0.483 ***
	(−2.76)	(−3.25)	(−2.76)	(−3.28)
Growth	0.001	−0.059 ***	0.001	−0.059 ***
	(0.11)	(−3.21)	(0.11)	(−3.24)
Cfo	−0.035	−0.015	−0.035	−0.014
	(−0.41)	(−0.17)	(−0.41)	(−0.16)
Loss	0.112 *	0.010	0.112 *	0.010
	(1.77)	(0.46)	(1.77)	(0.46)
Epe	−0.066	0.084	−0.067	0.085
	(−0.38)	(0.82)	(−0.38)	(0.83)
BM	0.091 *	0.162 ***	0.091 *	0.160 ***
	(1.91)	(2.73)	(1.91)	(2.71)
Director	−0.026	−0.172	−0.026	−0.172 *
	(−0.28)	(−1.64)	(−0.28)	(−1.65)
Idr	−0.265	−0.405 **	−0.266	−0.408 **
	(−1.41)	(−1.99)	(−1.41)	(−2.03)
Dual	0.003	−0.013	0.003	−0.012
	(0.13)	(−0.71)	(0.13)	(−0.65)
Top1	−0.000	0.001	−0.000	0.001
	(−0.14)	(0.74)	(−0.14)	(0.77)
Gqz	−0.008	−0.006	−0.008	−0.006
	(−0.28)	(−0.24)	(−0.29)	(−0.24)
Age	0.068 **	−0.021	0.068 **	−0.022
	(2.15)	(−0.50)	(2.14)	(−0.51)
GDPgrowth	−0.124	0.122	−0.124	0.119
	(−0.94)	(1.18)	(−0.94)	(1.15)
Constant	0.625	1.753 ***	0.623	1.748 ***
	(0.70)	(2.89)	(0.70)	(2.88)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind.	Yes	Yes	Yes	Yes
Obs.	2449	4520	2449	4520
Adj. R2	0.244	0.132	0.244	0.133
Empirical p-value	0.075 *		0.042 **

Note: We use Fisher’s permutation test for the coefficient difference testing and conduct self-sampling 1000 times to obtain the corresponding empirical p-value through the Bootstrap method. Reported in parentheses are t-values based on robust standard errors. *, **, *** represent statistically significantly different from zero at the 10%, 5%, and 1% level, respectively.

reports the results of the grouped tests. Columns (1) and (3) display the results for the high-Ana group, where the coefficients of AD1 and AD2 fail to show a statistical significance. Columns (2) and (4) report the results for the low-Ana group, where the coefficients of AD1 and AD2 are significantly negative at the 10% and 1% level, respectively (β = −0.291, t = −1.79; β = −0.422, t = −2.69). Fisher’s permutation test states that the coefficients are significantly different at the 10% and 5% level, respectively (p = 0.075; p = 0.042), with the appointed directors of the low-Ana group displaying greater significance.

Overall, heterogeneity exists in the relationship between appointed directors and carbon emission intensity concerning the analyst coverage. The reduction effect is more salient in firms with a lower level of analyst coverage. This aligns with our conjecture that a greater analyst coverage supervises the corporate environmental performance, thus limiting the additional effect of appointed directors. Conversely, firms with fewer analysts following may pay less attention to environmental protection due to the lack of external supervision, making appointed directors more effective.

6.4. Green Innovation Ability

Green innovation has been positioned as an effective way to balance economic development and environmental governance [109]. Its multifaceted impact encompasses reducing energy consumption [110], improving energy efficiency [111], reducing corporate environmental externalities [112], and mitigating carbon emissions [113]. Private industrial firms actively conducting green innovation may have cultivated a lower carbon emission intensity, which weakens the impact of appointed directors in carbon reduction.

Referring to Hu et al. [114], we adopt the number of green patent applications submitted by a firm in a given year to represent a firm’s green innovation ability (Green). We then partition observations based on the annual average of Green into the high-Green group and the low-Green group.

Table 16. Heterogeneous effects of corporate green innovation ability.

Variables	(1)	(2)	(3)	(4)
	High	Low	High	Low
	CO2	CO2	CO2	CO2
AD1	−0.208	−0.301 *
	(−1.35)	(−1.85)
AD2			−0.280	−0.475 **
			(−1.39)	(−2.30)
State	−0.002	−0.002	−0.003	−0.002
	(−0.93)	(−1.08)	(−0.99)	(−1.02)
Size	−0.093 *	0.003	−0.093 *	0.003
	(−1.81)	(0.12)	(−1.81)	(0.13)
Lev	0.269	0.003	0.265	0.002
	(1.25)	(0.03)	(1.23)	(0.02)
Roa	−0.063	−0.733 ***	−0.059	−0.736 ***
	(−0.20)	(−5.16)	(−0.19)	(−5.19)
Growth	−0.117 *	−0.033 **	−0.118 *	−0.034 **
	(−1.78)	(−2.50)	(−1.79)	(−2.52)
Cfo	−0.058	−0.028	−0.061	−0.028
	(−0.29)	(−0.35)	(−0.31)	(−0.36)
Loss	0.016	0.002	0.016	0.002
	(0.54)	(0.08)	(0.55)	(0.08)
Epe	0.350 *	0.042	0.349 *	0.042
	(1.80)	(0.46)	(1.80)	(0.45)
BM	0.001	0.097 **	0.003	0.095 **
	(0.03)	(2.17)	(0.06)	(2.14)
Director	0.037	−0.173 *	0.036	−0.172 *
	(0.57)	(−1.85)	(0.56)	(−1.85)
Idr	−0.260	−0.424 **	−0.242	−0.435 **
	(−1.40)	(−2.17)	(−1.32)	(−2.23)
Dual	−0.023	−0.019	−0.022	−0.018
	(−1.59)	(−1.09)	(−1.56)	(−1.05)
Top1	0.004	0.001	0.004	0.001
	(1.19)	(0.57)	(1.23)	(0.58)
Gqz	0.029	−0.010	0.032	−0.010
	(0.65)	(−0.39)	(0.71)	(−0.42)
Age	0.038	0.012	0.039	0.012
	(0.68)	(0.37)	(0.70)	(0.35)
GDPgrowth	0.288	−0.015	0.277	−0.017
	(1.54)	(−0.17)	(1.50)	(−0.18)
Constant	2.140 **	1.292 *	2.132 **	1.293 *
	(2.13)	(1.82)	(2.12)	(1.83)
Firm	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes
Ind	Yes	Yes	Yes	Yes
Obs.	936	6033	936	6033
Adj. R2	0.176	0.152	0.176	0.153
Empirical p-value	0.042 **		0.009 ***

Note: We use Fisher’s permutation test for the coefficient difference testing and conduct self-sampling 1000 times to obtain the corresponding empirical p-value through the Bootstrap method. Reported in parentheses are t-values based on robust standard errors. *, **, *** represent statistically significantly different from zero at the 10%, 5%, and 1% level, respectively.

reports the regression results of the grouped tests. Columns (1) and (3) furnish the results for the high-Green group, while Columns (2) and (4) outline the results for the low-Green group. The coefficients of AD1 and AD2 of the low-Green group are negative and significant at the 10% and 5% level, respectively (β = −0.301, t = −1.85; β = −0.475, t = −2.30), while those of the high-Green group are insignificantly negative. Fisher’s permutation test suggests that the coefficients are statistically significantly different at the 5% and 1% level, respectively (p = 0.042; p = 0.009), with the coefficients of AD1 and AD2 in the low-Green group being more significant.

Hence, heterogeneous effects of appointed directors exist in the context of firms’ green innovation ability. The relationship between appointed directors and carbon emission intensity is more pronounced in firms with a poor green innovation ability. Plausibly, firms with a strong green innovation ability have already engaged in carbon reduction activities, which limits the incremental effect of appointed directors. On the contrary, firms with a poor green innovation ability are not equipped with effective green technologies and innovations, thereby displaying a poor carbon performance. So, in these firms, the effect of appointed directors is more pronounced.

7. Conclusions and Implications

With increasing concerns about environmental protection, how to reduce corporate carbon emission intensity without jeopardizing corporate profits has become the focus of research. Using the data of A-share private industrial enterprises in the Chinese stock markets from 2008 to 2021, this study investigates the relationship between appointed directors, which is a growing type of mixed-ownership reform at the governance level, and corporate carbon emission intensity. The findings indicate that the appointed directors significantly reduce corporate carbon emission intensity, and with the help of appointed supervisors and executives, the appointed directors work more efficiently. Endogeneity tests and robustness tests confirm the stability of the linkage between appointed directors and corporate carbon emission intensity. The mechanism analyses indicate that the reduction effect of the appointed directors is achieved by fortifying environmental strategies and providing attractive compensation incentives, especially cash-based compensation incentives. In addition, the heterogeneity analyses reveal that the efficacy of appointed directors in curbing carbon emission intensity is more pronounced in private industrial enterprises with loose environmental regulation, operating in non-heavily polluting industries, with low analyst coverage, and with poor green innovation abilities.

Our findings may provide the following recommendations for the government:

First, the government should actively promote mixed-ownership reform in private enterprises, both at the equity level and at the governance level. While the mixed-ownership reform in SOEs is like a raging fire, the reform in private enterprises is still in the preliminary stage. At the policy level, regulations and guidance about mixed-ownership reform in private enterprises are not yet perfect, and the management system for state shareholders needs to be improved. In practice, state shareholders are minority shareholders with limited influence, and they generally have not realized the importance of having seats on the board of directors, which enables them to produce a marked effect. Therefore, further promotion of mixed-ownership reform is imminent. On the one hand, it is necessary to stimulate the enthusiasm of private enterprises to introduce state shareholders. On the other hand, attention should be paid to the arrangement of the power structure of the board of directors. Boosting mixed-ownership reform in private enterprises helps to fully leverage the resource and governance advantages of state-owned capital, achieve the integration of state-owned and non-state-owned capital, promote the efficient development of the private economy, and improve the healthy development of the national economy.

Second, more efforts should be made to improve the carbon performance of firms operating in heavily polluting industries. These firms are more likely to be the crux of carbon reduction. To facilitate the achievement of low-carbon goals and cultivate a sustainable economy, the government should apply differentiated and targeted management at the micro level and pay more attention to the environmental problems of these heavily polluting firms.

Third, our study can provide a feasible reference for developing countries assailed with environmental problems. It has been widely argued that carbon reduction and economic growth are irreconcilable, particularly in the early stage of economic development in developing countries. Our study suggests that strengthened mixed-ownership reform can help break the deadlock. As the appointed directors take on the social responsibilities of the government and the economic duties of directors, they are more capable of balancing the social objectives of the government and the profit requirements of enterprises. Therefore, mixed-ownership reform at the governance level provides developing countries an opportunity to reduce carbon emissions while maintaining economic development.

However, our study acknowledges certain inherent limitations. First, currently, the voluntary disclosure of carbon emission information is not a prevailing practice among Chinese firms. We calculate corporate carbon emission intensity based on the carbon information disclosed by the official yearbooks, which only cover Chinese industrial enterprises. Therefore, our study is confined to Chinese industrial firms. As corporate information disclosure evolves towards increased transparency, this constraint may be mitigated in future research. Second, since Chinese listed firms only disclose information about the top ten shareholders, our study focuses on the directors appointed by state shareholders among the top ten shareholders, rather than all shareholders. Therefore, we may miss some exceptions where the state shareholder holds very few shares but still has a seat on the board. Such special cases may deserve further research.