1. Introduction
On 1 January 2018, the environmental protection tax (EPT) law was enacted, marking a significant policy shift in China’s approach to environmental governance. This transition from a conventional sewage charge system to a more rigorous environmental tax framework aims to curb local government overreach, enhance corporate environmental accountability, and encourage the adoption of sustainable practices [1,2]. As China transitions from a quantity-driven growth model to one emphasizing quality and sustainability, fostering an ecological civilization becomes crucial [3,4]. This strategic pivot is essential for transforming China into having an eco-friendly economy and addressing the persistent ecological and environmental challenges that have accompanied the nation’s rapid economic and social development [5,6].
The concept of total factor productivity (TFP) is crucial in understanding the efficiency and performance of an economy. TFP measures how efficiently inputs are utilized in the production process, beyond the contributions of labor and capital alone [7]. Enhancing TFP is essential for sustainable economic growth, especially within ecological and environmental constraints [8]. With the implementation of the EPT in China, there is an opportunity to analyze its impact on TFP and its role in driving improvements in green technological innovation and alleviating financing constraints for businesses [9]. Our study aims to investigate whether the EPT has contributed to the improvement of TFP. Specifically, we explore the mechanisms through which the EPT enhances TFP, focusing on two mediating variables: the promotion of green technological innovation and the alleviation of financing constraints. Additionally, we examine the heterogeneity of the EPT’s impact on TFP, analyzing how the effects vary based on ownership structure and levels of financing constraints.
The debate on the impact of environmental taxes on enterprise development and economic growth is central to our investigation. Critics like Barbera and McConnell [10] have traditionally viewed environmental taxes as a potential drag on productivity due to their focus on pollution control. However, proponents of the Porter Hypothesis, such as Porter and Van Der Linde [11], argue that strategically designed environmental regulations can fuel innovation and enhance competitiveness, ultimately leading to a green transformation that boosts TFP. Recent studies have explored various factors influencing TFP. For instance, Shah et al. [12] investigated the impact of climate change and production technology heterogeneity on China’s agricultural TFP and production efficiency, concluding that technology is the main determinant of TFP change. Furthermore, environmental policies have been shown to incentivize technological innovation, which in turn boosts TFP. He and Jing [13] found that the EPT in China significantly increased the TFP of heavily polluting firms by promoting green technological innovation and optimizing capital allocation. This aligns with the Porter Hypothesis, suggesting that well-designed environmental regulations can drive technological progress and improve productivity. Additionally, the role of financial constraints in influencing TFP has been highlighted in the recent literature. Studies indicate that alleviating financing constraints can lead to significant improvements in TFP. For example, Sun and Zhang [9] demonstrated that the EPT helped firms overcome financial barriers, thereby facilitating investments in green technologies and enhancing overall productivity. This suggests that environmental taxes not only incentivize innovation but also play a critical role in easing financial constraints, further promoting TFP growth.
This study extends the debate by empirically examining the effects of the 2018 EPT reform in China through a quasi-natural experiment, focusing on its impact on TFP in heavily polluted industries. Using a difference-in-differences approach, we rigorously evaluate the EPT’s effect on TFP. Our methodology includes comprehensive data collection, careful variable selection, and robust analytical model construction. Through detailed empirical analysis with robustness checks and assessments of heterogeneity, we reveal how green innovation and financial constraints mediate this impact.
Our findings introduce evidence that the EPT has fostered the growth of TFP. We delineate the mechanisms behind this achievement by promoting green technological innovation and alleviating financing constraints, providing new insights into the interaction between environmental policies and economic development metrics. The literature on the topic appears to have an underrepresented discussion on financing constraints as a conduit for TFP enhancement, signaling a pivotal area of innovation in this research. Furthermore, the study dissects the heterogeneous impacts of tax implementation, revealing that the facilitative effects of the EPT on TFP are significantly present in state-owned enterprises and those with less-severe financing constraints but not as pronounced in non-state-owned enterprises and other types of businesses with stricter financing constraints. This selective efficacy underscores the nuanced understanding that our research contributes to policy implementation and its variably scaled economic consequences.
The innovative elements of our research can be succinctly encapsulated as follows: (1) Challenging prevailing views: We present new evidence countering the common belief that environmental taxes hinder TFP, showing a positive impact of the EPT. (2) Identifying key mechanisms: Our study uniquely demonstrates how the EPT boosts TFP through promoting green technological innovation and easing financing constraints. (3) Analyzing enterprise impact: We reveal the differential effects of the tax based on enterprise type and levels of financing constraints, highlighting significant benefits for state-owned enterprises and those with fewer financing constraints. This contributes valuable insights to environmental economics and policy making.
The structure of this paper is organized as follows: First, we present a focused literature review to establish a theoretical base and highlight the research gaps our work addresses. This is followed by our research hypotheses. Next, we detail our research methodology, including data collection, variable selection, and analytical model construction. The subsequent sections present our empirical results and discussion. Finally, we provide policy recommendations and conclude the study.
2. Literature and Hypothesis
2.1. Environmental Tax Reform and TFP Enhancement
In the absence of clear property rights, the ecological environment, considered a public good, does not incentivize enterprises to manage pollution effectively [14]. The existence of serious asymmetry between environmental governance costs and benefits makes enterprises reluctant to actively spend money on pollution governance [15]. The EPT reform in China represents a pivotal shift towards using fiscal policies to encourage environmental sustainability while simultaneously promoting economic productivity. This reform, transitioning from a conventional pollution fee system to a more structured environmental tax framework, aims to internalize the environmental costs of production, incentivizing enterprises towards cleaner and more efficient production methods. The design principle of the EPT law is to promote energy saving and emission reduction by enterprises and to force them to green transformation and upgrading [16]. The implementation of the EPT law is stricter than the sewage charge levy [17].
The literature provides substantial evidence supporting the positive impact of environmental taxes on TFP. For instance, Cai [3] and Li et al. [4] argue that transitioning towards a quality- and sustainability-focused growth model is crucial for China’s long-term economic development. In this context, Wang and Chen [5] highlight the role of fiscal instruments, such as environmental taxes, in driving this transition by addressing ecological challenges without compromising economic growth. Further, Li et al. [1] and Wen et al. [2] provide empirical evidence that the implementation of environmental taxes has led to significant improvements in corporate environmental accountability and sustainable practices.
Therefore, Hypothesis 1 is as follows:
The EPT reform plays a direct role in bolstering TFP among enterprises.
2.2. Heterogeneity in the Impact of the Environmental Tax Reform on TFP
The impact of the EPT reform on TFP exhibits significant heterogeneity across different types of enterprises. This variability is influenced by enterprise characteristics such as ownership structure and financing constraints. The literature suggests that state-owned enterprises (SOEs) and firms with less-severe financing constraints are more responsive to environmental taxes, benefiting more significantly from these policies in terms of TFP improvement. Ding and Petrovskaya [18] and Tingbani et al. [19] note that SOEs often receive preferential treatment in policy implementation, which can enhance their ability to invest in green technologies and improve productivity. Similarly, Zhang and Vigne [20] argue that firms facing fewer financing constraints are better positioned to respond to environmental regulations by investing in innovation and sustainable practices. These findings are supported by Sun and Zhang [21], who demonstrate the critical role of technological innovation and resource allocation in achieving TFP growth under environmental tax policies.
Therefore, Hypothesis 2 is as follows:
The influence of the EPT reform on TFP varies significantly among different enterprises, with state-owned enterprises and those facing fewer financing constraints experiencing a more pronounced enhancement in productivity.
2.3. Green Innovation as a Mediating Mechanism for TFP Enhancement
The EPT reform is designed to stimulate sustainable business practices, with a significant focus on fostering green innovation. This policy encourages enterprises to engage in environmentally friendly technological upgrades, which in turn serve as a crucial mediator enhancing TFP. The literature strongly supports the notion that green innovation plays a pivotal role in mediating the relationship between the EPT reforms and TFP improvement. For instance, Porter and Van Der Linde [11] contend that suitable environmental policies can stimulate innovation, leading to improved productivity and competitive advantage. Further, Liang et al. [22] highlight that the EPT laws enhance enterprises’ commitment to green technological innovation, thereby improving their production efficiency and TFP. This relationship is substantiated by Wu and Tal [23], who observe that technological innovation to phase out energy-consuming and inefficient equipment leads to significant improvements in enterprise productivity.
Therefore, Hypothesis 3 is as follows:
The EPT reform indirectly enhances the TFP of enterprises by incentivizing green innovation activities.
2.4. Financing Constraints as a Mediating Mechanism between the Environmental Tax Reform and TFP
Financing constraints significantly impact enterprises’ ability to innovate and comply with environmental regulations. The implementation of the EPT reform has been found to alleviate these constraints, thereby facilitating greater investment in R&D and green technologies. This relaxation of financing constraints acts as a mediator, enabling enterprises to improve their TFP through increased innovation. Studies such as those of Xiong et al. [24] and Hong et al. [25] demonstrate that easing financing barriers is essential for firms to pursue green innovation, with the EPT reform playing a crucial role in this process. Furthermore, Zheng et al. [26] and Bai and Lin [27] underscore the significance of green finance in supporting enterprises’ environmental governance efforts, suggesting that improved access to finance can lead to enhanced green innovation. This, in turn, contributes to TFP growth by fostering a more sustainable and innovative operational framework.
Therefore, Hypothesis 4 is as follows:
The EPT reform indirectly enhances the TFP of enterprises by reducing financing constraints.
3. Data and Methodology
3.1. Data Sources
Our analysis utilizes data spanning from 2009 to 2019 in pollution-intensive industries, derived from the China Stock Market and Accounting Research (CSMAR) database. This period was deliberately chosen to exclude the global financial crisis of 2008 and the unprecedented impacts of the COVID-19 pandemic in 2020. By avoiding these significant external economic events, we aim to ensure that our analysis of the EPT law’s impact remains stable and uninterrupted. This timeframe allows for a more accurate assessment, free from the distortions caused by these extraordinary circumstances.
To ensure data integrity and reliability, stringent measures were adopted throughout the data handling process. The study excluded all companies marked with ST and ST* (Special Treatment and Special Treatment Star), typically designated due to non-compliance with financial health standards. Additionally, data from the financial industry were omitted, likely due to the unique financial structures and market behaviors of this sector, which could skew the overall analysis. Furthermore, to mitigate the impact of outliers, the dataset was truncated at the 1st and 99th percentiles, enhancing the robustness of statistical analyses.
We focused on pollution-intensive industries because the EPT is specifically designed to target sectors with significant environmental impacts. These industries are expected to undergo substantial changes due to the tax, making them ideal for observing the EPT’s effects. Our selection of pollution-intensive industries was guided by previous studies that identified major polluting industries, such as chemical manufacturing, mining, and steel production [2,13,22]. Analyzing these industries allowed us to provide more relevant and actionable insights into the policy’s impact, directly aligning with the EPT’s objectives.
Despite the unbalanced nature of our sample, with only two post-implementation years out of eleven, we addressed this issue through the use of the difference-in-differences method. The difference-in-differences approach enabled us to compare the treatment group (firms affected by the EPT) with the control group (firms not affected), thereby mitigating the bias caused by the temporal imbalance. By incorporating time fixed effects and firm fixed effects into our model, we further controlled for other potential confounding factors that could influence the results. This ensures that our analysis more accurately isolates the impact of the EPT, despite the limited post-implementation data. All empirical analyses were conducted in the Stata16 software environment. Stata16 is a widely used statistical software that offers comprehensive tools for data analysis, management, and graphing. Conducting data analysis in such a software environment ensures the effectiveness and reliability of the research.
This approach to data selection and processing demonstrates the rigor and professionalism of the empirical analysis in the paper, ensuring the accuracy and credibility of the research findings.
3.2. Sample Selection
3.2.1. Explained Variables
In this research, TFP is employed as the explained variable to assess the development of heavily polluting enterprises. There exist multiple methodologies to gauge TFP, notably the Levinsohn–Petrin (LP) method and the Olley–Pakes (OP) method.
The OP method, as delineated by Olley and Pakes [28], mandates that the real investment of enterprises must exceed zero, a criterion that might lead to the exclusion of a substantial number of enterprise samples from the estimation. Conversely, the LP method, developed by Levinsohn and Petrin [29], effectively addresses this limitation. Consequently, this study adopts the LP method for measuring the TFP of enterprises, offering a more inclusive and robust analysis of their productivity dynamics. This method controls for intermediate inputs, labor, and capital, ensuring a comprehensive evaluation that captures a broader spectrum of enterprises, including those with minimal or no real investment.
Recent studies support the effectiveness of the LP method. Khan and Gulati [30] highlighted its robustness in analyzing productivity growth in Indian industries, while Haider and Bhat [31] demonstrated its applicability in assessing the impact of TFP on energy efficiency. Rostiana et al. [32] confirmed its suitability for Indonesian micro- and small-scale manufacturing industries, and Zheng et al. [33] emphasized its relevance in evaluating policy impacts on productivity in Chinese energy-intensive industries. Wu [34] also validated its utility in exploring the interaction between productivity and export behavior in Chinese electronics firms. These studies collectively validate the appropriateness of using the LP method in our research to capture the impact of the EPT on TFP comprehensively.
3.2.2. Explanatory Variables
The enactment of the EPT law on 1 January 2018, serves as a critical temporal marker in this research. We assigned a value of 1 to the time dummy variable of post-treatment effects for the years 2018 onward, and 0 for preceding years. Furthermore, the policy impact was examined by distinguishing between heavily polluting listed companies (experimental group, marked as 1) and the rest (control group, marked as 0) using a policy dummy variable treated. An interaction term, generated by multiplying these dummies, facilitated the analysis of the reform’s effect on the TFP of pollution-intensive firms.
3.2.3. Mediating Variables
In this study, we focused on two critical mediating variables: green innovation and financing constraints. Considering the inherent volatility in research and development (R&D) activities, as highlighted by Miao et al. [35], we quantified R&D efforts by the number of green patent applications (GI). This metric serves as a direct indicator of firms’ commitment to green technological advancement. Utilizing the number of green patent applications not only effectively captures the extent of a firm’s green innovation activities but also accurately reflects the output of R&D innovation, as supported by Ardito et al. [36], and further corroborated by Xu et al. [37].
To assess the degree of financing constraints faced by firms, we employed the Kaplan–Zingales (KZ) index [38]. This choice was motivated by the KZ index’s robustness in reflecting the multifaceted nature of financing constraints that firms encounter. The KZ index has been validated in various studies. For instance, Ren et al. [39] utilized the KZ index to examine the relationship between climate policy uncertainty and corporate debt in Chinese firms, highlighting its relevance. Similarly, Pham and Le [40] used the KZ index to analyze investment decisions under financial constraints in Vietnamese listed firms. Additionally, Liu et al. [41] applied the KZ index to evaluate financing constraints in agricultural companies, providing insights into the impact of entrepreneurship on firm performance. These studies collectively validate the appropriateness of using the KZ index in our research to comprehensively capture the financing constraints faced by firms.
The purpose of incorporating green innovation and financing constraints as mediating variables is to investigate the pathway through which EPT affects firms’ TFP. This analysis seeks to elucidate the mechanisms by which environmental taxation encourages improvements in corporate productivity, by promoting green innovation activities and addressing financial barriers. This approach allows for a comprehensive understanding of how environmental policies catalyze sustainable economic growth by influencing the innovative capacity and financial health of firms.
3.2.4. Control Variables
Drawing on the contemporary literature, several control variables were identified as potential influencers of firm-level TFP. These include Tobin’s Q, return on total assets, debt ratio, enterprise age, size, cash flow, book-to-market ratio, return on net assets, R&D investment, and fixed asset ratio. References and detailed descriptions, including variable names, symbols, and calculation methods, are provided in Table 1.
The introduction of these control variables allows for a more precise assessment of the EPT reform’s impact on enterprises’ TFP. By including these variables, the study not only isolates the specific effects of the tax reform but also accounts for other potential influencing factors. This comprehensive approach enhances the robustness and reliability of the research findings, ensuring a thorough exploration of the multifaceted impacts on enterprise productivity. Such a methodological framework is essential for drawing nuanced and informed conclusions in the realm of environmental economics.
Table 2 shows the descriptive statistics values of the variables.
The descriptive statistics (see Table 2) offer an in-depth overview of the data on total factor productivity (TFP_LP) among heavily polluting enterprises during the 2009 to 2019 period, revealing a moderate level of productivity on average and significant variability in productivity across the industry. The mean value of TFP (TFP_LP) for these enterprises is 9.369, indicating a moderate level of productivity on average. Moreover, the considerable variability in productivity among enterprises, further underscored by a standard deviation of 1.093, highlights the diversity in productivity levels within this sector.
Other variables, such as post-treatment effects (post), treatment status (treated), and difference-in-differences, present varied mean values, reflecting the heterogeneity in the dataset. For instance, the mean value of post-treatment effects is relatively low at 0.179, implying that these are limited in the sample. Similarly, treated has a mean of 0.381, indicating that less than half of the observations were subjected to the treatment in question.
Financial and operational metrics like debt ratio, market-to-book ratio, firm size, and age also show diverse ranges, suggesting variability in the financial health and scale of these enterprises. Regarding the negative values observed in some variables (notably the KZ index), this could signal that certain enterprises are encountering specific financial or operational challenges. Further exploration of the underlying reasons and their impact on the overall research findings is warranted.
Overall, the descriptive statistics provide valuable insights into the characteristics and performance variability of heavily polluting enterprises, which can be pivotal for further analytical or policy-oriented discussions.
3.3. Model Setting
To conduct the empirical regression analysis, this study adopted the difference-in-differences methodology. This approach allows for estimation of the policy’s effect by comparing changes across two sets of data before and after the policy implementation, as elucidated by Angrist and Pischke [50]. In our difference-in-differences model, we contrasted matched experimental group samples with control group samples to gauge the policy’s influence on the outcome variable. The basic regression model is outlined as follows:
Model (1)—basic regression model:
TFP-LPit = α0 + α1 DIDit + Controlsit + yeart + companyi + ε1(1)
TFP-LPit represents the dependent variable, measuring the TFP of heavily polluting enterprises using the LP method. DIDit is the core explanatory variable. Controlsit includes a series of control variables; yeart represents year fixed effects; companyi denotes enterprise fixed effects; and ε1 is the error term.
Model (2) and Model (3)—mediation by green innovation and financing constraints:
To explore the mediation roles of green innovation and financing constraints in the DID–TFP relationship, we draw on Baron and Kenny’s framework [51]. The models are specified as follows:
medit = β0 + β1DIDit + Controlsit + yeart + companyi + ε2(2)
TFP-LPit = γ0 + γ1 DIDit + γ2 medit + Controlsit + yeart + companyi + ε3(3)
In these models, medit represents the mediating variables, specifically green innovation and financing constraints. β0 and γ0 are constant terms; β1, γ1, and γ2 are coefficients; ε2 and ε3 are error terms. Model (2) explores DID’s influence on medit, whereas model (3) evaluates TFP’s dependence on both DID and medit, thus isolating direct (γ1) and mediated (γ2) effects. The model assesses DID’s overall impact on TFP as the aggregate of its direct (γ1) and mediated effects (β1 × γ2). Green innovation is hypothesized to positively mediate the relationship, indicating that the EPT enhances TFP by promoting environmentally friendly technological advancements. Easing financing constraints is also expected to positively mediate the relationship, suggesting that improved access to financing enables more investments in green technologies, which in turn enhances TFP. Through this refined modeling approach, the study aims to offer a nuanced understanding of policy impacts on enterprise productivity, accounting for both direct and indirect mechanisms within the context of environmental regulations.
4. Empirical Results
4.1. Parallel Trend Test
The validity of the difference-in-differences method hinges on the assumption of parallel trends—that is, the treatment and control groups would have followed similar trends in the absence of the policy. To verify this, an event study approach was employed, with the year immediately preceding the tax law’s introduction as the baseline.
Figure 1 displays the results of this test, showing the confidence intervals of estimated effects in pre-policy years. A crucial observation is that these intervals consistently encompass zero prior to the policy’s introduction, indicating no significant pre-policy differences between the groups and supporting the parallel trend assumption’s validity.
To provide a more detailed explanation of the lines in Figure 1:
Green solid line: Represents the estimated policy dynamic effects at different policy time points, showing the trend over time. The markers on the solid line indicate the estimated policy effect values for each specified time period (pre_5, pre_4, pre_3, pre_2, current, and post_1).
Blue dashed lines: Represent the 95% confidence intervals for the estimates at each time point. These confidence intervals show the statistical uncertainty of the point estimates. If the dashed lines (confidence intervals) cross the zero line, it indicates that the policy effect is not statistically significant at the 95% confidence level for that specific time period.
Red solid line: Represents the reference line where the policy effect is zero. This line serves as a baseline to help judge the significance of the policy effects. If the estimates and their confidence intervals lie entirely above or below this line, the estimates can be considered statistically significant.
Red dashed line: Represents the time point of policy implementation. This line helps distinguish between the periods before and after the policy implementation, allowing for observation of the dynamic changes before and after the policy is introduced.
The significance of confidence intervals including zero indicates that differences between the treatment and control groups before the policy were not statistically significant. This observation is vital for difference-in-differences analysis, supporting that any post-policy outcome deviations are attributable to the policy’s effect [50].
In conclusion, Figure 1’s graphical evidence strengthens the applicability of the parallel trend assumption to our study, ensuring the robustness of our difference-in-differences analysis and attributing post-policy changes clearly to the EPT law’s impact.
4.2. Basic Regression Results
Table 3 reports the regression results of the EPT reform on the TFP of heavy polluters.
In analyzing Table 3, the regression results from m1–m6 provide a comprehensive understanding of the EPT reform’s impact on the TFP of heavily polluting enterprises. Initially, m1–m3 without control variables reveal a significant positive effect of the reform on TFP. This initial finding establishes a baseline understanding of the policy’s impact.
As the analysis progresses to m4–m6, where control variables are included, the positive effect of the EPT reform remains consistent and significant. This consistency reinforces the robustness of the reform’s impact, even when accounting for various influencing factors. These models, through their incremental incorporation of control variables and fixed effects, offer a nuanced view of the policy’s effectiveness across different contexts.
Variable m1, focusing on year fixed effects, captures the temporal variations in the policy’s impact. Variable m2, with company fixed effects, sheds light on how individual company characteristics influence the reform’s effectiveness. Variable m3’s combination of both year and company fixed effects offers a more detailed picture, considering both time-related changes and unique company attributes. Variables m4–m6 continue this trend, each adding layers of complexity and control to refine the understanding of how the EPT reform impacts heavily polluting enterprises. The progression from m1–m6 illustrates the multi-dimensional nature of policy analysis, emphasizing the importance of considering various external and internal factors to fully grasp a policy’s impact on enterprise productivity. These consistent and significant results across all models provide strong evidence supporting Hypothesis 1.
4.3. Robustness Tests
To solidify the credibility of our findings, we undertook a series of robustness tests, each designed to challenge and validate our initial results from different angles. These tests are crucial for ensuring that our conclusions are not artifacts of specific model specifications or data peculiarities.
4.3.1. Replacing the Dependent Variable
The TFP was reassessed using the OP method, aligning with established methodologies for productivity assessment [28]. The results in m1 of Table 4 confirm the EPT reform’s significant promotion of TFP in polluting enterprises, consistent with findings that environmental regulations can enhance firm productivity [21]. This validation underscores the reform’s effectiveness in stimulating efficiency improvements.
4.3.2. Lagging All Explanatory Variables by One Period
To mitigate potential issues of time series correlation or reverse causality, we have lagged all explanatory variables by one period, following the approach recommended by Maçaira et al. [52]. By lagging the explanatory variables, this method enables a more accurate capture of the dynamic relationships between variables, reducing potential time-series correlation or reverse causality issues between the dependent and explanatory variables. This adjustment significantly enhances the accuracy and reliability of our model. In m2, presented in Table 4, where all explanatory variables are lagged by one period, the reform’s positive impact on TFP remains statistically significant. This finding underscores the robustness of the reform’s beneficial effects on TFP, even when accounting for time-lagged influences.
4.3.3. Regression Using the Propensity Score Matching–Difference-in-Differences Approach
The effectiveness of combining propensity score matchingwith difference-in-differences approach in reducing endogeneity and selection bias is well documented. For instance, Austin and Stuart [53] elaborate on the methodological foundations and advantages of integrating propensity score matching with difference-in-differences to enhance causal inference in observational studies. This integration not only addresses potential confounders but also improves the estimation of treatment effects by mimicking randomized controlled trial conditions. Variables m3 and m4 in Table 4, applying nearest neighbor matching and kernel radius matching, respectively, consistently indicate a significant enhancement of TFP in polluting enterprises due to the tax reform.
The comprehensive analysis across these varied robustness checks—from methodological shifts to advanced statistical treatments—collectively strengthens the conclusion that the EPT reform significantly enhances TFP in heavily polluting industries. These rigorous tests not only confirm the robustness of our initial findings but also provide a deeper understanding of the multifaceted impacts of environmental policies in the context of industrial productivity.
4.3.4. Placebo Test
To reinforce the credibility of empirical results, a placebo test was conducted. This involved randomizing the policy timing and areas across 500 trials and analyzing the distribution of estimated coefficients and p-values, as illustrated in Figure 2. The findings are enlightening: the coefficient concentration predominantly clusters around zero, most p-values exceed 0.1 indicating no statistical significance at the 10% level, and the normal distribution of estimated values in the placebo test further validates the robustness of the main findings. The methodology for this placebo test aligns with the framework introduced by Hagemann [54], which formalizes the notion of generating null distributions through placebo treatments in scenarios with a small number of large clusters, thus enhancing the reliability of our empirical findings and confirming that the effects observed in the benchmark regression are not products of random chance. This enhances confidence in the conclusions about the EPT reform’s impact on polluting enterprises’ TFP.
4.4. Heterogeneity Analysis
The heterogeneity analysis in our study focuses on two key aspects: property rights and financing constraints. This analysis helps to understand the differential impacts of the EPT reform on various types of enterprises.
4.4.1. Property Rights Heterogeneity
Research indicates a marked difference in the impact of the EPT reform on the TFP of state-owned and non-state-owned enterprises, as seen in m1 and m2 of Table 5. SOEs show a significant increase in TFP, possibly due to better alignment with government policies, access to resources, and government support for environmental reforms [55]. This alignment allows SOEs to adapt more efficiently to tax reforms, thereby enhancing productivity. In contrast, the effect on non-state-owned enterprises is less pronounced, likely due to lower levels of direct government intervention and support [56].
4.4.2. Financing Constraint Heterogeneity
Focusing on the impact of financing constraints on TFP, m3 and m4 utilize the KZ index. This study finds that enterprises with lesser financing constraints exhibit a more pronounced improvement in TFP post-tax reform. This improvement is attributed to their greater financial flexibility, enabling more significant investment in innovative and environmentally compliant technologies [20]. Easier access to external financing allows these firms to allocate resources efficiently towards meeting new environmental standards without disrupting operations [57].
This heterogeneity analysis provides a deeper insight into the differential impacts of environmental tax reform based on enterprise characteristics, emphasizing the importance of these factors in environmental policy design and implementation. These findings support Hypothesis 2.
4.5. Mechanism Testing
To understand the underlying mechanisms behind the EPT reform’s impact on enterprises, our study delves into two key areas: green innovation and financing constraints.
4.5.1. Green Innovation
This study measures green innovation through the number of green patent applications (GI). According to m2 in Table 6, the tax reform substantially increases green patent outputs, indicating a boost in green innovation. This significant effect, validated at a 1% significance level, implies that the tax reform incentivizes technological upgrades and green innovation, thus enhancing TFP. This aligns with findings in the literature, such as those by Deng and Yang [58] and Ma [59].
Despite m3 in Table 6 indicating a non-significant coefficient for green innovation (GI), the Sobel test (reflected in Table 7) was utilized to evaluate green innovation’s mediating role. The Sobel test is a statistical method used to determine the significance of a mediation effect. It calculates the indirect effect of an independent variable on a dependent variable through a mediator variable. The test combines the effect of the independent variable on the mediator (a) and the effect of the mediator on the dependent variable (b), adjusting for the variance of these effects to produce a z-value and a corresponding p-value [60].
In our study, the Sobel test yielded a z-value of 2.144 and a p-value of 0.032072. In hypothesis testing, a z-value greater than 1.96 indicates that the result is statistically significant at the 5% level, and a p-value less than 0.05 indicates that the observed data would be highly unlikely under the null hypothesis of no effect. Therefore, a z-value of 2.144, which is greater than 1.96, and a p-value of 0.032072, which is less than 0.05, confirm that the mediation effect of green innovation is statistically significant [61].
This finding underscores that the tax reform influences TFP not just directly but also indirectly by encouraging green innovation, with this mediation channel responsible for about 9.44% of the total effect, affirming Hypothesis 3. This means that green innovation acts as a significant mediator in the relationship between tax reform and TFP.
This analysis underscores the complexity of the policy’s impact, demonstrating that the EPT reform’s influence on enterprise TFP is multifaceted, involving both direct effects and indirect effects mediated by green innovation. This dual impact is consistent with recent research in the field, such as studies by Deng and Yang [58] and Ma [59], which emphasize the role of environmental policies in fostering innovation, especially in the context of green technologies and practices.
4.5.2. Financing Constraints
In this paper, the KZ index [38] is used to reflect the degree of financing constraints of firms. Variable m2 in Table 8 shows that the EPT reform significantly reduces the financing constraints of enterprises, as indicated by the negative coefficient of KZ and its statistical significance. This result supports the view that the EPT can promote TFP by alleviating financing constraints.
Variable m3 further demonstrates that when financing constraints are reduced, there is a corresponding significant increase in TFP. This is evidenced by the positive and significant coefficient of DID in m3, along with the negative and significant coefficient of KZ, which indicates that lower financing constraints lead to higher TFP.
Together, these results confirm that the EPT reform facilitates an improvement in TFP by reducing financing constraints, thereby supporting Hypothesis 4. This comprehensive analysis highlights the mediating role of financing constraints in the relationship between the EPT reform and enterprise performance.
This result is consistent with Zhang et al.’s study [62], who concluded that enterprises’ green innovation investment can effectively alleviate financing constraints. Enterprises can have a positive impact through the interaction of environmental information disclosure and green innovation to improve their financing conditions and promote their transformation and upgrading.
In addition, the financing constraint of enterprises is one of the important factors affecting their green innovation input. When enterprises face large financing constraints, their green innovation input may be limited. The implementation of the environmental protection fee-to-tax policy can reduce the environmental costs of enterprises and improve their profit level, thus increasing their R&D investment and green innovation investment.
5. Discussion
5.1. EPT and TFP
Our study indicates that the EPT has significantly promoted TFP in heavily polluting enterprises, supporting the Porter Hypothesis. This finding aligns with the results of Yang et al. [42] and Sun and Zhang [9], who also observed positive impacts of environmental tax reforms on firm productivity. These studies highlight that stringent environmental policies can drive innovation and improve efficiency, thereby enhancing TFP.
However, there are conflicting views in the literature. Dong et al. [63] found a U-shaped relationship between environmental regulation and industrial TFP, with negative spatial spillover effects at the provincial level in China, suggesting that stringent regulations may initially hinder productivity. Similarly, Ai et al. [64] reported that environmental regulations could reduce scale and technical efficiency, leading to an overall negative impact on TFP. Cai and Ye [65] noted that such regulations might tighten financial constraints and negatively impact technical innovation, thereby hindering TFP.
These discrepancies can be attributed to differences in methodology and industry focus. Dong et al. [63] used spatial econometric models to analyze provincial-level data, capturing spatial spillover effects not apparent in firm-level studies. Ai et al. [64] employed a panel data approach focusing on enterprise duration and survival, differing significantly from TFP-focused analyses. In contrast, our study employs a difference-in-differences approach, effective in isolating the causal impact of policy changes over time. Additionally, while Dong et al. [63] and Ai et al. [64] examined broader industrial sectors, our study specifically targets pollution-intensive industries, which might respond differently to environmental regulations.
5.2. Heterogeneity in TFP Improvements
Our heterogeneity analysis reveals that the positive impact of the EPT on TFP is more pronounced in state-owned enterprises and firms with fewer financing constraints. This finding is consistent with that of He and Jing [13], who emphasized that the effects of environmental policies vary depending on the organizational and financial characteristics of firms. Zhang et al. (2021) [20] also found that state-owned enterprises benefit more from such policies due to better access to resources and governmental support.
On the other hand, some studies suggest that private firms and SMEs might struggle more with the initial costs of compliance, which can delay the positive impacts on TFP. For instance, Lei and Wu [65] noted that the positive effects of environmental regulations on TFP were more significant in regions with lower initial levels of regulatory enforcement. These inconsistencies might stem from different regulatory environments and the varying ability of enterprises to adapt to new regulations. Additionally, the robustness of financial systems and access to capital can significantly influence how different types of enterprises respond to environmental policies.
5.3. Mechanisms of Impact
Our research demonstrates that the EPT promotes TFP through green innovation and the alleviation of financing constraints. This aligns with the findings of Qin et al. [66], who showed that local government competition and environmental regulations could enhance TFP by fostering innovation. Similarly, Zhang et al. [67] demonstrated that green innovation significantly reduces financing constraints for Chinese enterprises, further promoting TFP. Conversely, Cai and Ye [68] found that China’s new environmental protection law significantly hindered enterprises’ TFP, primarily through negative impacts on technical innovation, reduced resource allocation efficiency, and tightened financial constraints. Additionally, Yuan and Zhang [69] revealed that the allocation ratio of factors among different industries plays a crucial mediating role in the impact of environmental regulations on TFP. They found that while high-intensity regulations initially inhibit TFP, optimizing the ratio of factor allocation can reverse this effect, thus demonstrating that different mediating variables can lead to varied outcomes of environmental regulations on TFP.
In summary, our study provides robust evidence that EPT reforms can significantly enhance TFP, particularly through mechanisms of green innovation and the alleviation of financing constraints. This positive impact is more pronounced in state-owned enterprises and firms with fewer financing constraints. However, the effectiveness of these reforms can vary significantly based on industry focus, methodological approaches, and different mediating variables. Future research should continue to explore these complex relationships and consider additional mediating variables to fully understand the impact of environmental policies on productivity.
6. Conclusions and Policy Implications
6.1. Conclusions
This research, using difference-in-differences analysis on panel data from 2009 to 2019, examines the impact of China’s transition from environmental protection fees to taxes on the TFP of heavily polluting enterprises. The findings affirm the Porter Hypothesis, indicating that EPT reforms significantly enhance TFP through increased green innovation and reduced financing constraints. The study reveals heterogeneity in the tax reform’s impact, with state-owned enterprises and those with less-severe financing constraints benefiting more substantially. This underscores that EPT policies can serve as a catalyst rather than a barrier to enhancing TFP.
6.2. Policy Implications
Based on the findings of this study, we propose several recommendations for future policy formulation.
6.2.1. Implement and Refine Environmental Tax Policies
Based on our findings, we recommend the proactive implementation and continuous refinement of EPT policies. This includes enhancing collaboration between environmental and tax departments to improve tax collection and management efficiency [70]. Policies should be regularly reviewed and adjusted to ensure they effectively incentivize green innovation without overly burdening enterprises [71]. Establishing regular research and feedback mechanisms is crucial to ensure the dual benefits of EPT policies are realized [72].
6.2.2. Encourage and Support Green Innovation
To promote green innovation, enterprises should focus on developing talent for green technological innovation and adopt strategies for environmental sustainability. The government should bolster policies that specifically support green innovation initiatives, including the enhancement of technology management for environmental innovations and the creation of incentives for green technology development. Encouraging a culture of green innovation within firms can lead to sustainable TFP growth through focused R&D of environmentally friendly technologies.
6.2.3. Improve Financing Mechanism
To alleviate constraints, firms’ green innovation capabilities are diminished by higher financing constraints [73]. To alleviate financing constraints, the government should intensify its support for green technological innovation by increasing subsidies and establishing targeted subsidy mechanisms that prioritize projects with significant environmental impacts to ensure the effectiveness of these subsidies and mitigate risks associated with R&D. Furthermore, the expansion of green bonds and investment funds is crucial for providing enterprises, especially those that are state-owned or face fewer financing constraints, with more advantageous financing conditions. This strategy not only reduces financing barriers but also promotes private investment in green projects by making green bonds more attractive through tax incentives or guaranteed returns. Ultimately, this integrated approach aims to accelerate the green transformation of heavily polluting enterprises and advance sustainable development initiatives more effectively.
6.2.4. Tailor Policies to Enterprise Characteristics and Financing Constraints
Policy strategies should be fine-tuned to encourage green technological innovation across different enterprise types, with a particular focus on overcoming the unique challenges and leveraging the opportunities within firms’ financing landscapes. Special attention should be given to fostering an environment that supports green R&D efforts, especially in sectors and enterprises poised to benefit most from technological advancements in sustainability. For state-owned enterprises, which showed more pronounced TFP improvements, policies could further strengthen green innovation assessments to amplify their innovation capacity. Meanwhile, enterprises facing less-severe financing constraints might benefit from expanded access to green financing and credit support, continuously promoting green innovation and facilitating their green transformation.
These recommendations aim to balance environmental sustainability with economic growth by leveraging the positive impact of the EPT reform on enterprise TFP. Tailoring policies to the specific characteristics and challenges of enterprises can foster a conducive environment for green innovation and sustainable economic development.
6.3. Limitations and Further Research Prospects
Despite its strengths, this study faces limitations that open avenues for further research. First, while our difference-in-differences approach controls for observable factors, unobserved variables such as internal management changes or shifts in industry competition may also shape the effects of the environmental tax reform on TFP. Future studies should aim to uncover these hidden influences for a fuller understanding of the policy’s impact. Additionally, our analysis is based on a relatively short post-implementation period of the EPT. Extending the analysis to include more extensive post-reform data would provide a fuller understanding of the policy’s long-term impact.
Second, by concentrating on listed companies in heavily polluting industries, we gain specific insights but limit the broader applicability of our results. Exploring the effects of environmental regulations on a broader spectrum of firms affected would provide a more comprehensive understanding of their diverse effects. Lastly, our research focuses on the mediating roles of green technological innovation and financing constraints. However, it does not examine whether green management innovation and green finance might also serve as mediating or moderating factors in the relationship between the environmental tax reform and TFP. Future research should explore these aspects to provide a more comprehensive understanding of the mechanisms at play. Additionally, investigating the impact of carbon trading schemes on TFP could offer valuable insights into how different environmental policies interact to foster sustainable economic growth.
In sum, while this research adds valuable insights to the discourse of environmental policy and productivity, the outlined limitations and future research directions underscore the importance of continued study into how best to leverage environmental regulations for sustainable economic growth.
Conceptualization, J.W.; methodology, J.W.; software, J.W.; validation, J.W., D.T. and Y.P.; formal analysis, J.W., D.T. and Y.P.; investigation, J.W.; resources, J.W.; data curation, J.W.; writing—original draft preparation, J.W.; writing—review and editing, Y.P. and D.T.; visualization, Y.P. and D.T.; supervision, D.T.; project administration, D.T. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The raw data supporting the conclusions of this article will be made available by the authors on request.
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.
Variable name and definition.
| Type | Name | Abbreviation | Definition | Reference |
|---|---|---|---|---|
| Explained | Total Factor Productivity | TFP_LP | Total factor productivity calculated using the LP method | Levinsohn and Petrin [ |
| Explanatory | Time Dummy Variable | post | Set to 1 for the year 2018 and beyond, otherwise set to 0 | Yang et al., 2023 [ |
| Policy Dummy Variable | treated | Set to 1 for heavily polluted enterprises, otherwise set to 0 | Yang et al., 2023 [ | |
| Interaction Term | DID | Time dummy variable × policy dummy variable | Yang et al., 2023 [ | |
| Mediating | Green Innovation | GI | ln (number of green invention patent applications + 1) | Chen et al., 2021 [ |
| Financing Constraints | KZ | KZ index | Kaplan and Zingales [ | |
| Control | Tobin’s Q Ratio | qa | Market value of firm/replacement capital of firm | Rahman et al. [ |
| Return on Assets | roa | Net profit/average total assets | Tabash et al., 2020 [ | |
| Debt Ratio | debt | Total liabilities/total assets | Titman and Wessels [ | |
| Return on Equity | roe | Net profit/net assets | Tabash et al. [ | |
| Market-to-Book Ratio | mtb | Total assets/market value of the company | Titman and Wessels [ | |
| Firm Size | size | ln (total assets of the firm) | Suriawinata and Nurmalita [ | |
| Firm Age | lnage | ln (the number of years since firm’s inception) | Suriawinata and Nurmalita [ | |
| Fixed Asset Ratio | ppe | Fixed assets/total assets | Mollick and Haidar [ | |
| Cash Ratio | cash | (Cash + marketable securities)/current liabilities | Mustafa et al. [ |
Descriptive statistics of variables.
| Variable | N | Mean | SD | Min | Max |
|---|---|---|---|---|---|
| TFP_ LP | 8242 | 9.369 | 1.093 | 5.697 | 13.09 |
| post | 8328 | 0.179 | 0.383 | 0 | 1 |
| treated | 8328 | 0.381 | 0.486 | 0 | 1 |
| DID | 8328 | 0.0698 | 0.255 | 0 | 1 |
| GI | 8328 | 1.249 | 1.534 | 0 | 5.587 |
| KZ | 8328 | 1.509 | 2.126 | −9.921 | 8.948 |
| qa | 8328 | 1.948 | 1.208 | 0.855 | 7.722 |
| roa | 8328 | 0.0417 | 0.0462 | −0.102 | 0.204 |
| roe | 8328 | 0.0781 | 0.0845 | −0.265 | 0.331 |
| debt | 8328 | 0.483 | 0.190 | 0.0750 | 0.861 |
| mtb | 8328 | 0.311 | 0.142 | 0.0714 | 0.758 |
| size | 8328 | 22.60 | 1.322 | 19.90 | 26.39 |
| lnage | 8328 | 2.847 | 0.303 | 1.792 | 3.401 |
| ppe | 8328 | 0.249 | 0.180 | 0.00240 | 0.752 |
| rd | 8328 | 0.0152 | 0.0247 | 0 | 0.119 |
| cash | 8328 | 0.590 | 0.832 | 0.0289 | 5.566 |
Basic regression.
| m1 | m2 | m3 | m4 | m5 | m6 | |
|---|---|---|---|---|---|---|
| VARIABLES | TFP_LP | TFP_LP | TFP_LP | TFP_LP | TFP_LP | TFP_LP |
| DID | 0.0603 * | 0.4207 *** | 0.0603 *** | 0.0670 *** | 0.0611 *** | 0.0653 *** |
| (0.0344) | (0.0187) | (0.0205) | (0.0159) | (0.0160) | (0.0159) | |
| Controls | NO | NO | NO | YES | YES | YES |
| Observations | 8242 | 8242 | 8242 | 8242 | 8242 | 8242 |
| R-squared | 0.3144 | 0.0638 | 0.3144 | 0.5941 | 0.5871 | 0.5947 |
| Company FE | NO | YES | YES | NO | YES | YES |
| Year FE | YES | NO | YES | YES | NO | YES |
Robust standard errors in parentheses; *** p < 0.01, * p < 0.1.
Robustness test.
| m1 | m2 | m3 | m4 | |
|---|---|---|---|---|
| VARIABLES | TFP_OP | TFP_LP | TFP_LP | TFP_LP |
| DID | 0.0936 *** | 0.0595 ** | 0.0655 *** | 0.0653 *** |
| (0.0184) | (0.0265) | (0.0244) | (0.0244) | |
| Controls | YES | YES | YES | YES |
| Observations | 8242 | 7351 | 8219 | 8239 |
| R-squared | 0.4439 | 0.4555 | 0.5948 | 0.5947 |
| Company FE | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES |
Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05.
Heterogeneity test.
| m1 | m2 | m3 | m4 | |
|---|---|---|---|---|
| State-Owned | Non-State-Owned | High Financing Constraints | Low Financing Constraints | |
| VARIABLES | TFP_LP | TFP_LP | TFP_LP | TFP_LP |
| DID | 0.0697 ** | 0.0663 | 0.0360 | 0.0832 *** |
| (0.0275) | (0.0442) | (0.0424) | (0.0263) | |
| Observations | 5095 | 3147 | 4122 | 4120 |
| R-squared | 0.5717 | 0.6247 | 0.5505 | 0.6227 |
| Company FE | YES | YES | YES | YES |
| Year FE | YES | YES | YES | YES |
Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05.
Tests of mediating effects of green innovation.
| m1 | m2 | m3 | |
|---|---|---|---|
| VARIABLES | TFP_LP | GI | TFP_LP |
| DID | 0.0653 *** | 0.2015 *** | 0.0538 ** |
| (0.0244) | (0.0689) | (0.0235) | |
| GI | 0.0011 | ||
| (0.0061) | |||
| Observations | 8242 | 8328 | 8242 |
| R-squared | 0.5947 | 0.2475 | 0.6100 |
| Company FE | YES | YES | YES |
| Year FE | YES | YES | YES |
Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05.
Sobel mediation effect test results.
| Measurement | Coefficient | Standard Error | Z-Value | p-Value |
|---|---|---|---|---|
| Sobel | 0.00304926 | 0.00142255 | 2.144 | 0.032072 |
| Goodman-1 (Aroian) | 0.00294222 | 0.00145857 | 2.091 | 0.03656543 |
| Goodman-2 | 0.00304926 | 0.0013856 | 2.201 | 0.02775897 |
| a Coefficient | 0.175276 | 0.064387 | 2.72224 | 0.006484 |
| b Coefficient | 0.017397 | 0.005003 | 3.47729 | 0.000507 |
| Indirect Effect | 0.003049 | 0.001423 | 2.14351 | 0.032072 |
| Direct Effect | 0.029252 | 0.029094 | 1.00544 | 0.314685 |
| Total Effect | 0.032302 | 0.029101 | 1.11 | 0.267001 |
| Proportion of Mediation | 9.44% | - | - | - |
Tests of mediating effects of financing constraints.
| m1 | m2 | m3 | |
|---|---|---|---|
| VARIABLES | TFP_LP | KZ | TFP_LP |
| DID | 0.0653 *** | −0.2194 *** | 0.0504 ** |
| (0.0244) | (0.0663) | (0.0234) | |
| KZ | −0.0160 *** | ||
| (0.0043) | |||
| Observations | 8242 | 8328 | 8242 |
| R-squared | 0.5947 | 0.5453 | 0.6116 |
| Number of id | 797 | 797 | 797 |
| City FE | YES | YES | YES |
| Year FE | YES | YES | YES |
Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05.
References
1. Li, P.; Lin, Z.; Du, H.; Feng, T.; Zuo, J. Do environmental taxes reduce air pollution? Evidence from fossil-fuel power plants in China. J. Environ. Manag.; 2021; 295, 113112. [DOI: https://dx.doi.org/10.1016/j.jenvman.2021.113112] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34174685]
2. Wen, H.; Deng, W.; Guo, Q. The effects of the environmental protection law on heavily polluting firms in China. PLoS ONE; 2021; 16, e0261342. [DOI: https://dx.doi.org/10.1371/journal.pone.0261342]
3. Cai, F. From the demographic dividend to the reform dividend. Understanding China’s Economy; Springer: Singapore, 2021; [DOI: https://dx.doi.org/10.1007/978-981-33-6322-9_14]
4. Li, T.; Wang, P.; Liu, X.; Cheng, H. Quality-oriented growth: A new trend for Chinese firms. China Econ. J.; 2017; 10, pp. 6-20. [DOI: https://dx.doi.org/10.1080/17538963.2016.1274006]
5. Wang, X.; Chen, X. An evaluation index system of China’s development level of ecological civilization. Sustainability; 2019; 11, 2270. [DOI: https://dx.doi.org/10.3390/su11082270]
6. Zheng, Y.; Zhuang, G. Systemic governance of mountains, rivers, forests, farmlands, lakes and grasslands: Theoretical framework and approaches. Chin. J. Urban Environ. Stud.; 2021; 9, 2150021. [DOI: https://dx.doi.org/10.1142/S2345748121500214]
7. Filipenko, A. Economic productivity: Factor approaches. Actual Probl. Int. Relat.; 2021; 147, pp. 55-64. [DOI: https://dx.doi.org/10.17721/apmv.2021.147.1.55-64]
8. Trivić, N.; Todić, B. A decomposition of the total productivity factor into technical progress and technological efficiency—Methodological possibilities. Argum. Oecon.; 2022; 49, pp. 57-75. [DOI: https://dx.doi.org/10.15611/aoe.2022.2.03]
9. Sun, X.; Zhang, C. Environmental protection tax and total factor productivity—Evidence from Chinese listed companies. Front. Environ. Sci.; 2023; 10, 1104439. [DOI: https://dx.doi.org/10.3389/fenvs.2022.1104439]
10. Barbera, A.; McConnell, V. The impact of environmental regulations on industry productivity: Direct and indirect effects. J. Environ. Econ. Manag.; 1990; 18, pp. 50-65. [DOI: https://dx.doi.org/10.1016/0095-0696(90)90051-Y]
11. Porter, M.E.; Van der Linde, C. Toward a new conception of the environment-competitiveness relationship. J. Econ. Perspect.; 1995; 9, pp. 97-118. [DOI: https://dx.doi.org/10.1257/jep.9.4.97]
12. Shah, W.U.H.; Lu, Y.; Liu, J.; Rehman, A.; Yasmeen, R. The impact of climate change and production technology heterogeneity on China’s agricultural total factor productivity and production efficiency. Sci. Total Environ.; 2024; 907, 168027. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2023.168027] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37898215]
13. He, X.; Jing, Q. The impact of environmental tax reform on total factor productivity of heavy-polluting firms based on a dual perspective of technological innovation and capital allocation. Sustainability; 2022; 14, 14946. [DOI: https://dx.doi.org/10.3390/su142214946]
14. Chen, Y.; Zhang, J.; Tadikamalla, P.; Gao, X. The relationship among government, enterprise, and public in environmental governance from the perspective of multi-player evolutionary game. Int. J. Environ. Res. Public Health; 2019; 16, 3351. [DOI: https://dx.doi.org/10.3390/ijerph16183351] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31514308]
15. Gupta, K. Environmental Sustainability and Implied Cost of Equity: International Evidence. J. Bus. Ethics; 2018; 147, pp. 343-365. [DOI: https://dx.doi.org/10.1007/s10551-015-2971-z]
16. Carlson, A.-M.; Palmer, C. A qualitative meta-synthesis of the benefits of eco-labeling in developing countries. Ecol. Econ.; 2016; 127, pp. 129-145. [DOI: https://dx.doi.org/10.1016/j.ecolecon.2016.03.020]
17. Wong, W.; Batten, J.; Ahmad, A.H.; Mohamed-Arshad, S.B.; Nordin, S.; Abdul Adzis, A. Does ESG certification add firm value?. Financ. Res. Lett.; 2020; 39, 101593. [DOI: https://dx.doi.org/10.1016/j.frl.2020.101593]
18. Ding, X.; Petrovskaya, M. The relationship between environmental taxes, technological innovation and corporate financial performance: A heterogeneous analysis of micro-evidence from China. BRICS J. Econ.; 2022; 3, pp. 249-270. [DOI: https://dx.doi.org/10.3897/brics-econ.3.e91590]
19. Tingbani, I.; Salia, S.; Hussain, J.; Alhassan, Y. Environmental Tax, SME Financing Constraint, and Innovation: Evidence From OECD Countries. IEEE Trans. Eng. Manag.; 2021; 70, pp. 1006-1025. [DOI: https://dx.doi.org/10.1109/TEM.2021.3110812]
20. Zhang, D.; Vigne, S. How does innovation efficiency contribute to green productivity? A financial constraint perspective. J. Clean. Prod.; 2021; 70, pp. 1006-1025. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.124000]
21. He, Y.; Zhu, X.; Zheng, H. The influence of EPT law on total factor productivity: Evidence from listed firms in China. Energy Econ.; 2022; 113, 106248. [DOI: https://dx.doi.org/10.1016/j.eneco.2022.106248]
22. Liang, H.; Wang, Z.; Niu, R. Does environmental regulations promote the green transformation of high polluters?. Appl. Econ. Lett.; 2022; 30, pp. 927-931. [DOI: https://dx.doi.org/10.1080/13504851.2022.2030034]
23. Wu, J.; Tal, A. From pollution charge to EPT: A comparative analysis of the potential and limitations of China’s new environmental policy initiative. J. Comp. Policy Anal. Res. Pract.; 2018; 20, pp. 223-236. [DOI: https://dx.doi.org/10.1080/13876988.2017.1361597]
24. Xiong, X.; Masron, T.A.; Gondo, T.W. Can the green credit policy stimulate green innovation of heavily polluting enterprises in China?. Front. Environ. Sci.; 2023; 10, 1076103. [DOI: https://dx.doi.org/10.3389/fenvs.2022.1076103]
25. Hong, M.-K.; Li, Z.; Drakeford, B. Do the green credit guidelines affect corporate green technology innovation? Empirical research from China. Int. J. Environ. Res. Public Health; 2021; 18, 1682. [DOI: https://dx.doi.org/10.3390/ijerph18041682]
26. Zheng, C.; Deng, F.; Zhuo, C.; Sun, W. Green credit policy, institution supply, and enterprise green innovation. J. Econ. Anal.; 2022; 1, pp. 20-34. [DOI: https://dx.doi.org/10.58567/jea01010002]
27. Bai, R.; Lin, B. Access to credit and green innovation. J. Glob. Inf. Manag.; 2022; 30, pp. 1-21. [DOI: https://dx.doi.org/10.4018/JGIM.315022]
28. Olley, G.S.; Pakes, A. The dynamics of productivity in the telecommunications equipment industry. Econometrica; 1996; 64, pp. 1263-1297. [DOI: https://dx.doi.org/10.2307/2171831]
29. Levinsohn, J.; Petrin, A. Estimating production functions using inputs to control for unobservables. Rev. Econ. Stud.; 2003; 70, pp. 317-341. [DOI: https://dx.doi.org/10.1111/1467-937X.00246]
30. Khan, A.; Gulati, R. Productivity growth, catching-up and technology innovation in microfinance institutions in India: Evidence using a bootstrap Malmquist Index approach. Benchmarking: Int. J.; 2021; 28, pp. 210-230. [DOI: https://dx.doi.org/10.1108/BIJ-10-2020-0535]
31. Haider, S.; Bhat, J.A. The productivity impacts of energy efficiency programs in developing countries: Evidence from iron and steel firms in China. China Econ. Rev.; 2021; 59, 101364. [DOI: https://dx.doi.org/10.1016/j.chieco.2019.101364]
32. Rostiana, E.; Djulius, H.; Sudarjah, G.M. Total Factor Productivity Calculation of the Indonesian Micro and Small Scale Manufacturing Industry. Ekuilibrium J. Ilm. Bid. Ilmu Ekon.; 2022; 17, pp. 54-63. [DOI: https://dx.doi.org/10.24269/ekuilibrium.v17i1.2022.pp54-63]
33. Zheng, X.; Wu, C.; He, S. Impacts of China’s differential electricity pricing on the productivity of energy-intensive industries. Energy Econ.; 2020; 14, 105050. [DOI: https://dx.doi.org/10.1016/j.eneco.2020.105050]
34. Wu, X. The interaction between productivity and export behavior: Evidence from Chinese electronics firms. Am. J. Ind. Bus. Manag.; 2020; 10, pp. 1857-1869. [DOI: https://dx.doi.org/10.4236/ajibm.2020.1012116]
35. Miao, C.; Duan, M.; Zuo, Y.; Wu, X. Spatial heterogeneity and evolution trend of regional green innovation efficiency--an empirical study based on panel data of industrial enterprises in China’s provinces*. Energy Policy; 2021; 156, 112370. [DOI: https://dx.doi.org/10.1016/j.enpol.2021.112370]
36. Ardito, L.; Messeni Petruzzelli, A.; Pascucci, F.; Peruffo, E. Inter-firm R&D collaborations and green innovation value: The role of family firms’ involvement and the moderating effects of proximity dimensions. Bus. Strat. Environ.; 2018; 28, pp. 185-197. [DOI: https://dx.doi.org/10.1002/bse.2248]
37. Xu, J.; Liu, F.; Shang, Y. R&D investment, ESG performance and green innovation performance: Evidence from China. Kybernetes; 2020; 50, pp. 737-756. [DOI: https://dx.doi.org/10.1108/k-12-2019-0793]
38. Kaplan, S.; Zingales, L. Do investment-cash flow sensitivities provide useful measures of financing constraints?. Q. J. Econ.; 1997; 112, pp. 169-215. [DOI: https://dx.doi.org/10.1162/003355397555163]
39. Ren, X.; Qin, J.; Dong, K. How does climate policy uncertainty affect excessive corporate debt? The case of China. J. Environ. Assess. Policy Manag.; 2022; 24, 2250025. [DOI: https://dx.doi.org/10.1142/S1464333222500259]
40. Pham, X.Q.; Le, L.H. Investment in the context of financial constraints: A case of Vietnamese listed firms. Minist. Sci. Technol. Vietnam.; 2023; 65, pp. 8-15. [DOI: https://dx.doi.org/10.31276/VJST.65(10).08-15]
41. Liu, X.; Li, H.; Li, H. Analyzing relationship between financing constraints, entrepreneurship, and agricultural company using AI-based decision support system. Sci. Program.; 2022; 2022, 1634677. [DOI: https://dx.doi.org/10.1155/2022/1634677]
42. Yang, S.; Wang, C.; Lyu, K.; Li, J. EPT law and total factor productivity of listed firms: Promotion or inhibition?. Front. Environ. Sci.; 2023; 11, 1152771. [DOI: https://dx.doi.org/10.3389/fenvs.2023.1152771]
43. Chen, A.; Chen, H. Decomposition Analysis of Green Technology Innovation from Green Patents in China. Math. Probl. Eng.; 2021; 2021, 6672656. [DOI: https://dx.doi.org/10.1155/2021/6672656]
44. Rahman, H.U.; Zahid, M.; Al-Faryan, M.A.S. ESG and firm performance: The rarely explored moderation of sustainability strategy and top management commitment. J. Clean. Prod.; 2023; 404, 136859. [DOI: https://dx.doi.org/10.1016/j.jclepro.2023.136859]
45. Tabash, M.I.; Khan, S.U.; Daud, S. Impact of return on assets on firm performance: Evidence from Middle Eastern firms. Int. J. Financ. Stud.; 2020; 8, 57. [DOI: https://dx.doi.org/10.3390/ijfs8030057]
46. Titman, S.; Wessels, R. The determinants of capital structure choice. J. Financ.; 1988; 43, pp. 1-19. [DOI: https://dx.doi.org/10.1111/j.1540-6261.1988.tb02585.x]
47. Suriawinata, I.S.; Nurmalita, D.M. Ownership structure, firm value and the moderating effects of firm size: Empirical evidence from Indonesian consumer goods industry. J. Manaj. Dan Kewirausahaan; 2022; 24, pp. 91-104. [DOI: https://dx.doi.org/10.9744/jmk.24.1.91-104]
48. Mollick, A.V.; Haidar, M. Analyzing financial ratios as indicators to measure financial performance at PT. Industri Jamu dan Farmasi Sido Muncul Tbk. Asian J. Appl. Bus. Manag.; 2023; 2, pp. 173-188. [DOI: https://dx.doi.org/10.55927/ajabm.v2i2.3994]
49. Mustafa, M.; Rafiq, M.Y.; Khalid, S. Financial stability index for the financial sector of Pakistan. Economies; 2019; 7, 81. [DOI: https://dx.doi.org/10.3390/economies7030081]
50. Angrist, J.D.; Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion; Princeton University Press: Princeton, NJ, USA, 2008.
51. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol.; 1986; 51, pp. 1173-1182. [DOI: https://dx.doi.org/10.1037/0022-3514.51.6.1173]
52. Maçaira, P.; Thomé, A.M.T.; Oliveira, F.C.; Ferrer, A. Time series analysis with explanatory variables: A systematic literature review. Environ. Model. Softw.; 2018; 107, pp. 199-209. [DOI: https://dx.doi.org/10.1016/j.envsoft.2018.06.004]
53. Austin, P.C.; Stuart, E.A. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat. Med.; 2015; 34, pp. 3661-3679. [DOI: https://dx.doi.org/10.1002/sim.6607] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26238958]
54. Hagemann, A. Placebo inference on treatment effects when the number of clusters is small. J. Econom.; 2018; 213, pp. 190-209. [DOI: https://dx.doi.org/10.1016/j.jeconom.2019.04.011]
55. Zhou, J.; Mao, J.; Tian, M.; Wang, Z. Does informal environmental regulation matter? Evidence on the different impacts of communities and ENGOs on heavy-polluting firms’ green technology innovation. J. Environ. Plan. Manag.; 2022; 67, pp. 2668-2694. [DOI: https://dx.doi.org/10.1080/09640568.2023.2197554]
56. Guo, L.; Hu, C.; Fan, M.; Mao, J.; Tian, M.; Wang, Z.; Wei, Y. The spillover effects of environmental regulations: A perspective of Chinese unregulated firms’ tax burden. China World Econ.; 2023; 31, pp. 84-111. [DOI: https://dx.doi.org/10.1111/cwe.12465]
57. Fang, Y.L.; Shao, Z. Whether green finance can effectively moderate the green technology innovation effect of heterogeneous environmental regulation. Int. J. Environ. Res. Public Health; 2022; 19, 3646. [DOI: https://dx.doi.org/10.3390/ijerph19063646] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35329333]
58. Deng, J.; Yang, J. EPT and green innovation of heavily polluting enterprises: A quasi-natural experiment based on the implementation of China’s EPT law. PLoS ONE; 2023; 18, e0286253. [DOI: https://dx.doi.org/10.1371/journal.pone.0286253]
59. Ma, M. Study on the impact of environmental tax on corporate green innovation under the dual carbon target based on A-share mining listed companies. Highlights Bus. Econ. Manag.; 2023; 6, pp. 220-231. [DOI: https://dx.doi.org/10.54097/hbem.v6i.6321]
60. Sobel, M.E. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol.; 1982; 13, pp. 290-312. [DOI: https://dx.doi.org/10.2307/270723]
61. MacKinnon, D.P.; Lockwood, C.M.; Hoffman, J.M.; West, S.G.; Sheets, V. A comparison of methods to test mediation and other intervening variable effects. Psychol. Methods; 2002; 7, pp. 83-104. [DOI: https://dx.doi.org/10.1037/1082-989X.7.1.83]
62. Zhang, J.; Li, S. EPT, corporate ESG performance, and green technological innovation. Front. Environ. Sci.; 2021; 10, 982132. [DOI: https://dx.doi.org/10.3389/fenvs.2022.982132]
63. Dong, X.; Yang, Y.; Zhao, X.; Feng, Y.; Liu, C. Environmental Regulation, Resource Misallocation and Industrial Total Factor Productivity: A Spatial Empirical Study Based on China’s Provincial Panel Data. Sustainability; 2021; 13, 2390. [DOI: https://dx.doi.org/10.3390/su13042390]
64. Ai, H.; Hu, S.; Li, K.; Shao, S. Environmental regulation, total factor productivity, and enterprise duration: Evidence from China. Bus. Strategy Environ.; 2020; 29, pp. 2284-2296. [DOI: https://dx.doi.org/10.1002/bse.2502]
65. Lei, X.; Wu, S. Improvement of different types of environmental regulations on total factor productivity: A threshold effect analysis. Discret. Dyn. Nat. Soc.; 2019; 2019, 9790545. [DOI: https://dx.doi.org/10.1155/2019/9790545]
66. Qin, L.; Liu, S.; Zhan, C.; Duan, X.; Li, S.; Hou, Y. Impact of China’s local government competition and environmental regulation on total factor productivity. SAGE Open; 2023; 13, 21582440231160438. [DOI: https://dx.doi.org/10.1177/21582440231160438]
67. Zhang, Y.; Xing, C.; Wang, Y.-H. Does green innovation mitigate financing constraints? Evidence from China’s private enterprises. J. Clean. Prod.; 2020; 264, 121698. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.121698]
68. Cai, W.; Ye, P. How does environmental regulation influence enterprises’ total factor productivity? A quasi-natural experiment based on China’s new environmental protection law. J. Clean. Prod.; 2020; 276, 124105. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.124105]
69. Yuan, J.; Zhang, D. Research on the impact of environmental regulations on industrial green total factor productivity: Perspectives on the changes in the allocation ratio of factors among different industries. Sustainability; 2021; 13, 12947. [DOI: https://dx.doi.org/10.3390/su132312947]
70. Cai, W.; Bai, M.; Davey, H. Implementing EPT in China: An alternative framework. Pac. Account. Rev.; 2022; 34, pp. 479-513. [DOI: https://dx.doi.org/10.1108/PAR-02-2021-0021]
71. Diab, M.Y.; Khalifa, M.A.; Rady, T.A.; Abo Laban, A.M. Proposed framework for The Environmental Tax System to Reduce Environmental Pollution and Contribute to Sustainable Development. J. Environ. Sci.; 2022; 51, pp. 41-88. [DOI: https://dx.doi.org/10.21608/jes.2022.110369.1135]
72. Irianto, E.S.; Rosdiana, H.; Tambunan, M. On quest of environmental tax implementation in Indonesia. E3S Web Conf.; 2018; 52, 00013. [DOI: https://dx.doi.org/10.1051/e3sconf/20185200013]
73. Yu, C.-H.; Wu, X.; Zhang, D.; Chen, S.; Zhao, J. Demand for green finance: Resolving financing constraints on green innovation in China. Energy Policy; 2021; 153, 112255. [DOI: https://dx.doi.org/10.1016/j.enpol.2021.112255]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
1 School of Law and Business, Sanjiang University, Nanjing 210012, China;
2 School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China