Climate Risks and Common Prosperity for Corporate Employees: The Role of Environment Governance in Promoting Social Equity in China

Abstract

Promoting social equity is a global issue, and common prosperity is an important goal for human society’s sustainable development. This study is the first to examine climate risks’ impacts on common prosperity from the perspective of corporate employees, providing micro-level evidence for the coordinated development of climate governance and social equity. Employing data from companies listed on the Shanghai and Shenzhen stock exchanges from 2016 to 2023, a fixed-effects model analysis was conducted, and the results showed the following: (1) Climate risks are positively associated with the common prosperity of corporate employees in a significant way, and this effect is mainly achieved through employee guarantees, rather than employee remuneration or employment. (2) Climate risk will increase corporate financing constraints, but it will also force companies to improve their ESG performance. (3) The mechanism tests show that climate risks indirectly promote improvements in employee rights and interests by forcing companies to improve the quality of internal controls and audits. (4) The results of the moderating effect analysis show that corporate size and performance have a positive moderating effect on the relationship between climate risk and the common prosperity of corporate employees. This finding may indicate the transmission path of “climate pressure—governance upgrade—social equity” and suggest that climate governance may be transformed into social value through institutional changes in enterprises. This study breaks through the limitations of traditional research on the financial perspective of the economic consequences of climate risks, incorporates employee welfare into the climate governance assessment framework for the first time, expands the micro research dimension of common prosperity, provides a new paradigm for cross-research on ESG and social equity, and offers recommendations and references for different stakeholders.

1. Introduction

The climate has a significant impact on human society [1,2,3]. In response to climate risks, 17 Sustainable Development Goals (SDGs) (see the link for more details: https://sdgs.un.org/goals (accessed on 21 April 2025)) were proposed at the United Nations Sustainable Development Summit in 2015. And the Paris Agreement was adopted at the 21st United Nations Climate Change Conference. Ten years after the adoption of the SDGs and the Paris Agreement, sustainable development still faces substantial challenges despite the significant results achieved. According to the United Nations, 750 million people worldwide still have no access to electricity, and 2 billion people lack access to clean cooking facilities. Moreover, air pollution is increasing, affecting children all over the world. Accordingly, a call was made to encourage leaders to turn obstacles into business opportunities and promote real investments in climate and sustainable development at the fourth summit of the Partnering for Green Growth and the Global Goals (P4G). Therefore, studying climate risks’ impacts on business is important for the sustainability of the world.

China plays a role that cannot be ignored in the sustainable development proposed by the United Nations. According to the United Nations Development Programme’s Sustainable Development Goals Investor Map (China) Briefing (see the link for more details: https://www.undp.org/sites/g/files/zskgke326/files/migration/cn/SDG_Investors_Map_CN.pdf (accessed on 21 April 2025)), the Chinese version of the map is one of the 12 global maps produced by UNDP. Furthermore, the report notes that China’s Fourteenth Five-Year Plan for 2021–2025 is crucial and will send the right policy signals for the successful implementation of the UN Decade of Action, accelerating sustainable development investment. In addition, as the world’s largest developing country, the world’s second-largest economy (see the link for more details: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=CN&most_recent_value_desc=true (accessed on 21 April 2025)) and a major carbon emitter, China’s coordinated promotion of its “dual carbon” goals and rural revitalization strategy is not only a policy exploration at the national level but also constitutes a typical practical example of global climate governance.

It is worth mentioning that some of the SDGs proposed by the United Nations, such as “No poverty”, “Decent work and economic growth”, “Sustainable cities and communities” and “Partnerships for the goals”, coincide with China’s “common prosperity”. China first proposed the concept of common prosperity in 1953 [4]. Common prosperity refers to “the entire population working hard and helping each other to achieve a prosperous and abundant life (For example, the definition of this part is consistent with the SDGs’ ‘Decent work and economic growth’ and ‘Partnerships for the goals.’), spiritual confidence and self-reliance, a livable and business-friendly environment, social unity and harmony, universal access to public services, comprehensive human development and social progress, shared benefits from the reform, and living a happy and beautiful life” (see the link for more details: https://www.gov.cn/zhengce/2021-06/10/content_5616833.htm (accessed on 21 April 2025)). Common prosperity includes the material and the spiritual prosperity of the people. The realization of common prosperity cannot be achieved without the eradication of poverty and improvements in people’s livelihoods (see the link for more details: https://www.gov.cn/zhengce/2015-12/07/content_5020963.htm (accessed on 21 April 2025)); the approach to achieving common prosperity is consistent with the SDGs’ “No poverty.” In its 2020 recommendation document, the Central Committee of the Communist Party of China outlined strategic directives within the framework of formulating the Fourteenth Five-Year Plan for National Economic and Social Development and the Long-Term Vision and Objectives for 2035, explicitly advocating for “a better life for the people, and more tangible and substantial progress in the comprehensive development of the people and the common prosperity of all people.” (see the link for more details: https://www.gov.cn/zhengce/2020-11/03/content_5556991.htm (accessed on 21 April 2025)).

However, a study by Wan and Knight (2023) found that wealth inequality has increased [5]. Rising inequality in per capita household wealth poses a challenge to China’s realization of “common prosperity.” In light of the above realities, research on common prosperity in China has dual value: On the one hand, it reveals issues of uneven development, providing empirical cases from emerging economies for global poverty reduction and wealth distribution research. On the other hand, the policy adjustments explored by China in addressing inequality can also provide empirical evidence for other countries to balance sustainable development and social equity, thereby contributing to the achievement of global SDGs. Therefore, this study examines the impact of climate risks on common prosperity in the Chinese context.

Furthermore, past studies found that climate risks will affect companies in several ways, such as bank loan pricing [6], corporate social responsibility [7], the firm’s values [8], the firm’s leverage ratios [9], the sustainable development performance of companies [9] and annual reports’ readability [10]. Climate risk is the risk posed by climate change and extreme weather events [11,12]. Climate risks can be broadly categorized into physical and transition risks [12,13] (for a detailed definition and measurement of climate risks, see Section 3.2.2). There has been a lack of attention in the past literature on the impact of climate risks on firms’ employees. As one of the corporate stakeholders, employees may also be affected by climate risks. In addition, the literature on factors affecting common prosperity reveals that past studies focused more on the analysis of macro-level factors and measuring the common prosperity more in terms of the share of labor income. There are relatively few studies on micro-level factors and measuring the common prosperity of corporate employees from a multidimensional perspective. For example, Lyu et al. (2025) found that digital capability impacts common prosperity in a significantly positive way [14]. The study of Li et al. (2025) shows that employee stock ownership plans (ESOPs) can improve common prosperity proxied by the labor income share within the enterprise [15]. However, the process of realizing common prosperity is not exactly the same for people in different fields. And the realization of common prosperity not only includes income but also the standard of living of the people (see the link for more details: https://www.gov.cn/zhengce/2015-12/07/content_5020963.htm (accessed on 22 April 2025)). Therefore, it is of great significance to study the common prosperity of people in different fields and measure it in a multidimensional way.

The research objectives of this study are as follows: (1) To verify the direction and dimension specificity of climate risk’s impact on the common prosperity of corporate employees. Specifically, based on stakeholder theory, a fixed-effects model is used to examine whether climate risk affects the three dimensions of corporate employees’ common prosperity: employee compensation (H1), employment (H2), and guarantees (H3). Moreover, a fixed-effects model is used to examine whether climate risk affects the financial constraints (H4) and ESG performance (H5) of the company.

(2) To examine the mechanism and moderating effect of climate risk on employee common prosperity. Through the setting of interaction variables, we use a fixed-effects model to empirically examine the mediating effect of audit quality and internal control, as well as the moderating effect of corporate size and performance.

The regression analysis in this study is based on a fixed-effects model. Fixed-effects models are widely used in company-level research as they mitigate issues of endogeneity caused by omitted variables and individual and time differences, thereby increasing the reliability of causal inference [16,17,18,19,20]. For example, Fan et al. (2025) employed a fixed-effects model for regression analysis to mitigate the effects of individual and time differences [16]. Additionally, this study utilized F-tests, Hausman tests, and the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to assess the suitability of the fixed-effects model for the regression analysis. The results indicated that the fixed-effects model is more appropriate for this study’s regression analysis.

This study uses three indicators to measure common prosperity, including employee compensation, employee employment, and employee security (see Section 3.2.2 for specific variable measurements and relevant data sources). This study measures employee compensation based on the weighted calculation results of detailed indicators, such as employee profit sharing, the growth rate of employee compensation, and the ratio of the average compensation of directors, supervisors, and other employees. The measurement of employee compensation corresponds to the definitions of “prosperous and abundant life” and “shared benefits from the reform” in the definition of common prosperity. The measurement of employee employment indicators is based on the weighted calculation results of detailed indicators such as employee numbers and career development support, corresponding to “comprehensive human development” in the concept of common prosperity. The measurement of employee protection indicators is based on the weighted calculation results of detailed indicators, such as legal employment, protection of employees’ rights and interests, and occupational health protection. Employee protection indicators can, to a certain extent, measure “universal access to public services,” “social unity and harmony,” and “livable and business-friendly environment” as defined in common prosperity.

It is worth mentioning that the “common prosperity of corporate employees” focused on in this study is the specific manifestation of the macro concept of common prosperity in the corporate context, and its scope is significantly different from common prosperity at a broad social level. As micro entities in a market economy, corporations have a unique path to achieving common prosperity for their employees. For example, the “prosperous and abundant life” of employees is directly reflected in their salary levels, “comprehensive human development” depends on the jobs and vocational training provided by corporations, and “universal access to public services” relies on the protection provided by corporations to their employees. This special path to achieving common prosperity allows us to measure the common prosperity of corporate employees to a certain extent from a quantifiable perspective, rather than discussing the complex issues of corporate employee common prosperity from a broader social perspective.

The incremental contribution of this study is reflected in three aspects:

(1)

It breaks through the corporate financial perspective of research on the economic consequences of climate risk and expands the boundaries of research on the impact of climate risk on micro-level corporate entities by focusing on employees as core stakeholders (existing studies such as Ge et al. (2025) focus on the corporate-level consequences of financing costs [6]).

(2)

It expands the research on common prosperity from a micro level, using corporate employees as a sub-group to empirically test the impact of climate risk, thereby enriching the research on the micro-level driving mechanisms of common prosperity (existing research such as Lyu et al. (2025) focuses on common prosperity at the macro level [14]).

(3)

It innovatively constructs a measurement system for common prosperity among employees from the three dimensions of compensation, employment, and guarantees, overcoming the single reliance on labor income share in traditional research (such as Li et al. (2025) [15]) and more comprehensively capturing the rights and interests of employees.

2. Literature Review and Hypothesis Development

Past research has shown that climate risks impact companies in a significant way. On the one hand, climate risks negatively impact companies. According to the past literature, climate risks can decrease firms’ R&D expenditures and employment [21], cause a surge in corporate environmental litigation [22], decrease companies’ demand for debt [18], significantly increase the cost of capital [23,24], result in lower profitability and poor stock returns for the country’s food companies [25], increase audit fees [26], promote companies to disguise the pressure with increased carbon emissions [27], increase companies’ accruals-based and real earnings management [28] and result in more volatile and lower earnings and cash flows in companies [29]. For example, employing a country-level climate risk indicator, Ding et al. (2021) found that climate risks are positively associated with companies’ accruals-based and real earnings management as managers will choose accounting methods or take manipulation activities to serve their interests [28].

On the other hand, climate risks may prompt positive shifts in companies. For example, climate risks can increase companies’ sustainable activities and reduce firm information asymmetry. As a result, the concentration of the supply chain, downstream customers and upstream suppliers as a whole [12], analyst forecast errors and dispersion [30] and firm’s stock price crash risks [31] are reduced. Furthermore, firms’ ESG performance [32] and corporate green innovation are improved [33].

Multiple factors influence the process of common prosperity. These include both macro- and micro-factors. With regard to micro-factors, the results of Li et al. (2025) indicate that employee stock ownership plans (ESOPs) can improve the labor income share within the enterprise, which measures common prosperity [15]. This is achieved through the increased bargaining power of employees because, under the arrangement of ESOPs, employees’ participation and rights are increased. Using the share of labor income as a measure of common prosperity, Ma and Ma (2024) found that ESG ratings improve common prosperity within enterprises by reducing the constraints of the debt financing of the enterprise [34]. Furthermore, Yang et al. (2025) found that returning home entrepreneurship exerts positive impacts on rural common prosperity [35]. Moreover, entrepreneurship mainly affects rural common prosperity through government support, financial credit, and social networks.

In terms of macro-factors, the economic policy uncertainty [36], the economic openness [37], the strong financial regulatory policies [38], the population aggregation in central cities [39], the implementation of regional integration policies [40], the digital inclusive finance [41,42,43,44,45], the artificial intelligence [46], the accelerated depreciation policy [47], the elimination of energy poverty [48], the low-carbon energy transition [49], and the development of the digital economy [50,51] have impacts on common prosperity. For example, Guo et al. (2024) examined the impact of digital inclusive finance on the county’s common prosperity [41]. They found that digital inclusive finance provides the disadvantaged population with access to financial resources, which alleviates the problem of financial exclusion due to sectoral and regional differences. Moreover, Liu et al. (2023) found that the low-carbon energy transition positively affects common prosperity, which is achieved through expanded gross fixed capital formation, improved labor productivity, and upgraded industrial structure [49].

A review of the above studies on the economic consequences of climate risks reveals that climate risks affect companies in many ways, including corporate governance, financing costs, and corporate business management. However, there has been a lack of attention in the past literature on the impact of climate risks on firms’ employees. As one of the main stakeholders of a firm, climate risks are likely to have an impact on them.

Moreover, a review of the literature on factors affecting common prosperity reveals that the past literature has focused more on the analysis of macro-level factors. There are relatively few studies on micro-level factors. As a result, the measurement of common prosperity indicators in the past has also focused more on macroeconomic aspects. However, realizing common prosperity is a gradual process. The process of realizing common prosperity is not exactly the same for people in different fields. Therefore, it is of great significance to study the common prosperity of people in different fields from the micro perspective. The literature review is shown in Figure 1. Based on these points, the research question of this study was formulated as follows:

Research question: Will climate risks affect the common prosperity of corporate employees? If so, how will climate risks affect the common prosperity of corporate employees?

This study is based on stakeholder theory. Ansoff, who first introduced the term “stakeholders” into management and economics, stated that the stakeholders of a corporation may be managers, workers, shareholders, suppliers, and distributors [52]. It is considered that Freeman (2010) first established the stakeholder theory (this book was first published in 1984) [53]. He pointed out that stakeholders include individuals and groups. These individuals and groups can influence the achievement of organizational objectives or be affected by the process of achieving those objectives. Moreover, employees, as an essential part of the corporation, are one of the key stakeholders in the corporation. Climate risks‘ impacts on corporations inevitably spill over to corporate employees. The research framework of this study is shown in Figure 2.

The impact of climate risk on the common prosperity of corporate employees is multidimensional and heterogeneous, and its effect is closely related to the perception mechanism of employees. Climate risks will increase costs for companies, especially financing costs [6,7,8,24]. For example, Huynh et al. (2020) found that drought risk significantly increases the cost of equity capital [24]. Accordingly, companies may prioritize reducing flexible expenditures such as employee compensation to alleviate the financial pressure they face [21]. From the perspective of direct employee perception, salary levels, as the most intuitive economic outcome, are easily affected by corporate cost transmission: cost pressures such as extreme weather losses caused by climate risk and low-carbon transition investments will cause companies to prioritize reducing flexible expenditures such as salaries [24,25,29]. Employees can directly perceive the negative impact through slower salary increases and reduced performance bonuses.

The perception of job stability declines relatively. In order to avoid recruitment, training, and reduce the costs of existing employees, companies tend to reduce new positions and downsize or streamline existing personnel and departments, which leads to an overall decline in employees’ perception of their employment situation. For example, according to Li et al. (2024), in the face of climate risks, management may reduce employment by cutting back on new hires and downsizing, especially in labor-intensive areas, in order to reduce costs [21].

Improvements in employee protection (such as social security contributions and occupational health investments) are more likely to be perceived through indirect mechanisms: when climate risks force companies to strengthen governance [30,33], employees will indirectly feel that their rights and interests are better protected through specific measures such as upgrades to workplace safety and increased social security coverage. According to previous studies, climate risks will prompt corporations to adopt more sustainable measures to respond to the challenges [9,12,32,33]. For example, firms’ ESG performance [32] and corporate green innovation are improved [33]. Meanwhile, improving the guarantees of corporate employees falls within the scope of corporate sustainable development. Therefore, under the pressure of climate risks, the guarantees of corporate employees will be improved.

As climate risks force companies to strengthen governance, high climate risks will prompt companies to enhance external supervision by hiring the Big 4 accounting firms or increasing audit fees. This strengthened governance will reduce management’s encroachment on employee rights and interests, indirectly guaranteeing fair remuneration and investment, thereby ensuring the common prosperity of corporate employees. At the same time, companies will improve their internal control systems and ensure the fulfillment of rigid obligations such as social security contributions and occupational health protection through standardized processes. This will also directly improve the level of employee protection, thereby increasing the common prosperity of corporate employees. The hypothesis path is shown in Figure 3.

Based on the above analysis, the hypotheses regarding the common prosperity of corporate employees are proposed as follows:

H1. 

All other things being equal, climate risks will negatively affect company employee compensation.

H2. 

All other things being equal, climate risks will negatively affect company employee employment.

H3. 

All other things being equal, climate risks will positively affect company employee guarantee.

Faced with climate risks, corporate costs increase, further causing a decline in employee compensation and employment. However, under pressure from climate risks, companies have also adopted more sustainable behaviors to promote employee guarantees. In this regard, the following hypotheses are proposed:

H4. 

All other things being equal, climate risks will increase the financing costs for companies.

H5. 

All other things being equal, climate risks will increase the ESG performance of companies.

3. Materials and Methods

3.1. Data and Samples

The data from Shanghai and Shenzhen listed firms in this study come from the China Research Data Service Platform (CNRDS) [12,30,32,54,55] and the China Stock Market & Accounting Research Database (CSMAR) databases [31,32,36,56,57,58,59,60]. These two databases are widely used in research based on the Chinese context. The sample period is from 2016 to 2023. Data on climate risk indicators in the database start in 2016.

In this study, the data are processed as follows: (1) exclude the data of enterprises in the financial industry; (2) exclude the firms with missing data for the main variables; and (3) winsorize the upper and lower 1% for continuous variables. Data are processed using Stata MP 17.0. The data processing process is presented in Figure 4. Finally, this study obtained a total of 25,822 sample observations.

3.2. Methodology

3.2.1. The Fixed-Effects Model

In order to eliminate inherent differences at the individual or time level, to more accurately estimate the causal relationship between variables, and to address the problem of omitted variables, this study employed a fixed-effects model, which controls for year and firm fixed effects [16,17,18,19,20].

In order to further verify the rationality and applicability of the fixed-effects model for regression, this study used the F-test, Hausman test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The regression results for the three dependent variables (EMP_SALARY, EMP_EMPLOYMENT, EMP_GUARANTEE, SA and ESG as detailed in Section 3.2.2 Models and Variables) show that the p-values for the F-test are all 0.0000, strongly rejecting the null hypothesis, indicating that the fixed-effects model is significantly superior to the mixed OLS model [61,62]. The results of the Hausman test show that the p-values of the chi-square statistics for the regression of the five dependent variables are all 0.0000, rejecting the null hypothesis [63], indicating that the fixed-effects model is more suitable for this study.

The AIC value for the mixed OLS model with the dependent variable EMP_SALARY is 187,857.6, and the BIC value is 188,053.6. The AIC value for the fixed-effects model is 164,535.7, and the BIC value is 164,723.5. However, when estimating the AIC and BIC values for the random-effects model, an error is displayed (cannot be estimated). This indicates that the regression with the dependent variable EMP_SALARY is more suitable for the fixed-effects model.

The AIC is a standard for measuring the fitness of a statistical model, and the goal of using it for testing is to select the model with the smallest AIC [64,65,66]. BIC is similar to AIC and is used for model selection. The smaller the value, the better the statistical model fit [64,67,68]. The AIC value for the mixed OLS model with the dependent variable EMP_EMPLOYMENT is 179,501.3, and the BIC value is 179,697.2. The AIC value for the fixed-effects model is 150,156, and the BIC value is 150,343.7. However, when estimating the AIC and BIC values for the random-effects model, an error is displayed (cannot be estimated). This indicates that the regression with the dependent variable EMP_EMPLOYMENT is more suitable for the fixed-effects model.

For the OLS model with the dependent variable EMP_GUARANTEE, the AIC value is 147,158, and the BIC value is 147,353.9. For the fixed-effects model, the AIC value is 129,444.8, and the BIC value is 129,632.6. However, when estimating the AIC and BIC values for the random-effects model, the same error is displayed (cannot be estimated). Therefore, regression with the dependent variable EMP_EMPLOYEEMENT is more suitable for using the fixed-effects model.

When the dependent variable is SA, the AIC value for the regression using the OLS model is 956.5914, and the BIC value is 1152.407. The AIC and BIC values for regression using the fixed-effects model are −99185.98 and −98998.33, respectively. The AIC and BIC values for regression using the random-effects model cannot be estimated. Therefore, for the regression with the dependent variable SA, the fixed-effects model is more appropriate.

When the dependent variable is ESG, the AIC value for the regression using the OLS model is 50,179.51, and the BIC value is 50,345.5. The AIC and BIC values for the regression using the fixed-effects model are 42,124.31 and 42,283.38, respectively. The AIC and BIC values for the regression using the random-effects model cannot be estimated. Therefore, for the regression with the dependent variable being ESG, the fixed-effects model is more appropriate.

3.2.2. Models and Variables

The model employed in this research is as follows:

$$\begin{array}{c}{COM\_PROSPERITY}_{it}={\beta }_0+{\beta }_1 {CLIMATE\_RISK}_{it} \\ +{\displaystyle \sum _{i=1}^n}{\beta }_n{Controls}_{it}+{YEAR+FIRM+\epsilon }_{it} \end{array}$$

(1)

The proxy of employee common prosperity is the dependent variable in this study. This study measures the common prosperity of employees in three ways: remuneration (EMP_SALARY), employment (EMP_EMPLOYEEMENT), and guarantees (EMP_GURANTEE). The data source is the Enterprises’ Contribution to Common Prosperity Research Database in CSMAR. The Sustainability and CSR Research Team at East China Normal University and CSMAR developed the database. The above indicators are designed based on the following considerations:

(1) Material foundation: Compensation scores comprehensively reflect employee income levels (employee profit sharing, employee compensation growth rate, etc.), corresponding to the definition of “prosperous and abundant life” and “shared benefits from the reform” in the concept of common prosperity. (2) Development opportunities: Employment scores cover employee numbers, career development support, etc., corresponding to “comprehensive human development” in the concept of common prosperity. (3) Rights and interests bottom line: Guaranteed scores include legal employment, protection of employees’ rights and interests, occupational health protection, etc., corresponding to “universal access to public services”, “social unity and harmony” and “livable and business-friendly environment” in the definition of common prosperity.

Although common prosperity has rich connotations, in the micro context of corporate employees, these three dimensions can capture the core characteristics of well-being, and the data are reliable.

EMP_SALARY is the employee compensation score. The data are retrieved from the CSMAR database (the data for the other two indicators also come from the CSMAR database). According to the CSMAR database, it is calculated based on the weighted calculation (the Enterprises’ Contribution to Common Prosperity Research Database system was jointly developed by the Sustainable Development and Corporate Social Responsibility Research Team at East China Normal University and CSMAR; therefore, the CSMAR database does not disclose the specific weighting calculation method) of contribution to compensation per share (standardized; the standardized calculation method is the difference between the indicator value and the median value of that indicator divided by the average deviation between the indicator and the median value, and the same applies below), employee profit sharing (standardized), compensation per capita (standardized), the growth rate of employee compensation (standardized), and the ratio of the average compensation of directors, supervisors, and other employees (standardized). EMP_EMPLOYEEMENT is the employee employment score. It is weighted based on the number of employees at year-end (standardized), new jobs created (standardized), gender diversity in management (standardized), job competitiveness and career management (standardized), and care for vulnerable groups (standardized). EMP_GURANTEE is the employee guarantee score. It is based on a weighted calculation of legal employment (standardized), investment in work safety (standardized), level of work safety (standardized), occupational health protection (standardized), percentage of employee’s contribution to the Social Security Fund (standardized), commercial insurance (standardized), and protection of employees’ rights and interests (standardized).

The independent variable in this study is CLIMATE_RISK. Climate risks are induced by climate change [11,12]. Climate risks include physical and transition risks. Physical risks refer to the potential damage that climate phenomena (such as floods, droughts, and hurricanes) may cause to the economy, as well as significant disruptions to the ecological balance. Transition risk mainly focuses on potential losses that corporations may face during the transition to sustainable development, stemming from factors such as climate policy adjustments, technological innovation, and changes in market sentiment [12,13].

CLIMATE_RISK measures the number of climate risk words as a proportion of the total number of words in the text of the annual report. This measurement of climate risk can be found in the past literature (for example, Liu and Han (2025) [30] and Lin and Wu (2023) [31]). This indicator measures the climate risk faced by listed companies. According to Lin and Wu (2023), the frequency of certain keywords in the annual reports of Chinese listed companies was selected to measure corporate climate risk because annual reports are not limited to the industry in which the listed company operates and contain a large amount of financial information. Therefore, compared with other announcements, corporate annual reports receive more attention, and the quality of their information is guaranteed [31].

The data of CLIMATE_RISK are from the Climate Risk Research Database in the CNRDS database. The climate risk vocabulary was obtained in the following ways [30,31]: (1) The annual report corpus was trained by machine learning techniques, and the seed word set was expanded by obtaining the top ten similar words similar to the seed word set using the Continuous Bag-of-words Model (CBOW). (2) The final set of words for “climate risk” was determined by combining expert opinions. The seed and extended word sets for climate risks are shown in Appendix A. The references for the source of the words are Li et al. (2024) [21], Lin and Wu (2023) [31], Du et al. (2023) [69] and Li et al. (2024) [70].

The regression used to test hypotheses H4 and H5 also used the above model (1). According to the previous literature, financing constraints reflect the difficulty of obtaining external funds for corporations. When corporations face high financing constraints, it often means that their financing costs are also high [71,72,73,74]. Therefore, this study uses financing constraint indicators to measure corporate financing costs. Based on the research of Hadlock and Pierce (2010), this study uses SA as a measure of financing constraints [71] and uses SA as the dependent variable in the regression. The independent variable remains CLIMATE_RISK. The data source for the financing constraint indicator SA is the CSMAR database. The higher the SA index, the more severe the corporate financing constraints and the higher the corporate financing costs. This study uses ESG scores to measure corporate ESG performance. When using model (1) for regression, ESG scores are used as the dependent variable and CLIMATE_RISK as the independent variable. ESG score data are sourced from Bloomberg (https://www.bloomberg.com). Previous ESG research has also used this database [75,76]. The higher the ESG score, the better the corporate ESG performance.

The regressions in this study also include the following control variables: ROA, LEV, SIZE, GROWTH, DTURN, REC, INV, BM, HHI, SOE, DUAL, MAGTMALER, OPACITY, INDDIRR, AUD_FEE. See

Table 1. Variable description.

Type	Name	Explanation
Dependent variable	EMP_SALARY	Employee compensation score.
	EMP_EMPLOYEEMENT	Employee employment score.
	EMP_GURANTEE	Employee guarantee score.
	SA	Financing Constraint Index = 0.737 × Size + 0.043 × Size2 − 0.040 × Age. Among them, Size is the natural logarithm of the total assets of the enterprise; Age is the number of years the enterprise has been in operation.
	ESG	ESG ratings from Bloomberg.
Independent variable	CLIMATE_RISK	Climate risk keyword frequency divided by the number of words in the text of the annual report.
Control variables	ROA	The net profit of the firm divided by the total assets of the firm.
	SIZE	The logarithm of the firm’s total assets.
	LEV	The equity of the firm divided by the total liabilities of the firm.
	GROWTH	Operating income for the year divided by operating income for the previous year less 1.
	DTURN	Average monthly turnover rate of current year’s stocks—average monthly turnover rate of last year’s stocks.
	REC	Accounts receivable as a percentage of total assets.
	INV	Inventory to total assets.
	BM	Market-to-book ratio.
	HHI	Total assets of individual firms as a share of total assets of firms in the industry.
	SOE	Dummy variables. If the firm is a state-owned enterprise, the value is taken as 1. Otherwise, it is taken as 0.
	DUAL	Dummy variables. Chairman and CEO are the same person, the value takes 1. Otherwise, it takes 0.
	MAGTMALER	Percentage of males in management.
	OPACITY	Subject to disclosure by the Shenzhen Stock Exchange (SZSE) and Shanghai Stock Exchange (SHSE). 1 = Excellent, 2 = Good, 3 = Pass, 4 = Fail.
	AUD_FEE	Audit costs in logarithmic terms.
	YEAR_FE	Year fixed effects.
	FIRM_FE	Firm fixed effects.

for detailed explanations of the control variables. In addition, this study controls for year and firm fixed effects. The standard errors in this study are clustered at the firm level.

4. Results and Analysis

4.1. Analysis of Main Regression Results

The variables’ summary statistics and the year and industry distribution of the sample are presented in

Table 2. Summary statistics and the year and industry distribution of the sample.

Panel A
Variable Name	Observation Number	Mean	P25	Median	P75	SD
EMP_SALARY	25822	53.85	46.81	53.37	60.54	9.75
EMP_EMPLOYEEMENT	25822	55.86	47.72	55.45	63.25	11.35
EMP_GURANTEE	25822	52.04	50.50	50.50	51.23	4.43
SA	25822	−3.91	−4.08	−3.91	−3.75	0.27
ESG	7450	35.09	28.48	32.31	39.18	9.08
CLIMATE_RISK	25822	0.37	0.15	0.25	0.42	0.38
ROA	25822	0.04	0.01	0.04	0.07	0.07
LEV	25822	0.42	0.26	0.41	0.56	0.20
SIZE	25822	22.36	21.42	22.15	23.09	1.33
GROWTH	25822	0.15	−0.04	0.09	0.25	0.38
DTURN	25822	−0.18	−0.32	−0.07	0.07	0.55
REC	25822	0.13	0.04	0.11	0.18	0.10
INV	25822	0.13	0.05	0.11	0.17	0.11
BM	25822	0.63	0.44	0.63	0.82	0.26
HHI	25822	0.07	0.02	0.05	0.09	0.07
SOE	25822	0.31	0.00	0.00	1.00	0.46
DUAL	25822	0.30	0.00	0.00	1.00	0.46
MAGTMALER	25822	78.97	71.43	80.00	87.50	11.65
OPACITY	25822	1.57	1.00	2.00	2.00	0.97
INDDIRR	25822	37.90	33.33	36.36	42.86	5.38
AUD_FEE	25822	13.98	13.53	13.86	14.31	0.65
Panel B
Year	Frequency		Percentage		Cumulative Percentage
2016	2218		8.59		8.59
2017	2400		9.29		17.88
2018	2877		11.14		29.03
2019	2927		11.34		40.36
2020	3125		12.1		52.46
2021	3451		13.36		65.83
2022	4193		16.24		82.07
2023	4631		17.93		100
Total	25,822		100
Panel C
Industry	Frequency		Percentage		Cumulative Percentage
A:Agriculture/forestry/livestock	276		1.07		1.07
B: Mining	516		2		3.07
C1: Manufacturing	1567		6.07		9.14
C2: Manufacturing	4967		19.24		28.37
C3: Manufacturing	10,330		40		68.38
C4: Manufacturing	567		2.2		70.57
D: Electricity/gas/water (Utilities)	805		3.12		73.69
E: Construction	588		2.28		75.97
F: Wholesale/retail trade	1135		4.4		80.36
G: Transportation/storage	692		2.68		83.04
H: Hotels and catering	57		0.22		83.26
I: Telecommunications, radio, television and satellite transmission services	1914		7.41		90.67
K: Real estate	745		2.89		93.56
L: Leasing	306		1.19		94.74
M: Research and experimental development	360		1.39		96.14
N: Social service	410		1.59		97.73
O: Residential services, repairs and other services	5		0.02		97.75
P: Education	31		0.12		97.87
Q: Healthcare	70		0.27		98.14
R: Communication/cultural	368		1.43		99.56
S: Integrated business	113		0.44		100
Total	25,822		100

. According to the results presented in Table 2, the employee compensation score has an average value of 53.85. The average value of the employee employment score is 55.86, and the average value of the employee guarantee score is 52.04. The average frequency of the number of climate risk words is 37%. The average value of the SA indicator is −3.91. The average ESG score is 35.09 points. According to Panel B and C in Table 2, the sample size shows an increasing trend from year to year, and the sample is mainly concentrated in the manufacturing industry.

The untabulated Pearson correlation coefficient results show that the correlation coefficient of EMP_SALARY and CLIMATE_RISK is −0.010, which is insignificant. The correlation coefficient of EMP_EMPLOYEEMENT and CLIMATE_RISK is 0.036, which is significant at the 1% level. The correlation coefficient of EMP_GURANTEE and CLIMATE_RISK is 0.151, which is significant at the 1% level. The correlation coefficient between SA and CLIMATE_RISK is 0.103, which is significant at the 1% level. The correlation coefficient between ESG and CLIMATE_RISK is 0.082, which is significant at the 1% level. Moreover, this study tested the variance inflation factors (VIFs) of the regression. The maximum value of the VIF of the regression in this study is 3.87. It is generally accepted that there is no serious covariance problem if the value of the VIF is less than 10.

The results of the basic regression for H1-H3 are shown in

Table 3. Basic regression results for H1–H3.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE
CLIMATE_RISK	−0.3414	1.5610 ***	1.6035 ***	−0.3896	0.4241	1.3191 ***
	(0.3947)	(0.3603)	(0.2352)	(0.3915)	(0.2630)	(0.2260)
ROA				−0.4903	2.4318 ***	0.8507 *
				(1.1718)	(0.8857)	(0.4665)
LEV				2.6086 ***	−0.1514	−2.3098 ***
				(0.7874)	(0.6063)	(0.3728)
SIZE				−0.3782	5.5342 ***	1.3931 ***
				(0.2483)	(0.2276)	(0.1266)
GROWTH				1.7422 ***	2.0710 ***	−0.2532 ***
				(0.1591)	(0.1317)	(0.0655)
DTURN				0.0479	−0.3588 ***	−0.2616 ***
				(0.0862)	(0.0664)	(0.0413)
REC				5.8493 ***	1.4709	−0.0178
				(1.5638)	(1.1567)	(0.7002)
INV				3.0404 **	3.9630 ***	0.9200
				(1.3267)	(1.1436)	(0.6221)
BM				0.4351	−2.6647 ***	0.6024 **
				(0.4689)	(0.3653)	(0.2434)
HHI				0.6799	2.8827	−1.8167
				(2.9536)	(1.8574)	(1.2178)
SOE				1.4118 ***	1.2516 ***	−0.0214
				(0.4307)	(0.3608)	(0.2114)
DUAL				0.0425	0.3707 **	0.1440
				(0.1938)	(0.1490)	(0.0931)
MAGTMALER				−0.0005	−0.5157 ***	0.0088
				(0.0104)	(0.0084)	(0.0055)
OPACITY				−0.0166	−0.1230 **	0.1539 ***
				(0.0657)	(0.0504)	(0.0352)
INDDIRR				−0.0191	0.0408 ***	0.0041
				(0.0165)	(0.0131)	(0.0101)
AUD_FEE				1.4938 ***	0.7374 ***	−0.5301 ***
				(0.3453)	(0.2641)	(0.1772)
Constant	54.0179 ***	55.3420 ***	51.4343 ***	39.1554 ***	−39.0606 ***	27.2193 ***
	(0.1445)	(0.1319)	(0.0861)	(6.1100)	(5.5408)	(3.3173)
Observations	25,391	25,391	25,391	25,391	25,391	25,391
R-squared	0.6400	0.7722	0.5404	0.6463	0.8503	0.5501
FIRM FE	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level. The same as below.

. Columns (1) to (3) in the table are results regressed without control variables. The regression coefficients for EMP_EMPLOYEEMENT and EMP_GURANTEE are all positive and significant. With the addition of control variables, only the correlation coefficient for EMP_GURANTEE and CLIMATE_RISK is positively significant at the 1% level. The economic significance of the regression coefficients for EMP_GURANTEE and CLIMATE_RISK is 2.53% (1.3191/52.04). When the proportion of CLIMATE_RISK increases by 1 percentage point, the score of EMP_GURANTEE increases by 2.53%.

The results of the basic regression for H4 and H5 are shown in

Table 4. Basic regression results for H4 and H5.

	(1)	(2)	(3)	(4)
VARIABLES	SA	ESG	SA	ESG
CLIMATE_RISK	0.0144 ***	2.4793 ***	0.0141 ***	1.9109 ***
	(0.0031)	(0.5837)	(0.0031)	(0.5561)
ROA			−0.0192 **	−3.0468 *
			(0.0095)	(1.6961)
LEV			−0.0214 **	−6.7747 ***
			(0.0105)	(1.2610)
SIZE			0.0055	3.3263 ***
			(0.0045)	(0.3737)
GROWTH			−0.0048 ***	−0.5610 ***
			(0.0012)	(0.1936)
DTURN			−0.0091 ***	−0.4278 **
			(0.0006)	(0.2156)
REC			0.0117	−0.8295
			(0.0216)	(2.5545)
INV			−0.0176	3.7941 **
			(0.0150)	(1.9180)
BM			−0.0169 ***	−1.9696 ***
			(0.0042)	(0.7015)
HHI			0.0815 **	−4.1635
			(0.0320)	(3.0658)
SOE			−0.0116 ***	−0.4933
			(0.0033)	(0.6103)
DUAL			0.0004	0.1903
			(0.0016)	(0.2944)
MAGTMALER			0.0000	−0.0128
			(0.0001)	(0.0166)
OPACITY			0.0016 ***	−0.4245 ***
			(0.0005)	(0.1121)
INDDIRR			0.0001	0.0276
			(0.0002)	(0.0219)
AUD_FEE			0.0072 *	0.5462
			(0.0042)	(0.4891)
Constant	−3.9136 ***	34.0475 ***	−4.1305 ***	−46.1949 ***
	(0.0011)	(0.2387)	(0.1263)	(9.8388)
Observations	25,391	7411	25,391	7411
R-squared	0.9813	0.7835	0.9818	0.7960
FIRM FE	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level.

. According to the regression results in Table 4, it can be seen that, regardless of whether control variables are included in the regression, the regression coefficient of CLIMATE_RISK and SA is always significantly positive at the 1% level. The regression coefficient of CLIMATE_RISK and ESG is also always significantly positive at the 1% level. The economic significance of the CLIMATE_RISK and SA regression coefficient is 0.36% (0.0141/3.91 × 100). When the climate risk index rises by 1 percentage point, the level of financing constraints faced by corporations will rise by 0.36 percentage points. The economic significance of the regression coefficient of CLIMATE_RISK and SA is 5.45% (1.9109/35.09 × 100). When the climate risk index rises by 1 percentage point, the ESG score of the corporate will rise by 5.45 percentage points. The above regression results support H4 and H5.

4.2. Mechanism Analysis

Section 2 suggests that climate risks will motivate companies to respond proactively. As a company’s employees are one of the key stakeholders in its operations and development, companies will take proactive measures to promote the development of their employees, which will allow them to show greater resilience when facing climate risks. In this part of the article, we will continue to explore whether climate risks affect the common prosperity of employees by affecting the quality of a firm’s audit and internal controls. Corporate audits and internal controls are both part of corporate governance. The former is an external oversight, and the latter is an internal oversight. Both provide oversight of corporate governance, thereby protecting corporate stakeholders.

Therefore, this study hypothesized that climate risks have prompted firms to strengthen their internal and external oversight. Strengthening the quality of corporate audits and internal controls makes it less likely that firms will engage in behaviors such as whitewashing financial statements, fraud, overriding controls, and extracting private benefits from the firm. Therefore, better-run companies’ employees will enjoy fairer rights and benefits and be more engaged in the organization’s operations. The common prosperity of employees will, therefore, be improved.

Therefore, this study set BIG4 and AUD_FEE. The former is a dummy variable. If the auditor is a Big 4 accounting firm, it is taken as 1. Otherwise, it is taken as 0. The latter is the logarithm of the audit fees. Higher audit fees and the fact that the auditor is from a Big 4 company represent higher audit quality. This study set the cross-multiplier variable of BIG4 (AUD_FEE) with the independent variable. If climate risks promote the common prosperity of a firm’s employees by strengthening the firm’s audit quality, then the regression coefficient of the cross-multiplier variable should be positive and significant.

Table 5. Regression results on the mediating role of audit quality.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE
BIG4_CLIMATE	−0.2406	−0.3789	1.3000 **
	(0.7665)	(0.4726)	(0.6557)
AUDFEE_CLIMATE				−0.7089 **	0.4683 **	1.6460 ***
				(0.3086)	(0.1914)	(0.3287)
CLIMATE_RISK	−0.3673	0.4591 *	1.1989 ***	9.7316 **	−6.2626 **	−22.1817 ***
	(0.4048)	(0.2738)	(0.2244)	(4.4211)	(2.7618)	(4.6470)
BIG4	0.5737	0.1959	0.7542
	(0.7947)	(0.5767)	(0.4718)
AUD_FEE	1.4483 ***	0.7302 ***	−0.6356 ***	1.7907 ***	0.5412 *	−1.2194 ***
	(0.3512)	(0.2678)	(0.1785)	(0.3679)	(0.2863)	(0.1990)
ROA	−0.4983	2.4315 ***	0.8274 *	−0.4145	2.3817 ***	0.6746
	(1.1723)	(0.8856)	(0.4650)	(1.1709)	(0.8856)	(0.4647)
LEV	2.6196 ***	−0.1581	−2.2383 ***	2.5570 ***	−0.1173	−2.1900 ***
	(0.7876)	(0.6066)	(0.3710)	(0.7875)	(0.6068)	(0.3686)
SIZE	−0.3813	5.5350 ***	1.3791 ***	−0.3909	5.5426 ***	1.4225 ***
	(0.2483)	(0.2277)	(0.1260)	(0.2480)	(0.2278)	(0.1260)
GROWTH	1.7453 ***	2.0710 ***	−0.2432 ***	1.7363 ***	2.0750 ***	−0.2393 ***
	(0.1593)	(0.1316)	(0.0655)	(0.1592)	(0.1316)	(0.0650)
DTURN	0.0487	−0.3591 ***	−0.2576 ***	0.0386	−0.3526 ***	−0.2399 ***
	(0.0862)	(0.0664)	(0.0413)	(0.0860)	(0.0664)	(0.0410)
REC	5.8437 ***	1.4747	−0.0563	5.7828 ***	1.5148	0.1365
	(1.5632)	(1.1562)	(0.6937)	(1.5626)	(1.1568)	(0.6898)
INV	3.0324 **	3.9666 ***	0.8748	3.0278 **	3.9714 ***	0.9494
	(1.3280)	(1.1431)	(0.6194)	(1.3252)	(1.1435)	(0.6208)
BM	0.4403	−2.6624 ***	0.6066 **	0.4624	−2.6828 ***	0.5389 **
	(0.4690)	(0.3651)	(0.2427)	(0.4691)	(0.3649)	(0.2411)
HHI	0.6827	2.8753	−1.7679	0.6751	2.8859	−1.8053
	(2.9533)	(1.8571)	(1.2159)	(2.9469)	(1.8548)	(1.1970)
SOE	1.4143 ***	1.2500 ***	−0.0048	1.4078 ***	1.2543 ***	−0.0119
	(0.4305)	(0.3611)	(0.2100)	(0.4324)	(0.3606)	(0.2075)
DUAL	0.0416	0.3704 **	0.1425	0.0438	0.3698 **	0.1410
	(0.1939)	(0.1490)	(0.0928)	(0.1938)	(0.1490)	(0.0925)
MAGTMALER	−0.0005	−0.5157 ***	0.0090	−0.0005	−0.5156 ***	0.0089
	(0.0104)	(0.0084)	(0.0055)	(0.0104)	(0.0084)	(0.0055)
OPACITY	−0.0162	−0.1225 **	0.1527 ***	−0.0102	−0.1272 **	0.1390 ***
	(0.0657)	(0.0504)	(0.0351)	(0.0656)	(0.0504)	(0.0348)
INDDIRR	−0.0190	0.0409 ***	0.0042	−0.0192	0.0409 ***	0.0044
	(0.0165)	(0.0131)	(0.0101)	(0.0165)	(0.0131)	(0.0101)
Constant	39.8073 ***	−38.9927 ***	28.9192 ***	35.2512 ***	−36.4813 ***	36.2845 ***
	(6.1659)	(5.5958)	(3.3330)	(6.3052)	(5.6694)	(3.5147)
Observations	25,391	25,391	25,391	25,391	25,391	25,391
R-squared	0.6463	0.8503	0.5510	0.6464	0.8504	0.5533
FIRM FE	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level.

shows the regression results. The regression results in Table 5 show that the regression coefficients of BIG4_CLIMATE and EMP_GURANTEE are positively significant. The regression coefficient of AUDFEE_CLIMATE and EMP_EMPLOYEEMEE (EMP_GURANTEE) is positively significant. The results of the regressions support the above analysis. In other words, climate risks will contribute to the common prosperity of corporate employees by improving the quality of corporate audits. However, the correlation coefficients of EMP_SALARY and AUDFEE_CLIMATE are negative and significant. Since the regression coefficients of EMP_SALARY and CLIMATE_RISK in the main regression are insignificant, it cannot be concluded that the improvement in audit quality will weaken the relationship between climate risks and the common prosperity of employees.

For internal control, this study set IC_SCORE. The data for this variable are obtained from the Shenzhen Dibao Internal Control Quality Index Database. In order to test whether climate risks affect the common prosperity of corporate employees through internal control, this study set the cross-multiplier variable of IC_SCORE and climate risks. If climate risks contribute to the common prosperity of employees through improvements in the quality of internal control, the coefficient of this cross-multiplier variable should be positive and significant.

The regression results are presented in

Table 6. Regression results on the mediating role of internal controls.

	(1)	(2)	(3)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE
IC_CLIMATE	−1.5378	−0.4831	1.8831 **
	(1.2038)	(0.9731)	(0.7680)
CLIMATE_RISK	0.5211	0.7056	0.1598
	(0.8680)	(0.7162)	(0.5027)
IC_SCORE	1.1814 *	0.7616	−0.1032
	(0.6321)	(0.5061)	(0.3108)
ROA	−0.6422	2.2545 **	0.7600
	(1.1861)	(0.8855)	(0.4650)
LEV	2.6052 ***	−0.0330	−2.2278 ***
	(0.7933)	(0.6093)	(0.3749)
SIZE	−0.4098	5.5129 ***	1.3709 ***
	(0.2506)	(0.2270)	(0.1281)
GROWTH	1.7151 ***	2.0350 ***	−0.2587 ***
	(0.1613)	(0.1327)	(0.0662)
DTURN	0.0381	−0.3570 ***	−0.2592 ***
	(0.0866)	(0.0666)	(0.0416)
REC	6.0524 ***	1.4411	−0.0522
	(1.5654)	(1.1587)	(0.7050)
INV	2.9931 **	3.8858 ***	0.8601
	(1.3291)	(1.1541)	(0.6251)
BM	0.4731	−2.5863 ***	0.6439 ***
	(0.4728)	(0.3652)	(0.2454)
HHI	0.5413	2.7924	−1.9372
	(2.9525)	(1.8641)	(1.2249)
SOE	1.4145 ***	1.2518 ***	0.0053
	(0.4290)	(0.3615)	(0.2111)
DUAL	0.0475	0.3353 **	0.1402
	(0.1953)	(0.1497)	(0.0940)
MAGTMALER	−0.0001	−0.5159 ***	0.0090
	(0.0104)	(0.0084)	(0.0055)
OPACITY	0.0033	−0.1196 **	0.1630 ***
	(0.0660)	(0.0506)	(0.0357)
INDDIRR	−0.0206	0.0409 ***	0.0053
	(0.0165)	(0.0132)	(0.0101)
AUD_FEE	1.5700 ***	0.7504 ***	−0.5275 ***
	(0.3481)	(0.2652)	(0.1793)
Constant	38.0712 ***	−39.2792 ***	27.6119 ***
	(6.1387)	(5.5570)	(3.3329)
Observations	25,117	25,117	25,117
R-squared	0.6460	0.8504	0.5501
FIRM FE	YES	YES	YES
YEAR FE	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level.

. Based on the regression results in Table 6, it can be found that only the regression coefficient of IC_CLIMATE and EMP_GURANTEE is positive and significant. The regression coefficients of the remaining two cross-multiplication terms are insignificant. The above regression results support the above analysis. That is, climate risks will contribute to the common prosperity of corporate employees by promoting improvements in corporate internal control.

4.3. Moderating Effect Analysis

Corporate size and performance may have a moderating effect on the results of the main regression. On the one hand, corporate size and performance have a significant positive moderating effect on the relationship between climate risk and employee common prosperity. Large-scale enterprises, with their more abundant resource reserves and stronger risk resistance capabilities, are better able to transform risk pressure into a driving force for improving employee protection when faced with climate risks, such as increasing investment in occupational health and ensuring full social security contributions, thereby strengthening the positive effects of climate risks on employee protection. At the same time, their large scale of operation can effectively mitigate cost pressures, reduce salary compression caused by climate risks, and weaken the negative impact of climate risks on employee salaries. High-performance companies can use retained earnings to withstand the short-term impact of climate risks, reducing the need to cut employee salaries and enhancing team stability by increasing employee benefits and improving working conditions, thereby further amplifying the positive effect of climate risks on the common prosperity of employees.

On the other hand, corporate size and performance may also have a negative impact on the relationship between climate risk and employee common prosperity. Some large corporations may suffer from inefficient governance when responding to climate risks due to complex organizational hierarchies and long decision-making chains, which may undermine the actual effectiveness of audit quality improvement and internal control optimization, making it impossible to effectively translate risk pressure into improvements in employee rights and interests. They may even neglect the demands of grassroots employees due to “big company disease,” thereby weakening the positive impact of climate risks on employee protection. High-performance companies that overly pursue short-term profit maximization may pass on the costs of responding to climate risks to their employees. For example, they may maintain profit levels by controlling salary increases and reducing non-mandatory benefits, thereby exacerbating the negative effects of climate risks on employee compensation. In addition, some high-performance companies may be sensitive to market fluctuations and prioritize the interests of shareholders in the face of climate risks, reducing long-term investments in employee training and career development, which in turn has a negative impact on the common prosperity of employees.

To validate this analysis, we set up an interaction variable between company size (SIZE) and climate risk (CLIMATE_RISK_SIZE). We also set up an interaction variable between company performance (ROA) and climate risk (CLIMATE_RISK_ROA). The regression results are shown in Table 6.

According to the regression results in

Table 7. Regression results on the moderating role of company size and performance.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE
CLIMATE_RISK_SIZE	0.0430	0.3110 **	1.4297 ***
	(0.2029)	(0.1396)	(0.1417)
CLIMATE_RISK_ROA				4.6695	1.6582	5.2040 ***
				(3.7414)	(2.3380)	(1.8063)
CLIMATE_RISK	−1.3855	−6.7716 **	−31.7612 ***	−0.5184	0.3783	1.1755 ***
	(4.7227)	(3.2393)	(3.2009)	(0.4096)	(0.2745)	(0.2260)
ROA	−0.4966	2.3867 ***	0.6430	−1.7808	1.9735 *	−0.5875
	(1.1710)	(0.8852)	(0.4616)	(1.5143)	(1.1034)	(0.6210)
LEV	2.6129 ***	−0.1203	−2.1669 ***	2.7030 ***	−0.1179	−2.2046 ***
	(0.7883)	(0.6064)	(0.3679)	(0.7929)	(0.6112)	(0.3709)
SIZE	−0.3934	5.4247 ***	0.8895 ***	−0.4026	5.5255 ***	1.3659 ***
	(0.2587)	(0.2304)	(0.1305)	(0.2490)	(0.2286)	(0.1262)
GROWTH	1.7429 ***	2.0759 ***	−0.2306 ***	1.7369 ***	2.0691 ***	−0.2592 ***
	(0.1592)	(0.1316)	(0.0646)	(0.1593)	(0.1316)	(0.0655)
DTURN	0.0492	−0.3497 ***	−0.2196 ***	0.0475	−0.3590 ***	−0.2621 ***
	(0.0861)	(0.0664)	(0.0406)	(0.0862)	(0.0664)	(0.0414)
REC	5.8504 ***	1.4794	0.0215	5.7804 ***	1.4464	−0.0945
	(1.5642)	(1.1557)	(0.6903)	(1.5637)	(1.1568)	(0.6999)
INV	3.0480 **	4.0179 ***	1.1724 *	2.9683 **	3.9374 ***	0.8396
	(1.3281)	(1.1444)	(0.6305)	(1.3296)	(1.1431)	(0.6214)
BM	0.4314	−2.6915 ***	0.4791 **	0.4759	−2.6502 ***	0.6479 ***
	(0.4699)	(0.3658)	(0.2402)	(0.4710)	(0.3643)	(0.2426)
HHI	0.6810	2.8903	−1.7818	0.6474	2.8711	−1.8529
	(2.9544)	(1.8534)	(1.1875)	(2.9612)	(1.8553)	(1.2153)
SOE	1.4128 ***	1.2588 ***	0.0116	1.4330 ***	1.2591 ***	0.0022
	(0.4304)	(0.3615)	(0.2085)	(0.4294)	(0.3605)	(0.2110)
DUAL	0.0422	0.3685 **	0.1343	0.0409	0.3701 **	0.1423
	(0.1939)	(0.1490)	(0.0918)	(0.1939)	(0.1490)	(0.0930)
MAGTMALER	−0.0005	−0.5157 ***	0.0087	−0.0003	−0.5156 ***	0.0090
	(0.0104)	(0.0084)	(0.0055)	(0.0104)	(0.0084)	(0.0055)
OPACITY	−0.0172	−0.1269 **	0.1361 ***	−0.0198	−0.1241 **	0.1504 ***
	(0.0655)	(0.0504)	(0.0346)	(0.0657)	(0.0504)	(0.0352)
INDDIRR	−0.0191	0.0410 ***	0.0046	−0.0193	0.0408 ***	0.0038
	(0.0165)	(0.0131)	(0.0100)	(0.0165)	(0.0131)	(0.0101)
AUD_FEE	1.4950 ***	0.7461 ***	−0.4901 ***	1.4940 ***	0.7374 ***	−0.5299 ***
	(0.3456)	(0.2639)	(0.1709)	(0.3452)	(0.2642)	(0.1775)
Constant	39.4840 ***	−36.6864 ***	38.1342 ***	39.6812 ***	−38.8739 ***	27.8053 ***
	(6.2587)	(5.6139)	(3.3540)	(6.1238)	(5.5629)	(3.3081)
Observations	25,391	25,391	25,391	25,391	25,391	25,391
R-squared	0.6463	0.8504	0.5567	0.6463	0.8503	0.5504
FIRM FE	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level.

(1)–(3), it can be seen that the regression coefficient between CLIMATE_RISK_SIZE and EMP_EMPLOYEEMENT is positively significant at the 5% level. However, the regression coefficient between CLIMATE_RISK and EMP_EMPLOYEEMENT in the main regression is not insignificant. Therefore, we cannot conclude that company size has a negative or positive moderating effect on the main regression results. The regression coefficient between CLIMATE_RISK_SIZE and EMP_GURANTEE is significantly positive at the 1% level. Combined with the results of the main regression, this indicates that company size has a positive moderating effect on employee guarantees. In other words, large-scale corporations are better able to transform risk pressure into a driving force for improving employee guarantees. According to the regression results in Table 7 (4)–(6), only the regression coefficient between CLIMATE_RISK_ROA and EMP_GURANTEE is significantly positive at the 1% level. Combining the results of the main regression, we can see that corporate performance has a positive moderating effect on the results of the main regression. In other words, high-performance companies can use retained earnings to withstand the short-term impact of climate risk and further amplify the positive effect of climate risk on employee guarantees.

4.4. Robustness Tests

This section provides a robustness check of the results in the main regression.

First, this study takes the independent and dependent variables forward and backward two periods, respectively, and regresses them again. This test mitigates the endogeneity problem arising from causal inversion. This study sets up the independent variables in periods t − 1 and t − 2 and regresses them again. This study also sets up the dependent variables in periods t + 1 and t + 2 and regresses them again. The regression results are shown in Appendix

Table A3. Regression results on pushing two periods forward and backward for H1–H3.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
VARIABLES	F1EMP_SALARY	F1EMP_EMPLOYEEMENT	F1EMP_GURANTEE	F2EMP_SALARY	F2EMP_EMPLOYEEMENT	F2EMP_GURANTEE	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE
CLIMATE_RISK	−0.5546	0.3448	0.5931 **	1.0177 **	−0.0380	1.5846 ***
	(0.4148)	(0.3493)	(0.2420)	(0.4592)	(0.4213)	(0.2839)
L1CLIMATE_RISK							−0.4059	−0.0031	0.5427 **
							(0.3947)	(0.2887)	(0.2253)
L2CLIMATE_RISK										0.8094 *	−0.1955	1.4325 ***
										(0.4352)	(0.3227)	(0.2597)
Controls	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
Constant	49.8597 ***	24.6614 ***	26.1635 ***	80.0491 ***	41.1551 ***	25.4498 ***	41.6271 ***	−39.3034 ***	23.6131 ***	46.1789 ***	−42.5943 ***	23.1317 ***
Observations	20,171	20,171	20,171	16,134	16,134	16,134	22,391	22,391	22,391	17,884	17,884	17,884
R-squared	0.6544	0.8117	0.5632	0.6634	0.8118	0.5798	0.6591	0.8562	0.5660	0.6671	0.8635	0.5833
FIRM FE	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level. The same as below.

and

Table A4. Regression results on pushing two periods forward and backward for H4 and H5.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
VARIABLES	F1SA	F2SA	SA	SA	F1ESG	F2ESG	ESG	ESG
CLIMATE_RISK	0.0053 **	0.0103 ***			1.8272 ***	1.5768 **
	(0.0026)	(0.0027)			(0.5366)	(0.6839)
L1CLIMATE_RISK			0.0064 **				1.7945 ***
			(0.0026)				(0.5553)
L2CLIMATE_RISK				0.0123 ***				1.4823 **
				(0.0025)				(0.7015)
Controls	YES	YES	YES	YES	YES	YES	YES	YES
Constant	−4.3623 ***	−4.4706 ***	−4.0938 ***	−4.2415 ***	−30.9612 ***	−14.5145	−48.9394 ***	−49.6705 ***
Observations	(0.1040)	(0.0805)	(0.1346)	(0.1590)	(11.3712)	(12.0446)	(11.3396)	(13.8412)
R-squared	20,156	16,111	22,391	17,884	6326	5220	6343	5258
FIRM FE	0.9846	0.9868	0.9844	0.9863	0.8056	0.8142	0.8008	0.8119
YEAR FE	YES	YES	YES	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “**” indicates significance at the 5% level, and “***” indicates significance at the 1% level.

.

In accordance with the regression results in Appendix Table A3, it is found that the regression coefficient between CLIMATE_RISK and F1EMP_GURANTEE (F2EMP_GURANTEE) is positive and significant. The regression coefficient between L1CLIMATE_RISK (L2CLIMATE_RISK) and EMP_GURANTEE is positive and significant. These regression results further support the test’s results in the main regression. In addition, it is found that the effect of climate risks on employee compensation indicators becomes positive at period t + 2 or t − 2. This is because it takes a period for firms to react to an increase in employee compensation.

In accordance with the regression results in Appendix Table A4, the regression coefficients for CLIMATE_RISK and F1SA/F2SA (F1ESG/F2ESG) are positively significant. The regression coefficients for L1CLIMATE_RISK (L2CLIMATE_RISK) and SA (ESG) are significantly positive. The above regression results further support H4 and H5.

Second, this study used the entropy-balanced method. Firms with high climate risks inherently differ from those with low climate risks. This study used the entropy balance method to mitigate the endogeneity caused by this issue. The regression results are shown in Appendix

Table A5. Regression results using EBM.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	SA	ESG
CLIMATE_RISK	−0.2658	0.2730	1.2040 ***	0.0110 ***	1.5825 ***
	(0.4041)	(0.2699)	(0.2457)	(0.0031)	(0.5575)
Controls	YES	YES	YES	YES	YES
Constant	43.8628 ***	−45.6672 ***	24.2821 ***	−4.6701 ***	−53.4380 ***
	(6.9202)	(6.4205)	(4.2087)	(0.1407)	(10.8313)
Observations	25,391	25,391	25,391	25,391	7411
R-squared	0.6541	0.8593	0.5616	0.9838	0.8029
FIRM FE	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “***” indicates significance at the 1% level.

. As shown in the regression results in Appendix Table A5, it is found that the regression coefficients of CLIMATE_RISK and EMP_GURANTEE remain positive and significant. The remaining two regression coefficients are insignificant. The regression coefficients for CLIMATE_RISK and SA are significantly positive at the 1% level. The regression coefficients for CLIMATE_RISK and ESG are significantly positive at the 1% level. The above regression results continue to support the results in the main regression.

Third, this study replaced the measurement of the independent variable. We calculated the industry climate risk average by industry to calculate the difference between the firm’s climate risk and the industry average. The independent variable was replaced with the difference between the climate risks of companies and the industry average. The regression results are presented in Appendix

Table A6. Regression results with replaced measurement of the independent variable.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	SA	ESG
AVECLIMATE_RISK	−0.7987 *	0.4640	1.0579 ***	0.0141 ***	1.9109 ***
	(0.4263)	(0.2832)	(0.2265)	(0.0031)	(0.5561)
Controls	YES	YES	YES	YES	YES
Constant	38.6791 ***	−38.9557 ***	27.1520 ***	−4.1305 ***	−46.1949 ***
	(6.1154)	(5.5435)	(3.3601)	(0.1263)	(9.8388)
Observations	25,391	25,391	25,391	25,391	7411
R-squared	0.6463	0.8503	0.5496	0.9818	0.7960
FIRM FE	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “*” indicates significance at the 10% level, and “***” indicates significance at the 1% level.

. In accordance with the regression results in Appendix Table A6, the regression coefficients for AVECLIMATE_RISK and EMP_GURANTEE (SA/ESG) are positively significant at the 1% level. The results continue to support the results in the main regression.

Fourth, we replaced the sample period and regressed. The pandemic, which began in 2021, significantly impacted the economy. In order to exclude impacts that arose after the pandemic, we excluded the sample in 2022 and 2023. The regression results are shown in Appendix

Table A7. Regression results with changed sample period.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	EMP_SALARY	EMP_EMPLOYEEMENT	EMP_GURANTEE	SA	ESG
CLIMATE_RISK	−0.1174	0.2312	0.6815 ***	0.0108 ***	1.4315 ***
	(0.5016)	(0.3410)	(0.2185)	(0.0032)	(0.4132)
Controls	YES	YES	YES	YES	YES
Constant	28.9270 ***	−41.8688 ***	39.8843 ***	−3.8904 ***	−38.0200 ***
	(8.4803)	(6.7289)	(2.9601)	(0.1389)	(9.0177)
Observations	16,557	16,557	16,557	16,557	5938
R-squared	0.7009	0.8671	0.6070	0.9843	0.8509
FIRM FE	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES

Note: Robust standard errors are clustered at the firm level. “***” indicates significance at the 1% level.

. According to the regression results in Appendix Table A7, the regression coefficients of CLIMATE_RISK and EMP_GURANTEE (SA/ESG) remain significant at the 1% level. The above regression results continue to support the results in the main regression.

5. Discussions

Based on stakeholder theory and using a fixed-effects model, this study empirically examines the impact of climate risk on the common prosperity of corporate employees and the underlying mechanisms. Its core findings engage in a multidimensional dialogue with the existing literature, both validating some theoretical expectations and presenting new research insights.

For the core conclusions, the positive impact of climate risk on the common prosperity of employees is mainly achieved through the corporate employee guarantee dimension (supporting H3), while it has no significant effect on compensation (H1) and employment (H2). This finding is partly consistent with the research of Li et al. (2024), who pointed out that corporations will prioritize reducing flexible expenditures (such as R&D expenses) under climate risk [21]. This study further found that compensation, as a typical flexible expenditure, is insignificantly affected, while guarantees are strengthened due to governance upgrades. In addition, the results of the main regression test show that climate risk increases corporate financing costs; i.e., the financing constraints faced by companies increase (supporting H4). This result further confirms why climate risk does not significantly increase the compensation of corporate employees. However, according to the results of the main regression, climate risk forces corporations to improve their ESG performance (supporting H5). This result echoes the finding that climate risk can significantly improve employee guarantees. This shows the duality of the impact of climate risk on corporations. At the same time, this result complements and echoes the conclusion of Liu et al. (2025) that climate risk promotes corporate ESG performance [32]. The improvement in ESG performance is specifically reflected in the area of governance protection, rather than in the expansion of compensation or employment [32]. Furthermore, the conclusion that climate risk increases corporate financing constraints echoes the findings of Huynh et al. (2020). Huynh et al. (2020) found that drought risk significantly increases the cost of equity capital [24].

At the mechanism level, climate risk indirectly improves employee guarantees by enhancing audit quality and internal control quality (supporting mechanism analysis), echoing Su et al.’s (2025) conclusion that climate risk drives corporate governance optimization [12]. However, this study is the first to focus the effects of governance upgrades under climate risks on employee rights protection, confirming that external audit supervision (such as Big 4 audits) and internal process standardization (such as internal control systems) can effectively constrain management’s encroachment on employee rights, transforming climate pressure into institutional safeguards. This mechanism explains why, even without a significant increase in compensation, common prosperity among employees can still be achieved through the guarantee dimension. Such findings also supplement the path proposed by Ma and Ma (2024) that ESG improves the share of labor income by alleviating financing constraints [34] and reveals the social value of governance upgrades in the non-income dimension.

From the perspective of research innovation, this study breaks through the macro focus of the existing literature on common prosperity (such as Guo et al. (2024) discussing the impact of digital inclusive finance on common prosperity in counties [41]) and confirms, for the first time, the possibility of synergy between climate governance and social equity at the micro level of corporate employees. Unlike Li et al. (2025), who only measured common prosperity at the corporate level based on the share of labor income [15], this study uses a three-dimensional indicator system to find that the path to achieving common prosperity has dimensional specificity. When faced with climate risks, corporations are more inclined to balance risk response and employee rights through strengthening guarantees rather than increasing compensation, which provides micro-level evidence for understanding the “differentiated paths to achieving common prosperity among different groups.”

It is worth noting that this study found no significant impact of climate risk on employment, which differs from the conclusion of Li et al. (2024) that “climate risk reduces employment” [21]. This discrepancy may stem from differences in the research context: this study is based on the research context in China, while Li et al. (2024) based their study on the context in the United States [21]. The difference in context may have led to different results due to differences in institutions and policies, which in turn led to different responses to climate risk, further contributing to the difference in results. This also provides directions for follow-up research.

In addition, the positive moderating effect of corporate size and performance (which is significant in terms of guarantees) confirms that large-scale enterprises can buffer risk shocks with more abundant resources, and high-performance enterprises can strengthen the implementation of governance upgrades through retained earnings. This is consistent with Ge et al.’s (2025) view that corporate resource endowments affect climate risk response capabilities [6], further illustrating that resource abundance is an important boundary condition for the transformation of climate stress into employee welfare.

In summary, the findings of this study not only verify the applicability of stakeholder theory in the context of climate governance but also provide new evidence for the transmission chain of “climate risk-corporate governance-social value,” indicating that climate risk not only brings cost pressure but also has the potential to generate positive social spillover effects through governance change.

6. Conclusions

This study uses data from Shanghai and Shenzhen listed companies from 2016 to 2023 as samples and empirically tests the impact of climate risks on the common prosperity of corporate employees through a fixed-effects model. The main conclusions are as follows:

(1)

This study confirms our research objectives. That is, this study confirms that climate risk has a significant impact on the common prosperity of corporate employees. Moreover, overall, climate risk has a certain promotional effect on the common prosperity of corporate employees.

(2)

This study does not confirm the impact of climate risk on corporate employee compensation (H1). According to the regression results in Table 3, the regression coefficient between climate risk and corporate employee compensation is negative but insignificant. Therefore, this study cannot conclude that climate risk has a negative impact on corporate employee compensation.

(3)

This study also did not confirm the impact of climate risk on corporate employee employment (H2). According to the regression results in Table 3, the regression coefficient between climate risk and corporate employee employment is insignificant. Therefore, this study cannot conclude that climate risk has a negative impact on corporate employee employment.

(4)

This study confirmed that climate risk has a positive effect on corporate employee guarantees (H3). According to the regression results in Table 3, the regression coefficient between climate risk and corporate employee guarantees is positive and significant. Combined with the robustness test of this study, it can be concluded that climate risk can positively promote corporate employee guarantees.

(5)

This study confirmed that climate risk increases the financial constraints of the company (H4), which increases financial costs for the company, preventing increases in the compensation of corporate employees.

(6)

This study confirmed that climate risk improves the ESG performance of the company (H5). This conclusion illustrates that climate risks will push corporations to adopt more sustainable measures, thereby promoting improvements in employee guarantees.

(7)

This study confirms the mediating role of internal supervision (internal control, IC_SCORE) and external supervision (audit quality, BIG4, AUD_FEE) in corporations. According to the regression results in Table 4 and Table 5, the interaction variable between corporate audit quality and internal control and climate risk is significantly positive. Combined with the results of the main regression, it can be concluded that climate risk forces corporations to improve corporate governance, thereby promoting the common prosperity of corporate employees.

(8)

This study confirms the positive moderating effect of corporate size and corporate performance on the relationship between climate risk and the common prosperity of corporate employees. According to the results in Table 6, the interaction variables between corporate size and performance proxies and climate risk are significantly positive. Combined with the results of the main regression, it can be concluded that the larger the corporate size and the higher the corporate performance, the more obvious the promoting effect of climate risk on the common prosperity of corporate employees.

7. Limitations and Implications

7.1. Limitations

Although this study employs a fixed-effects model to test the impact of climate risk on the common prosperity of corporate employees, the following limitations remain:

(1)

This study uses text analysis to measure climate risks. Future research may consider combining text analysis with remote sensing data and field survey data to measure climate risks.

(2)

This study examines corporate employees without distinguishing among different levels of employees. Follow-up research may consider further studying common prosperity among employees at different levels within a corporation.

(3)

This study’s measurement of common prosperity among employees is limited to three quantifiable dimensions: compensation, employment, and security. It does not fully cover the subjective dimensions of “spiritual confidence and self-reliance,” “social unity and harmony,” and “living a happy and beautiful life.” Future research can combine questionnaire survey data to explore the relationship between climate risk and common prosperity from a more comprehensive perspective.

(4)

This study focuses on measuring common prosperity in terms of compensation, employment and guarantees. It is a preliminary exploration of the micro-level dimensions of common prosperity, rather than a comprehensive portrayal of common prosperity in society as a whole. Future research could combine social survey data with subjective well-being indicators to construct a more comprehensive measurement system.

(5)

In terms of industry selection, high-carbon and low-carbon industries may respond differently to climate risks, which in turn may have different impacts on the common prosperity of employees.

7.2. Implications

(1)

Implications for corporate managers: Companies need to incorporate climate risk into their strategic management systems and transform environmental pressures into institutional advantages that protect employee rights and interests by improving internal control processes and enhancing audit independence.

(2)

Implications for policymakers: The government can guide corporations to fulfill their responsibilities for employee rights through climate regulation and ESG incentive policies. For example, it can incorporate employee protection indicators into corporate climate rating systems and provide tax incentives or green financing support for investments in employee rights in high-climate-risk industries.

(3)

Implications for social governance entities: As coordinators of multiple stakeholder interests, social governance entities (such as industry associations and trade unions) should focus on reducing information asymmetry and power imbalances. By establishing platforms for employees, corporations, and regulatory agencies to consult on climate risk response measures, trade unions can work with corporations to design climate-adaptive welfare programs (such as flexible work arrangements during extreme weather) to meet employee needs. In addition, non-governmental organizations (NGOs) can publish corporate “climate fairness rankings” to highlight best practices in balancing climate governance and employee rights. This public pressure can complement formal regulation and urge underperforming companies to improve.

(4)

Implications for corporate employees: As key stakeholders affected by climate risks and corporate responses, employees can actively participate in relevant processes to protect their rights and interests. By raising awareness of their rights, employees can understand the link between climate risks and workplace benefits. For example, they can recognize that improvements in internal controls (such as standardized safety protocols) and external audits (such as third-party oversight) should translate into better protection of their legal rights (such as occupational health and social security). In addition, through participation in feedback mechanisms and channels such as trade unions or employee representative meetings, they can express their opinions on climate risk adaptation measures and advocate for the satisfaction of employee needs, such as flexible work arrangements during extreme weather events or transparent disclosure of the impact of climate-related investments on employee welfare. This active participation helps to align corporate climate strategies with employee welfare.