Racing to Be Green? How Industry Tournament Incentives Shape Corporate Environmental Performance

Racing to Be Green? 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How Industry Tournament Incentives Shape Corporate Environmental Performance Ding Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7645315/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In this paper, we explore how industry‐level tournament incentives influence firms’ environmental outcomes, drawing on a dataset of 9,644 Chinese A‐share firm‐year observations spanning 2010 through 2020. Our empirical analysis reveals that stronger competitive incentives within an industry are linked to superior environmental performance at the firm level. We then decompose executive competitive orientation into three facets—ownership type, geographic location of the firm, and the degree of industry homogeneity—to assess how these characteristics shape the core relationship. The results indicate that when executives possess a pronounced competitive drive, the beneficial effect of industry tournament incentives on environmental performance is amplified. Moreover, this reinforcing effect is particularly evident for state‐controlled enterprises, companies headquartered in eastern provinces, and sectors exhibiting high similarity among rivals. To ensure the robustness of our conclusions, we employ a variety of techniques—including tests for reverse causality, checks for omitted‐variable bias, two‐stage least squares estimation, and propensity‐score matching. Overall, our findings furnish solid empirical support for the role of tournament‐style compensation in advancing environmental objectives and offer practical guidance for regulators and corporate boards seeking to refine executive pay schemes and strengthen environmental governance. Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Industry tournament incentives Environmental performance Sustainable development 1. Introduction Presently, there is increased pressure worldwide to achieve carbon neutrality. Environmental governance is a highly valued topic. Governments, academic scholars, NGOs, and managers have proven environmental performance a vital strategic implication. Khan et al. (2020) find environmental actions including environmentally friendly technologies, recycling, and CO2 and GHG emission reduction may encourage corporate environmental performance. Therefore, future companies should reflect the concept of a green economy, while also balancing environmental protection and economic development (Dong et al., 2020). Furthermore, social capital will also tend to favor companies with excellent environmental performance, helping companies achieve sustainable development (Delmas et al., 2010). The investment in environmental performance is long-term. The environmental management and control behavior of executives is vulnerable to the impact of principal-agent problems (Wang et al.,2022). CEOs are prone to short-sighted behavior while making decisions, and often favor short-term economic benefits (Arianpoor et al.,2022). The increased investment towards environmentally conscious practices of listed companies will reduce the negative public opinion on listed companies in society and help enhance the company's image (Liu et al,2009). The executive compensation incentive system can be affected by external factors such as public opinion (Berrone et al,2009), which is also a factor that affects the level of environmental performance. We extend this work to tournament theory and investigate whether industry tournament incentives based on executive behavior affect environmental performance levels in the Chinese context. Tournament‐style compensation remains a burgeoning area of inquiry within executive pay structures. Fu et al. (2022) highlight that both the absolute level and the internal composition of salary contracts shape managerial choices, demonstrating that a strategically tiered pay framework bolsters organizational decision‐making. Shen et al. (2018) employ the pay differential between CEOs and their subordinates as a proxy for intra‐firm tournament pressures, revealing that wider internal salary spreads stimulate higher rates of innovation. Similarly, Cheng et al. (2016) show that such internal competition curbs opportunistic earnings management. Beyond the boundaries of a single firm, executives also respond to competitive salary dynamics prevailing across their industry. Mei Chun et al. (2022) segment firms by geographic region—reflecting the tendency of Chinese executives to move within local labor markets—and report that stronger regional pay contests foster greater innovation output. Coles (2013) further extends the tournament framework to the inter‐firm arena by measuring the gap between the top‐paid CEO and peer‐industry CEOs, finding that external tournament incentives enhance both operating performance and firm valuation. Subsequent studies have linked industry‐level pay disparities to advances in innovation capability (Mei Chun et al., 2019), reductions in accrual‐based earnings management (Park, 2017), lower incidence of stock‐price collapses (Sun et al., 2019), and more aggressive merger and acquisition activity (Nguyen et al., 2015). Collectively, this body of work suggests that tournament incentives—whether internal or external—serve to elevate corporate value. Yet, despite the recognized importance of environmental stewardship for long‐term firm worth, little attention has been paid to how industry‐wide competitive pay schemes affect environmental performance. Addressing this omission, our study investigates the link between external tournament incentives and firms’ environmental outcomes. China serves as an ideal platform for studying the impact of industry tournament incentives on environmental performance. There are several reasons to study Chinese listed firms. First, Chinese companies are in short supply of executives, which in most cases will increase executive pay to widen the pay gap with competitive companies. Therefore, executives are affected by industry tournament incentives when they are not yet employed (Banker, Bu, and Mehta, 2016). From the perspective of corporate governance, it is meaningful to study the industry tournament incentive in China. In addition, the 20th National Congress of China has recently pointed out that the Chinese government should promote the process of ecological civilization construction. To this end, China has launched a series of concepts and legal regulations such as " double carbon target " and " two mountains theory " to help China transform into an environment-friendly economy. Chinese enterprises in order to be able to long-term development must respond to government calls to follow the laws and regulations. Coupled with China 's public opinion continues to require enterprises to accelerate the pace of energy conservation and emission reduction green cycle, China 's listed companies to fulfill their social responsibility has become a top priority. The most intuitive and effective way is to improve environmental performance (Cao, Lemmon, Pan, Qian, and Tian, 2019; Chen, Kim, Li, and Liang, 2018). Our analysis of Chinese publicly traded companies from 2010 through 2020 reveals a positive association between industry‐level tournament incentives and firms’ environmental performance. Several distinctive aspects of the Chinese setting make it particularly well suited for examining this relationship. First, as one of the world’s most rapidly expanding economies, China plays a pivotal role in global markets, amplifying the relevance of any insights drawn from its firms. Second, the China Securities Regulatory Commission has implemented a comprehensive framework of executive‐compensation rules that closely mirrors international practice, yielding a transparent, market‐driven pay structure. Third, the introduction of the Shanghai–Shenzhen–Hong Kong Stock Connect program provides a quasi‐natural experiment that allows us to isolate the causal impact of financial‐market integration on industry tournament incentives. According to our theoretical prediction, heightened competition among CEOs spurred by these incentives motivates them to advance environmental initiatives more swiftly in order to boost firm value. Finally, we confirm that our core result—a positive link between industry tournament incentives and environmental performance—persists when we substitute an alternative metric for measuring those incentives. The following methods are used to analyze endogeneity issues. First, we control firm fixed-effect and industry fixed-effect to mitigate omitted variables issues. There may be a reverse causality concern between industry tournament incentives and environmental performance. For example, CEOs have incentives to improve corporate environmental performance to enhance corporate image to change the company value and win the tournament. Furthermore, firms with high reputations often have more defined salary structures for their executives. We attempt to mitigate endogeneity concerns by employing the instrumental variables method and propensity score matching method. These empirical results show that our findings are robust by analyzing potential endogeneity issues. Moreover, we divide CEO’s competition willingness into three dimensions to test the impact of the CEO’s competition willingness on the association between the industry tournament incentives and environmental performance. The Chinese government holds absolute control of Chinese state-owned enterprises, and can directly appoint CEOs. Working in a state-owned enterprise means a stable working environment, however there is often less opportunity for career growth. Though these are virtually isolated from the externally managed labor market, managers of state-owned enterprises work in the Chinese government's political hierarchy, and there are cases of secondment and temporary appointment. Executives with such experience are more likely to advance in their future careers. Getting promoted within the Chinese political ranking system means more power, social status, privileges, and pecuniary and non-pecuniary benefits (Chen et al., 2018). Therefore, industry tournament incentives are also included. Managers in SOEs may have a stronger willingness to win tournaments. Our empirical result suggests that the positive effect of industry tournament incentives on environmental performance is more pronounced in SOEs than non-SOEs. Next, we study the impact of industry homogeneity. In a homogenous industry, goods of different brands imitate each other in all aspects, including performance, appearance, and marketing strategies. On the enterprise side, there exist similar business operation and management methods. During the homogeneous competition, executives are more sensitive to compensation and job-hopping phenomenon is more obvious (Kong et al.,2022). Thus, managers need to further compete with external candidates when the CEO position becomes available. This expands subordinate managers' perceived probability of promotion. If a CEO wants to get a promotion, they must increase the value of their company. Therefore, we expect a more pronounced positive association between industry tournament incentives and environmental performance in more homogenous industries. Our results remain consistent with our prediction. Finally, we study the impact of the companies’ geographic location. Since the reform and development, there has been an obvious gap between the western China and the eastern China in the allocation of market resources and the process of marketization (Guo et al.,2022). The chemical industry in the western region is developed, and the natural resource endowment remains rich, but the chemical industrial structure in west of China is simple, and the enterprises adopt extensive development. The eastern region has formed a complete modern industrial system and has a reasonable industrial layout, and the high-tech industry has gradually become its leading industry. Due to the differences in resource allocation and industrial structure, compared with the central and western regions, the labor market in the eastern region is more developed, and executives are more mobile in the labor market. CEOs are more likely to improve their value through labor market mobility, and they are more willing to participate in industry tournament. Therefore, we expect a more pronounced positive association between industry tournament incentives and environmental performance in eastern China. Our results are consistent with this prediction. Our paper makes significant contributions to the related literature. First, the existing literature lacks research on executive compensation and environmental performance. Current studies mostly focus on the impact of corporate governance factors such as government audits on environmental performance (Gao et al,2021). Executive compensation incentive system remains an important factor affecting executive behavior decisions (Coles et al,2018). Some researchers have studied the relationship between executive compensation level, executive compensation sensitivity, and environmental performance (Berrone et al,2009; Zou et al.,2015), but less attention has been given to whether the executive compensation gap will affect environmental performance. To the best of our knowledge, our study is the first to explore the interaction between industry tournament incentives and environmental performance and suggests that CEO compensation gap plays a significant role in enhancing firms' overall environmental performance. Second, the Chinese government strictly controls ecological development nowadays. It has become a trend for China's traditional economic development model to transform into an environmentally friendly one. The future development of enterprises should not only focus on company performance or innovation ability (Jin and Choi,2019). Environmental performance is an important indicator to measure a company's implementation of social responsibility (Melo et al, 2012). Investment in environmental management and control can improve a company's reputation and strengthen its relationship with the government, the public, and, other stakeholders (Aguilera and Guerrero,2018). Currently, publicly listed firms face growing pressure to boost their corporate value by pursuing energy efficiency, emissions reduction, and green growth. This evolving corporate landscape thus offers a timely context in which to examine how industry-level tournament incentives shape environmental outcomes. To our knowledge, our study is among the first to center explicitly on environmental performance when investigating the effects of executive compensation pay structures on firms’ sustainability practices. The remainder of the study is organized as follows. Section 2 provides a brief literature review and hypotheses. Section 3 presents the samples and empirical models. Section 4 reports empirical results. Section 5 conducts a further analysis, and Section 6 concludes the paper. 2. Literature Review and Hypothesis Development 2.1 Contextual background At the moment of the "environmental protection storm", governments at all levels in China have investigated environmental pollution problems, and have implemented measures such as coal pressing to reduce emissions, limit production, and suspend production (Shen and Lisa). Gilal et al. (2018) suggests companies carrying out environmental control work to improve environmental performance and achieve corporate social responsibility. Furthermore, a higher level of environmental performance will be a competitive advantage for enterprises. Because of the importance of environmental performance to enterprise development, researchers have conducted a tremendous amount of research investigating the factors influencing enterprise environmental performance. Corporate governance, senior executives' hometown identity, institutional investors' shareholding, government environmental audit, financial performance, environmental information disclosure, and other factors will have a positive impact on the environmental performance of listed companies. Based on the research on the high-level team theory, Shahab et al. (2019) found that the characteristics of executives, including executive gender, executive age, and executive compensation, can all affect the level of environmental performance. A compensation incentive system is an important way to motivate executives. Some studies have found that the level of executive compensation can affect the environmental performance of listed companies, and found that executives with higher-than-average compensation promote the improvement of environmental performance (Zou et al.,2015). Compensation incentives include not only executive compensation levels but also compensation structures. Tournament incentive is to motivate corporate executives to make decisions by constructing a competitive compensation structure (Zhang et al.,2022). When the tournament theory was proposed, academics measured the internal tournament incentives by the pay gap between CEOs and non-CEOs. Vieito (2012) finds that internal tournament incentives can improve corporate performance. Zhang et al. (2022) find internal tournament incentives are to enhance corporate value in a way that promotes innovative output. In addition, in the external labor market, due to the existence of competition among executives, there is a pay gap among executives. Coles et al (2013) put forward the industry tournament theory on the executive compensation gap between industries, and study the influence of the executive compensation gap on executive behavior under industry tournament incentives. Coles et al (2018) studied the relationship between industry tournament incentives and financial performance and found that industry tournament incentives can positively moderate financial performance. Park (2016) found that executive CEOs are influenced by industry tournament incentives in earnings management, and improve the company's financial performance by changing financial statement data. Most of the research on industry tournament incentives focuses on the mechanism for enhancing corporate value. Zhang et al. (2022) found that industry tournament incentives can motivate companies to fulfill their social responsibilities. As a variable reflecting corporate social responsibility, the environmental performance also affects corporate value. 2.2 Hypothesis Development The emotional psychology analysis tournament incentives use executives ' competitive emotions on pay differences to influence executives' decision-making behavior. There is information asymmetry between executives and shareholders in the external labor market. Business owners are unable to directly measure the CEO when assessing the CEO's true ability. Cichello et al. (2009) suggest that business owners evaluate CEO capabilities largely through business value. Therefore, higher enterprise value can help executive CEOs gain higher market evaluations and higher remuneration packages. Coles et al. (2018) believe that executives with lower salaries in the industry will be motivated by industry tournament incentives. Executives will be motivated to enhance corporate value to change their unfavorable situation during the enterprise assessment. CEOs can adopt environmental management strategies to improve environmental performance and fulfill corporate social responsibility to enhance corporate value. To increase the probability of promotion and obtain rewards from industry tournaments, CEOs will choose environmental investment, through environmental regulation, to enhance their companies’ environmental image and reduce the companies risk-taking. This allows for stronger ties with governments, the public, and other stakeholders in a socially responsible manner (Melo et al,2012). Although environmental governance will increase cost which may affect the company's economic performance in the short-term business cycle, therefore affecting executive compensation under the performance-oriented evaluation policy. However, several mechanisms explain why industry tournament pressure can instead push CEOs toward better environmental performance rather than cost-cutting that sacrifices sustainability. First, environmental performance has become a salient signal to external monitors-regulators, investors, customers and rating agencies-so that strong environmental governance improves a firm’s reputation and market valuation, which are precisely the outputs by which CEOs are evaluated in tournament settings. Second, poor environmental performance exposes firms to asymmetric downside risks (fines, litigation, regulatory restrictions, supply-chain loss, and reputational shocks) that can destroy firm value quickly; under tournament incentives, avoiding these large downside losses is as important as pursuing short-term gains. Third, many investors and lenders increasingly price ESG and environmental risk into capital costs, so investing in environmental management can improve access to lower-cost financing and long-term performance prospects-again improving the CEO’s comparative standing. Finally, when external stakeholders reward verifiable environmental improvements (through procurement, ratings, activism or regulatory recognition), the competitive payoff for “winning” the tournament becomes aligned with sustainable choices. Taken together, these channels can convert what looks like a short-term, competitive incentive into a driver of longer-term, environment-friendly strategies-especially in contexts where environmental KPIs enter the performance evaluation or where regulatory/market monitoring is strong. Company executives are not only the operators of the company, but also the executors of corporate strategies (Fu-Jin et al.,2010). Increasing investment in environmental protection will reduce the negative externalities of listed companies and achieve sustainable development of the company (Ziolo et al.,2019). Therefore, it is speculated that CEOs will pay close attention to the interests of the company, and adopt practical strategies to increase investment in environmental governance, which can enhance the environmental performance of the company, the promotion probability, and the possibility of obtaining incentives from industry tournaments. Hypothesis 1 . Industry tournament incentives have a positive and significant relation with corporate environmental performance. In China, SOEs and non-SOEs are subject to different degrees of market constraints. Ullah et al. (2022) have found that state-owned enterprises show less incentive willingness to win industry tournaments than non-state-owned enterprises. The reason is that their executive pays more attention to the development of political causes, so the external labor market has less influence on such executives. However, due to the introduction of the Environmental Protection Law of the People's Republic of China, environmental governance requires full participation. Unlike non-state-owned enterprises, which aim at a profit, state-owned enterprises undertake a significant amount of social responsibilities (Khalid et al.,2021). State-owned enterprises are required to be more responsive to the call to protect the environment based on government and social support. In China's political system, executive secondment has become an important way for senior executives of state-owned enterprises to be promoted. Similar to executive job-hopping in non-state-owned enterprises, executives may be seconded to other departments only if the value of the company they work for increases. Chen and Ma (2021) find that SOEs are more willing to invest in green investments to improve their environmental performance. Therefore, industry tournament incentives are also a way to stimulate the executives of state-owned enterprises to improve their environmental performance. Hypothesis 2 . The positive association between industry tournament incentives and corporate environmental performance is more pronounced in SOEs than non-SOEs. The more homogeneous companies in the industry, the higher the industry homogeneity, and the stronger the industry liquidity. Therefore, in industries with high homogeneity, CEOs should improve their management ability to enhance the possibility of obtaining tournament awards (Kale and Reis,2009). In the external labor market, enterprises have a higher willingness to hire external personnel as executives of the company. For CEOs in the same industry, the higher the homogeneity of the industry, the higher the employment opportunities in the external labor market. For the company's executive CEO, the more likely it is that executives "change jobs" (Liu,2014). Park (2017) found that the higher the industry homogeneity, the stronger the willingness of CEOs to promote industry tournament incentives. Hence, we expect industry homogeneity affects the positive relationship between industry tournament incentives and the environmental performance of listed firms. Hypothesis 3 . The positive association between industry tournament incentives and corporate environmental performance is more pronounced in higher homogenous industries. Because of China's large geographical area, the gap between east and west is pronounced, which is also reflected in the degree of labor market development. Compared with the central and western regions, the eastern region enjoys a superior geographical location and developed economy, so the labor market in the eastern region has a higher degree of development (Wang et al,2013). Due to the relatively sound labor market allocation, the CEOs of companies in the eastern region can improve their self-worth through the liquidity of the labor market. The corporate culture of protecting the environment is more complete (Banker et al.,2016). To move this decomposition beyond description, we frame the east–central/western contrast using two complementary theoretical lenses. From an institutional theory perspective, regional differences in formal and informal institutions-regulatory enforcement, market-supporting rules, disclosure norms, and civic pressure-alter the payoffs of environmental investment. Stronger institutions in the eastern region increase the returns to verifiable environmental governance (through regulatory compliance, reputational benefits, and better access to ESG-aware capital), so tournament-driven efforts to improve firm value are more likely to be channeled into environmental performance there. From an upper-echelons perspective, the composition and career incentives of senior managers vary across regions: more fluid labor markets and market-oriented managerial cohorts in the east mean CEOs face greater outside options and career concerns, making them more responsive to signals from industry tournaments. Together, institutional constraints and managerial orientations generate a theory-driven expectation that the tournament-environment link will be stronger in the east than in less institutionally developed regions. Therefore, we predict that the correlation between industry tournament incentives and corporate environmental performance in the eastern region is more significant than that in the central and western regions. Hypothesis 4 . The positive association between industry tournament incentives and corporate environmental performance is more pronounced in eastern China. 3. Research design 3.1 Data and Sample We take the data of listed companies in China's Shanghai and Shenzhen stock exchanges from 2010 to 2020 as the research sample. To improve the validity of the data, this paper uses the following criteria to eliminate the research sample: (1) Eliminate the data of listed companies with incomplete data; (2) Eliminate the data of listed companies ST and ST* company data; (3) Winsorized all the relevant variables of interest at a 1 % level and attained a final sample of 7844 observations between 2010 and 2020. The data in this paper was obtained through the following ways: (1) The executive compensation data is from the Chinese Security Market and Accounting Research (CSMAR) database; (2) Environmental performance data and other enterprise data were collected, collated and verified manually by Zhou and Deng (2017). 3.2 Measuring industry tournament incentives We adopt the approach of Coles et al. (2018) to construct our key independent variable, industry tournament incentives ( INTOURINC ). Specifically, we calculate INTOURINC as the natural logarithm of the difference between (1) the total annual pay awarded to the highest-paid CEO within an industry (or size-based subgroup) and (2) the total annual pay of the industry’s second-highest-paid CEO. By using the runner-up CEO’s compensation rather than the industry average or minimum, we reduce distortion from extreme top-pay figures. Applying a log transformation further attenuates the impact of any remaining outliers in the raw gap measure. Following Coles et al.’s reasoning, a larger compensation spread between the top-paid and the next-highest-paid CEO signifies a more intense “tournament” for the promotion prize, so that higher values of INTOURINC indicate stronger industry-level tournament incentives. 3.3 Measuring environmental performance According to China’s “Guidelines for the Preparation of Corporate Environmental Reports” (Ministry of Environmental Protection), environmental performance encompasses the quantifiable outcomes that a firm attains via its resource use, pollution control, and environmental protection activities. Common proxies for corporate environmental performance include: (1) pollutant‐discharge metrics, (2) composite environmental‐index scores, and (3) ecological‐benefit assessments. China’s pollutant‐discharge approach remains constrained by the absence of a comprehensive toxic‐release inventory, while index‐based evaluations often entail subjective weighting decisions. To overcome these limitations, we adopt the World Business Council for Sustainable Development’s eco‐benefit methodology. This approach captures a firm’s environmental performance by quantifying the environmental influences on its products or services, offering a holistic and objective gauge of corporate eco‐outcomes. Referring to Zhou and Deng (2017), we employ two indicators to construct a composite measure of environmental performance. First, total firm revenue serves as the proxy for operating outcomes. Second, we use the pollutant‐discharge fee—which firms remit in proportion to the volume and toxicity of emissions—as the proxy for environmental impact. Because this fee is calibrated to the quantity of harmful substances released, it provides an objective, comprehensive reflection of a firm’s pollution footprint. We combine these two elements into a single index: higher index values correspond to superior environmental performance. Formally: 3.4 Model specification We test our hypotheses using variations of the following ordinary least squares regression model: (1) Where ENVIRO it is the ratio of the natural log of the current year's operating revenue to the natural log of the current year's sewage expenses; Industry tournament incentives are measured using the industry pay gap. We use a large number of controls informed by prior researches (e.g., Cui et al., 2019; Francoeur et al., 2021; Zhao et al., 2021). First, we control firm size ( SIZE ) and industry-adjusted ROA ( ROA ). Furthermore, we control for market-to-book ratio ( MTB ) and a Big Four auditor indicator ( BIG4 ). Finally, we control for CEO departures ( CEORETIRE ), CEO TENURE ( CEOTENURE ), age of CEO ( LNAGE ), CEO ownership ratio ( CEOSTOCK ), formula independent director number proportion ( DUDO ), CEO concurrently serving as chairman ( CHAIR ) and formula property right nature ( STATE ). We also control for several industries and year fixed effects. 4. Empirical results 4.1 Descriptive statistics Panel A of Table 1 reports the descriptive statistics for all variables—dependent, independent, and controls—employed in our principal regression models. For each variable it lists the number of observations, the mean, the standard deviation, and the minimum and maximum values. TABLE 1. Descriptive Statistics and Variable Correlations Panel A: Descriptive Statistics Variables N Mean Median Max Min S.D 𝐸𝑁𝑉𝐼𝑅𝑂 7,844 14.82 14.97 18.37 9.886 1.813 INTOURINC 7,844 13.27 13.45 16.54 10.03 1.109 BIG4 7,844 0.0680 0 1 0 0.252 SIZE 7,844 22.58 22.32 25.78 20.14 1.285 CEOSTOCK 7,844 0.0190 0 0.307 0 0.0600 ROA 7,844 0.0350 0.0300 0.210 -0.106 0.0500 CEORETIRE 7,844 0.0500 0 1 0 0.217 CEOTENURE 7,844 5.304 5 13 1 3.135 CHAIR 7,844 0.215 0 1 0 0.411 DUDO 7,844 0.917 1 1 0 0.276 STATE 7,844 0.501 1 1 0 0.500 MTB 7,844 1.609 1.211 7.438 0.213 1.369 LNAGE 7,844 3.925 3.932 4.304 3.497 0.166 Panel B: Pearson Correlations Variables 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC IOS SIZE CEOSTOCK ROA CEORETIRE CEOTENURE CHAIR DUDO STATE MTB LNAGE 𝐸𝑁𝑉𝐼𝑅𝑂 1 INTOURINC 0.129 *** 1 IOS 0.202 *** 0.078 *** 1 SIZE 0.655 *** 0.107 *** 0.296 *** 1 CEOSTOCK -0.085 *** -0.041 *** -0.087 *** -0.248 *** 1 ROA -0.066 *** 0.116 *** -0.028 *** -0.172 *** 0.115 *** 1 CEORETIRE -0.049 *** 0.014 -0.062 *** -0.007 0.012 -0.003 1 CEOTENURE 0.142 *** 0.057 *** -0.053 *** 0.107 *** 0.108 *** -0.049 *** 0.039 *** 1 CHAIR -0.006 0.031 *** 0.031 *** -0.080 *** 0.367 *** 0.061 *** 0.123 *** 0.230 *** 1 DUDO 0.009 -0.092 *** 0.006 -0.029 *** 0.003 -0.020 * -0.014 -0.037 *** -0.022 ** 1 STATE 0.308 *** -0.064 *** 0.115 *** 0.469 *** -0.315 *** -0.165 *** 0.009 -0.049 *** -0.169 *** 0.033 *** 1 MTB -0.365 *** -0.043 *** -0.128 *** -0.552 *** 0.208 *** 0.439 *** 0.025 ** -0.115 *** 0.081 *** -0.138 *** -0.296 *** 1 LNAGE 0.044 *** -0.059 *** 0.039 *** 0.103 *** -0.012 -0.057 *** 0.083 *** 0.034 *** -0.037 *** 0.007 0.092 *** -0.097 *** 1 4.2. Regression results Table 2 reports pooled OLS estimates of how industry‐level tournament pay gaps affect firms’ environmental performance. Column (1) estimates a simple regression of environmental performance on INTOURINC , omitting both fixed effects and additional covariates. Column (2) augments the first model with a full suite of firm‐level controls as well as industry and year dummy variables. In the bivariate model, the coefficient on industry tournament incentives is 0.1749 and is highly significant (t = 24.937), providing clear support for Hypothesis 1 that greater pay‐gap “prizes” correspond with stronger environmental outcomes. When controls and fixed effects are introduced in Column (2), the coefficient remains positive and statistically significant, indicating that our core result is not driven by omitted variables. TABLE 2. Regression results for the impact of industry tournament incentives on environmental performance Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.1749 *** 0.0368 *** (24.937) (4.958) ISO 0.0940 ** (2.410) SIZE 0.9887 *** (106.259) CEOSTOCK 0.3285 ** (2.333) ROA 1.1345 *** (6.736) CEORETIRE -0.1231 *** (-3.612) CEOTENURE -0.0070 ** (-2.519) CHAIR 0.2124 *** (8.001) DUDO -0.0843 ** (-2.351) STATE 0.0819 *** (3.935) MTB -0.0050 (-0.633) LNAGE 0.0245 (0.475) Constant -12.1506 *** -6.6510 *** (-40.429) (-12.221) Industry effect Yes Yes Year effect Yes Yes Observations 7,844 7,844 R-SQUARED 0.478 0.079 4.3 Heterogeneity over state ownership We predict that industry tournament incentives have a more significant positive correlation with the environmental performance of state-owned enterprises than non-state-owned enterprises. To verify this point, we divide property rights into state-owned enterprises and non-state-owned enterprises and generate a dummy variable ( STATEDUM ). We assign a value of 1 to a company with this variable as a state-owned enterprise and 0 to a company with this variable as a non-state-owned enterprise. The regression results are shown in Table 3. The results show that the regression coefficients of INTOURINC of state-owned enterprises in column (1) are significantly positive, while the regression coefficients of INTOURINC of non-state-owned enterprises in column (2) are not significant, Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 2. TABLE 3 Moderating effect of state ownership Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.0978 *** 0.0769 (4.343) (1.043) IOS -0.2970 *** 0.0849 (-4.118) (0.248) SIZE 1.0611 *** 1.0286 *** (47.069) (9.523) CEOSTOCK -11.8655 -0.7855 (-1.513) (-0.496) ROA 3.6145 *** 1.2948 (7.312) (0.555) CEORETIRE -0.6482 *** 0.0844 (-7.665) (0.189) CEOTENURE 0.0076 -0.0275 (1.102) (-0.786) CHAIR -0.1271 ** 0.1141 (-2.093) (0.393) DUDO 0.1160 0.2421 (1.298) (0.683) MTB -0.0303 0.0605 (-1.025) (1.139) LNAGE -0.1699 -0.1382 (-1.288) (-0.885) Constant -8.8958 *** -7.9576 *** (-10.517) (-2.969) Industry effect Yes Yes Year effect Yes Yes Observations 2,465 5,379 R-SQUARED 0.640 0.559 empirical P-value 0.0193** 4.4 Heterogeneity over industry homogeneity Industry homogeneity was quantified by estimating each firm’s stock‐to‐market regression slope and then aggregating those betas across all firms in the same industry (Kale, 2009). First, we calculate the median of partial regression coefficients of stock and market for all companies in the travel industry by industry and year and then generate a dummy variable ( HOMODUNMEDIAN ). Secondly, this variable is compared with the industry median as the partial regression coefficient of the firm in that year. We dichotomize industry homogeneity by comparing each firm’s beta to the cross‐industry median: firms with a beta above the industry median receive a value of 1, while those at or below the median are coded as 0. The regression results are shown in Table 4. Column (1) is the regression results of enterprises whose partial regression coefficient is higher than the median, and column (2) is the regression results of enterprises whose partial regression coefficient is lower than the median. The coefficient of INTOURINC in column (1) is significantly positive, indicating that industry tournament incentives have a stronger positive relationship with the environmental performance of listed companies in industries with higher homogeneity. Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 3. TABLE 4 Moderating effect of industry homogeneity Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.1082 *** 0.0769 (5.509) (1.043) IOS 0.1394 * 0.0849 (1.686) (0.248) SIZE 0.9769 *** 1.0286 *** (41.790) (9.523) CEOSTOCK 4.5043 *** -0.7855 (13.157) (-0.496) ROA 1.6208 *** 1.2948 (3.540) (0.555) CEORETIRE -0.4415 *** 0.0844 (-4.960) (0.189) CEOTENURE 0.0417 *** -0.0275 (5.936) (-0.786) CHAIR -0.1168 ** 0.1141 (-2.122) (0.393) DUDO 0.4431 *** 0.2421 (4.305) (0.683) STATE 0.1679 *** 0.2266 (3.336) (0.861) MTB -0.0098 0.0605 (-0.421) (1.139) LNAGE -0.3088 *** -0.1382 (-2.699) (-0.885) Constant -6.7660 *** -7.9576 *** (-8.970) (-2.969) Industry effect Yes Yes Year effect Yes Yes Observations 4,643 3,201 R-SQUARED 0.517 0.559 empirical P-value 0.036* 4.5 Heterogeneity over geographical location It is expected that compared with the central and western regions, the correlation between industry tournament incentives and corporate environmental performance is more significant in the eastern region. To verify this, the samples in the benchmark model are geographically grouped and regional dummy variables ( LOCALDUM ) are generated. This variable is then assigned a value of 1 for firms in the eastern region and 0 for firms in the central and western regions. The regression results are shown in Table 5. The results show that the regression coefficients of INTOURINC in the eastern region of column (1) are all significantly positive, while the regression coefficients of INTOURINC in the central and western regions of column (2) are not significant. Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 4. TABLE 5 Moderating effect of geographical location Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.0960 *** 0.0769 (6.077) (1.043) IOS 0.0457 0.0849 (0.634) (0.248) SIZE 0.9253 *** 1.0286 *** (47.972) (9.523) CEOSTOCK 2.3857 *** -0.7855 (8.958) (-0.496) ROA 0.7097 * 1.2948 (1.938) (0.555) CEORETIRE -0.3878 *** 0.0844 (-4.291) (0.189) CEOTENURE 0.0271 *** -0.0275 (4.601) (-0.786) CHAIR -0.0569 0.1141 (-1.255) (0.393) DUDO 0.2485 *** 0.2421 (3.190) (0.683) STATE 0.2130 *** 0.2266 (5.074) (0.861) MTB 0.0287 * 0.0605 (1.724) (1.139) LNAGE -0.2133 ** -0.1382 (-2.223) (-0.885) Constant -6.3391 *** -7.9576 *** (-10.294) (-2.969) Industry effect Yes Yes Year effect Yes Yes Observations 6,437 1,407 R-SQUARED 0.488 0.559 empirical P-value 0.029* 4.6 Endogeneity 4.6.1 Instrumental variable To solve the problem that our hypothesis is not affected by the omitted variables problem, we use two kinds of instrumental variables. Firstly, according to the research of Huang et al. (2019), the number of CEOs of other companies in the same industry whose salary is higher than that of their own company is used as the first instrumental variable. To win the highest incentive prize for tournaments, the lower-paid CEOs keep raising their salaries and thus winning more tournaments. When the higher-paid CEOs getting more in the same industry, CEO of the company will be more influenced by incentives. Therefore, the level of incentive degree of the industry tournament incentives changes with the number of this kind of CEO. Secondly, according to the study of Coles et al. (2018), we sum up the compensation of all CEOs in the same industry and use the sum of the compensation of CEOs in the same industry as the second instrumental variable. We use the two-stage least squares method (2SLS) to empirically analyze the model, and the analysis results are shown in Table 7. According to table 6 in the column (1) and (2) of the regression results, the dependent variable in the regression coefficients is significantly positive, considering the omission caused by the variable under the condition of endogenous problems, the instrumental variable method, the model industry tournament incentives with the company's environmental performance is significant positive correlation results show that the hypothesis is still established. TABLE 6 Endogeneity check Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.2425 *** 0.4553 *** (9.543) (5.095) ISO 0.1028 * 0.0710 (1.709) (1.119) SIZE 0.9382 *** 0.9141 *** (54.851) (45.349) CEOSTOCK 3.0756 *** 3.3467 *** (11.338) (11.137) ROA 1.2549 *** 0.4007 (3.591) (0.805) CEORETIRE -0.3126 *** -0.3285 *** (-4.529) (-4.590) CEOTENURE 0.0197 *** 0.0172 *** (3.946) (3.289) CHAIR -0.0893 ** -0.1181 *** (-2.309) (-2.841) DUDO 0.3396 *** 0.3780 *** (5.032) (5.296) STATE 0.2105 *** 0.2720 *** (5.795) (6.056) MTB -0.0128 0.0063 (-0.789) (0.340) LNAGE -0.2169 ** -0.1386 (-2.547) (-1.485) Constant -8.1026*** -10.8041*** (-13.129) (-8.590) Industry effect Yes Yes Year effect Yes Yes Observations 7,844 7,844 R-SQUARED 0.513 0.481 4.6.2 PSM Although control variables such as CEO and company characteristics are added to the baseline model, this does not rule out the existence of a nonlinear relationship between industry tournament incentives and environmental performance. To this end, by referring to the method of Mei Chun et al. (2019), we match the propensity scores of the disposal group (high industry tournament incentives companies) and the control group (low industry tournament incentives companies) in the sample. Then, the least square regression is performed on the benchmark model based on the matched samples to control for endogeneity problems caused by systematic differences in CEO and company characteristics between the two groups of samples. As shown in Table 7, the regression coefficients of explanatory variables are all significantly positive, which supports our view. These results indicate that industry tournament incentives are significantly positively correlated with environmental performance when considering the possible nonlinear relationship of model variables. TABLE 7 PSM results for the impact of industry tournament incentives on environmental performance. Variables (1) (2) 𝐸𝑁𝑉𝐼𝑅𝑂 𝐸𝑁𝑉𝐼𝑅𝑂 INTOURINC 0.1010 *** 0.0115 ** (7.689) (2.132) ISO 0.1147 * 0.0018 (1.867) (0.112) SIZE 0.9896 *** 0.0099 ** (58.143) (2.482) CEOSTOCK 2.2556 *** 0.0297 (9.145) (0.427) ROA 1.5007 *** 0.0196 (4.545) (0.265) CEORETIRE -0.3193 *** -0.0277 (-4.529) (-0.758) CEOTENURE 0.0201 *** 0.0196 (3.956) (0.265) CHAIR -0.0490 -0.0013 (-1.246) (-0.133) DUDO 0.3215 *** -0.0240 (4.670) (-1.634) STATE 0.1413 *** -0.0035 (3.870) (-0.388) MTB 0.0136 0.0045 (0.930) (1.185) LNAGE -0.2613 *** -0.0277 (-3.074) (-0.758) Constant -7.3524 *** -0.7454 *** (-13.348) (-4.240) Industry effect Yes Yes Year effect Yes Yes Observations 4,248 3,596 R-SQUARED 0.514 0.440 5. Conclusions and future research directions Drawing on insights from Tan et al. (2021), Chowdhury et al. (2022), and Anton et al. (2004), we propose that firms which strategically invest in environmental initiatives gain a reputational edge that executives can exploit to secure larger tournament‐style rewards. Accordingly, CEOs have a strong incentive to bolster their companies’ environmental outcomes in order to win these competitive prizes. We further hypothesize that this positive linkage intensifies when regulatory pressures are more stringent and when external labor markets exhibit greater maturity and mobility. Our analysis of A-share firms over 2010–2020 demonstrates a robust, positive link between industry-level tournament incentives and corporate environmental performance. We validate this relationship through a battery of robustness checks—including tests for omitted-variable bias, reverse-causality estimations, two-stage least-squares regressions, and propensity-score matching—all of which yield consistent results. Further subgroup analyses reveal that the incentive effect is especially pronounced among state-owned enterprises, companies operating in highly homogeneous industries, and firms headquartered in China’s eastern provinces. Finally, although we tried our best to determine the unexplored relation (particularly in China) between industry tournament incentives and environmental performance, we believe that our study has certain limitations that should be addressed in the future. First, we did not consider internal tournament incentives in this study because it was difficult to obtain compensation data for company employees during the study period. Second, the CEO's short-sighted behavior remains an interesting topic in the recent literature. Therefore, we suggest further research into the moderating effect of CEO short-sighted behavior. Declarations AUTHOR CONTRIBUTIONS The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. CONFLICT OF INTEREST STATEMENT The author declares no relevant financial or nonfinancial conflict of interest to disclose. He certifies that he has no affiliations with or involvement in any organization or entity with any financial or nonfinancial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interest in any material discussed in this article. DATA AVAILABILITY STATEMENT The datasets generated and/or analyzed during the current study are available from the author upon reasonable request. References Aguilera‐Caracuel J, Guerrero‐Villegas J., 2018. How corporate social responsibility helps MNEs to improve their reputation. The moderating effects of geographical diversification and operating in developing regions. Corporate social responsibility and environmental management, 25(4), 355-372. Anton W R Q, Deltas G, Khanna M., 2004. Incentives for environmental self-regulation and implications for environmental performance. Journal of environmental economics and management, 48(1), 632-654. Arianpoor, A., & Yazdanpanah, R.,2022. 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How Industry Tournament Incentives Shape Corporate Environmental Performance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePresently, there is increased pressure worldwide to achieve carbon neutrality. Environmental governance is a highly valued topic. Governments, academic scholars, NGOs, and managers have proven environmental performance a vital strategic implication. Khan et al. (2020) find environmental actions including environmentally friendly technologies, recycling, and CO2 and GHG emission reduction may encourage corporate environmental performance. Therefore, future companies should reflect the concept of a green economy, while also balancing environmental protection and economic development (Dong et al., 2020). Furthermore, social capital will also tend to favor companies with excellent environmental performance, helping companies achieve sustainable development (Delmas et al., 2010).\u003c/p\u003e\n\u003cp\u003eThe investment in environmental performance is long-term. The environmental management and control behavior of executives is vulnerable to the impact of principal-agent problems (Wang et al.,2022). CEOs are prone to short-sighted behavior while making decisions, and often favor short-term economic benefits (Arianpoor et al.,2022). The increased investment towards environmentally conscious practices of listed companies will reduce the negative public opinion on listed companies in society and help enhance the company\u0026apos;s image\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(Liu et al,2009). The executive compensation incentive system can be affected by external factors such as public opinion (Berrone et al,2009), which is also a factor that affects the level of environmental performance. We extend this work to tournament theory and investigate whether industry tournament incentives based on executive behavior affect environmental performance levels in the Chinese context.\u003c/p\u003e\n\u003cp\u003eTournament‐style compensation remains a burgeoning area of inquiry within executive pay structures. Fu et al. (2022) highlight that both the absolute level and the internal composition of salary contracts shape managerial choices, demonstrating that a strategically tiered pay framework bolsters organizational decision‐making. Shen et al. (2018) employ the pay differential between CEOs and their subordinates as a proxy for intra‐firm tournament pressures, revealing that wider internal salary spreads stimulate higher rates of innovation. Similarly, Cheng et al. (2016) show that such internal competition curbs opportunistic earnings management. Beyond the boundaries of a single firm, executives also respond to competitive salary dynamics prevailing across their industry. Mei Chun et al. (2022) segment firms by geographic region\u0026mdash;reflecting the tendency of Chinese executives to move within local labor markets\u0026mdash;and report that stronger regional pay contests foster greater innovation output. Coles (2013) further extends the tournament framework to the inter‐firm arena by measuring the gap between the top‐paid CEO and peer‐industry CEOs, finding that external tournament incentives enhance both operating performance and firm valuation. Subsequent studies have linked industry‐level pay disparities to advances in innovation capability (Mei Chun et al., 2019), reductions in accrual‐based earnings management (Park, 2017), lower incidence of stock‐price collapses (Sun et al., 2019), and more aggressive merger and acquisition activity (Nguyen et al., 2015). Collectively, this body of work suggests that tournament incentives\u0026mdash;whether internal or external\u0026mdash;serve to elevate corporate value. Yet, despite the recognized importance of environmental stewardship for long‐term firm worth, little attention has been paid to how industry‐wide competitive pay schemes affect environmental performance. Addressing this omission, our study investigates the link between external tournament incentives and firms\u0026rsquo;\u0026nbsp;environmental outcomes.\u003c/p\u003e\n\u003cp\u003eChina serves as an ideal platform for studying the impact of industry tournament incentives on environmental performance. There are several reasons to study Chinese listed firms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, Chinese companies are in short supply of executives, which in most cases will increase executive pay to widen the pay gap with competitive companies. Therefore, executives are affected by industry tournament incentives when they are not yet employed (Banker, Bu, and Mehta, 2016). From the perspective of corporate governance, it is meaningful to study the industry tournament incentive in China. In addition, the 20th National Congress of China has recently pointed out that the Chinese government should promote the process of ecological civilization construction. To this end, China has launched a series of concepts and legal regulations such as \u0026quot; double carbon target \u0026quot; and \u0026quot; two mountains theory \u0026quot; to help China transform into an environment-friendly economy.\u0026nbsp;Chinese enterprises in order to be able to long-term development must respond to government calls to follow the laws and regulations.\u0026nbsp;Coupled with China \u0026apos;s public opinion continues to require enterprises to accelerate the pace of energy conservation and emission reduction green cycle, China \u0026apos;s listed companies to fulfill their social responsibility has become a top priority. The most intuitive and effective way is to improve environmental performance (Cao, Lemmon, Pan, Qian, and Tian, 2019; Chen, Kim, Li, and Liang, 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis of Chinese publicly traded companies from 2010 through 2020 reveals a positive association between industry‐level tournament incentives and firms\u0026rsquo;\u0026nbsp;environmental performance. Several distinctive aspects of the Chinese setting make it particularly well suited for examining this relationship. First, as one of the world\u0026rsquo;s most rapidly expanding economies, China plays a pivotal role in global markets, amplifying the relevance of any insights drawn from its firms. Second, the China Securities Regulatory Commission has implemented a comprehensive framework of executive‐compensation rules that closely mirrors international practice, yielding a transparent, market‐driven pay structure. Third, the introduction of the Shanghai\u0026ndash;Shenzhen\u0026ndash;Hong Kong Stock Connect program provides a quasi‐natural experiment that allows us to isolate the causal impact of financial‐market integration on industry tournament incentives. According to our theoretical prediction, heightened competition among CEOs spurred by these incentives motivates them to advance environmental initiatives more swiftly in order to boost firm value. Finally, we confirm that our core result\u0026mdash;a positive link between industry tournament incentives and environmental performance\u0026mdash;persists when we substitute an alternative metric for measuring those incentives.\u003c/p\u003e\n\u003cp\u003eThe following methods are used to analyze endogeneity issues. First, we control firm fixed-effect and industry fixed-effect to mitigate omitted variables issues. There may be a reverse causality concern between industry tournament incentives and environmental performance. For example, CEOs have incentives to improve corporate environmental performance to enhance corporate image to change the company value and win the tournament. Furthermore, firms with high reputations often have more defined salary structures for their executives. We attempt to mitigate endogeneity concerns by employing the instrumental variables method and propensity score matching method. These empirical results show that our findings are robust by analyzing potential endogeneity issues.\u003c/p\u003e\n\u003cp\u003eMoreover, we divide CEO\u0026rsquo;s competition willingness into three dimensions to test the impact of the CEO\u0026rsquo;s competition willingness on the association between the industry tournament incentives and environmental performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Chinese government holds absolute control of Chinese state-owned enterprises, and can directly appoint CEOs. Working in a state-owned enterprise means a stable working environment, however there is often less opportunity for career growth. Though these are virtually isolated from the externally managed labor market, managers of state-owned enterprises work in the Chinese government\u0026apos;s political hierarchy, and there are cases of secondment and temporary appointment. Executives with such experience are more likely to advance in their future careers. Getting promoted within the Chinese political ranking system means more power, social status, privileges, and pecuniary and non-pecuniary benefits (Chen et al., 2018). Therefore, industry tournament incentives are also included. Managers in SOEs may have a stronger willingness to win tournaments. Our empirical result suggests that the positive effect of industry tournament incentives on environmental performance is more pronounced in SOEs than non-SOEs.\u003c/p\u003e\n\u003cp\u003eNext, we study the impact of industry homogeneity. In a homogenous industry, goods of different brands imitate each other in all aspects, including performance, appearance, and marketing strategies. On the enterprise side, there exist similar business operation and management methods. During the homogeneous competition, executives are more sensitive to compensation and job-hopping phenomenon is more obvious (Kong et al.,2022). Thus, managers need to further compete with external candidates when the CEO position becomes available. This expands subordinate managers\u0026apos; perceived probability of promotion. If a CEO wants to get a promotion, they must increase the value of their company. Therefore, we expect a more pronounced positive association between industry tournament incentives and environmental performance in more homogenous industries. Our results remain consistent with our prediction.\u003c/p\u003e\n\u003cp\u003eFinally, we study the impact of the companies\u0026rsquo; geographic location. Since the reform and development, there has been an obvious gap between the western China and the eastern China in the allocation of market resources and the process of marketization\u0026nbsp;(Guo et al.,2022). The chemical industry in the western region is developed, and the natural resource endowment remains rich, but the chemical industrial structure in west of China is simple, and the enterprises adopt extensive development. The eastern region has formed a complete modern industrial system and has a reasonable industrial layout, and the high-tech industry has gradually become its leading industry. Due to the differences in resource allocation and industrial structure, compared with the central and western regions, the labor market in the eastern region is more developed, and executives are more mobile in the labor market. CEOs are more likely to improve their value through labor market mobility, and they are more willing to participate in industry tournament. Therefore, we expect a more pronounced positive association between industry tournament incentives and environmental performance in eastern China. Our results are consistent with this prediction.\u003c/p\u003e\n\u003cp\u003eOur paper makes significant contributions to the related literature. First, the existing literature lacks research on executive compensation and environmental performance. Current studies mostly focus on the impact of corporate governance factors such as government audits on environmental performance (Gao et al,2021). Executive compensation incentive system remains an important factor affecting executive behavior decisions (Coles et al,2018). Some researchers have studied the relationship between executive compensation level, executive compensation sensitivity, and environmental performance (Berrone et al,2009; Zou et al.,2015), but less attention has been given to whether the executive compensation gap will affect environmental performance. To the best of our knowledge, our study is the first to explore the interaction between industry tournament incentives and environmental performance and suggests that CEO compensation gap plays a significant role in enhancing firms\u0026apos; overall environmental performance.\u003c/p\u003e\n\u003cp\u003eSecond, the Chinese government strictly controls ecological development nowadays. It has become a trend for China\u0026apos;s traditional economic development model to transform into an environmentally friendly one. The future development of enterprises should not only focus on company performance or innovation ability (Jin and Choi,2019). Environmental performance is an important indicator to measure a company\u0026apos;s implementation of social responsibility (Melo et al, 2012). Investment in environmental management and control can improve a company\u0026apos;s reputation and strengthen its relationship with the government, the public, and, other stakeholders (Aguilera and Guerrero,2018). Currently, publicly listed firms face growing pressure to boost their corporate value by pursuing energy efficiency, emissions reduction, and green growth. This evolving corporate landscape thus offers a timely context in which to examine how industry-level tournament incentives shape environmental outcomes. To our knowledge, our study is among the first to center explicitly on environmental performance when investigating the effects of executive compensation pay structures on firms\u0026rsquo;\u0026nbsp;sustainability practices.\u003c/p\u003e\n\u003cp\u003eThe remainder of the study is organized as follows. Section 2 provides a brief literature review and hypotheses. Section 3 presents the samples and empirical models. Section 4 reports empirical results. Section 5 conducts a further analysis, and Section 6 concludes the paper.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cp\u003e\u003cem\u003e2.1 Contextual background\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt the moment of the \u0026quot;environmental protection storm\u0026quot;, governments at all levels in China have investigated environmental pollution problems, and have implemented measures such as coal pressing to reduce emissions, limit production, and suspend production (Shen and Lisa). Gilal et al. (2018) suggests companies carrying out environmental control work to improve environmental performance and achieve corporate social responsibility. Furthermore, a higher level of environmental performance will be a competitive advantage for enterprises. Because of the importance of environmental performance to enterprise development, researchers have conducted a tremendous amount of research investigating the factors influencing enterprise environmental performance. Corporate governance, senior executives\u0026apos; hometown identity, institutional investors\u0026apos; shareholding, government environmental audit, financial performance, environmental information disclosure, and other factors will have a positive impact on the environmental performance of listed companies. Based on the research on the high-level team theory, Shahab et al. (2019) found that the characteristics of executives, including executive gender, executive age, and executive compensation, can all affect the level of environmental performance.\u003c/p\u003e\n\u003cp\u003eA compensation incentive system is an important way to motivate executives. Some studies have found that the level of executive compensation can affect the environmental performance of listed companies, and found that executives with higher-than-average compensation promote the improvement of environmental performance (Zou et al.,2015). Compensation incentives include not only executive compensation levels but also compensation structures. Tournament incentive is to motivate corporate executives to make decisions by constructing a competitive compensation structure (Zhang et al.,2022). When the tournament theory was proposed, academics measured the internal tournament incentives by the pay gap between CEOs and non-CEOs. Vieito (2012) finds that internal tournament incentives can improve corporate performance. Zhang et al. (2022) find internal tournament incentives are to enhance corporate value in a way that promotes innovative output. In addition, in the external labor market, due to the existence of competition among executives, there is a pay gap among executives. Coles et al (2013) put forward the industry tournament theory on the executive compensation gap between industries, and study the influence of the executive compensation gap on executive behavior under industry tournament incentives. Coles et al (2018) studied the relationship between industry tournament incentives and financial performance and found that industry tournament incentives can positively moderate financial performance. Park (2016) found that executive CEOs are influenced by industry tournament incentives in earnings management, and improve the company\u0026apos;s financial performance by changing financial statement data. Most of the research on industry tournament incentives focuses on the mechanism for enhancing corporate value. Zhang et al. (2022) found that industry tournament incentives can motivate companies to fulfill their social responsibilities. As a variable reflecting corporate social responsibility, the environmental performance also affects corporate value.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Hypothesis Development\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe emotional psychology analysis tournament incentives use executives \u0026apos; competitive emotions on pay differences to influence executives\u0026apos; decision-making behavior. There is information asymmetry between executives and shareholders in the external labor market. Business owners are unable to directly measure the CEO when assessing the CEO\u0026apos;s true ability. Cichello et al. (2009) suggest that business owners evaluate CEO capabilities largely through business value. Therefore, higher enterprise value can help executive CEOs gain higher market evaluations and higher remuneration packages. Coles et al. (2018) believe that executives with lower salaries in the industry will be motivated by industry tournament incentives. Executives will be motivated to enhance corporate value to change their unfavorable situation during the enterprise assessment.\u003c/p\u003e\n\u003cp\u003eCEOs can adopt environmental management strategies to improve environmental performance and fulfill corporate social responsibility to enhance corporate value. To increase the probability of promotion and obtain rewards from industry tournaments, CEOs will choose environmental investment, through environmental regulation, to enhance their companies\u0026rsquo; environmental image and reduce the companies risk-taking. This allows for stronger ties with governments, the public, and other stakeholders in a socially responsible manner (Melo et al,2012). Although environmental governance will increase cost which may affect the company\u0026apos;s economic performance in the short-term business cycle, therefore affecting executive compensation under the performance-oriented evaluation policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, several mechanisms explain why industry tournament pressure can instead push CEOs toward better environmental performance rather than cost-cutting that sacrifices sustainability. First, environmental performance has become a salient signal to external monitors-regulators, investors, customers and rating agencies-so that strong environmental governance improves a firm\u0026rsquo;s reputation and market valuation, which are precisely the outputs by which CEOs are evaluated in tournament settings. Second, poor environmental performance exposes firms to asymmetric downside risks (fines, litigation, regulatory restrictions, supply-chain loss, and reputational shocks) that can destroy firm value quickly; under tournament incentives, avoiding these large downside losses is as important as pursuing short-term gains. \u0026nbsp;Third, many investors and lenders increasingly price ESG and environmental risk into capital costs, so investing in environmental management can improve access to lower-cost financing and long-term performance prospects-again improving the CEO\u0026rsquo;s comparative standing. Finally, when external stakeholders reward verifiable environmental improvements (through procurement, ratings, activism or regulatory recognition), the competitive payoff for\u0026nbsp;\u0026ldquo;winning\u0026rdquo;\u0026nbsp;the tournament becomes aligned with sustainable choices. Taken together, these channels can convert what looks like a short-term, competitive incentive into a driver of longer-term, environment-friendly strategies-especially in contexts where environmental KPIs enter the performance evaluation or where regulatory/market monitoring is strong.\u003c/p\u003e\n\u003cp\u003eCompany executives are not only the operators of the company, but also the executors of corporate strategies (Fu-Jin et al.,2010). Increasing investment in environmental protection will reduce the negative externalities of listed companies and achieve sustainable development of the company (Ziolo et al.,2019). Therefore, it is speculated that CEOs will pay close attention to the interests of the company, and adopt practical strategies to increase investment in environmental governance, which can enhance the environmental performance of the company, the promotion probability, and the possibility of obtaining incentives from industry tournaments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e. Industry tournament incentives have a positive and significant relation with corporate environmental performance.\u003c/p\u003e\n\u003cp\u003eIn China, SOEs and non-SOEs are subject to different degrees of market constraints. Ullah et al. (2022) have found that state-owned enterprises show less incentive willingness to win industry tournaments than non-state-owned enterprises. The reason is that their executive pays more attention to the development of political causes, so the external labor market has less influence on such executives. However, due to the introduction of the Environmental Protection Law of the People\u0026apos;s Republic of China, environmental governance requires full participation. Unlike non-state-owned enterprises, which aim at a profit, state-owned enterprises undertake a significant amount of social responsibilities (Khalid et al.,2021). State-owned enterprises are required to be more responsive to the call to protect the environment based on government and social support.\u003c/p\u003e\n\u003cp\u003eIn China\u0026apos;s political system, executive secondment has become an important way for senior executives of state-owned enterprises to be promoted. Similar to executive job-hopping in non-state-owned enterprises, executives may be seconded to other departments only if the value of the company they work for increases. Chen and Ma (2021) find that SOEs are more willing to invest in green investments to improve their environmental performance. Therefore, industry tournament incentives are also a way to stimulate the executives of state-owned enterprises to improve their environmental performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e. The positive association between industry tournament incentives and corporate environmental performance is more pronounced in SOEs than non-SOEs.\u003c/p\u003e\n\u003cp\u003eThe more homogeneous companies in the industry, the higher the industry homogeneity, and the stronger the industry liquidity. Therefore, in industries with high homogeneity, CEOs should improve their management ability to enhance the possibility of obtaining tournament awards (Kale and Reis,2009). In the external labor market, enterprises have a higher willingness to hire external personnel as executives of the company. For CEOs in the same industry, the higher the homogeneity of the industry, the higher the employment opportunities in the external labor market. For the company\u0026apos;s executive CEO, the more likely it is that executives \u0026quot;change jobs\u0026quot; (Liu,2014). Park (2017) found that the higher the industry homogeneity, the stronger the willingness of CEOs to promote industry tournament incentives. Hence, we expect industry homogeneity affects the positive relationship between industry tournament incentives and the environmental performance of listed firms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e. The positive association between industry tournament incentives and corporate environmental performance is more pronounced in higher homogenous industries.\u003c/p\u003e\n\u003cp\u003eBecause of China\u0026apos;s large geographical area, the gap between east and west is pronounced, which is also reflected in the degree of labor market development. Compared with the central and western regions, the eastern region enjoys a superior geographical location and developed economy, so the labor market in the eastern region has a higher degree of development (Wang et al,2013). Due to the relatively sound labor market allocation, the CEOs of companies in the eastern region can improve their self-worth through the liquidity of the labor market. The corporate culture of protecting the environment is more complete (Banker et al.,2016). To move this decomposition beyond description, we frame the east\u0026ndash;central/western contrast using two complementary theoretical lenses. From an institutional theory perspective, regional differences in formal and informal institutions-regulatory enforcement, market-supporting rules, disclosure norms, and civic pressure-alter the payoffs of environmental investment. Stronger institutions in the eastern region increase the returns to verifiable environmental governance (through regulatory compliance, reputational benefits, and better access to ESG-aware capital), so tournament-driven efforts to improve firm value are more likely to be channeled into environmental performance there. \u0026nbsp; From an upper-echelons perspective, the composition and career incentives of senior managers vary across regions: more fluid labor markets and market-oriented managerial cohorts in the east mean CEOs face greater outside options and career concerns, making them more responsive to signals from industry tournaments. Together, institutional constraints and managerial orientations generate a theory-driven expectation that the tournament-environment link will be stronger in the east than in less institutionally developed regions. Therefore, we predict that the correlation between industry tournament incentives and corporate environmental performance in the eastern region is more significant than that in the central and western regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e. The positive association between industry tournament incentives and corporate environmental performance is more pronounced in eastern China.\u003c/p\u003e"},{"header":"3. Research design","content":"\u003cp\u003e\u003cem\u003e3.1 Data and Sample\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe take the data of listed companies in China\u0026apos;s Shanghai and Shenzhen stock exchanges from 2010 to 2020 as the research sample. To improve the validity of the data, this paper uses the following criteria to eliminate the research sample: (1) Eliminate the data of listed companies with incomplete data; (2) Eliminate the data of listed companies ST and ST* company data; (3) Winsorized all the relevant variables of interest at a 1 % level and attained a final sample of 7844 observations between 2010 and 2020.\u003c/p\u003e\n\u003cp\u003eThe data in this paper was obtained through the following ways: (1) The executive compensation data is from the Chinese Security Market and Accounting Research (CSMAR) database; (2) Environmental performance data and other enterprise data were collected, collated and verified manually by Zhou and Deng (2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2 Measuring industry tournament incentives\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe adopt the approach of Coles et al. (2018) to construct our key independent variable, industry tournament incentives (\u003cem\u003eINTOURINC\u003c/em\u003e). Specifically, we calculate \u003cem\u003eINTOURINC\u003c/em\u003e as the natural logarithm of the difference between (1) the total annual pay awarded to the highest-paid CEO within an industry (or size-based subgroup) and (2) the total annual pay of the industry\u0026rsquo;s second-highest-paid CEO. By using the runner-up CEO\u0026rsquo;s compensation rather than the industry average or minimum, we reduce distortion from extreme top-pay figures. Applying a log transformation further attenuates the impact of any remaining outliers in the raw gap measure. Following Coles et al.\u0026rsquo;s reasoning, a larger compensation spread between the top-paid and the next-highest-paid CEO signifies a more intense \u0026ldquo;tournament\u0026rdquo; for the promotion prize, so that higher values of \u003cem\u003eINTOURINC\u003c/em\u003e indicate stronger industry-level tournament incentives.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 Measuring environmental performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to China\u0026rsquo;s\u0026nbsp;\u0026ldquo;Guidelines for the Preparation of Corporate Environmental Reports\u0026rdquo;\u0026nbsp;(Ministry of Environmental Protection), environmental performance encompasses the quantifiable outcomes that a firm attains via its resource use, pollution control, and environmental protection activities. Common proxies for corporate environmental performance include: (1) pollutant‐discharge metrics, (2) composite environmental‐index scores, and (3) ecological‐benefit assessments. China\u0026rsquo;s pollutant‐discharge approach remains constrained by the absence of a comprehensive toxic‐release inventory, while index‐based evaluations often entail subjective weighting decisions. To overcome these limitations, we adopt the World Business Council for Sustainable Development\u0026rsquo;s eco‐benefit methodology. This approach captures a firm\u0026rsquo;s environmental performance by quantifying the environmental influences on its products or services, offering a holistic and objective gauge of corporate eco‐outcomes.\u003c/p\u003e\n\u003cp\u003eReferring to Zhou and Deng (2017), we employ two indicators to construct a composite measure of environmental performance. First, total firm revenue serves as the proxy for operating outcomes. Second, we use the pollutant‐discharge fee\u0026mdash;which firms remit in proportion to the volume and toxicity of emissions\u0026mdash;as the proxy for environmental impact. Because this fee is calibrated to the quantity of harmful substances released, it provides an objective, comprehensive reflection of a firm\u0026rsquo;s pollution footprint. We combine these two elements into a single index: higher index values correspond to superior environmental performance. Formally:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"205\" height=\"41\" 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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4 Model specification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe test our hypotheses using variations of the following ordinary least squares regression model:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cimg width=\"493\" height=\"21\" 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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003eENVIRO\u003csub\u003eit\u0026nbsp;\u003c/sub\u003e\u003c/em\u003eis the ratio of the natural log of the current year\u0026apos;s operating revenue to the natural log of the current year\u0026apos;s sewage expenses; Industry tournament incentives are measured using the industry pay gap. We use a large number of controls informed by prior researches (e.g., Cui et al., 2019; Francoeur et al., 2021; Zhao et al., 2021). First, we control firm size (\u003cem\u003eSIZE\u003c/em\u003e) and industry-adjusted ROA (\u003cem\u003eROA\u003c/em\u003e). Furthermore, we control for market-to-book ratio (\u003cem\u003eMTB\u003c/em\u003e) and a Big Four auditor indicator (\u003cem\u003eBIG4\u003c/em\u003e). Finally, we control for CEO departures (\u003cem\u003eCEORETIRE\u003c/em\u003e), CEO TENURE (\u003cem\u003eCEOTENURE\u003c/em\u003e), age of CEO (\u003cem\u003eLNAGE\u003c/em\u003e), CEO ownership ratio (\u003cem\u003eCEOSTOCK\u003c/em\u003e), formula independent director number proportion (\u003cem\u003eDUDO\u003c/em\u003e), CEO concurrently serving as chairman (\u003cem\u003eCHAIR\u003c/em\u003e) and formula property right nature (\u003cem\u003eSTATE\u003c/em\u003e). We also control for several industries and year fixed effects.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Empirical results","content":"\u003cp\u003e\u003cem\u003e4.1 Descriptive statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePanel A of Table 1 reports the descriptive statistics for all variables\u0026mdash;dependent, independent, and controls\u0026mdash;employed in our principal regression models. For each variable it lists the number of observations, the mean, the standard deviation, and the minimum and maximum values.\u003c/p\u003e\n\u003cp\u003eTABLE 1. Descriptive Statistics and Variable Correlations\u003c/p\u003e\n\u003cp\u003ePanel A: Descriptive Statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1053%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003eS.D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e14.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e18.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e9.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e13.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e13.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e16.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e10.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eBIG4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e22.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e22.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e25.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e20.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e-0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.0500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e5.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e3.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e7.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e1.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2632%;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.1053%;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e3.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e3.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2105%;\"\u003e\n \u003cp\u003e4.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e3.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1053%;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePanel B: Pearson Correlations\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"964\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e \u003cem\u003e\u0026nbsp;Variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e\u003cem\u003eIOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.129\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eIOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.202\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.078\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.655\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.107\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.296\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.085\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.041\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.087\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.248\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.066\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.062\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e0.107\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.108\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e-0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n 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\u003cp\u003e0.031\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.031\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.080\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.367\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e0.061\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.123\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e0.230\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.029\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e-0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e-0.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.022\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.308\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.064\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e0.469\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e-0.315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e-0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e-0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e0.033\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.365\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e-0.128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e-0.552\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e0.208\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e0.439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.025\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e-0.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e0.081\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.138\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.296\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.044\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.74274%;\"\u003e\n \u003cp\u003e0.039\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.12033%;\"\u003e\n \u003cp\u003e0.103\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.88382%;\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.43154%;\"\u003e\n \u003cp\u003e-0.057\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.09129%;\"\u003e\n \u003cp\u003e0.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.71369%;\"\u003e\n \u003cp\u003e0.034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e0.092\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.84647%;\"\u003e\n \u003cp\u003e-0.097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.91286%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.2. Regression results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 reports pooled OLS estimates of how industry‐level tournament pay gaps affect firms\u0026rsquo; environmental performance. Column (1) estimates a simple regression of environmental performance on \u003cem\u003eINTOURINC\u003c/em\u003e, omitting both fixed effects and additional covariates. Column (2) augments the first model with a full suite of firm‐level controls as well as industry and year dummy variables. In the bivariate model, the coefficient on industry tournament incentives is 0.1749 and is highly significant (t = 24.937), providing clear support for Hypothesis 1 that greater pay‐gap\u0026nbsp;\u0026ldquo;prizes\u0026rdquo;\u0026nbsp;correspond with stronger environmental outcomes. When controls and fixed effects are introduced in Column (2), the coefficient remains positive and statistically significant, indicating that our core result is not driven by omitted variables.\u003c/p\u003e\n\u003cp\u003eTABLE 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegression results for the impact of industry tournament incentives on environmental performance\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.1749\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.0368\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e(24.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(4.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eISO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.0940\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(2.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.9887\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(106.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.3285\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(2.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e1.1345\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(6.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e-0.1231\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(-3.612)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e-0.0070\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(-2.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.2124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(8.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e-0.0843\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(-2.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.0819\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(3.935)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e-0.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(-0.633)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.0245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(0.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-12.1506\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e-6.6510\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e(-40.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e(-12.221)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.3 Heterogeneity over state ownership\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe predict that industry tournament incentives have a more significant positive correlation with the environmental performance of state-owned enterprises than non-state-owned enterprises. To verify this point, we divide property rights into state-owned enterprises and non-state-owned enterprises and generate a dummy variable (\u003cem\u003eSTATEDUM\u003c/em\u003e). We assign a value of 1 to a company with this variable as a state-owned enterprise and 0 to a company with this variable as a non-state-owned enterprise. The regression results are shown in Table 3. The results show that the regression coefficients of \u003cem\u003eINTOURINC\u003c/em\u003e of state-owned enterprises in column (1) are significantly positive, while the regression coefficients of \u003cem\u003eINTOURINC\u003c/em\u003e of non-state-owned enterprises in column (2) are not significant, Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 2.\u003c/p\u003e\n\u003cp\u003eTABLE 3\u003c/p\u003e\n\u003cp\u003eModerating effect of state ownership\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.0978\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(4.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eIOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-0.2970\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-4.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e1.0611\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.0286\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(47.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(9.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-11.8655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.7855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-1.513)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-0.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e3.6145\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.2948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(7.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-0.6482\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-7.665)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(1.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-0.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-0.1271\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.1141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-2.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.1160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.2421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(1.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-0.0303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-1.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-0.1699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.1382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-1.288)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e-8.8958\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-7.9576\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e(-10.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-2.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e2,465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e5,379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eempirical P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 399px;\"\u003e\n \u003cp\u003e0.0193**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.4 Heterogeneity over industry homogeneity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndustry homogeneity was quantified by estimating each firm\u0026rsquo;s stock‐to‐market regression slope and then aggregating those betas across all firms in the same industry (Kale, 2009). First, we calculate the median of partial regression coefficients of stock and market for all companies in the travel industry by industry and year and then generate a dummy variable (\u003cem\u003eHOMODUNMEDIAN\u003c/em\u003e). Secondly, this variable is compared with the industry median as the partial regression coefficient of the firm in that year. We dichotomize industry homogeneity by comparing each firm\u0026rsquo;s beta to the cross‐industry median: firms with a beta above the industry median receive a value of 1, while those at or below the median are coded as 0.\u003c/p\u003e\n\u003cp\u003eThe regression results are shown in Table 4. Column (1) is the regression results of enterprises whose partial regression coefficient is higher than the median, and column (2) is the regression results of enterprises whose partial regression coefficient is lower than the median. The coefficient of \u003cem\u003eINTOURINC\u003c/em\u003e in column (1) is significantly positive, indicating that industry tournament incentives have a stronger positive relationship with the environmental performance of listed companies in industries with higher homogeneity. Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 3.\u003c/p\u003e\n\u003cp\u003eTABLE 4\u003c/p\u003e\n\u003cp\u003eModerating effect of industry homogeneity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.1082\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(5.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eIOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.1394\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(1.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.9769\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.0286\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(41.790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(9.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e4.5043\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.7855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(13.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e1.6208\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.2948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(3.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.4415\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-4.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.0417\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(5.936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.1168\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.1141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-2.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.4431\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.2421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(4.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.1679\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.2266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(3.336)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.0098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-0.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.3088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.1382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-2.699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-6.7660\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-7.9576\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-8.970)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-2.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e4,643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e3,201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eempirical P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 399px;\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.5 Heterogeneity over geographical location\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIt is expected that compared with the central and western regions, the correlation between industry tournament incentives and corporate environmental performance is more significant in the eastern region. To verify this, the samples in the benchmark model are geographically grouped and regional dummy variables (\u003cem\u003eLOCALDUM\u003c/em\u003e) are generated. This variable is then assigned a value of 1 for firms in the eastern region and 0 for firms in the central and western regions. The regression results are shown in Table 5. The results show that the regression coefficients of \u003cem\u003eINTOURINC\u003c/em\u003e in the eastern region of column (1) are all significantly positive, while the regression coefficients of \u003cem\u003eINTOURINC\u003c/em\u003e in the central and western regions of column (2) are not significant. Moreover, the empirical P-value used to test the difference of coefficient of industry tournament incentives between groups is significant, which verifies hypothesis 4.\u003c/p\u003e\n\u003cp\u003eTABLE 5\u003c/p\u003e\n\u003cp\u003eModerating effect of geographical location\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.0960\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(6.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(1.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eIOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.0457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.9253\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.0286\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(47.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(9.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e2.3857\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.7855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(8.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.7097\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1.2948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(1.938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.3878\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-4.291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.0271\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(4.601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.0569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.1141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.2485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.2421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(3.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.2130\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.2266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(5.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(0.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.0287\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.0605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(1.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-0.2133\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-0.1382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-2.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e-6.3391\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e-7.9576\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e(-10.294)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-2.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e6,437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e1,407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eempirical P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 399px;\"\u003e\n \u003cp\u003e0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.6 Endogeneity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.6.1 Instrumental variable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo solve the problem that our hypothesis is not affected by the omitted variables problem, we use two kinds of instrumental variables. Firstly, according to the research of Huang et al. (2019), the number of CEOs of other companies in the same industry whose salary is higher than that of their own company is used as the first instrumental variable. To win the highest incentive prize for tournaments, the lower-paid CEOs keep raising their salaries and thus winning more tournaments. When the higher-paid CEOs getting more in the same industry, CEO of the company will be more influenced by incentives. Therefore, the level of incentive degree of the industry tournament incentives changes with the number of this kind of CEO.\u003c/p\u003e\n\u003cp\u003eSecondly, according to the study of Coles et al. (2018), we sum up the compensation of all CEOs in the same industry and use the sum of the compensation of CEOs in the same industry as the second instrumental variable. We use the two-stage least squares method (2SLS) to empirically analyze the model, and the analysis results are shown in Table 7. According to table 6 in the column (1) and (2) of the regression results, the dependent variable in the regression coefficients is significantly positive, considering the omission caused by the variable under the condition of endogenous problems, the instrumental variable method, the model industry tournament incentives with the company\u0026apos;s environmental performance is significant positive correlation results show that the hypothesis is still established.\u003c/p\u003e\n\u003cp\u003eTABLE 6\u003c/p\u003e\n\u003cp\u003eEndogeneity check\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.2425\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.4553\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(9.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(5.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eISO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.1028\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(1.709)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(1.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.9382\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.9141\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(54.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(45.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e3.0756\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e3.3467\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(11.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(11.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.2549\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.4007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(3.591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.3126\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.3285\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-4.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-4.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0197\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0172\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(3.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(3.289)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.0893\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.1181\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-2.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-2.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.3396\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.3780\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(5.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(5.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.2105\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.2720\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(5.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(6.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-0.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(0.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.2169\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.1386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-2.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-1.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-8.1026***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e-10.8041***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-13.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e(-8.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e7,844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.6.2 PSM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough control variables such as CEO and company characteristics are added to the baseline model, this does not rule out the existence of a nonlinear relationship between industry tournament incentives and environmental performance.\u003c/p\u003e\n\u003cp\u003eTo this end, by referring to the method of Mei Chun et al. (2019), we match the propensity scores of the disposal group (high industry tournament incentives companies) and the control group (low industry tournament incentives companies) in the sample. Then, the least square regression is performed on the benchmark model based on the matched samples to control for endogeneity problems caused by systematic differences in CEO and company characteristics between the two groups of samples. As shown in Table 7, the regression coefficients of explanatory variables are all significantly positive, which supports our view. These results indicate that industry tournament incentives are significantly positively correlated with environmental performance when considering the possible nonlinear relationship of model variables.\u003c/p\u003e\n\u003cp\u003eTABLE 7\u003c/p\u003e\n\u003cp\u003ePSM results for the impact of industry tournament incentives on environmental performance.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cem\u003e𝐸𝑁𝑉𝐼𝑅𝑂\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTOURINC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.1010\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0115\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(7.689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(2.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eISO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.1147\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(1.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eSIZE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.9896\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0099\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(58.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(2.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOSTOCK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.2556\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(9.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(0.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.5007\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(4.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(0.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEORETIRE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.3193\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.0277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(-4.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-0.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eCEOTENURE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.0201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(3.956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(0.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eCHAIR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.0490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(-1.246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eDUDO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.3215\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.0240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(4.670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-1.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eSTATE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.1413\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(3.870)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-0.388)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eMTB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.0136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(0.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(1.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eLNAGE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.2613\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.0277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(-3.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-0.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-7.3524\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e-0.7454\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e(-13.348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e(-4.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eIndustry effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eYear effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4,248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e3,596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-SQUARED\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"5. Conclusions and future research directions","content":"\u003cp\u003eDrawing on insights from Tan et al. (2021), Chowdhury et al. (2022), and Anton et al. (2004), we propose that firms which strategically invest in environmental initiatives gain a reputational edge that executives can exploit to secure larger tournament‐style rewards. Accordingly, CEOs have a strong incentive to bolster their companies\u0026rsquo;\u0026nbsp;environmental outcomes in order to win these competitive prizes. We further hypothesize that this positive linkage intensifies when regulatory pressures are more stringent and when external labor markets exhibit greater maturity and mobility.\u003c/p\u003e\n\u003cp\u003eOur analysis of A-share firms over 2010\u0026ndash;2020 demonstrates a robust, positive link between industry-level tournament incentives and corporate environmental performance. We validate this relationship through a battery of robustness checks\u0026mdash;including tests for omitted-variable bias, reverse-causality estimations, two-stage least-squares regressions, and propensity-score matching\u0026mdash;all of which yield consistent results. Further subgroup analyses reveal that the incentive effect is especially pronounced among state-owned enterprises, companies operating in highly homogeneous industries, and firms headquartered in China\u0026rsquo;s eastern provinces.\u003c/p\u003e\n\u003cp\u003eFinally, although we tried our best to determine the unexplored relation (particularly in China) between industry tournament incentives and environmental performance, we believe that our study has certain limitations that should be addressed in the future. First, we did not consider internal tournament incentives in this study because it was difficult to obtain compensation data for company employees during the study period. Second, the CEO\u0026apos;s short-sighted behavior remains an interesting topic in the recent literature. Therefore, we suggest further research into the moderating effect of CEO short-sighted behavior.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no relevant financial or nonfinancial conflict of interest to disclose. He certifies that he has no affiliations with or involvement in any organization or entity with any financial or nonfinancial interest in the subject matter or materials discussed in this manuscript. The author has no financial or proprietary interest in any material discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguilera‐Caracuel J, Guerrero‐Villegas J., 2018. How corporate social responsibility helps MNEs to improve their reputation. The moderating effects of geographical diversification and operating in developing regions. Corporate social responsibility and environmental management, 25(4), 355-372.\u003c/li\u003e\n\u003cli\u003eAnton W R Q, Deltas G, Khanna M., 2004. Incentives for environmental self-regulation and implications for environmental performance. 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Management Decision.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Industry tournament incentives, Environmental performance, Sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-7645315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7645315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, we explore how industry‐level tournament incentives influence firms’ environmental outcomes, drawing on a dataset of 9,644 Chinese A‐share firm‐year observations spanning 2010 through 2020. Our empirical analysis reveals that stronger competitive incentives within an industry are linked to superior environmental performance at the firm level. We then decompose executive competitive orientation into three facets—ownership type, geographic location of the firm, and the degree of industry homogeneity—to assess how these characteristics shape the core relationship. The results indicate that when executives possess a pronounced competitive drive, the beneficial effect of industry tournament incentives on environmental performance is amplified. Moreover, this reinforcing effect is particularly evident for state‐controlled enterprises, companies headquartered in eastern provinces, and sectors exhibiting high similarity among rivals. To ensure the robustness of our conclusions, we employ a variety of techniques—including tests for reverse causality, checks for omitted‐variable bias, two‐stage least squares estimation, and propensity‐score matching. Overall, our findings furnish solid empirical support for the role of tournament‐style compensation in advancing environmental objectives and offer practical guidance for regulators and corporate boards seeking to refine executive pay schemes and strengthen environmental governance.","manuscriptTitle":"Racing to Be Green? How Industry Tournament Incentives Shape Corporate Environmental Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 08:40:00","doi":"10.21203/rs.3.rs-7645315/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T17:02:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T03:23:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196404831347301149427085388855071295539","date":"2025-11-10T02:00:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88921266427298698968421681697479351909","date":"2025-10-14T05:45:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T07:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315470439411017135137265424599469961080","date":"2025-10-09T01:24:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T01:20:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T18:42:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T03:03:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-18T04:59:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f74e94f2-1049-45bf-934f-9704f13a165e","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56531248,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":56531249,"name":"Scientific community and society/Geography"},{"id":56531250,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-03-12T06:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 08:40:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7645315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7645315","identity":"rs-7645315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}