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Article

Sustainability in Question: Climate Risk, Environment, Social and Governance Performance, and Tax Avoidance

by
Yuxuan Zhang
1,
Leihong Yuan
2,*,
Idawati Ibrahim
1 and
Ropidah Omar
1
1
Tunku Puteri Intan Safinaz School of Accountancy, College of Business, Universiti Utara Malaysia, Sintok 06010, Kedah Darul Aman, Malaysia
2
Financial Department, Chaohu University, No. 1, Bantang Street, Hefei Chaohu Economic Development Zone, Chaohu City, Hefei 238014, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(4), 1400; https://doi.org/10.3390/su17041400
Submission received: 10 December 2024 / Revised: 29 January 2025 / Accepted: 5 February 2025 / Published: 8 February 2025
Versions Notes

Abstract

:
This study examines whether firm managers strategically use tax avoidance to address climate risks, with a specific focus on strategies employed to reduce corporate income tax liabilities, and this study incorporates the moderating role of ESG performance and is ground in stakeholder theory to highlight the balance between sustainability and corporate profit expectations. Using the secondary data from Chinese A-listed companies during 2017–2023, the findings reveal that firms increasingly adopt tax avoidance practices in response to rising climate risks. More specifically, strong ESG performance positively moderates this relationship, underscoring its role in shaping socially and ethically responsible strategies to tackle sustainability challenges. By employing panel data analysis and addressing endogeneity through instrumental variable tests, Propensity Score Matching, and the Heckman test, this study provides robust results. These findings contribute to the literature on tax avoidance and provide practical insights for actionable ESG initiatives. For firms, these include improving transparency in tax reporting and integrating sustainability metrics into corporate ESG framework for firms. For tax authority, they involve upgrading the tax-related big data supervision system and fostering alignment between corporate practices and government policies.

1. Introduction

One of the most important issues facing the globe today is climate risk, which is also a major area of study for sustainability scholars [1,2,3]. Recent extreme climate events, such as the elevation of sea levels and the capriciousness of climatic patterns, have profoundly impacted all aspects of human society [4]. These risks not only threaten the natural environment, but also exert substantial influence on global economies and business operations [5]. Among relevant prior studies, climate risk has increasingly influenced the way businesses make decisions. For example, climate change has promoted governments to introduce stricter environmental regulations and tax policies, including carbon taxes or carbon trading systems [6]. Such policies may increase the tax burden on companies and then prompt some companies to reduce their burden through tax avoidance strategies [7]. Second, climate risk may negatively drive up operating costs, destabilize supply chains, and reduce market demand, thereby diminishing profitability [8]. Such financial pressures may prompt companies to adopt tax avoidance as a strategy to maintain financial stability [9,10]. Moreover, increased climate risk has intensified public and investor scrutiny of corporate social responsibility performance [11,12]. High-profile tax avoidance companies may face the risk of reputation damage, while firms with better ESG performance may reduce their tax avoidance behavior in order to enhance their social reputation and market recognition [13,14].
Thus, these factors may serve as catalysts for firms to adopt tax avoidance strategies. Taxes are an important cost expense for companies and can impact cash flow and financial stability. Corporate tax avoidance is used as a tax planning strategy to save on corporate tax expenses and preserve resources [15,16]. Grounded in stakeholder theory, this study aims to explore the implications of climate risk on tax avoidance.
This study identifies three critical research gaps that warrant further investigation. First, existing research on tax avoidance primarily focuses on traditional tax planning strategies, with limited emphasis on the influence of climate risk on corporate tax behavior [17,18]. Empirical studies specifically examining how climate risk factors affect tax avoidance decisions remain scarce. For example, Song and Xian [3] briefly discuss firm-level climate change and tax avoidance, but the measurements of variables and theoretical framework differ significantly from this study. Second, much of the current literature is concentrated on developed economies, neglecting the distinct characteristics of emerging markets such as China; these markets’ regulatory frameworks and stakeholder pressures present unique challenges and opportunities, which differ markedly from those in developed nations. Furthermore, while ESG performance is increasingly recognized as a key driver of corporate responsibility, its potential moderating effect on the relationship between climate risk and tax avoidance has been insufficiently explored [19]. This study seeks to address these gaps by providing valuable insights into the intersection of climate risk, ESG performance, and corporate tax strategies, with a particular focus on the context of emerging markets.
Three potential marginal contributions to this study include the following: First, the literature has extensively examined corporate tax avoidance in terms of internal corporate factors, stakeholders, and external institutional environment. This study investigates how climate risk affects the tax avoidance in the context of Chinese corporations. This is particularly relevant given the vulnerabilities in corporate governance and sustainable consciousness in China. Second, while ESG performance reflects corporate efforts in addressing climate risks and fulfilling social responsibilities, this study investigates its moderating effect on the relationship between climate risk and corporate tax avoidance, providing fresh insights into ESG’s strategic role in financial decision-making. Third, this study integrates ESG performance into the framework of the relevance between climate risk and corporate tax avoidance, demonstrating the important role that corporate sustainability has in shaping tax decision-making. Finally, the findings of this study also have important policy implications. The positive correlation between increasing climate risk and corporate tax avoidance underscores challenges for tax collection and management. Policymakers should encourage firms to adopt low-carbon transformation while facilitating bank loans to such enterprises, so as to alleviate the problems of financing difficulties and high financing costs caused by climate risk.
The structure of this study is outlined as follows: Section 2 provides a review of the relevant literature and the development of hypotheses. Section 3 outlines the data and research methodology. Section 4 presents the empirical findings including descriptive statistics, correlation analysis, baseline regression, endogeneity test, Heckman test, robustness tests, and heterogeneity analysis. Section 5 discusses the results. Finally, Section 6 concludes with the findings, study limitations, and directions for future research.

2. Related Literature and Hypotheses Development

2.1. Climate Risk and Corporate Tax Avoidance

An increasing number of studies have explored the popular research topic of climate risk, a key external factor linked to uncertainty and losses to the globe and the society, which subsequently influences corporate decisions and actions.
The prior literature on climate risks or changes highlights significant implications for corporate performance and strategies including tax avoidance. Climate risks, which manifest as increasing temperatures, changing rainfall patterns, and extreme weather events, have been shown to exert pervasive effects on various aspects of economic and corporate activity. Understanding these effects is essential to analyzing their connection to corporate tax avoidance.
For example, increasing temperatures have been empirically linked to declines in corporate financial performance. For example, Pankratz, Bauer [20] demonstrated that a one standard deviation increase in hot days results in a 0.3% decline in corporate revenues, reflecting the adverse impact of temperature on productivity and operations. Similarly, Dell, Jones [21] found that rising temperatures suppress economic growth and industrial output, with particularly severe effects on low-income countries. These findings imply that corporations in climates vulnerable to rising temperatures face tighter financial constraints, potentially prompting them to seek aggressive tax strategies to preserve resources and offset operational losses. Moreover, rainfall variability further exacerbates these financial pressures in specific regions. Barrios, Bertinelli [22] highlighted the role of rainfall decline in hindering economic growth in sub-Saharan Africa, noting that the absence of such declines could have narrowed the GDP per capita gap of developing regions by 15–40%. In the corporate context, such climatic stressors may intensify the need for tax avoidance as firms strive to maintain profitability under adverse conditions.
Extreme climate events amplify the financial vulnerabilities of corporations. Fankhauser and Tol [23] observed that such events damage corporate financial assets, while Cevik and Miryugin [24] revealed that firms in developing countries that were vulnerable to climate change faced heightened borrowing costs and limited access to debt financing. These challenges restrict firms’ ability to adapt to climate risks, potentially incentivizing tax avoidance as a strategy to conserve cash flows and navigate liquidity constraints. Benincasa, Betz [25] further documented that firms experiencing monetary losses due to extreme weather events alter their investment and financing decisions, suggesting that climate-induced financial stress drives a shift in corporate behavior like tax avoidance. In addition to direct financial impacts, climate risks intersect with corporate sustainability practices, which influence tax behavior. Velte [26] reported that sustainable institutional investors discourage tax avoidance and that strong corporate sustainability performance amplifies this effect in Europe. However, the risk of green washing persists, especially if firms symbolically adopt sustainability practices without integrating tax strategies into their overall management process.
Thus, climate risks significantly affect corporate financial performance, financing opportunities, and operational behavior, creating pressures that can encourage or discourage tax avoidance depending on the specific circumstances. The interplay between financial vulnerability, sustainability practices, and tax strategies underscores the need for a nuanced understanding of how firms respond to the multifaceted challenges posed by climate change.
In various corporate-level strategies, tax planning or tax avoidance, as a crucial role of corporate strategy and overall cost savings, holds a particular significance in corporate decision-making. There has been an extensive study of climate risk in the various fields above, but its intersection with tax avoidance research remains limited.
Existing studies on climate risk and tax avoidance reveal a dual perspective: On one hand, firms often adopt conservative tax strategies to align with their sustainability goals and maintain their reputation to deal with climate risk. This may be because such firms prioritize long-term sustainability and stakeholder trust over short-term financial gains. For example, Athira and Ramesh [27] examined this relationship from an international viewpoint, aligning with the agency motive, and found that as economic policy uncertainty increases, firms tend to reduce their tax avoidance practices. Similarly, ref. [28] highlighted that firms mitigate financial and climate risks by decreasing their reliance on debt-related tax shields, reflecting cautious financial strategies. Moreover, Shen, Xu [29] revealed that firms located in Chinese cities with severe air pollution engage in lower levels of tax avoidance and pay higher corporate effective tax rates, potentially as a response to heightened regulatory scrutiny and societal expectations. Xu and Ren [18] state that some firms located in earthquake-affected areas are less inclined to adopt tax avoidance practices compared to those in unaffected regions. Moreover, Siyi, Ruoyu [17] documented that the influence of typhoons on corporate tax avoidance indicates that, with the augmentation of the damage inflicted by typhoons, the degree of tax avoidance among local enterprises declines, especially for state-owned, politically affiliated, and leading corporations. Consistent with Xu and Ren [18], firms that pay more taxes after natural disasters are more likely to receive government grants and credit, creating mutual benefits for both local governments and businesses.
At the other end of the spectrum, enterprises confronted with greater climate risk might undertake aggressive tax planning to conserve resources for future uncertainties. For instance, Adrian, Garg [30] provided evidence that, in the U.S., when firms faced a negative cash flow, they demonstrated a propensity to engage in a higher level of tax avoidance. This reaction seemingly serves as a means to bolster financial positions during challenging times. Amin, Akindayomi [31] further explored the U.S. context and uncovered a novel finding: during climate policy uncertainty, firms adopted more aggressive tax planning techniques, such as tax shelters. This behavior might be a strategic response to the ambiguity surrounding future regulatory landscapes. Interestingly, Chatjuthamard, Chintrakarn [32] posited that companies that successfully achieve significant savings through tax avoidance can redirect those resources. Specifically, they have more capital at their disposal to invest in climate-related initiatives that align with the Paris Agreement, which ultimately has the potential to enhance shareholder value. In the European Union, Compagnie, Struyfs [33] observed that pollution-intensive firms covered by the EU Emission Trading Scheme (EU ETS), the world’s largest multinational emission trading system, reacted to the sharp rise in carbon prices following the EU Council’s decisions on February 28, 2017. These firms increased their corporate tax avoidance, perhaps in an attempt to offset the additional costs imposed by the carbon pricing mechanism and maintain profitability. Overall, these studies highlight the complex interplay between firms’ financial management, tax avoidance strategies, and responses to climate policies. Lassoued, Souguir [34] explored that a rise in carbon risk was linked to increased tax avoidance among 854 American firms from 2015 to 2021. What is more, Ni, Chen [4] utilized a substantial international sample and discovered that firms characterized by higher levels of climate risk are inclined to adopt more tax avoidance strategies. This inclination is spurred by the necessity to maintain cash reserves in the context of a tightened financial crunch rather than by government tax incentives. Meanwhile, this effect is stronger in regions with vulnerable corporate governance, limited financial information transparency, and greater policy uncertainty, which could also be applicable in a Chinese context.
Some of the prior literature have also shown the effects of some climate-related risks on corporate tax avoidance in a Chinese context. Tang, Yang [35] showed the influence of temperature on tax avoidance and revealed increases in tax avoidance activities in response to high temperatures especially in high labor-intensive firms. Song and Xian [3] showed that companies facing higher firm-level climate change risk (CCR) possess a stronger inclination to partake in tax avoidance as a means to address financing concerns. This study provides differences on the comprehensive indicator of climate risk compared with [35] and offers alternative measurements of tax avoidance and the moderating role of ESG performance compared with [3].
The current literature offers a foundation for understanding the association between climate risk and corporate tax avoidance, highlighting firms’ cautious strategies amid environmental and societal pressures. However, further research should address contextual differences, broader climate risks, and long-term tax strategy implications to better capture the complexities of this relationship. Moreover, the prior literature has provided a rather inconsistent body of evidence regarding the association between climate-related risks and corporate tax avoidance within the global panorama and the increasing concern in China. Nevertheless, the empirical evidence concerning the influence of climate risks on corporate tax avoidance among large-sample firms in emerging markets remains relatively scarce. China is the biggest emerging market and contains firms where there is still a lack of mature corporate governance and high-level solution abilities about financial affairs that are concerning. Therefore, this section offers the relationships for climate risks and tax avoidance based on agency theory.
Hypothesis H1:
Climate risk exerts a positive influence on corporate tax avoidance.

2.2. Moderating Role of ESG Performance Between Climate Risk and Corporate Tax Avoidance

In the existing body of literature, the function of ESG as a moderating variable within the relationship between climate risk and tax avoidance has been relatively underexplored. ESG performance assumes a crucial role in mitigating corporate tax avoidance. The key mechanisms through which this occurs include easing financing dilemma, enhancing internal control mechanism, and enhancing external oversight [36].
Firms facing higher climate risks may enhance their ESG performance to strengthen their social responsibility, which could, in turn, impact their tax decisions. However, the specific mechanisms through which ESG moderates the influence between climate risk and tax avoidance remain a relatively new area of research. Some previous researches have explored that ESG performance moderates the intersection between firm characteristics and tax avoidance. For instance, the integration of ESG disclosures exhibits a compounded effect on the association between corporate ownership and tax avoidance [37,38]. Many of the institutional investors become more ESG-centered. Consequently, they perceive risk management and active engagement, as opposed to divestment, as a more preferable strategy for confronting climate risks [39]. Naseer, Guo [40] also revealed the moderating role of ESG performance influencing a correlation between climate risk and stock market volatility (SMVOL) in 488 UK-listed companies from 2012 to 2021.
While ESG has been extensively studied in other various fields, its impact on the association of climate risk and corporate tax avoidance still remains underexplored. Thus, this part formulates the hypothesis below:
Hypothesis H2:
ESG performance moderates the association between climate risk and corporate tax avoidance.

3. Data and Methodology

3.1. Data

This study employs the panel analysis method, taking a sample of A-share listed companies in Shanghai and Shenzhen from 2017 to 2023, to investigate the link between climate risk and tax avoidance. On December 2016, China issued the Environmental Protection Tax Law, which marked a substantial reform of China’s tax and fee system in the field of environmental protection [41]. Through the principle of ‘polluter pays’, the Environmental Protection Tax Law makes it mandatory for companies to take responsibility for pollution control. By focusing on the post-2017 sample, it is an opportunity to examine the tax avoidance behaviors of companies when facing higher environmental responsibilities.
From the setting of hypotheses to the following empirical analysis, this study aims to test the following two hypotheses with the following methodology: To test Hypothesis H1, we estimated a baseline regression model where the dependent variable is tax avoidance and the main independent variable is climate risk. This model incorporates control variables to account for firm-specific characteristics. For hpothesis H2, we introduced interaction terms between climate risk and ESG performance to assess the moderating effect, as outlined in the econometric framework.
Most of the data originate from the China Stock Market & Accounting Research (CSMAR) database and annual reports of companies. For the purpose of avoiding the influence of abnormal samples on the analysis results, this paper processed the collected data. Firstly, it excluded financial industry samples and ST samples. Subsequently, it excluded samples with severely missing financial data and climate risk indicator data. Finally, to reduce the influences of extreme values on the samples, the continuous type of data were subjected to a 1% shrinkage treatment. After a series of treatments, 17,208 observations were finally obtained. Table 1 reflects the observations for each of the years 2017–2023 after processing.

3.2. Variable Measurement

3.2.1. Dependent Variable

National and international academics on corporate tax avoidance have four main measurements [42,43,44,45]: (1) By computing the gap between the nominal and effective income tax rates, one can determine the degree of corporate tax evasion (RATE). (2) To smooth out short-term fluctuations, the five-year mean value of the gap in the difference in the nominal and effective rates of income taxes is used to measure corporate tax avoidance (LRATE). (3) The ratio of period-end total assets is used to calculate the distinction between accounting earnings before taxes and taxable income (BTD). It serves as a proxy for tax planning activities and reflects structural and discretionary differences between accounting and tax reporting. An organization is more inclined to partake in tax avoidance actions when the ratio is elevated, which indicates a larger discrepancy between accounting theory and taxable income. (4) To gauge the degree of corporate tax evasion, the accounting and tax discrepancy is calculated after subtracting the impact of accrued gains (DDBTD), which introduced by Desai and Dharmapala, was designed to exclude the effects of earnings manipulation and control for the firms’ own characteristics. This allows for a more accurate measure of discretionary book-tax differences, isolating the impact of tax avoidance strategies from other factors that may influence reported earnings [46].
Drawing on the studies of Lopes Dias and Gomes Reis [44], Ao, Ong [45], this study uses RATE to measure corporate tax avoidance. Compared with other measurement methods, this approach is simple and straightforward to calculate and can intuitively reflect the extent of tax avoidance. It can reflect the impact of alterations in tax policies on corporate tax avoidance practices. Therefore, the degree of tax avoidance increases with the size of the indicator.

3.2.2. Independent Variable

Climate change has always been a popular research topic among scholars. There is a high degree of uncertainty about the influence of climate risk on businesses due to inadequate disclosure of climate risk information. Therefore, there is still a big controversy on the measurement of climate risk. Through city mortality rates and economic losses [5], the average temperature of a region [28], greenhouse gas (GHG) emissions and energy consumption [47], third-party-provided physical climate data [48], among others, can be used as proxy variables for climate risk. However, these variables are by no means necessarily applicable at the corporate level and do not realistically reflect the exposure of Chinese listed companies to climate risk.
In this study, we learned from the Yin, Deng [1] research methodology. It adopts text analysis and machine learning techniques for building climate risk metrics, thereby studying the implications of climate risk on corporations. Following with Li, Shan [49], they constructed a dictionary through textual analysis of earnings call transcripts of U.S. listed companies and measured climate risk by calculating the frequency of keywords related to climate risks. First of all, it compiled the listed corporations’ annual reports. Then, using the information revealed by manual reading and the National Meteorological Science Data Center, the terms related to climate risk as mentioned in the annual reports were established, and then the data were processed using the Jieba word segmentation tool. Finally, these terms were used to structure the climate risk index for publicly traded companies. The climate risk index is determined by the ratio of the frequency of terms related to climate risk to the total word count in the annual report. A greater value of this index indicates a higher level of climate risk for the company.
Text analysis enables the precise extraction of climate risk-related terms from annual reports. By calculating the ratio of these terms to the total word count, the measure reflects a firm’s attention to and disclosure of climate risks. Annual reports, as key channels for corporate disclosure, often include extensive information on risk management and sustainability [50]. Using the annual report data to construct a climate risk index highlights firms’ focus on climate risk and aligns well with studies on tax avoidance, as climate risk considerations may influence overall strategic decisions, including tax strategies [28].
To control for systematic industry effects on term frequency, the study incorporates industry fixed effects into the model, isolating firm-level differences in climate risk disclosures. Additionally, heterogeneity analyses comparing state-owned and non-state-owned enterprises, as well as high-carbon and low-carbon industries, were conducted to validate the climate risk index across different industry contexts.
Appendix A shows the terms related to climate risk.

3.2.3. Moderating Variable

The climate risk would increase social media’s attention to companies, which makes companies improve their ESG performance with high attention [1]. In this study, we refer to the research methodology of Xie, Qin [51] and select the Huazheng ESG rating database as a measure of actual ESG performance of the company. The ESG index is characterized by a high frequency of updating, broad coverage, and strong data availability. In addition to taking into account the global ESG rating system, the utilization of Huazheng ESG rating data also account for the real state of the Chinese capital market and business attributes. Nine grades make up this system: C, CC, CCC, B, BB, BBB, A, AA, and AAA. In this study, each of the nine ratings is assigned a number from 1 to 9, signifying a progression from poor to high, that is, a higher score corresponds to a stronger ESG performance.
While this study focuses on climate risk, we chose to use the aggregate ESG performance score because climate-related issues often intersect with social (S) and governance (G) factors as well. Governance practices (G) can significantly influence a firm’s ability to develop and implement effective climate risk management strategies, ensuring that climate-related goals are aligned with overall corporate governance. Similarly, social factors (S), such as stakeholder engagement and community impact, are closely tied to a firm’s approach to addressing and mitigating climate risks. By considering the aggregate ESG score, we capture a more comprehensive view of how these interconnected dimensions contribute to managing climate risk.

3.2.4. Control Variables

Considering that the existing literature from other scholars were used as a reference, there is a need to control other factors to prevent them from having an impact on tax avoidance. The control variables encompassed in the study are as follows: growth level (Grow), firm size (Size), financial leverage (Lev), return on net assets (Roa), cash flow from operations (Cash), and Shareholding of the largest shareholder (Top1). The specific measurement methods are presented in Table 2.

3.3. Model Design

Corporate tax avoidance degree is subject to the influence of climate risk. In this research, an Ordinary Least Squares (OLS) model is utilized to estimate such a connection. The following is the baseline regression model (1) that was created:
R A T E i , t = α 0 + α 1 R i s k i , t + α 2 C o n t r o l i , t + ∑ Y e a r i , t + ∑ I n d i , t + ε i , t
In the equation, RATE represents the extent of corporate tax avoidance. Risk denotes climate risk. The control variables, namely Grow, Size, Lev, Roa, Cash, and Top1, are collectively referred to as “control”. Year serves as the control variable for the time dimension, and Ind functions as the control variable for the industry.

4. Empirical Results

4.1. Descriptive Statistics

Table 3 displays the findings of a descriptive analysis of the study’s main variables. RATE’s mean value is 0.012, which is greater than zero. This suggests that there is a phenomena of tax avoidance and that the nominal income tax rate of the majority of China’s listed companies is higher than the effective income tax rate. The fact that the smallest value is 0.428 and the largest value is 0.220 suggests that there are significant differences in the level of tax evasion amongst businesses. Climate risk has a mean of 0.211 and a median of 0.141. It illustrates how listed companies are exposed to climate risk, which varies widely throughout companies. Other variables essentially resemble the analysis of previous research [51].

4.2. Correlation Analysis

Table 4 displays the findings of the main variables’ correlation test. A positive association between RATE and risk is shown in Table 4. Furthermore, the correlation coefficients among the other variables are all below 0.8. The variance inflation factor (VIF) is less than 10, illustrating that there is no multicollinearity.

4.3. Baseline Regression

On the basis of 17,208 observations from 2017 to2023, and using the OLS methodology, this study investigates the effect of climate risk on the level of corporate tax avoidance. Table 5 presents the baseline regression outcomes regarding the impact of climate risk on corporate tax avoidance. In column (1) of Table 5, only the influence of climate risk on corporate tax avoidance is fixed with respect to year and industry. With t-values of 0, 014, and 0.20, the findings in columns (1) and (2) show a positive association between corporate tax avoidance and climate risk at the 1% level of significance. It claims that, both in univariate estimate and in estimation with control variables included, there is a significant positive correlation between corporate tax avoidance and climate risk. To put it another way, corporate tax evasion rises in tandem with climate danger. The hypothesis H1 is supported.
The climate risk from natural disasters may cause damage to the firm’s equipment and plant, which in turn leads to the inability of the firm to carry out normal production operations and face the possibility of shutting down production [52]. Simultaneously, climate risks could threaten the lives of employees and indirectly affect the management of business personnel [53,54]. If the operational risk of a company increases, then the uncertainty of obtaining profits also increases. Firms have a powerful incentive to hedge against risk and increase corporate cash flow through tax avoidance. Therefore, when facing climate risk, companies may pursue tax avoidance strategies to mitigate the potential negative impacts.
In terms of control variables, Size and Roa exhibit a markedly positive correlation with RATE. In contrast, Lev and Cash display a significantly negative correlation with RATE. This implies that, for listed companies, a larger firm size and a higher return on net assets are related to a greater degree of tax avoidance. Conversely, a higher financial leverage and a larger operating cash flow led to a lower level of tax avoidance.

4.4. Endogeneity Test

4.4.1. Instrumental Variables Test

The issue of endogeneity, which can arise from reverse causation and bias from omitted variables, is discussed. In this research, instrumental variables are estimated by means of two-stage least squares (2SLS). Taking reference from Yin, Deng [1], climate risk lagged by one period is used as an instrumental variable. Lagged climate risk is correlated with current climate risk due to its temporal continuity. Climate risk information disclosed in annual reports is typically influenced by prior risk conditions, making lagged climate risk a reasonable proxy for current climate risk and satisfying the relevance for instrumental variables.
First-stage regression, which accounts for climate risk and includes controls for year and industry, has been performed, and the outcome is presented in Table 6, column (1). Lagged climate risk is significantly associated with current climate risk (p < 0.01), with an F-statistic greater than 10, indicating no weak instrument problem.
Firms’ tax planning strategies are more likely to be adjusted based on the current external environment and policy pressures rather than directly driven by the previous year’s climate risk level. For instance, Peters and Romi [55] highlighted that the impact of climate risk on corporate behavior typically arises through real-time effects of information disclosure and external regulatory pressures. This suggests that lagged climate risk is unlikely to have a direct causal relationship with current tax planning behavior.
The coefficient for LRisk and Risk is 0.955, attaining statistical significance at the 1% level, according to the findings of the first-stage regression in column (1) of Table 6. The regression findings for instrumental variables shown in column (2) manifest that the coefficient of Risk is 0.021 and is likewise statistically significant at the 1% level. Consequently, the conclusion that climate risk contributes to increased corporate tax avoidance remains valid.

4.4.2. PSM Test

The endogeneity test was conducted using the propensity to match score method (PSM) with reference to [47]. First, we generated a dummy variable for climate risk. The control variable represents the covariate in the current analysis, with the median value of climate risk being taken as the cut-off threshold. The sample firms were divided into experimental and control groups. Those exceeding the median of climate risk were categorized into the high climate risk group and assigned a value of 1. Conversely, those below the median of climate risk were grouped into the low climate risk group and assigned a value of 0, serving as the control group. The sample firms were scored by the Logit model. Subsequently, the samples were matched employing the 1:1 nearest neighbor matching technique. Finally, a regression analysis was then performed on the collected samples. The estimation results of the Logit model for climate risk are presented in Table 6, column (3). At the 5% significance level, the coefficient of Risk_dum is 0.005, exhibiting a notably positive value. It demonstrates that, after dealing with the sample self-selection problem, the conclusions of this paper remain valid, and the findings are robust.

4.4.3. Heckman Test

With reference to Zhang, Zhou [56], the Heckman two-stage model was utilized to solve the endogeneity issue brought on by sample selection bias. To select a dummy variable for the mean climate risk at the industry level, we used 1 for those greater than the mean risk at the industry level and 0 otherwise. We constructed the Probit model for regression analysis to obtain IMR. Following that, to repeat the regression analysis, we added the first stage’s IMR to model (1). The results of this regression are shown in columns (4) and (5) of Table 6, revealing that the coefficients of the IMR are significantly positive.
This supports the presence of sample selection bias and validates the application of the Heckman model to account for bias. The coefficient on climate risk remains significantly positive. It further illustrates that the results regarding climate risk and corporate tax avoidance are in accordance with those of the baseline regression, even after accounting for selection bias through the application of the Heckman model.

4.5. Robustness Tests

4.5.1. Replacement of Dependent Variable

In this study, baseline regression analyses are conducted using the first method to assess the degree of corporate tax avoidance. The latter is ascertained by deducting the effective income tax rate from the nominal income tax rate. The other three techniques are used to validate the decision to replace the dependent variable during the robustness analysis. Columns (1– 3) in Table 7 present the regression outcomes subsequent to the substitution of the dependent variable. At the 1% statistical level, the correlation between corporate tax evasion and climate risk is still strongly positive. The findings of this paper are further supported by the statement that climate risk does indeed heighten the extent of corporate tax avoidance.

4.5.2. Removing the Impact of COVID-19

In 2019, with the occurrence of COVID-19, there was an increased socio-economic uncertainty, which exposes companies to great risks, and business conditions were influenced [57]. Therefore, this paper excludes the effect of COVID-19 and re-examines the regression test by excluding the observations after 2019. The regression outcomes subsequent to the reduction in the sample size are displayed in column (4) of Table 7, indicating a coefficient for Risk of 0.016, maintaining a positive correlation at the 5% significance level. Furthermore, it demonstrates that elevated climate risk leads to an increase in tax avoidance. Hypothesis 1 was tested in this study.

4.5.3. Fixed Effects

In order to address the issue of omitted variables that do not change over time, this paper reanalyses the sample using a fixed effects model, leveraging the research carried out by Ozkan, Temiz [2]. The findings are displayed in column (5), which reveals that the coefficient for Risk is 0.030 and is statistically significant at 1%. These findings align with the baseline regression outcomes detailed within this paper.

4.6. Heterogeneity Analysis

4.6.1. Analysis of Property Rights Heterogeneity

The influence of climate risk on corporate investment varies considerably depending on the property rights structure of the enterprises. Within China’s prevailing economic framework, enterprises are categorized into two main categories: SOEs and non-SOEs. The degree of government intervention and the way it intervenes in SOEs and non-SOEs are different, so these two types of enterprises tend to differ in their responses to climate-related policies. This examination examines a level of corporate tax evasion under different property rights as well as the variability of climate risk. Columns (1) and (2) of Table 8 present the results of the regressions for SOEs and non-SOEs, respectively. The findings show that corporate tax avoidance and climate risk have a positive but negligible correlation in SOEs. At the 1% level of significance, there is a clear correlation between climate risk and tax avoidance in non-SOEs, with a coefficient of 0.024. This suggests that climate risk exerts a more pronounced influence on corporate tax avoidance strategies among non-SOEs. This could be due to the fact that non-SOEs do not have sufficient funds in terms of resources, technology, and human resources to deal with the risks caused by climate change [58]. SOEs have a special position in terms of access to resources and social responsibility, so it has less incentive to avoid taxes than non-SOEs.

4.6.2. Analysis of Industry Heterogeneity

There are variations in the sensitivity to climate risks and the degree to which different industries are affected. In general, for high-carbon industries such as mining, electricity, gas and water production, and transport, they tend to be dependent on specific environmental conditions and natural resources. When climate change has a substantial impact on these industries, they may not be able to sustain competing production. As a result, climate risk has a large impact on production processes and costs in these high-carbon industries, and tax avoidance may be used as a coping strategy. For low-carbon industries such as services and education, climate change has less of an impact on their operations. For this reason, these industries are less exposed to climate risk [58]. Group tests are performed on the sample enterprises in this article, which are separated into high-carbon and low-carbon industries. Table 8 displays the regression findings in (3) and (4). Within the high-carbon industry, the coefficient for the relationship between climate risk and corporate tax avoidance is 0.053, exceeding that of the low-carbon industry. These results indicate that climate risk exerts a more conspicuous influence on corporate tax avoidance strategies in high-carbon industries.

4.7. Analysis of Moderating Effects

For the testing of Hypothesis 2, ESG performance as a moderating role in the relationship between climate risk and corporate tax avoidance was examined. The presence of the moderating mechanism was explored by introducing ESG as a moderating variable into the baseline regression model, along with the interaction term between ESG and Risk. The reformulated regression model is presented as follows:
R A T E i , t = β 0 + β 1 R isk i , t + β 2 E S G i , t + β 3 E S G i , t * R isk i , t + β 4 C o n t r o l i , t + ∑ Y e a r i , t + ∑ I n d i , t + ε i , t
The outcomes of the regression analysis are presented in Table 9. At the 1% level, the ESG coefficients in columns (1) and (2) are both positive and statistically significant. This implies that enhanced ESG performance is positively correlated with increased tax avoidance behavior. To put it another way, when climate risk escalates, enterprises tend to be more prone to tax avoidance. This may be related to tax planning employed by high-risk firms to alleviate financial pressure or to safeguard liquidity. In column (3) after adding control moderator variables, the interaction term between ESG and Risk exhibits a positive correlation at the 10% significance level. To account for potential differences among ESG rating agencies and attempt to test the alternative noted ESG rating in this study, this study also tests the model using the Wind ESG score as the main ESG measurement. As shown in column (4) of Table 9, the results are consistent with the regression findings based on the Huazheng ESG rating presented in column (3). This consistency suggests that the choice of either the Huazheng ESG index or the Wind ESG index does not affect the robustness of the findings. It illustrates that ESG performance somewhat moderates the implications for corporate tax avoidance of climate risk. Among high-risk companies, good ESG performance may further promote tax avoidance behavior. This could result from the fact that high investments in ESG by companies require financial support, and tax avoidance transforms into a source of funding.

5. Discussion

Based on the empirical analysis, this study examines the relationship between climate risk and corporate tax avoidance among Chinese listed firms from 2017 to 2023.
The findings reveal that heightened climate risk is related to increased tax avoidance behavior, supporting the hypothesis H1. This suggests that firms, driven by a desire to enhance shareholder value and protect their interests, are more likely to adopt tax avoidance strategies under rising climate risk. These results align with the findings of prior research in Chinese contexts, including Song and Xian [3], Tang, Yang [35], while also corroborating findings from the prior studies conducted in the U.S. and Europe [30,31,32,33].
The positive association between climate risk and corporate tax avoidance remained robust across rigorous endogeneity tests, including instrumental variable analysis, PSM, and the Heckman test. Additional robustness checks, such as replacing the dependent variable, applying fixed effects, and testing the COVID-19 impacts, consistently supported this finding.
Comparative analysis highlights notable differences in the drivers and implications of this relationship between China and other countries [59]. In developed markets like the U.S. and Europe, stricter enforcement of environmental regulations and more comprehensive disclosure requirements influence firms’ tax avoidance strategies. In contrast, Chinese firms face unique challenges, such as uncertainty in regulatory oversight [60], the dual pressures of achieving economic growth and addressing environmental concerns [44], and the dominance of state-owned enterprises (SOEs) [61]. The findings are derived from a unique institutional setting, China, characterized by its transition economy, specific corporate governance challenges, and regulatory landscape, including the 2017 Environmental Tax Law, offering insights into how regulatory changes influence corporate behavior. The results have limited generalizability due to variations in corporate governance systems, cultural norms, and regulatory environments across countries.
Additionally, this empirical study incorporates SOE or non-SOEs in unique institutional contexts in China using heterogeneous effect analysis, consistent with prior studies [18,62], which also examined the importance of state ownership in China. State-owned enterprises (SOEs) are central to China’s economy, managing key industries and implementing government policies, including those targeting climate initiatives. Tasked with balancing economic goals and social responsibilities, SOEs face heightened scrutiny, which limits tax avoidance and ensures stronger compliance with climate regulations. Moreover, SOEs could benefit from direct access to policy support, funding, and resources. In contrast, non-SOEs are more influenced by external factors like policy changes and climate risks, often prioritizing resource reallocation and tax incentives to reduce their tax burden. This focus on cost-cutting can overshadow social responsibility, as these firms emphasize financial performance and short-term gain. These factors partially explain the nuanced ways climate risks shape tax strategies in China compared to other regions.
Interestingly, the moderating role of ESG performance showed a slight but positive significance, supporting the hypothesis H2, which is a novel finding compared to prior studies, suggesting that firms with higher ESG investments may use tax savings to fund their sustainability initiatives. It indicates that efforts to enhance ESG performance while socially beneficial, may inadvertently encourage tax avoidance under heightened climate risk.
Moreover, heterogeneity analysis revealed that climate risk’s influence on tax avoidance is more pronounced in non-SOEs and high-carbon industries, where financial constraints and regulatory pressures are greater. In contrast, SOEs typically benefit from stronger government support and operate different incentive structures. These findings underscore the interplay between climate risk, ownership structure, and industry characteristics, emphasizing the need for policies that balance sustainability goals with responsible tax practices.

6. Conclusions

This study provides new insights into the influence of climate risk on tax avoidance within the context of China, offering unique evidence from a developing country perspective. It emphasizes the importance of considering the distinct circumstances of developing countries when addressing climate and tax issues at both national and corporate levels. Furthermore, the study introduces ESG performance as a moderating factor and conceptualizes climate risk as a comprehensive construct. ESG performance can alleviate financing constraints, improve internal controls, and strengthen supervision, thereby hindering corporate tax avoidance. This inhibitory effect is particularly evident in firms located in regions with underdeveloped corporate governance, higher agency costs, and relatively lower audit quality in most emerging countries such as China. In response to external risks such as climate change, companies can take microlevel initiatives such as raising environmental awareness, improving corporate governance, optimizing the use of production inputs, focusing on ESG reporting, and avoiding inappropriate practices like green washing. These actions enhance their competitiveness and better prepare them to face market or external challenges.
Unlike previous studies that focus on specific climate-associated risks, we have studied such as extreme weather phenomena, temperature fluctuations, and natural calamities. These contributions underscore the novelty of this research.
While this study provides insights into the relationship between climate risk and corporate tax avoidance in China, its recommendations can be adapted for other countries with high-carbon industries, non-state-owned enterprises, and emerging environmental tax policies, making the study relevant beyond the context of China. To enhance the findings relevant beyond the Chinese context, we have incorporated the insights from global research on corporate tax avoidance, climate risk, and ESG performance. This study aims to provide valuable implications and future research conducted in diverse regions, including Europe, the United States, and emerging markets, thereby establishing the global relevance of the study’s contributions.
Developing countries should prioritize sustainable development by learning from the corporate governance practices and risk management strategies of developed nations. At the same time, they must consider their unique circumstances and adopt tailored solutions to effectively address external risks, such as climate change, and internal challenges, such as tax avoidance.
Overall, this study aims to provide several meaningful contributions. First, tax authorities should incorporate climate risks into their assessment of corporate tax avoidance behaviors, leveraging climate risk as a reference index to improve the efficiency of tax audits. Second, enterprises should conduct comprehensive assessments and predictions of climate risks, strengthen collaboration with government agencies, research institutions, investors, and other stakeholders, develop robust risk response strategies, and establish cross-border and cross-sector risk response mechanisms, which can further enhance resilience. Third, governments should encourage and support well-developed low-carbon enterprises in raising funds through carbon markets and other financial channels. This includes financing research and development for critical low-carbon technologies and alleviating financing constraints for low-carbon transformation. Such measures would help specific regions, industries, and firms successfully facilitate their low-carbon transition.
This study has several limitations and recommendations that warrant acknowledgment. First, the methodology relies primarily on secondary data sourced from the CSMAR database, and the conducted relevant empirical analysis was constrained by the limited availability and accessibility of detailed data, which restricted the depth of our exploration. Second, while offering valuable insights into the Chinese context, differences in regulations, governance practices, and cultural norms may limit the findings’ generalizability. Future research should examine these relationships in other countries to assess their global applicability. Third, the future studies may also examine how ESG sub dimension affects the study variables. Fourth, while this study examines the relationship between climate risk and tax avoidance, future research could incorporate other moderating factors based on corporate governance perspectives, namely, board governance characteristics (e.g., board size and gender diversity) or CEO demographic and organizational attributes (e.g., age, gender, tenure, compensation, or ownership), to provide deeper and more nuanced insights into these dynamics. Last but not least, one main limitation is the weak reliability of climate risk measurement, as it relies on voluntary self-reported disclosures, which may be subject to selective reporting and inconsistent standards. This measure reflects firms’ perceptions rather than their actual exposure. Strengthening disclosure practices can help integrate climate risk into environmental management, and additionally future studies could explore alternative proxies to improve measurement accuracy as the concern for climate risk grows.

Author Contributions

Conceptualization, Y.Z. and L.Y.; methodology, Y.Z.; software, L.Y.; validation, Y.Z.; data curation, L.Y.; writing, Y.Z. and L.Y.; investigation, I.I. and R.O.; supervision, I.I. and R.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available from the corresponding author on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Climate Risk Key Words

Table A1. Climate Risk Key Words.
Table A1. Climate Risk Key Words.
Risk TypeClimate Risk Key Words
Energyenergy saving, clean, ecological, environmental, transformational solar, upgrading, recycling, utilization, nuclear, natural gas, efficiency, oil, regeneration, consumption reduction, water saving, photovoltaic, retrofitting, fuel consumption, power consumption, energy consumption, wind power, intensification.
Climate Changedisasters, earthquakes, typhoons, tsunamis, droughts, extreme, severe, inundation, gales, dust, hurricanes, frost, storms, mudslides, landslides, freezing, heavy rain, snow, ice, snowstorm, sand, wind, climate, weather, humidity, water temperatures, frigid temperatures, rainfall, temperature, rainy season, precipitation, extremely cold, winter, flood season, high humidity, water conditions, water level, light, water shortage, high temperature, subsidence, groundwater.
Low carbonemission reduction, environmental protection, green, low carbon, carbon neutral, carbon trading.

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Table 1. Sample distribution.
Table 1. Sample distribution.
YearFrequencyPercent (%)
2017205411.94
2018214512.47
2019252214.66
2020252614.68
2021257214.95
2022261115.17
2023277816.14
Total17,208100
Table 2. Summary of main variable measurements.
Table 2. Summary of main variable measurements.
VariablesCorresponding AbbreviationsMeasurement
Dependent VariableTax avoidanceRATENominal income tax rate—effective income tax rate
Independent VariableClimate riskRiskThe frequency of terms related to climate risk/total word count in the annual report
Moderating VariableESG performanceESGAssign values from C–AAA to 1–9
Control VariablesGrowth levelGrow(Current main operating income—previous main operating income)/previous main operating income
Firm sizeSizeNatural logarithm of total assets
LeverageLevTotal debt/total assets
Return on AssetsRoaNet profit/total assets
Cash FlowCashCash flows from operating activities/total assets
Shareholdings of the largest shareholderTop1Shareholdings of the largest shareholder/total shareholdings of the company
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
VariableNMeanSDMinMedianMax
RATE17,2080.0120.106−0.4280.0210.220
LRATE17,2080.0050.072−0.2580.0100.178
BTD17,2080.0060.027−0.0660.0030.097
DDBTD17,2080.0050.027−0.0710.0030.096
Risk17,2080.2110.2120.0300.1411.204
ESG17,2084.2541.0101.0004.0006.000
Grow17,2080.1730.353−0.4620.1122.125
Size17,20822.5701.31920.20022.38026.600
Lev17,2080.4130.1880.0660.4080.857
Roa17,2080.0540.0420.0010.0440.217
Cash17,2080.0610.064−0.1150.0580.252
Top117,2080.2610.1910.0000.2650.706
Table 4. Correlation analysis.
Table 4. Correlation analysis.
RATERiskESGGrowSizeLevRoaCashTop1
RATE1
Risk0.027 ***1
ESG0.037 ***0.038 ***1
Grow0.041 ***0.023 ***−0.050 ***1
Size−0.041 ***0.256 ***0.231 ***0.019 **1
Lev−0.150 ***0.210 ***−0.040 ***0.089 ***0.520 ***1
Roa0.253 ***−0.075 ***0.116 ***0.168 ***−0.079 ***−0.370 ***1
Cash0.067 ***0.0020.081 ***0.016 **0.041 ***−0.176 ***0.501 ***1
Top1−0.0050.062 ***0.081 ***−0.021 ***0.200 ***0.065 ***0.056 ***0.089 ***1
The values in parentheses represent the corresponding t-values. Symbols *** and ** indicate significance at the 1% and 5% levels, respectively. *** p < 0.01 and ** p < 0.05.
Table 5. The result of baseline regression.
Table 5. The result of baseline regression.
(1)(2)
RATERATE
Risk0.014 ***0.020 ***
(3.149)(4.532)
Grow 0.003
(1.407)
Size 0.003 ***
(4.395)
Lev −0.024 ***
(−4.379)
Roa 0.674 ***
(28.998)
Cash −0.158 ***
(−11.042)
Top1 0.003
(0.571)
_cons0.055 ***−0.029
(6.073)(−1.644)
YearYesYes
IndYesYes
N1720817208
R20.0580.118
Adj R20.05720.1161
F46.37978.902
Note: The table is a baseline regression to validate Model 1. The t statistics are in parentheses and *** p < 0.01.
Table 6. The result of endogeneity test.
Table 6. The result of endogeneity test.
Instrumental VariablePSMHeckman
(1)(2)(3)(4)(5)
RiskRATERATERiskRATE
LRisk0.955 ***
(326.024)
Risk 0.021 *** 0.019 ***
(3.986) (4.258)
Risk_dum 0.005 **
(2.384)
IMR 0.098 ***
(3.263)
Grow0.010 ***0.0010.0030.0040.003
(5.595)(0.474)(1.067)(0.127)(1.453)
Size0.003 ***0.005 ***0.004 ***−0.209 ***−0.008 **
(6.171)(6.150)(3.707)(−21.514)(−2.273)
Lev0.010 ***−0.032 ***−0.028 ***−0.488 ***−0.048 ***
(2.661)(−5.007)(−3.724)(−6.712)(−5.233)
Roa−0.0060.585 ***0.690 ***0.847 ***0.719 ***
(−0.389)(21.958)(21.314)(2.686)(26.622)
Cash0.001−0.155 ***−0.162 ***0.190−0.148 ***
(0.073)(−9.449)(−8.231)(0.994)(−10.155)
Top1−0.0000.002−0.004−0.055−0.000
(−0.095)(0.285)(−0.480)(−0.753)(−0.065)
_cons−0.062 ***−0.009−0.0324.988 ***0.160 ***
(−5.323)(−0.440)(−1.317)(21.875)(2.648)
ControlsYesYesYesYesYes
YearYesYesYesYesYes
IndYesYesYesYesYes
N121451214590931720817208
R20.9350.1230.122——0.118
F6224.858——43.482——76.670
Note: The table shows the results of the endogeneity test. LRisk refers to the lagging Risk by one period when performing the 2SLS analysis; Risk_dum refers to taking the median of Risk when performing the PSM analysis. The t statistics are in parentheses. ** p < 0.05, and *** p < 0.01.
Table 7. The results of the robustness analyses.
Table 7. The results of the robustness analyses.
Replace Dependent VariableRemoving the Impact of COVID-19Fixed Effects
(1)(2)(3)(4)(5)
LRATEBTDDDBTDRATERATE
Risk0.010 ***0.003 ***0.004 ***0.016 **0.030 ***
(3.391)(3.148)(3.811)(2.236)(2.605)
Grow0.003 *−0.003 ***−0.003 ***0.010 ***−0.005 **
(1.853)(−5.153)(−5.496)(2.845)(−2.263)
Size0.005 ***0.001 ***0.001 ***0.006 ***0.017 ***
(10.267)(3.184)(3.493)(4.834)(6.899)
Lev−0.046 ***−0.004 ***−0.006 ***−0.037 ***−0.038 ***
(−12.575)(−2.804)(−4.013)(−4.115)(−3.496)
Roa0.339 ***0.273 ***0.195 ***0.743 ***0.746 ***
(21.746)(47.329)(33.011)(19.522)(26.739)
Cash−0.089 ***−0.037 ***0.013 ***−0.106 ***−0.077 ***
(−9.273)(−10.337)(3.549)(−4.565)(−5.285)
Top10.006 *−0.007 ***−0.007 ***−0.001−0.048 ***
(1.693)(−5.135)(−4.874)(−0.082)(−2.887)
_cons−0.056 ***−0.0010.000−0.077 ***−0.383 ***
(−4.789)(−0.285)(0.023)(−2.735)(−6.971)
YearYesYesYearYesYes
IndYesYesIndYesYes
N172081720817208672117208
R20.1340.1830.1390.1230.061
F91.952132.78895.81337.712123.209
Note: This table presents the results of the robustness analyses, where (1)–(3) are the replacement of explanatory variables; (4) is the exclusion of the post-2019 sample due to the COVID-19 effect; and (5) is the result of the analysis using fixed effects. The t statistics are in parentheses. * p < 0.1, ** p < 0.05, and *** p < 0.01.
Table 8. The results of the heterogeneity analysis.
Table 8. The results of the heterogeneity analysis.
(1)(2)(3)(4)
SOENon-SOEHigh-CarbonLow-Carbon
Risk0.0090.024 ***0.053 ***0.013 ***
(1.005)(3.504)(4.517)(2.823)
Grow0.0040.001−0.0020.005 *
(0.752)(0.202)(−0.331)(1.877)
Size0.005 ***0.008 ***0.004 **0.003 ***
(3.528)(5.436)(2.030)(4.021)
Lev−0.041 ***−0.011−0.041 ***−0.019 ***
(−3.257)(−1.052)(−3.449)(−3.197)
Roa0.616 ***0.454 ***1.175 ***0.557 ***
(11.085)(12.159)(21.464)(21.672)
Cash−0.168 ***−0.107 ***f−0.151 ***−0.150 ***
(−5.068)(−4.390)(−5.184)(−9.168)
Top1−0.015−0.009−0.0070.007
(−1.194)(−0.955)(−0.526)(1.144)
_cons−0.037−0.072 **−0.032−0.148 ***
(−0.918)(−2.137)(−0.853)(−8.127)
YearYesYesYesYes
IndYesYesYesYes
N77649444374913459
R20.1540.1160.2430.075
F20.45517.85249.75464.356
Note: The table presents the results of the tests for which heterogeneity analyses were conducted. Columns (1) and (2) are the results of the test under SOEs and non-SOEs, respectively; columns (3) and (4) are the results of the test in high-carbon and low-carbon sectors, respectively. The t statistics are in parentheses. * p < 0.1, ** p < 0.05, and *** p < 0.01.
Table 9. The results of the moderating effects analysis.
Table 9. The results of the moderating effects analysis.
(1)(2)(3)(4)
RATERATERATERATE
Risk0.012 ***0.012 ***0.020 ***0.020 ***
(2.798)(2.752)(4.458)(4.476)
ESG0.005 ***0.005 ***0.0010.000
(5.670)(5.667)(0.701)(0.355)
Risk*ESG 0.0040.006 *0.001 *
(1.159)(1.700)(1.725)
Grow 0.0030.003
(1.421)(1.408)
Size 0.003 ***0.003 ***
(4.003)(4.059)
Lev −0.023 ***−0.023 ***
(−4.228)(−4.261)
Roa 0.674 ***0.674 ***
(28.899)(28.917)
Cash −0.158 ***−0.157 ***
(−11.043)(−11.035)
Top1 0.0030.003
(0.581)(0.574)
_cons0.040 ***0.040 ***−0.028−0.031 *
(4.254)(4.215)(−1.571)(−1.653)
YearYesYesYearYear
IndYesYesIndInd
N17208172081720817208
R20.0600.0600.1180.118
F45.86744.08773.92773.917
Note: The table is a study of the impact of climate risk and corporate tax avoidance using ESG as a moderating variable, and columns (3) and (4) are data from the Huazheng ESG and Wind ESG measures, respectively. The t statistics are in parentheses. * p < 0.1, and *** p < 0.01.
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Zhang, Y.; Yuan, L.; Ibrahim, I.; Omar, R. Sustainability in Question: Climate Risk, Environment, Social and Governance Performance, and Tax Avoidance. Sustainability 2025, 17, 1400. https://doi.org/10.3390/su17041400

AMA Style

Zhang Y, Yuan L, Ibrahim I, Omar R. Sustainability in Question: Climate Risk, Environment, Social and Governance Performance, and Tax Avoidance. Sustainability. 2025; 17(4):1400. https://doi.org/10.3390/su17041400

Chicago/Turabian Style

Zhang, Yuxuan, Leihong Yuan, Idawati Ibrahim, and Ropidah Omar. 2025. "Sustainability in Question: Climate Risk, Environment, Social and Governance Performance, and Tax Avoidance" Sustainability 17, no. 4: 1400. https://doi.org/10.3390/su17041400

APA Style

Zhang, Y., Yuan, L., Ibrahim, I., & Omar, R. (2025). Sustainability in Question: Climate Risk, Environment, Social and Governance Performance, and Tax Avoidance. Sustainability, 17(4), 1400. https://doi.org/10.3390/su17041400

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