The Interaction Effects of Income Tax Incentives and Environmental Tax Levies on Corporate ESG Performance: Evidence from China

Abstract

The enhancements of tax policies and their coordination have emerged as a significant way to promote corporate sustainability, especially in developing economies worldwide. Using panel data from Chinese non-financial A-share listed companies from 2009 to 2022, this study empirically explores the promoting effects of corporate income tax (CIT) incentives and environmental protection tax (EPT) levies on corporate ESG performance. We find that the CIT incentive has a notable positive impact on firms’ ESG behavior, acting on the micro-mechanisms of increasing corporate cash flow and reducing agency costs, and its promoting effect is more salient with regard to the social and governance dimensions. This study also traces the interactive effects between the EPT levy and CIT incentive policies, which boost corporate ESG behavior synergistically. Heterogeneity analyses reveal that these effects are more noticeable in manufacturing firms and non-state-owned firms with severe financing constraints. Environmental tests show that CIT incentive policies have positive effects on green technological innovation, and Chinese enterprises are still experiencing relatively serious negative impacts. The conclusions of this study are conducive to providing theoretical support and policy suggestions for encouraging the sustainable development of companies through the policy combination of environmental regulation and tax incentives.

1. Introduction

Improving corporate sustainability is a worldwide approach to tackling global issues such as economic downturn, environmental pollution, and technological revolution. In light of these existential threats, investors are increasingly turning to investment products and companies that consider environmental, social, and governance (ESG) issues in their business models [1,2]. Under the continuous influence of the intensive economic policies of the Chinese government, A-share listed companies have realized the significance of ESG performance in demonstrating their sustainability capabilities, and the number of companies publishing ESG reports has grown from 371 in 2009 to 1755 in 2023. Despite this rapid growth, the number of ESG disclosures is still insufficient (about 34.5% in 2023). On the whole, improvements in ESG quality still lag behind the improvements in its quantity [3], indicating that managers have neglected a critical effect of ESG: it can bring about financial risks, including cash flow risks and financing constraints. As a consequence, the ESG performance of a significant number of companies is insufficient due to their neglect of a critical component of ESG: tax.

For enterprises worldwide, tax practices are essential to their ESG performance, both in terms of measuring their sustainability impact and assessing their financial risk. Many countries have adopted a carbon tax or a cap-and-trade system to make polluters pay for their emissions. Moreover, many OECD countries have seen a greater reliance on tax incentives to support corporate R&D over the past two decades, as R&D tax incentives represented around 60% of total government support for business R&D in 2019, compared to 36% in 2006 [4]. Through its Tax Cuts and Jobs Act in 2017, the United States permanently reduced its corporate income tax rate from the fourth highest in the world to a moderate level of 21%, which would invigorate companies and benefit workers [5]. In the Chinese mainland, the still-developing market economy, coupled with the government’s institutional characteristics, makes it more necessary for the government to regulate firms and to influence their development [6]. A pollution discharge fee system was established in 1979 and was changed into an environmental protection tax (hereafter EPT) through legislation in 2018, stipulating that firms that directly discharge pollutants into the environment must pay the tax. Beyond the legislation of environmental taxes such as the EPT, the greening transformation of non-environmental taxes has also continued, forming a combined green tax system to cultivate a sustainable economy. Unified since 2008, the corporate income tax (hereafter CIT) is one of the main categories for greening transformation, which can indirectly promote the sustainable development of firms by regulating the strength of its levy and using tax incentive tools. The unification of CIT and the implementation of EPT provide an opportunity to further analyze the impact of environmental and non-environmental tax policies on corporate ESG performance.

The existing theoretical and empirical literature mainly focuses on the economic consequences of corporate ESG performance [7,8,9]. Meanwhile, there is limited research on the ESG influencing factors, which can be divided into firm and market perspectives, including firms’ innovations [10], mergers and acquisitions [11], internal governance [12,13,14], and investors [15,16]; there is a lack of literature from the government perspective, except for government green procurement [17]. As for the Chinese context, the existing literature has focused primarily on the implementation of EPT and the water resources tax reform, arguing that these environmental taxation processes are conducive to the promotion of corporate technological innovations and have a catalytic effect on ESG performance [18,19,20]. Compared with these environmental taxation levies that convert firms’ external costs into internal costs to save energy and reduce emissions, non-environmental tax policies, such as the CIT incentive, are also important measures for the government to activate economic growth and to develop a sustainable economy. Though the direct impact of tax incentives on corporate ESG performance is overlooked, existing studies have confirmed the facilitating effect of tax incentives on corporate R&D investment, energy savings, emissions reduction, and financing constraints [21,22,23,24], all of which can help to enhance enterprises sustainability. There is also preliminary research on the overall influence of tax incentives on corporate ESG performance; however, it ignores the most essential impact mechanisms of taxation, such as the cash flow effect and the agency cost effect [25]. Still, the existing literature pays little attention to the mechanism of tax incentives on corporate ESG performance, or to the interaction between environmental and non-environmental tax policies, which provides a breakthrough space for this research. Consequently, this study constructs a model in which Chinese firms adopt different ESG performance selection strategies under CIT incentives and EPT levies to explain the mechanism by which tax policy affects corporate sustainable development.

Using panel data from the database of non-financial A-share listed companies in China from 2009–2022, this study empirically examines the economic effect of CIT incentive and the interaction between CIT and EPT policies on corporate ESG performance. The robustness of the results is tested by replacing the core explanatory variables and the measurement model, excluding special firms, by conducting lagging tests and using the Heckman two-stage model. In particular, this study separates the “time effect” from the “policy effect” and groups the firms according to whether they belong to the key inspection taxpayers in order to explore the impact of environmental tax levies (e.g., EPT) and non-environmental tax incentives (e.g., CIT) on corporate ESG performance.

The contributions of this study to the existing literature are threefold. First, it provides rigorous and comprehensive empirical evidence regarding the unexpected impact of CIT cuts on corporate ESG strategies. The effects of the CIT incentive on corporate financing constraints, R&D investment, energy efficiency, productivity, and innovation have been the focus of previous studies [26,27,28]. Given that the CIT reform provides more opportunities for firms’ ESG activities, we extend the research to the influence of CIT incentives on corporate ESG performance and further analyze its mechanisms.

Second, the study empirically tests a range of views on the interaction between environmental and non-environmental tax policies. Scholars have realized that the combination of multiple policy instruments has become the key to corporate sustainability [29], but existing studies mainly focus on the interaction effects between different environmental policies, including command-and-control policies, taxation on energy efficiency and emissions reductions, and climate incentives [30,31,32,33], leaving the interactions between environmental and non-environmental policies largely untouched. This study explores the importance of the interaction effects of CIT incentives and EPT levies, thereby providing significant evidence of the interaction effects between environmental and non-environmental policies.

Third, this study contributes to the improvement of corporate ESG performance. The existing studies mainly focus on the economic consequences of corporate ESG performance, and only a few studies discuss the enhancement of ESG performance from the market and corporate perspectives [11,13]. However, they do not acknowledge the role of government in the Chinese context and ignore the influence of combined tax policies, which are discussed in detail in this study. This study has considerable implications for developing countries that mainly depend on command-and-control policies to achieve business performance improvement.

2. Research Hypotheses

2.1. Corporate Income Tax (CIT) in China and Its Incentive Policies

Within the national green tax system, CIT, as a representative of non-environmental taxes, forms a linkage with environmental taxes and jointly influences the sustainability of firms. As the most important direct tax in the Chinese mainland, CIT directly originates from microeconomic agents and acts on the market through related policies. The current CIT law of mainland China was enforced in 2008, realizing for the first time the unified application to domestic and foreign-funded firms, the unification and standardization of pre-tax deduction criteria, and preferential taxation, thereby establishing a set of tax incentive policies that are generally applicable to all types of firms. Since 2008, relevant incentive measures have been added or adjusted for optimization. In general, the CIT incentive encourages firms to invest funds in technological R&D, energy and water conservation, environmental protection, and safe production, all of which are core parts of ESG activities. It also encourages firms’ social(S) and governance(G) activities, such as pre-tax deductions for employee welfare expenses, employee education expenses, and public welfare donation expenditures; firms can also add deductions for wages paid for the employment of disabled personnel. Empirical studies have shown that the 2008 CIT consolidation policy can reduce pollutant emissions [28] and promote technological innovation [34]. The main purpose of CIT incentives is to promote the sustainable development of firms, such as by promoting environmental protection, stabilizing social employment, and releasing enterprise vitality, all of which are consistent with the objectives of the corporate ESG goals. This raises the question of whether CIT incentives can affect corporate ESG performance, meaning that its mechanisms should be explored.

2.2. CIT Incentive and Corporate ESG Performance

Since tax is typically listed as a governance factor in ESG ratings, tax planning practice is also at the heart of tax responsibility. As a key measure in the tax reforms implemented to benefit enterprises, the CIT reduction practice not only directly reduces the burden on firms but also stimulates their vitality in many respects. Though existing studies have not yet directly discussed the impact of CIT incentives on corporate ESG performance, they have confirmed its facilitating effect on enterprise R&D investment, greening transformation, energy saving, emissions reduction, financing constraints, and other aspects [21,22,23,24], which all help firms to achieve sustainable development and good ESG performance. Summarizing the existing literature, this study argues that the mechanism of CIT incentives that promote corporate ESG performance has three aspects. The first one is its cash flow effect. In general, Chinese enterprises have more urgent capital needs in terms of personnel training, technological upgrading, and other constructive aspects, leading to the insufficiency of cash for seemingly high-input and low-output ESG activities with strong externality. Cash flow effects brought about by the CIT reduction and rebate can satisfy firms’ capital demands and provide a resource buffer for enterprises to invest in sustainable development [35]. Therefore, CIT incentives can enhance firms’ motivation and ability to carry out ESG activities.

Second, CIT incentives can reduce firms’ external costs and ease their capital pressure, which is the key tax-planning practice used to solve the problem of financing constraints. As a type of direct tax preference, the reduction in CIT is passed on to firms through the price mechanism, which reduces the marginal cost of ESG activities as a whole, easing the cost pressure brought about by energy savings, emissions reduction, waste disposal, internal control, and other activities, thus generating cost utility [36,37]. As a result, corporate profitability is strengthened and sufficient profit guarantees the financial and material resources needed to carry out technological change and transformation, which in turn creates the expectation of a stable cash flow for firms and alleviates the financial pressure on corporate ESG activities to a significant degree. Therefore, firms will have higher motivation to invest funds in projects closely related to their ESG performance, such as environmental protection, workers’ welfare, and social services, ultimately achieving the optimization of firms’ internal resource allocation.

Third, ESG activities can enhance corporate reputations, at the cost of uncertain returns. From the perspective of managerial motivation, in firms with higher macro tax burdens, managers reap higher marginal returns from engaging in rent-seeking ESG activities. These firms tend to have more room for tax avoidance operations, which is usually associated with higher levels of opportunism, moral hazard, and agency problems, significantly crowding out productive ESG activities [38]. In contrast, firms with higher incentives and thus lighter tax burdens have lower marginal returns from engaging in tax avoidance activities and tend to be more willing to respond to policies for higher firm value and reputation, therefore actively engaging in productive ESG activities. As agency costs are an essential part of governance, reduced agency costs translate into better governance scores. As a result, CIT incentives can not only reduce the “negative effect” of corporate ESG activities but also enhance the intrinsic motivation of their ESG investment, which in turn improves corporate ESG performance. Therefore, we propose the following hypothesis:

H1. 

CIT incentives can promote corporate ESG performance, with a greater impact on social and governance dimensions than the environmental dimension.

2.3. Policy Interaction Effects of CIT Incentives and EPT Levies

One of the main challenges in the implementation of corporate ESG strategies is reducing environmental tax levies to a lower cost [39]. The Environmental Protection Tax Law enforced in 2018 transformed the pollutant discharge fee into an environment protection tax, which taxes air pollutants, water pollutants, solid waste, and noise. As the country’s first tax directly targeting environmental protection, EPT marks an important initiative for China to utilize economic means to incentivize firms to reduce pollutant emissions. By increasing the operating costs of firms to discharge taxable pollutants, EPT can prompt them to allocate resources reasonably and to proactively reduce emissions, thus achieving sustainable development [20,40]. With legal rigidity enhanced and the collection rate increasing, EPT inevitably changes the pattern of economic resource allocation for firms. As a kind of Environmental regulation, it increases the costs of pollution control and institutional compliance for firms. Under higher pressure of environmental regulation, firms are forced to take measures such as production cuts and work stoppages, reducing the funds available for firms to invest in innovation [41,42,43,44,45]. In addition, Compliance costs caused directly or indirectly by environmental regulations can crowd out enterprises’ R&D investment, thereby inhibiting their green innovation [46].

Managers face important cost trade-off issues when choosing between environmental protection taxes and ESG behaviors. When a company blindly pursues cost-effectiveness, it may damage its corporate reputation and thereby reduce its attractiveness in the capital market. Still, the increase in corporate pollution control costs and institutional compliance costs remains the main reason why enterprises are unable to engage in ESG activities. In many cases, policymakers can use a variety of tools to address the sustainable development issues for businesses. The interaction of different tax policies can significantly change corporate perceptions of environmental costs, in response to the changes in capital brought about by the EPT levy. In the context of China’s multi-tax and multi-policy green tax system, firms that meet the government’s criteria can receive overlapping policy support in taxation. In practice, the incentives of non-environmental tax policies can interact with China’s first environmental protection tax levy and can achieve the expected governance effects. Therefore, the policy combination under the green tax system can be transformed into an impetus for firms to conduct transformation and to improve their ESG performance.

On the one hand, the interaction of different tax policies may result in lower environmental costs for firms than other non-market instruments. One potential direction for improving corporate ESG performance is the incentives of non-environmental tax policies, primarily aimed at reducing the implementation costs of firm externalities. The CIT incentive can provide firms with an opportunity to adjust their ESG strategies to address or synergize with the externality costs associated with EPT levies. In this way, non-environmental tax reductions can directly stimulate firms to increase productivity and reduce costs by scaling up investment, thereby releasing the redundant resources available to firms for ESG activities. In addition, the cash flow compensation created by non-environmental tax incentives can outweigh the costs of EPT levies, thus further providing firms with opportunities to adjust their development strategies to cope with the rising costs of polluting emissions due to increased external environmental regulation.

On the other hand, enterprises that enjoy tax incentives perform better in aspects such as scientific and technological innovation and social responsibility, which indicates that enterprises pay more attention to their corporate reputation and the reactions of the capital market. Therefore, when weighing the reputation issues caused by the costs of environmental protection taxes, enterprises are more inclined to invest more in ESG activities. Overall, the interplay between environmental and non-environmental tax policies may enhance corporate ESG behavior complementarily. Figure 1 shows the research framework.

H2. 

The interaction effect of non-environmental and environmental tax policies (represented by CIT and EPT, respectively) complementarily contributes to enhancing corporate ESG performance.

3. Data and Methods

3.1. Sample and Data

Aiming to create an environment of fair competition among enterprises with different ownership structures, a new corporate income tax law was implemented in 2008, which allow us to study the impact of CIT cuts on promoting corporate ESG performance and improving their sustainability. To unify the criteria of financial data and consider that China’s corporate income tax reduction policy matured in 2022, this study takes A-share listed companies from 2009–2022 as the sample. We drew ESG ratings, CIT incentives, and financial-related data from the China Stock Market and Accounting Research Database (CSMAR), Wind, and other databases; we then manually collated EPT and other data based on governmental documents. Referring to the common practice in existing studies, the raw data are processed in the following four ways. (1) We exclude the listed companies in the financial sector, as their report structure is obviously different from companies of other industries. (2) We exclude the ST, *ST, and PT companies with abnormal financial systems during the sample period. (3) We exclude firms listed in the B-share market during the sample period because of the differences in its requirements for companies’ financial data. (4) We Winsorize all continuous variables at 1% and 99% to eliminate the influence of extreme values on the results. In total, there were 27,494 observations.

3.2. Model Construction

To test Hypothesis 1, we construct the following panel regression model (1):

(1)$ES{G}_{it}=\alpha +{\partial }_{1}Incentiv{e}_{it}+Contro{l}_{it}+{\theta }_t+{\theta }_n+{\epsilon }_{it}$

In the model, the subscript “i” represents the firm, and “t” represents the year. The explained variable ${\mathrm{ESG}}_{\mathrm{it}}$ represents the ESG ratings of the firm “i” in period “t”, and the explanatory variable ${\mathrm{Incentive}}_{\mathrm{it}}$ represents the CIT incentive of the firm “i” in period “t”. ${\mathrm{Control}}_{\mathrm{it}}$ represents a set of control variables for firms in terms of firm characteristics, the financial situation, and internal governance, as specified above. Moreover, $\mathsf{\alpha }$ is the intercept term of the model, ${\mathsf{\theta }}_{\mathrm{t}}$ is a year fixed effect to control for common shocks at the year level that do not vary with firms (e.g., macroeconomic situation, monetary and fiscal policies, etc.), and ${\mathsf{\theta }}_{\mathrm{n}}$ is an industry fixed effect to control for common shocks at the industry level that do not vary with firms (e.g., technological advances, changes in business models, the upgrading of consumption, rising labor costs, etc.).

To analyze the interaction effect of policy mix between CIT and EPT, this study sets up the following model (2) without considering the key-monitoring enterprises as the experimental group:

(2)$ES{G}_{it}=\alpha +{\beta }_{1}Incentiv{e}_{it}+{\beta }_{2}Pos{t}_{it}+{\beta }_{3}Incentiv{e}_{it}\times Pos{t}_{it}+Contro{l}_{it}+{\theta }_t+{\theta }_n+{\epsilon }_{it}$

When considering the key-monitoring enterprises as the experimental group, this study sets up the following model (3):

(3)$ES{G}_{it}=\alpha +{\delta }_{1}Incentiv{e}_{it}+{\delta }_{2}Trea{t}_{it}\times Pos{t}_{it}+{\delta }_{3}Incentiv{e}_{it}\times Trea{t}_{it}\times Pos{t}_{it}+Contro{l}_{it}+{\theta }_t+{\theta }_n+{\epsilon }_{it}$

where Post_it is a proxy variable for EPT. After the implementation of the EPT in 2018, the time dummy variable Post takes the value of 1; otherwise, it is 0. As for Treat_it, if an enterprise is a heavily polluting one, it takes the value of 1; otherwise, it is 0. In Model (1), we add EPT, heavily polluting enterprises, and their cross-multiplier terms to test the policy interactive effect of CIT and EPT on corporate ESG ratings, the results of which are denoted by ${\mathsf{\beta }}_3$ and ${\mathsf{\delta }}_3$. If the estimated coefficients ${\mathsf{\beta }}_1$ and ${\mathsf{\beta }}_3$ or ${\mathsf{\delta }}_1$ and ${\mathsf{\delta }}_3$ are significantly positive at the same time, it can indicate the existence of a positive interaction effect, thereby supporting Hypothesis 2.

3.3. Variable Definition

3.3.1. Explained Variable: ESG Rating Indicators

This study adopts the ESG ratings from the Sino-Securities Index, one of the most mature ESG rating systems in China, as the core explained variables to measure corporate ESG performance. Covering all Chinese A-share listed firms and comprehensively showing ESG performance with four levels of key indicators, the Sino-Securities Index ESG rating system is widely applied in ESG-related studies in China. This rating system classifies the ESG performance of Chinese firms into nine grades from C to AAA, which are sequentially assigned values of 1–9 in the empirical test carried out here, with higher scores indicating better ESG performance. In addition, this study takes the E(nvironmental), S(ocial), and G(overnance) indicator scores as the explanatory variables, to explore the impact of CIT incentives in depth.

3.3.2. Explanatory Variable: Corporate Income Tax Incentive

This study measures the CIT incentive effect from two perspectives. First, from the perspective of cash flow, the actual CIT rate of the firms is calculated according to CSMAR and compared with the statutory tax rate (25%), so as to derive the extent of the CIT incentive. The formula is as follows: Incentive = ln[Profit before tax × (25% − ETR)]. ETR represents the effective corporate tax rate, which is the prevailing measure of a firm’s tax burden worldwide. Second, a dummy variable is introduced to measure whether a firm has enjoyed the CIT incentive, which is 1 if the firm’s effective CIT rate is less than 25%, and 0 otherwise.

Currently, there are four common methods used to calculate the ETR of a company [47,48]: (1) ETR1 = income tax expense/profit before tax, (2) ETR2 = income tax expense/earnings before interest and tax; (3) ETR3 = (income tax expense − deferred income tax expense)/earnings before interest and tax; (4) ETR4 = income tax expense/(profit before tax − deferred income tax expense/statutory tax rate). In this study, the first method is used to calculate the company’s effective tax rate (ETR1), and the other three methods are used to check the stability of the empirical research results.

3.3.3. Moderator Variable: Environmental Protection Tax Levy

The moderator variable of this study, Treat×Post, is based on the EPT. To build an administration system with differential management and risk-oriented levies, the taxpayers were categorized into key-monitoring taxpayers and non-key-monitoring taxpayers, according to their different management levels of environmental impact assessment. According to the environmental information disclosure guidance for listed companies issued by the Ministry of Ecology and Environment of the PRC in 2010 and the Guidelines for the Industry Classification of Listed Companies of the CSRC, which were revised in 2012, the listed companies in the heavily polluting industries in this study include mining, textiles, papermaking and study products, petroleum, chemicals, chemical fibers, ferrous and non-ferrous metals, ore mining and dressing, rubber and plastic products, pharmaceuticals, and fur-related products. With the heavily polluting listed companies in the above industries as the experimental group, and non-heavily polluting listed companies in other industries as the control group, we investigated the impact of EPT on ESG ratings. This study constructs the group dummy variable Treat, the time dummy variable Post, and the interaction term policy variable Treat×Post. If an enterprise is a heavy polluter, Treat takes the value of 1; otherwise, it is 0. After the implementation of the EPT in 2018, the time dummy variable Post takes the value of 1; otherwise, it is 0. This study focuses on the positiveness of the coefficient of the interaction term Treat×Post.

3.3.4. Mechanism Variables

According to the theoretical analysis of Hypothesis 1, this study conducts mechanism tests from the cash flow effect and agency cost effect. In terms of the cash flow effect, a firm’s current cash flow (Cash) is measured using the ratio of its monetary funds and trading financial assets to its total assets [49]. In addition, the firm’s return on net assets (ROA) is used to measure its profitability, in order to verify the effect of CIT incentives on firm profitability.

The existing studies mainly use the management cost ratio (MCR) as a proxy variable for agency costs [50,51], which is the ratio of a company’s management expenses to its operating revenue, and this is followed by a test from the degree of managerial power (Power). The greater the degree of power, the higher the agency costs. Managerial power is calculated through principal component regression using five indicators: CEO duality, board size, the proportion of internal directors, equity dispersion, and management shareholding [52].

3.3.5. Control Variables

To alleviate the endogenous interference of omitted variables and improve the efficiency of the regression analysis, the following control variables are introduced: (1) enterprise size (Size), i.e., the natural logarithm of an enterprise’s total assets; and (2) enterprise nature (SOE), i.e., whether the enterprise is a state-owned one. In this study, the SOE is set as a dummy variable. If the enterprise is a state-owned one, the SOE = 1; otherwise, the SOE = 0; (3) solvency (Lev), or the debt-to-asset ratio, is calculated by dividing the total company assets by its total liabilities; (4) liquidity (Liq), which is measured by the current ratio; (5) financial risk (FinR), i.e., the firm’s financial leverage ratios; (6) operational efficiency (EFF), i.e., the turnover rate of accounts receivable; (7) market value (BM): net assets/total market capitalization; and (8) ownership concentration (TTH), i.e., the percentage of shares held by the top ten shareholders. To eliminate the effect of extreme values, the variables are indented by 1% before and after. The variables are defined in Table 1.

4. Empirical Results

4.1. Descriptive Statistics

Table 2 shows the descriptive statistics of the main variables. The mean ESG ratings of Chinese firms during the sample period is 4.210, the standard deviation (SD) is 1.064, and the median is 4, indicating that the ESG ratings of the sample firms as a whole are in B grade. The A-share listed firms in China have moderate ESG performance; therefore, further stimulation measures are needed. In addition, the companies’ ratings vary widely in terms of environmental, social, and governance indicators (with SDs of 7.819, 10.534, and 6.782 respectively). The variance in the CIT incentive of the sample enterprises (Incentive 1) is 1.530, with the maximum value of 22.896, which marks a big difference from the minimum value of 6.499. The variance of whether the incentive exists (Incentive 2) is 0.402, indicating that the degree of CIT incentive varies significantly among the sample enterprises. Thus, based on the characteristics of China’s economic system, this study explores ways to promote corporate ESG performance through the perspective of tax policies. In addition, the descriptive statistical results of the mechanism variables and control variables are consistent with the existing studies.

4.2. Benchmarking Regression

To test Hypothesis 1, model (1) is regressed and Table 3 presents the full sample regression results for the effect of CIT incentives on ESG ratings. The hypothesis is tested in terms of the extent of the CIT incentive (Incentive 1), as shown in columns (1) to (4); and the presence of the CIT incentive (Incentive 2), as shown in columns (5) to (8). The regressions are all based on control variables, year-fixed and industry-fixed effects. The results in columns (1) and (5) show that the coefficients of Incentive 1 and Incentive 2 on ESG are 0.108 (t-values = 16.823) and 0.211 (t-values = 12.433), respectively, which are significant at the 1% statistical level, indicating that the presence of CIT incentives has a significantly positive impact on firms’ ESG ratings; this suggests that CIT reductions can significantly incentivize firms’ sustainable development. As discussed in the second part of this study, CIT incentives can trigger a cash flow effect, allowing firms to reach resource redundancy and reduce their adverse selection problems, which promotes managers to conduct more ESG activities, thereby confirming Hypothesis 1. The estimated coefficients are all negative and pass the test of significance at the 1% level. The marginal coefficient of Incentive 1 in column (1) is 0.108, which implies that, keeping all else constant, for every unit increase in CIT incentive, firms’ ESG ratings may increase significantly by 10.8% on average. As for the presence of CIT incentives, the marginal coefficient of Incentive 2 in column (5) is 0.211, which implies that, keeping other conditions constant, the presence of CIT incentives may significantly increase firms’ ESG ratings by 21.1% on average. These results suggest that firms are more sensitive to the presence or absence of CIT incentive policies, and, therefore, the positive effect of CIT incentives on firms’ sustainability should be actively promoted.

Further regression results of the E, S, and G ratings separately, as shown in columns (2)–(4) and (6)–(8) in Table 3, indicate that CIT incentives make a significant contribution to the environmental, social, and governance aspects of enterprises and all are significant at the 1% level. CIT incentives have a more significant impact on firms’ social responsibility scores and governance scores than on environmental scores. This is consistent with the existing literature showing that tax incentives can promote corporate social responsibility [53,54] and reduce rent-seeking problems [55]. In addition, the regression results of the control variables are also consistent with previous studies.

4.3. Robustness Tests

4.3.1. Replacing the Measurement Model and Explanatory Variables

Since there are often heteroskedasticity, autocorrelation, and other problems in the data that may lead to inaccurate estimated standard errors, the samples are tested again using robust standard errors to replace the original standard errors, instead of running pooled OLS regression models with year- and industry-fixed effects. The results are shown in Table 4, indicating that CIT incentives still show promotion effects on corporate ESG ratings even after replacing the measurement model, thus confirming Hypothesis 1.

As shown in Section 3.3.2, there are four methods for measuring ETR and thus four scores for CIT incentives. In the benchmarking test, the first method is used to calculate the effective tax rate of the company (Incentive 1 and Incentive 2), and the other three methods are used in this part to conduct a robustness test (Incentive 3, Incentive 4, and Incentive 5). The results are shown in Table 4. They show that the CIT incentive makes a significant contribution to ESG ratings under multiple calculation methods.

4.3.2. Eliminating Special Samples

First, we eliminate high-tech companies. Existing studies have shown that high-tech companies are more willing to invest their redundant resources in technological innovation, product upgrading, and other aspects to address social responsibility issues [56], thereby enhancing ESG performance. This study conducts a regression test, eliminating high-tech firms according to the Regulation on the Identification of High-tech Enterprises issued by the government in 2016. The results are shown in columns (1) and (4) of Table 5, with both the degree and the presence of CIT incentives being significantly positive at the 1% level, thus confirming Hypothesis 1.

Second, we eliminate heavily polluting firms, since they usually pay more attention to investing in environmental performance to reverse stereotypical impressions of them and to create an eco-friendly impression for the public. A regression test is conducted, eliminating heavily polluting enterprises from 16 sub-industries according to the Guidelines for the Industry Classification of Listed Companies issued in 2012. The results are shown in columns (2) and (5) of Table 5. Whether the degree of CIT incentive is higher or as long as it exists, it can significantly enhance the ESG performance of non-heavy polluters. This highlights the generalized significance of CIT incentives for corporate sustainability.

Third, we eliminate high-performance companies. Considering that high-performing firms may also be more likely to receive such incentives, we excluded firms with superior performance by comparing the mean values of corporate performance. Columns (3) and (6) in Table 5 show that the relevant results still significantly promote corporate ESG performance. Therefore, the results support Hypothesis 1.

4.3.3. Lag Tests

Considering that there may be a certain lag in the promotion effect of CIT incentives on ESG performance, we add lags by one, two, and three periods in the benchmarking test for the degree and the presence of CIT incentives, in order to test the robustness of the benchmarking regression. The results shown in Table 6 indicate that CIT incentives still have a significant promotion effect on firms’ ESG ratings considering the lag effect. In addition, the promotion effect is characterized by first increasing and then weakening, indicating that CIT incentives can be reflected quickly in corporate sustainability. Overall, the conclusions of the benchmark regression remain robust when considering the time lag scenario.

4.3.4. Heckman Two-Stage Model Test

As a preferential policy enjoyed by firms that meet the tax relief criteria, CIT incentives can alter corporate strategic choices in response to changes in the policy environment, and thus there also remains a sample self-selection problem. This study adopts the Heckman two-stage regression model to further test the sample selection bias. In the first stage, the probability of firms receiving CIT incentives is estimated, and the inverse Mills ratio (IMR1 and IMR2) is obtained based on controlling the control variables of the benchmarking regression model. We use the first and second calculations of CIT incentives as presented in Section 3.3.2 (Incentive 1 and Incentive 3), and the results are shown in columns (1) and (3) of Table 7. In the second stage, the inverse Mills ratio is added as a control variable to model (1) for regression, and the results are shown in columns (2) and (4) of Table 7. After introducing IMR1 and IMR2, the regression coefficients of CIT incentive and ESG ratings are still significantly positive at the 1% level, with coefficients of 0.092 and 0.110, indicating that CIT incentives can enhance corporate ESG performance after considering the sample self-selection problem; thus, the robustness of our findings is confirmed.

4.3.5. Instrumental Variable Approach

To better identify the policy effects, we use the instrumental variable (IV) approach to avoid the bias from endogeneity. Enterprises with better ESG performance may have more disposable capital to obtain tax incentives, thus leading to a two-way causality problem. We use the statutory corporate tax rate for industry as an instrumental variable. The rationale is as follows: enterprises in the same industry share strong similarities in production and operations, and industry attributes influence how enterprises in the industry utilize tax policies. However, the statutory corporate tax rate in the industry is unrelated to the ESG performance of individual enterprises. Therefore, we employ the two-stage least-squares (2SLS) method to test the instrumental variable IV. In Table 8, columns (1) and (3) present the first-stage regression results, while columns (2) and (4) show the second-stage regression results. The results indicate that, after considering the instrumental variable, tax incentives are significantly and positively correlated with corporate ESG performance, which is consistent with the above conclusions.

4.4. Interaction Effect of CIT Incentive and EPT Levy Policies

In terms of the corporate environmental monitoring mechanism, the implementation of the Environmental Protection Tax Law in 2018 marks the rise of environmental protection from the administrative to the legal levels, which is an important factor for the ESG activities of Chinese companies. This judgment is tested by regressing model (2). To mitigate the multicollinearity problem caused by the introduction of interaction terms, the relevant variables are centered in this study. The interaction effects of the CIT incentive and EPT levy policies are tested without considering heavily polluting companies, and the results are shown in Table 9. Columns (1) and (2) show that the coefficients of the interaction terms POST*Incentive 1 and POST*Incentive 2 are 0.042 and 0.191, respectively, and the interaction effect between the two is significant at the 1% level, suggesting that CIT incentives and EPT levies significantly contribute to firms’ ESG behavior. Under the cost pressure caused by the EPT levy, firms may place greater emphasis on utilizing the positive effects of CIT incentives to enhance sustainability, thus improving their ESG behavior. Additionally, the samples are divided into heavily polluting and non-heavily polluting firms and tested using model (3) to further ensure the robustness of the findings. Columns (3) and (4) of Table 8 show the results, in which the coefficient of the interaction term (POST*Treat*Incentive 1) in column (4) is 0.114, and the interaction effect is significant at the 5% level; however, the interaction term (POST*Treat*Incentive 2) in column (3) is not significant. This shows that the CIT incentive and EPT levy form a synergistic effect to enhance firms’ ESG behavior when both the “time effect” and the “policy effect” are taken into account, thus validating Hypothesis 2. Given that the difference-in-differences (DlD) framework can be sensitive to specific event windows (e.g., three years before and after an event), we further selected samples from 2015 to 2022 for verification. The test results in columns (5)–(8) remain consistent with the above findings and have larger coefficients, supporting Hypothesis 2.

5. Additional Tests

5.1. Heterogeneity Tests

5.1.1. Under Financing Constraints: CIT Incentives on SOEs and Non-SOEs

Financing constraints are the main reason why firms are less likely to invest in ESG activities [57]. Therefore, the type of firm is selected to measure whether they are in financing constraints. In the Chinese mainland, state-owned enterprises (SOEs) more easily obtain financing than non-SOEs due to their political connections and information channels [58]. The results are shown in Table 10. The promotion effects of the degree and presence of CIT incentive on SOEs’ ESG ratings are 2.2% and 5.3%, respectively, and they are significant at the 5% level, while the incentives for non-SOEs are more significant (14.6% and 35.7%) and significant at the 1% level. This suggests that the cash flow effect released by CIT incentives is more effectual to non-SOEs, and it also indicates the more serious financing problems faced by non-SOEs.

5.1.2. Inside the Largest Manufacturing Nation: CIT Incentives in Manufacturing and Non-Manufacturing Firms

The current rise in environmental costs has become one of the main factors increasing the burden of manufacturing firms; thus, finding a way to promote their sustainable transformation through non-environmental tax policies has become an urgent task. We further explore this issue with a focus on manufacturing companies. Columns (1) and (2) of Table 11 show that, at the 1% significant level, the coefficient of the degree of CIT incentive (Incentive 1) on non-manufacturing companies’ ESG ratings is 0.057; for manufacturing enterprises, it is 0.135, indicating that manufacturing enterprises are more sensitive to the promotion effect of CIT incentives. The coefficient of the presence of CIT incentives (Incentive 2) on non-manufacturing enterprises is 0.144, and, on manufacturing enterprises, it is 0.261, which also supports the conclusion.

5.2. Mechanism Tests

The CIT incentive’s promoting impact on corporate ESG performance has been tested in multiple ways, and the reliability of the results has been verified through a series of robustness tests. A related new question is raised: how does the CIT incentive influence corporate ESG performance? And what is the specific transmission mechanism? According to the theoretical analysis in Section 2.2, the main driving factors are the cash flow effect and agency cost effect. Consequently, the following section mainly discusses the influence channels of CIT incentive on corporate ESG performance, using the following intermediary model to test its internal mechanism.

5.2.1. Cash Flow Effect

As an innovation process of efficiency improvement and management optimization, long-term and continuous ESG activities require firms to have sufficient cash flow as a guarantee. To test this mechanism, the cash flow status and profitability of firms is examined, and the results are shown in Table 11. Column (1) indicates that CIT incentives can indeed reduce the pressure of the corporate tax burden and increase companies’ disposable cash flow. The coefficients in column (4) of Table 12 demonstrate that CIT cuts significantly contribute to firms’ enhancement of profitability. Column (2) presents the impact of CIT incentives on corporate ESG performance based on model (1). When adding mechanism variables into model (1), both regression coefficients shown in columns (3) and (5) (0.162 and 0.211 respectively) are lower than the value of 0.211 in column (2), supporting the existence of a mediating effect. This is precisely because CIT incentives facilitate the improvement of corporate productivity and profitability; thus, the expectation of a stable cash flow, a sufficient material basis for ESG activities and sustainable development, becomes a reality. This verifies Hypothesis 1, i.e., that there is a cash flow effect of CIT incentives.

5.2.2. Agency Cost Effect: The Governance Effect of CIT Incentives

Whether CIT incentives have an agency cost effect is reflected in their ability to alleviate agency problems such as opportunism and moral hazards of companies, which is further tested in this study. We conduct one test with the management cost ratio (MCR) as the mechanism variable, and another test based on the degree of separation between ownership and control of the major shareholders (Power), and the results are shown in columns (6) and (8) of Table 12. These results indicate that CIT incentives are significantly negatively correlated with both MCR and Power and are significant at the 1% level. This proves that CIT incentives can alleviate corporate agency problems by reducing the MCR and Power. When adding the agency mechanism variable into model (1), the correlation regression results are shown in columns (7) and (9) of Table 11, with the coefficients of 0.204 and 0.205, which are lower than the coefficient of 0.211 in column (2), thereby supporting the existence of a mediating effect. The results indicate that CIT incentives increase corporate ESG performance by reducing agency costs and enhancing the governance effect, thus supporting Hypothesis 1.

5.3. Environmental Tests

Previous studies have shown that tax incentives have a promoting effect on the environmental, social, and governance (ESG) aspects of enterprises, and this effect is more significant at the social level, and the governance level to a lesser extent. On the one hand, this finding indicates that the ESG of Chinese enterprises is the inheritor of the concept of corporate social responsibility (CSR) and has not broken through the existing CSR framework. On the other hand, the mechanism test shows that, although tax incentives have released cash flows and have shown a governance effect on enterprises, the environmental problems of Chinese enterprises are still relatively serious. Therefore, it is necessary to further explore whether the substantive environmental problems faced by Chinese enterprises are affected by tax incentives. This study further conducts tests from the positive promotion indicators and negative environmental indicators at the environmental level, namely, green technology innovation, environmental violation incidents, and violations of environmental information disclosure. The results are shown in Table 13.

The positive environmental indicators in columns (1) and (2) show that the tax reduction incentives of non-environmental taxes have a significant promoting effect on the green technology innovation of enterprises, which are significant at the 1% and 10% levels, respectively. However, the tax reduction incentives at the non-environmental level show an insignificant inhibitory effect on the negative environmental indicators of enterprises. The test results shown in columns (3) and (4) indicate that tax incentives have an inhibitory effect on environmental violation incidents, but it is not significant. From columns (5) and (6), it can be deduced that tax reduction incentives can significantly reduce the problem of environmental information violations at the 5% level, which indicates that the cash flows and governance effects brought about by the tax incentives of non-environmental taxes have a promoting effect on the environmental aspects of enterprises, but mainly in terms of promoting the positive green technology innovation of enterprises. In a word, Chinese enterprises are still experiencing relatively serious negative impacts at the environmental level, and the green transformation of Chinese enterprises is often goal-oriented, manifesting as short-term compliance adjustments (such as “perfunctory” environmental protection investments) rather than systematic environmental strategies.

6. Discussion

This study aims to reveal the unexpected effects of how CIT incentives improved corporate ESG performance after the 2008 CIT reform in the Chinese mainland, as well as the interactive effect between the policies of the CIT incentive and EPT levy within China’s green tax system. The validity of the results is confirmed by Heckman two-stage model tests, heterogeneity tests, and other additional robustness checks. As far as the authors know, this study provides some of the most comprehensive empirical evidence on the impact of CIT incentives on ESG ratings, and it includes micro-mechanism evidence of the cash flow and corporate governance effects upon the influence of CIT incentives on corporate ESG ratings within the Chinese context. Specifically, it takes the lead in revealing the interplay between environmental and non-environmental tax policies and their effect on corporate ESG performance.

Given that existing studies on corporate sustainability and ESG performance mainly focus on environmental taxation [59,60], this study tries to present new perspectives and findings by expanding its research target to more commonly used tax incentive policies and their policy combinations, aiming to provide an academic reference for the Chinese enterprises and government to promote corporate ESG development in the long run. First, this study finds in its mechanism analysis that CIT incentives promote firms’ ESG performance through two main channels. Previous studies have mainly emphasized the role of the CIT incentive on corporate financing constraints, R&D investment, energy efficiency, productivity, and other financial indexes [21,22]. Given that the CIT reform provides more unexpected opportunities with governance effect on firms, we extend our research to the influence of CIT incentives on corporate ESG performance by increasing cash flow and reducing agency costs, with a more significant effect on the social and governance dimensions.

On this basis, this study analyzes the effect of CIT and EPT policy interactions on boosting corporate ESG performance. Existing studies mainly focus on the interaction effects between different environmental policies [30,31]. However, in the context of China’s large-scale tax reduction and fee reduction, it is necessary to consider the interaction effects of CIT incentives, one of China’s most important tax reduction policies, and environmental taxes. In this study, the interaction effect of CIT incentives and the EPT levy significantly boost corporate ESG performance by increasing the scale of investment, directly improving productivity, reducing the implementation cost of business externalities, and generating additional ESG performance.

In addition, the heterogeneity analysis shows that the CIT incentive and its interaction effect with EPT are more significant in manufacturing firms and non-SOEs with severe financing constraints. Given that China’s manufacturing industry is facing a full-scale green transformation, and the current rise in environmental costs has become one of the main factors increasing the burden on manufacturing firms and non-SOEs, finding a way to promote their sustainable transformation through non-environmental tax policies has become highly urgent. Enterprises can increase their effective management of internal resources under low tax rates and the regulation of environmental taxes, thereby improving corporate ESG performance.

7. Conclusions and Policy Proposals

Using data from Chinese non-financial A-share listed firms from 2009 to 2022, this study reveals the micro-mechanisms whereby CIT incentives promote corporate ESG behavior. It also explores the cash flow and agency cost effects of the CIT incentive on corporate sustainability. The incentives of non-environmental tax policies, represented by the CIT incentive in this study, have more significant effects on the social and governance aspects of ESG activities. In addition, there is an interaction effect for non-environmental and environmental tax policies (represented by the CIT incentive and EPT levy, respectively), which has a better promotion effect on corporate ESG ratings. Heterogeneity tests show that the CIT incentive and its interaction effect with the EPT levy are more significant in manufacturing firms and non-state-owned firms with severe financing constraints. Environmental tests show that CIT incentive policies have a positive effect on green technological innovation, and Chinese enterprises are still experiencing relatively serious negative impacts. Overall, this study offers new insights into enhancing corporate ESG behavior from the perspective of tax policies, with the aim of promoting sustainable business development.

Given that developing countries often face similar institutional capacity and tax compliance environments, this study can provide reference opinions for China and other developing countries to improve the corporate income tax system in two main ways. The first is the enrichment of tax incentive measures related to CIT. The CIT reform should focus on tax reduction to moderately reduce the tax burden of firms, which can not only maintain tax competitiveness but also lower the threshold of preferential policies for firms. To encourage environmental protection activities, consideration can be given to granting income tax credit preferences to firms for the amount of investment in renewable resources and to enterprises engaged in the production of energy-saving and eco-friendly products. We suggest leveraging differentiated tax incentive tools to establish an “environmental-financial” dual-dimension incentive framework, which includes tax preferences graded by environmental performance and links corporate income tax (CIT) reductions to substantive environmental indicators (e.g., carbon intensity, pollutant emission reductions), rather than using merely “whether an enterprise belongs to an environmental protection industry” as the threshold.

The second way is to optimize the mix of tax policies that can promote corporate ESG performance, continue to improve the green tax system in general, and facilitate the green transformation of non-environmental taxes, so that multiple tax policies can form a synergy to promote sustainability. To reduce the financing and innovation costs of firms (especially manufacturing and non-state-owned enterprises), more efforts should be made to provide incentive measures in non-environmental tax policies. It is necessary to give full play to the role of CIT as the core fulcrum of incentive tax policies, to form a combination of preferential policies with environmental taxes and enhance their coordination, and to improve the applicability of preferential tax policies to reduce the compliance cost of firms in enjoying them.

At the corporate level, ESG activities need to be viewed as an important way to enhance enterprise value. To this end, firms should first improve their internal environment and make full use of their redundant financial resources to promote ESG-related activities such as personnel training, technological innovation, and governance structure improvements. In addition, companies (particularly those in the manufacturing industry) need to pay close attention to and respond rapidly to government policies such as tax and fee reductions, taking advantage of the cash flow generated by tax incentives to improve their sustainability, especially in terms of social and governance aspects. Meanwhile, as China’s tax policy is an important factor for foreign investment, which mainly affects the cost and benefit of investment through measures such as CIT rates, pre-tax deduction, and tax incentives, this study can also provide certain reference value for foreign enterprises in the Chinese market from a tax perspective.

This study is not without its limitations. There are several ESG rating organizations and many differences in the rating systems of each organization, which may mean that ESG rating data from a single organization struggle to comprehensively reflect a company’s true ESG performance, leading to possible limitations in our calculations. This study only discusses the corporate income tax, which is the most important direct tax in the Chinese mainland, and subsequent studies need to be conducted to discuss the role of other non-environmental taxes and their interaction with environmental taxes, in order to fully portray the impact of China’s tax system on corporate ESG performance.

Author Contributions

Conceptualization, W.W., F.M. and S.G.; methodology, W.W. and F.M.; software, W.W. and S.G.; validation, W.W., F.M. and S.G.; formal analysis, W.W. and S.G.; investigation, W.W. and S.G.; resources, W.W.; data curation, W.W. and F.M.; writing—original draft preparation, W.W.; writing—review and editing, F.M. and S.G.; visualization, W.W.; supervision, F.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figure and Tables

Figure 1 Research framework.

Table 1

Variable definitions.

Variables	Symbol	Definition
Explained Variables	ESG	ESG rating indicators by the Sino-Securities Index
	ESG_E_R	Scores of ESG-E indicators
	ESG_S_R	Scores of ESG-S indicators
	ESG_G_R	Scores of ESG-G indicators
Explanatory Variables	Incentive 1	Natural logarithm of the CIT incentive a corporation enjoyed
	Incentive 2	Dummy variable, 1 if Incentive1 is below the statutory income tax rate (25%), 0 otherwise
Moderator variables	Treat	1 if the firm is a heavy polluter, 0 otherwise.
	Post	1 after the implementation of EPT in 2018, 0 before 2018.
Mechanism Variables	Cash	Cash to total assets ratio
	ROA	Return on net assets
	MCR	Ratio of a company’s management expenses to its operating revenue
	Power	Managerial power is calculated through principal component regression using five indicators: CEO duality, board size, proportion of internal directors, equity dispersion, and management shareholding.
Control Variables	Size	Natural logarithm of an enterprise’s total assets
	SOE	1 if an enterprise is a state-owned one, 0 otherwise.
	Lev	Total company liabilities divided by total company assets
	Liq	Current ratio
	FinR	Financial leverage ratios
	EFF	Turnover rate of accounts receivable
	BM	Book value divided by market capitalization
	TTH	Percentage of shares held by the top ten shareholders
	Year	Year-fixed effects
	Industry	Industry-fixed effects

Table 2

Descriptive statistics.

Variables	Mean	SD	Min.	P50	Max.
ESG	4.210	1.064	1.000	4.000	8.000
ESG_E_R	60.261	7.819	29.460	59.970	95.160
ESG_S_R	74.184	10.534	0.000	74.665	100.000
ESG_G_R	79.906	6.782	23.870	81.200	99.000
Incentive 1	16.753	1.530	6.499	16.707	22.896
Incentive 2	0.797	0.402	0.000	1.000	1.000
Cash	0.359	0.639	0.001	0.204	49.969
ROA	0.053	0.044	−0.074	0.044	0.786
MCR	0.084	0.084	0.001	0.068	7.284
Power	4.770	7.550	0.000	0.000	60.323
Size	22.026	1.369	0.000	21.847	28.636
Liq	2.818	4.120	0.038	1.755	190.869
Lev	0.406	0.202	0.008	0.398	1.136
EFF	2.057	1.466	−1.184	1.711	18.799
SOE	0.362	0.481	0.000	0.000	1.000
FinR	0.195	0.459	−8.805	0.068	7.784
BM	0.619	0.247	0.011	0.618	1.559
TTH	0.170	0.118	0.001	0.142	0.810

Notes: N = 27,494. This table presents the descriptive statistics of the full samples. It provides the mean value, standard deviation (SD), minimum value (Min.), median value (P50), and maximum value (Max.) of the variables.

Table 3

Benchmarking regression results.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	ESG	ESG_E_R	ESG_S_R	ESG_G_R	ESG	ESG_E_R	ESG_S_R	ESG_G_R
Incentive 1	0.108 ***	0.480 ***	0.655 ***	0.508 ***
	(16.823)	(9.901)	(11.398)	(13.267)
Incentive 2					0.211 ***	1.044 ***	1.105 ***	1.059 ***
					(12.433)	(8.291)	(7.161)	(10.258)
Size	0.052 ***	0.635 ***	0.603 ***	−0.147 ***	0.168 ***	1.135 ***	1.311 ***	0.409 ***
	(5.529)	(8.932)	(7.154)	(−2.612)	(26.051)	(23.840)	(22.471)	(10.477)
Liq	0.001	−0.073 ***	−0.041 **	0.055 ***	0.002	−0.080 ***	−0.031 *	0.074 ***
	(0.122)	(−5.396)	(−2.569)	(5.166)	(1.252)	(−6.276)	(−1.957)	(6.969)
Lev	−0.674 ***	1.704 ***	0.242	−8.528 ***	−0.752 ***	0.722 **	0.114	−8.826 ***
	(−12.716)	(4.259)	(0.511)	(−26.975)	(−16.124)	(2.089)	(0.268)	(−31.167)
EFF	−0.002	−0.226 ***	−0.210 ***	0.197 ***	0.004	−0.230 ***	−0.096 **	0.194 ***
	(−0.284)	(−5.340)	(−4.187)	(5.893)	(0.764)	(−6.602)	(−2.262)	(6.809)
SOE	0.042 **	−0.373 ***	−1.511 ***	1.607 ***	0.072 ***	−0.156	−1.576 ***	1.874 ***
	(2.492)	(−2.967)	(−10.127)	(16.165)	(4.937)	(−1.435)	(−11.829)	(21.038)
FinR	−0.160 ***	0.298 *	−0.617 ***	−1.524 ***	−0.216 ***	−0.095	−1.168 ***	−1.593 ***
	(−7.953)	(1.957)	(−3.418)	(−12.678)	(−13.904)	(−0.825)	(−8.295)	(−16.921)
BM	0.365 ***	4.193 ***	−0.459	2.061 ***	0.277 ***	3.623 ***	−0.924 ***	1.775 ***
	(10.182)	(15.506)	(−1.432)	(9.645)	(8.597)	(15.160)	(−3.156)	(9.062)
TTH	0.299 ***	−0.932 **	−1.706 ***	4.700 ***	0.339 ***	−0.175	−1.780 ***	4.728 ***
	(4.921)	(−2.032)	(−3.134)	(12.960)	(6.253)	(−0.435)	(−3.616)	(14.359)
_cons	1.153 ***	34.547 ***	43.902 ***	79.013 ***	0.184	30.854 ***	38.476 ***	73.931 ***
	(5.602)	(22.219)	(23.805)	(64.302)	(1.018)	(23.018)	(23.421)	(67.295)
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	22,184	22,184	22,184	22,184	27,494	27,494	27,494	27,494
Adj. R2	0.105	0.107	0.230	0.198	0.120	0.113	0.262	0.196

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 4

Results after replacing the measurement model and explanatory variables.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Heteroskedasticity Robust Standard Errors		Cluster Robust Standard Errors		Replacing Explanatory Variables
Incentive 1	0.050 ***		0.108 ***
	(7.517)		(11.513)
Incentive 2		0.083 ***		0.211 ***
		(5.107)		(8.687)
Incentive 3					0.097 ***
					(12.552)
Incentive 4						0.074 ***
						(9.960)
Incentive 5							0.077 ***
							(10.148)
_cons	2.621 ***	2.678 ***	1.153 ***	0.184	0.083	0.021	−0.099
	(8.675)	(10.077)	(3.965)	(0.649)	(0.400)	(0.098)	(−0.462)
control	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	22,184	27,494	22,184	27,494	16,545	15,634	15,215
Adj. R2	0.032	0.028	0.105	0.120	0.130	0.121	0.125

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01

Table 5

Results after eliminating special samples.

	(1)	(2)	(3)	(4)	(5)	(6)
	Eliminating High-Tech Companies	Eliminating Heavily Polluting Companies	Eliminating High-Performance Companies	Eliminating High-Tech Companies	Eliminating Heavily Polluting Companies	Eliminating High-Performance Companies
Incentive 1	0.060 ***	0.100 ***	0.064 ***
	(6.131)	(12.532)	(5.845)
Incentive 2				0.147 ***	0.213 ***	0.143 ***
				(6.588)	(10.145)	(6.693)
_cons	−0.220	0.909 ***	−0.016	−0.857 ***	0.025	−0.740 ***
	(−0.733)	(3.685)	(−0.048)	(−3.449)	(0.116)	(−2.716)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	7676	13,385	7874	11,086	16,888	11,700
Adj. R2	0.173	0.119	0.114	0.194	0.143	0.150

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 6

Results of lag tests.

	(1)	(2)	(3)	(4)	(5)	(6)
	Lags by One Period	Lags by Two Periods	Lags by Three Periods	Lags by One Period	Lags by Two Periods	Lags by Three Periods
Incentive 1	0.104 ***	0.109 ***	0.098 ***
	(14.509)	(13.825)	(11.066)
Incentive 2				0.176 ***	0.186 ***	0.147 ***
				(9.253)	(9.026)	(6.469)
_cons	0.207	−0.096	−0.498 *	−0.587 ***	−0.892 ***	−1.179 ***
	(0.921)	(−0.387)	(−1.808)	(−2.970)	(−4.094)	(−4.905)
control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	17,695	14,959	12,534	21,908	18,783	15,812
Adj. R2	0.109	0.113	0.112	0.124	0.131	0.128

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 7

Results of the Heckman two-stage model test.

	(1)	(2)	(3)	(4)
	IMR1	ESG	IMR2	ESG
Incentive 1	4.894 ***	0.092 ***
	(41.886)	(11.603)
Incentive 3			4.842 ***	0.110 ***
			(43.879)	(11.105)
IMR1		−0.013
		(−0.489)
IMR2				−0.017 *
				(−1.731)
_cons	−81.145 ***	0.219	−78.996 ***	0.328
	(−42.138)	(0.984)	(−43.951)	(1.460)
Control	Yes	Yes	Yes	Yes
Year FE	No	Yes	No	Yes
Industry FE	No	Yes	No	Yes
N	14,728	14,728	16,545	14,465
Adj. R2		0.113		0.114
Pseudo R2	0.846		0.848

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 8

Results of the instrumental variable test.

	(1)	(2)	(3)	(4)
	First Stage	Second Stage	First Stage	Second Stage
IV	3.203 ***		1.498 ***
	(0.210)		(0.063)
Incentive 1		0.113 *
		(0.065)
Incentive 2				0.392 ***
				(0.120)
_cons	−4.482 ***	1.986 ***	0.423 ***	0.724 ***
	(−0.235)	(0.315)	(0.072)	(0.225)
Control	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	14,662	14,662	18,467	18,467
Adj. R2	0.520	0.060	0.176	0.062

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 9

Results of the interaction effect of the CIT incentive and EPT levy policies.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Incentive 1	0.108 ***		0.108 ***		0.127 ***		0.133 ***
	(16.901)		(16.881)		(14.899)		(15.960)
Post	−0.355 **	−0.276 *			−0.043	0.066
	(−2.273)	(−1.932)			(−0.331)	(0.535)
POST*Incentive 1	0.042 ***				0.048 ***
	(4.627)				(4.114)
Incentive 2		0.223 ***		0.213 ***		0.277 ***		0.288 ***
		(13.040)		(12.529)		(11.681)		(12.271)
POST*Incentive 2		0.191 ***				0.193 ***
		(6.208)				(4.490)
POST*Treat			−0.049 **	−0.053 ***			−0.044	−0.058 **
			(−2.315)	(−2.705)			(−1.533)	(−2.191)
POST*Treat*Incentive 1			0.011				0.024
			(0.927)				(1.496)
POST*Treat*Incentive 2				0.114 **				0.166 **
				(2.459)				(2.494)
_cons	1.209 ***	0.1566	1.141 ***	0.156	1.178 ***	−0.154	1.008 ***	−0.185
	(5.866)	(0.866)	(5.537)	(0.863)	(4.388)	(−0.676)	(3.791)	(−0.810)
control	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	22,184	27,494	22,184	27,494	14,804	17,690	14,804	17,690
Adj. R2	0.106	0.121	0.105	0.120	0.098	0.104	0.097	0.104

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 10

Results of the CIT incentive effect test of SOEs and non-SOEs.

	(1)	(2)	(3)	(4)
	SOE		Non-SOE
Incentive 1	0.022 **		0.146 ***
	(2.193)		(17.944)
Incentive 2		0.053 **		0.357 ***
		(2.295)		(14.718)
_cons	−2.082 ***	−2.219 ***	2.776 ***	1.802 ***
	(−6.806)	(−8.527)	(9.483)	(7.010)
control	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	6955	9869	15,229	17,625
Adj. R2	0.215	0.226	0.081	0.087

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 11

Results of the CIT incentive effect test of manufacturing/non-manufacturing firms.

	(1)	(2)	(3)	(4)
	Manufacturing Firms		Non-Manufacturing Firms
Incentive 1	0.135 ***		0.057 ***
	(16.734)		(5.334)
Incentive 2		0.261 ***		0.144 ***
		(11.076)		(5.910)
_cons	2.252 ***	1.236 ***	−0.198	−0.720 ***
	(10.810)	(6.317)	(−0.635)	(−2.792)
Control variables	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes
N	15,533	17,848	6651	9646
Adj. R2	0.072	0.069	0.190	0.216

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 12

Results of mechanism tests (cash flow and agency cost effects).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	Cash	ESG	ESG	ROA	ESG	MCR	ESG	Power	ESG
Incentive 2	0.020 **	0.211 ***	0.211 ***	0.017 ***	0.162 ***	−0.008 ***	0.204 ***	−0.365 ***	0.205 ***
	(2.186)	(12.433)	(12.400)	(28.742)	(9.442)	(−7.512)	(12.026)	(−2.845)	(11.818)
Cash			0.028 **
			(2.562)
ROA					2.879 ***
					(16.796)
MCR							−0.891 ***
							(−9.120)
Power									−0.004 ***
									(−4.543)
_cons	0.927 ***	0.184	0.158	−0.077 ***	0.406 **	0.389 ***	0.531 ***	−5.312 ***	0.158
	(9.373)	(1.018)	(0.872)	(−12.120)	(2.246)	(34.819)	(2.873)	(−3.863)	(0.850)
control	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	27,494	27,494	27,494	27,494	27,494	27,494	27,494	26,452	26,452
Adj. R2	0.268	0.120	0.120	0.359	0.129	0.257	0.123	0.055	0.120

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

Table 13

Results of environmental tests.

	(1)	(2)	(3)	(4)	(5)	(6)
	Green Technology Innovation		Environmental Violation		Environmental Information Disclosure Violation
Incentive 1	1.746 ***		−0.000		−0.004 **
	(11.335)		(−0.492)		(−2.120)
Incentive 2		0.837 *		−0.002		−0.005
		(1.858)		(−0.787)		(−0.773)
_cons	−34.022 ***	−7.060 ***	−0.007	−0.008	0.089 *	0.045
	(−9.142)	(−2.918)	(−0.278)	(−0.457)	(1.885)	(1.243)
Control	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
N	5031	6652	5031	6652	5031	6652
Adj. R2	0.045	0.020	0.010	0.009	0.025	0.018

z statistics in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

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