Introduction

To achieve harmonious coexistence between humans and nature, green transformation and low-carbon development are challenges that all countries must confront. In the face of escalating environmental, ecological, and climate change issues, countries around the world have taken measures to reduce carbon emissions. According to estimates by the China Hua Economic Industry Research Institute, China’s carbon dioxide emissions reached 9.894 billion tons in 2020, ranking first globally. The emissions from the electricity and industrial sectors collectively contribute to more than two-thirds of the total emissions. As the largest global emitter of greenhouse gases, China introduced dual carbon objectives during the 2020 United Nations General Assembly. With the aim of reducing carbon emissions and facilitating green transformation, the Chinese government proposed and implemented additional environmental regulatory measures (Gyamfi, 2022).

The emission trading scheme (ETS), which serves as a market-based instrument, has been extensively embraced by nations globally. Its main function is to determine the appropriate price for carbon emissions, address the negative externalities of carbon emissions, and encourage companies to protect the environment and reduce greenhouse gas emissions. It has become one of the most important policy instruments for addressing climate problems and is highly important for the sustainable development of human society (Klenert et al. 2018).

While enterprise development is undeniably crucial, China must address the enduring dilemma between economic growth and environmental protection to achieve its dual carbon objectives (Johnstone et al. 2017). To reduce carbon emissions, stricter environmental policy constraints are imposed on companies, intensifying internal pressures and directly inflating emission reduction costs, thereby straining production capital and further impacting green innovation and productivity levels within companies (Zhou et al. 2023). Nevertheless, green innovation necessitates substantial financial backing and enduring investment; thus, profit-driven companies are often dissuaded from embracing green innovation initiatives. Consequently, additional policy incentives are essential to encourage companies to adopt green innovation practices (Hong et al. 2024; Wang and Zhang, 2024; Wasiq et al. 2023).

In recent years, however, government subsidies for energy savings and emission reductions have gradually been withdrawn. Moreover, environmental regulatory approaches that utilize market mechanisms to address negative externalities are gradually being introduced. For example, China introduced the ETS in 2013 in an attempt to reduce carbon emissions through market allocation, market pricing, and voluntary transactions. The advantages of this system lie in its ability to efficiently control carbon emissions and reduce enterprise costs. However, as market trading replaces direct government subsidies, the choice between engaging in emission reduction measures or facing trading quotas on the market means a direct increase in operating costs for industrial companies, thus impairing their green transformation efforts.

Scholars have examined the impact of the ETS policy on carbon reduction from various angles. While some scholars posit that the ETS can lower CO2 emissions by fostering technological innovation within enterprises (Bai and Ru, 2024; Guerriero and Pacelli, 2023; Kruse-Andersen and Sørensen, 2024; Liu et al. 2024; Lv and Bai, 2021), others contend that enterprises tend to prioritize reducing output over investing in green technological advancements to meet emission reduction targets (Chen et al. 2021; Deng et al. 2023; Wang et al. 2023). A consensus on the extent to which the ETS stimulates green technological innovation has not yet been reached in the literature.

This study aims to explore the following questions: Will the ETS facilitate the reduction of government subsidies? How will green innovation in industrial enterprises be influenced? What is the operational mechanism? Which industrial enterprises are poised to benefit the most from ETS pilot initiatives? Against the backdrop of ETS deployment in China, this research employs ETS pilot policies as a quasinatural experiment to examine the causal links between the ETS, government subsidies, and green innovation and to delve into their operational mechanisms.

The subsequent chapters are outlined as follows: The second section provides the literature review and introduces the research hypotheses. The third section details the methodology and design, primarily presenting the data sources, models, and variable selection for this study. In the fourth section an empirical analysis of the effects of ETS pilot policies on government subsidy income and the green innovation of enterprises is presented. The fifth section presents the conclusions and discussion.

Literature review and research hypotheses

Literature review

The policy effect of the ETS

Presently, scholars are extensively deliberating on the policy implications of ETSs, primarily focusing on their economic, innovative, and environmental effects. From an economic perspective, there are various views among scholars regarding the correlation between ETSs and economic development. Dong and Xiao (2024) reported that carbon emission trading policies negatively impact GDP, undermining climate justice and resulting in inevitable welfare losses (Hübler et al. 2014). Additionally, dynamic shifts in economic growth targets and the intensity of environmental regulation demonstrate varying phases, particularly in relation to the green development industry (Li et al. 2022). Evaluating the impact of environmental regulation on economic growth from the standpoints of formal and informal environmental regulations leads to the conclusion that environmental regulation policies restrain economic growth (Xiao et al. 2021). Another perspective posits that carbon emission trading fosters economic growth, creating employment opportunities, especially in the development of a low-carbon economy (Liu and Sun, 2021). Dechezleprêtre et al. (2023) discovered that carbon emission trading not only reduces emissions but also augments the income and fixed assets of relevant companies. From a microeconomic perspective, these benefits are derived primarily from reducing a company’s emission reduction costs and selling emissions permits and quotas for economic gain (Oke et al. 2024; Tang et al. 2023). Moreover, Hao et al. (2023) confirmed the dynamic relationship between environmental regulation intensity and the rate of economic growth, revealing an inverted U-shaped curve between environmental regulation and the rate of economic growth (Lyu et al. 2022).

From an innovation standpoint, the importance of low-carbon technologies in attaining carbon neutrality goals and sustainable development objectives is increasing (Shang and Lv, 2023). The European Union Emission Trading Scheme (EU ETS) has not only reduced greenhouse gas emissions but also amplified the yield of green technology innovation (Egenhofer et al. 2011). China can glean valuable insights from the EU’s achievements. Numerous scholars have researched the innovation-promoting impacts of China’s ETS pilot policies. One perspective posits that the ETS does not significantly foster technological innovation and may even exert a restraining influence (Ren et al. 2022; Xin-gang et al. 2023; Zhang, 2024; Zhou and Wang, 2022). Yi et al. (2019) revealed that China’s environmental policy instruments do not sufficiently drive green innovation; only market-based environmental incentives within a reasonable scope can stimulate green innovation. An alternative viewpoint lends support to the Porter hypothesis by suggesting that the implementation of the ETS plays a considerable role in fostering sustainable and eco-friendly development. Zhang et al. (2021) noted that China’s ETS has strengthened green total factor productivity (GTFP) and reduced the number of carbon-intensive enterprises, thereby improving the effectiveness of green development and promoting regional carbon equity. Chen et al. (2024) contend that an increase in carbon prices has a notably positive effect on green innovation within enterprises, especially nonstate-owned enterprises, encouraging substantial green innovation endeavors. Companies with a limited ability to shift costs demonstrate increased initiative in implementing significant green innovations. Zhai et al. (2023) revealed that the ETS has a more pronounced effect on the efficiency of green innovation in eastern regions, economically developed areas, cities rich in resources, and those with strong environmental governance.

From an environmental perspective, the majority of studies indicate that the implementation of an ETS leads to a reduction in carbon emissions and generates positive policy outcomes. Zhang et al. (2020) conducted estimations of the practical implementation of a carbon emissions trading system in seven regions via a DID model, with existing research indicating a 24.2% decrease in carbon emissions in pilot provinces due to ETS implementation. However, scholars have argued that environmental policies aimed at saving energy exhibit rebound effects. For example, Lange et al. (2020) discovered that improvements in energy efficiency result in rebound effects that lead to increased energy consumption. Moreover, Pang and Timilsina (2021) assessed the impact of carbon emissions trading policies on the basis of a general equilibrium model, revealing a reduction in carbon emissions resulting from such policies, along with the attainment of economic efficiency (Chang et al. 2023).

Influence of Environmental Regulation on Sustainable Innovation

Many studies employ the “compliance cost” theory or the “innovation compensation” theory to validate the Porter hypothesis. Current research on the associations between environmental regulation and corporate green innovation generally falls into four categories: promotive effects, inhibitory effects, nonlinear relationships, and nonsignificant relationships (Chen et al. 2022; Liu et al. 2023; Tan and Zhu, 2022; Wang and Shao, 2019). Studies examining policy tools reveal that different environmental policy instruments yield varying outcomes. Command-and-control policies and market-driven approaches, for instance, demonstrate a positive promoting effect on green innovation. Bian and Zhao (2020), J. Zhang et al. (2020), and Fan et al. (2023) assessed the impact of tools such as command controls, pollution taxes, subsidies, and pollution permits on corporate green technology. Their findings indicate that auctioned permits have the most significant promoting effect, followed by pollution taxes and subsidies. In contrast, the role of direct regulation is suboptimal. Only when green sensitivity reaches a certain level can it foster the development of green supply chains and a stable strategy benefiting enterprises and consumers be realized while expediting the government’s enhancement of green supply chain management (Long et al. 2021). Bauman et al. (2008) argues that command-and-control policies have the potential to spur innovation in the pollution control cost curve, rendering them more effective than market-driven policies in stimulating innovation.

The existing research has not produced consistent findings on the influence of the ETS on corporate green innovation. What factors contribute to the variations in companies’ innovation behaviors? In China, the government and enterprises are the primary stakeholders in carbon reduction efforts. The government extends support to enterprises through subsidies, tax incentives, and discounted loans (Shao and Wang, 2023). Government subsidies serve a dual purpose: they stimulate green innovation within enterprises, thus driving the advancement of energy-efficient and emission-reducing technologies, and enhance production efficiency (Hu et al. 2023). Additionally, these subsidies play a pivotal role in improving surplus management, boosting competitiveness, and enhancing overall corporate performance; thus, they effectively bolster corporate profits (Zhao et al. 2022). Consequently, government subsidies represent a crucial incentive policy aimed at fostering green innovation and facilitating carbon reduction initiatives within enterprises.

Existing research has focused predominantly on the role of the ETS, with limited exploration of how environmental regulations influence the reduction of government subsidies and drive industrial enterprises to increase internal green innovation after subsidy withdrawal. In contrast to the current literature, this paper makes significant contributions in the following areas. First, it investigates the shift from traditional command-and-control environmental regulations to market-based approaches, where ETS pilot policies leverage market mechanisms to lessen enterprises’ dependence on regulatory burdens and government subsidies, thereby fostering a competitive environment conducive to “top-by-top competition” in green innovation among enterprises. Second, it delves into the mechanisms through which ETS pilot policies enhance the green innovation performance of enterprises; operational cost mechanisms and green R&D investment strategies are analyzed to elucidate how ETS pilot policies enhance green innovation outcomes. Third, this paper examines the diverse impacts of ETS pilot policies on government subsidies and green innovation performance, providing insights into the contextual factors influencing the effectiveness of ETS pilot policies. Finally, it empirically evaluates the selective impact of ETS pilot policies, which are crucial for traditional excess capacity reduction and enhancing the competitiveness of high-quality enterprises, and offers valuable practical guidance.

Research hypothesis

In traditional command-and-control environmental regulations, when local firms face soft budget constraints, the government compensates loss-making firms through financial subsidies, tax incentives, and credit concessions (Tenev et al. 2002). Owing to information asymmetry, local governments cannot effectively determine whether a company’s losses result from bearing policy burdens. Under the guidance of relevant policies, local governments can only continuously subsidize loss-making enterprises. Under market-oriented reforms, the soft budget constraint of enterprises becomes a hard constraint to improve resource allocation efficiency (Jin and Zou, 2003). The ETS pilot policy, as a market-oriented environmental regulation instrument, aims to reduce corporate carbon emissions through bilateral market transactions. Unlike the direct closing of high-emission and high-pollution enterprises or granting environmental subsidies or tax breaks for certain environmental behaviors, the ETS policy no longer focuses on regulating the improvement of enterprises’ environmental equipment or targeting emission reduction technologies. Instead, companies are given greater autonomy under a market-based mechanism to promote green innovation through market transactions. Moreover, pilot provinces and cities have formulated detailed implementation rules for carbon reduction accounting, introduced methods to quantify enterprises’ carbon emissions, and monitored and exposed enterprises’ carbon emissions, thereby reducing the moral hazard of the state paying for enterprises to a certain extent. This alleviates the problem of incentive incompatibility under asymmetric information conditions between the government and enterprises. The institutional development of the ETS can reduce government subsidy revenues for enterprises to a certain extent. Therefore, the following hypothesis is proposed in this study:

H1: The ETS pilot policy has led industrial companies in the pilot provinces and cities to opt out of state subsidies.

In the absence of environmental regulations, external environmental impacts lead to insufficient incentives for green innovation in companies. Since the benefits generated by corporate green innovation activities have societal impacts, the private sector lacks the motivation to bear the costs of green innovation. Environmental regulatory measures help promote the engagement of companies in green innovation activities by correcting negative externalities; thus, they stimulate green innovation (Peng et al. 2021). Therefore, as a market-oriented environmental policy instrument, the ETS can effectively price carbon emissions, correct environmental market failures, and promote green innovation in enterprises (Hu et al. 2023). According to the innovation-induced theory proposed by Hicks, (1963), the purpose of technological innovation introduction by companies is to reduce the use of relatively expensive production factors. The direction of technological innovation by firms is influenced by changes in the relative prices of factors. The ETS leads to a relative increase in the price of emission allowances as a factor. To save costs, companies engage in green innovation by developing new technologies to reduce carbon dioxide emissions and reduce operating costs. In other words, companies are willing to pay the cost of green innovation to reduce the cost of carbon emissions. Therefore, this study hypothesizes the following:

H2: The ETS pilot policy promotes the green innovation performance of industrial companies in pilot provinces and cities.

If the ETS pilot policy leads to a reduction in government subsidy revenue for industrial firms in the pilot area while promoting green innovation performance, this study further investigates the internal mechanism of the impact of the ETS pilot policy on firms’ green innovation performance. According to the “Porter hypothesis”, environmental regulations increase firms’ operating costs, forcing them to compensate for these regulatory costs through green innovation and modernization. This shows a cost channel through which environmental regulatory policy influences firms’ green innovation performance. The ETS quotas affect firms’ operating costs in the following ways. First, when production is organized by carbon emission quotas, capacity cannot be fully utilized, and production costs cannot be diluted by economies of scale. Second, when the ETS quotas are exceeded, enterprises must purchase additional quotas in the market, which increases operating costs. Third, the short-term adjustments enterprises make when facing ETS quotas, may cause them to incur friction costs.

Moreover, the alleviation of the cost channel of the “Porter hypothesis” depends on whether the cost compensation enterprises utilize is innovation driven. Increasing green R&D investment enhances the green innovation performance of enterprises (Zhang et al. 2023). Under carbon emission restrictions, the ETS pilot policy increases the compliance cost of the inefficient capacity of zombie enterprises, releases dormant resources, boosts market competition, improves voluntary allocation and efficiency, and encourages enterprises to increase their green R&D investment to promote green innovation, thereby improving production efficiency. The increase in operating costs caused by the ETS pilot policy exacerbates budget constraints for enterprises. Under the objective function of profit maximization, the optimal choice for enterprises is to achieve technological renewal by increasing their investment in green R&D. By increasing investment in green R&D, firms can also create a surplus of carbon quotas and generate additional revenue and benefits through market transactions. From the perspective of intertemporal optimization, firms trade short-term losses and R&D costs for long-term green innovation performance. Therefore, this study hypothesizes the following:

H3: The ETS pilot policy can improve the green innovation performance of companies through both the “pressure effect” of increased operating costs and the “dynamic effect” of increased green R&D investments.

Methods and data

Data sources

This study selects the first group of major emitting industrial enterprises identified as study subjects under the ETS pilot policy in 7 pilot provinces and cities (Beijing, Tianjin, Hubei, Chongqing, Guangdong, Shenzhen, and Shanghai). First, with the exception of Shenzhen, the other pilot areas are provinces and directly controlled municipalities. To ensure the consistency of the study scope, Shenzhen was merged with Guangdong Province. Second, the list of major emitting enterprises is identified on the basis of the carbon emission trading platform and the national enterprise information disclosure system. Third, on the basis of the 8 major emitting industries covered by the comprehensive implementation of China’s emissions trading system (petrochemical, steel, nonferrous metals, papermaking, chemical industry, building materials, aviation, and electricity), the relevant enterprises in the list are selected as the treatment group. Finally, listed companies in related industries from nonpilot provinces are selected as the control group.

The data selected for this study come from the WIND and CSMAR databases. Using 2008 as the base year, this study sampled 8 listed companies in key energy-consuming industries in the A-share market from 20082018. The data were subjected to the following treatments: exclusion of financial, ST, and *ST samples; removal of samples with significant missing information; and truncation of tails for the main continuous variables at the 1% level on both sides. Finally, 78 listed companies were selected as the treatment group and 466 listed companies were selected as the control group, yielding a total of 5984 panel data points.

Model Setting and Variable Selection

Data sources

The ETS pilot policy has only been introduced in some provinces and cities, and the establishment of the pilot zones is an exogenous, quasinatural experiment within the economic system that fulfills the condition of exogeneity. This study chooses 2011 (Liu et al. 2020) rather than 2013 (Dong et al. 2020) as the starting year for the ETS pilot project. The reason for this is that although formal emissions trading did not begin until 2013, the preparatory work and the most important steps for emissions trading took place in 2011. These include the establishment of some trading systems, the formulation of carbon trading schemes, the development of procedures for handling carbon emission pilots, the setting of overall control targets, and the establishment of mechanisms for monitoring, reporting, and verification (MRV). As the key aspects of setting a carbon quota, measuring carbon emissions, and accounting for carbon trading, these schemes and processes are essential components of the systematic implementation of carbon emissions trading. Owing to the “announcement effect” (Xiao et al. 2021), when the National Development and Reform Commission announced the “Notice on Carrying Out Carbon Emission Trading Pilot Work,” enterprises across the country adjusted their production and investment behavior according to this policy.

Thus, this study builds the following difference-in-differences (DID) regression model to confirm whether the ETS pilot policy has caused government subsidies for major emitting industrial companies in the pilot areas to end:

$$\begin{array}{l}{{GS}}_{{ijkt}}=\beta +\alpha {Treat}\times {Time}+\delta X+{{Firm}}_{k}+{{Pro}}_{j}\times {{Year}}_{t}\\\qquad\quad\;\;\,+\,{{Ind}}_{i}\times {{Year}}_{t}+\varepsilon _{{ijkt}}\end{array}$$
(1)

where the subscripts i, j, k, and t represent industry, province, enterprise, and year, respectively. \({{GS}}_{{ijkt}}\) is the logarithm of government subsidy income. \({Treat}\times {Time}\) is the key explanatory variable, where Time is a dummy variable for policy implementation time, taking a value of 0 before the implementation of the carbon emission trading pilot policy and 1 in the implementation year and thereafter. Treat is a dummy variable for pilot enterprises; it has a value of 1 for businesses in provinces that have carbon emission trading pilot programs and 0 for businesses outside of pilot regions. Other control variables are denoted by X, \({{Firm}}_{k}\) is the individual fixed effect, \({{Pro}}_{j}\times {{Year}}_{t}\) is the province-time fixed effect, and \({{Ind}}_{i}\times {{Year}}_{t}\) is the industry-year fixed effect.

Equation (2) is used to test the impact of the ETS pilot policy on the green innovation performance of key-emitting industrial enterprises.

$$\begin{array}{l}{{GP}}_{{ijkt}}={\beta }_{1}+{\alpha }_{1}{Treat}\times {Time}+{\delta }_{1}{X}_{1}+{{Firm}}_{k}+{{Pro}}_{j}\times {{Year}}_{t}\\\qquad\quad\;\;\,+\,{{Ind}}_{i}\times {{Year}}_{t}+\varepsilon _{{ijkt}}\end{array}$$
(2)

where \({{GP}}_{{ijkt}}\) represents the logarithm of the green innovation level of the enterprise, which is used to measure green innovation performance. \({X}_{1}\) represents other control variables, and the remaining variables are the same as those in Eq. (1).

Variable selection

(1) Dependent variables. First, there are government subsidies to enterprises (GSs). This study uses the logarithm of the amount of government subsidies received by enterprises as a measure of the enterprise’s budget constraint. Government subsidies are derived from nonoperating income in the form of government grants. The second factor is enterprise green innovation performance (GP). Considering that green patents represent the core content of environmental technology and have natural advantages in standardization, informatization, and scale, this study measures enterprise green innovation performance on the basis of these advantages. Furthermore, this study opts for using the relatively timely, stable, and reliable quantity of green patent applications over the number of green patents granted. The reasons are as follows: first, there is a certain time lag and uncertainty in patent grants (Lian et al. 2022; Xu et al. 2023); second, patent applications serve as evidence of the development and introduction of innovative technologies by enterprises. This innovative technology is often applied to the production process of the enterprise before the patent application is submitted, thereby influencing the enterprise’s green innovation performance before a patent is granted. In this study, the identification and matching of the green invention patents of listed companies are based on the International Patent Classification (IPC) codes in the “International Patent Classification Green List”.

(2) Control variables. Companies with higher debt-to-equity ratios often receive more government subsidies (Lukas and Thiergart, 2019). The amount of government subsidies received in a given year is also related to the business performance and growth of a company (Bhagat and Bolton, 2008). Therefore, this study selects a firm’s debt-equity ratio, long-term debt ratio, net profit growth rate, fixed asset investment ratio, net cash flow from operating activities, firm size, number of employees, and firm age as control variables in Eq. (1). Research has shown that a firm’s green innovation performance is related to its financial leverage, growth, profitability, and audit quality (Aguilera-Caracuel and Ortiz-de-Mandojana, 2013). Therefore, in Eq. (2), the company’s debt ratio, the proportion of long-term debt, the net profit growth rate, the proportion of investment in fixed assets, audit quality, company size, the number of employees, and the age of the company are selected as control variables. Table 1 shows the descriptive results of the main variables.

Table 1 Descriptive statistics (N = 5984).
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Empirical analysis

Benchmark regression results

Table 2 presents the regression analysis examining the impact of ETS pilot policy implementation on government subsidies and green innovation performance among industrial enterprises (H1 and H2). Models 1–3 show the baseline regression results for government subsidies. The findings demonstrate a statistically significant negative association at the 1% significance level between ETS pilot policy implementation and government subsidies, as indicated by the \({Treat}\times {Time}\) coefficient. The results suggest that following ETS pilot policy implementation, the industrial enterprises in the pilot regions received notably lower government subsidies than those in nonpilot areas. Industrial enterprises exhibiting higher asset‒liability ratios (Lev), faster net profit growth (Ni), larger fixed asset investments (Rar), larger company sizes (Size), greater numbers of employees (Noe), and higher net cash flows from operating activities (Opncf) tended to obtain increased government subsidies. Models (4)-(6) present the regression results for green innovation performance. The results show a statistically significant positive relationship at the 1% level for the \({Treat}\times {Time}\) coefficient. These results indicate that after executing the ETS pilot policy, the industrial firms in the pilot regions exhibited markedly greater green innovation performance than did those in nonpilot areas. The findings indicate that the ETS pilot policy increased competitiveness among and motivated greater green innovation in industrial enterprises. Additionally, the analysis shows that green innovation performance maintained positive associations with the net profit growth rate (Ni), fixed asset investment share (Rar), number of employees (Noe), and audit quality (Audit). The primary contributing factor could be the healthy financial status of the enterprise and its substantial pool of talent, which offer ample external resources for promoting green innovation, consequently enhancing the company’s performance in this realm (Wang et al. 2024). In contrast to government subsidies, a higher company asset‒liability ratio (Lev) is associated with lower green innovation performance.

Table 2 Baseline regression results.
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Parallel trends test

The application of the DID model necessitates the fulfillment of the parallel trends’ assumption by both the control group and the experimental group to ensure unbiased estimation. This assumption guarantees that any preexisting differences between the groups are not caused primarily by the policy under examination but rather by other unobserved common factors or random errors. As a result, when observing discrepancies following policy implementation, there is a greater degree of confidence in attributing them to the policy’s effect rather than other potential factors. If the treatment group and control group exhibit divergent trends prior to policy implementation, these dissimilarities may be mistakenly interpreted as part of the policy effect, consequently leading to biased estimation results. The fulfillment of the parallel trends assumption significantly enhances the accuracy of DID estimation. In particular, in the baseline regression model employed in this study, the parallel trends assumption reflects the expectation that the patterns exhibited in terms of government subsidies and green innovation in both pilot cities and nonpilot cities should demonstrate similar temporal trends before the implementation of the ETS pilot policy. However, subsequent to policy implementation, the parallel trends between the experimental and control groups are anticipated to be disrupted, resulting in significant changes in government subsidies and green innovation within the pilot cities compared with the nonpilot cities, making the policy evaluation results meaningful. We use an event study to assess prepolicy coefficient estimates (Jacobson et al. 1993) and test parallel trends. The results show no significant prepilot differences in GS(a) or GP(b), as shown in Fig. 1. Postlaunch, GS and GP became significantly negative and positive, respectively, satisfying parallel trends.

Fig. 1
figure 1

Parallel trend test.

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Robustness test

To ensure the reliability of the research findings, this study conducted a series of robustness tests.

The impact of the ETS pilot policy on government subsidies for industrial enterprises

First, to increase the robustness of the variable measurement, this study reevaluates government subsidies by logarithmically transforming the amount of government subsidies into nonoperating income (GS1). The regression results are presented in Model 1 of Table 3, which shows that different measurement approaches minimally affect the \({Treat}\times {Time}\) regression coefficient for government subsidies. Second, we exclude the impact of nonpilot-affiliated enterprises (GS2). Among the treatment group samples obtained by matching affiliated companies of listed companies, 18 are located in nonpilot provinces. We are concerned that affiliated companies may be less affected by carbon regulation than government subsidies for listed companies, potentially interfering with the baseline regression results. Therefore, Model 2 of Table 3 presents the results excluding this portion of the sample, and the \({Treat}\times {Time}\) regression coefficient remains significant at the 1% level. Third, we eliminate the potential for government subsidy leakage. To mitigate the reduction in government subsidy income for industrial enterprises participating in the pilot project, offset by low-interest loans provided to the enterprises, we conduct a more in-depth analysis of the impact of the ETS pilot policy on the industrial debt financing cost. The regression results in Model 3 of Table 3 indicate that the coefficient of \({Treat}\times {Time}\) is not significant. This suggests that the reduction in government subsidies for industrial enterprises within the pilot project is unrelated to government low-interest loans.

Table 3 Robustness test for government subsidies in industrial firms.
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The impact of the ETS pilot policy on the green innovation performance of industrial enterprises

First, from the perspective of variable replacement, this study measures green innovation performance by selecting the ratio of the number of green invention patent applications to the total number of patent applications (GP1). The regression results are presented in Model 1 of Table 4. The \({Treat}\times {Time}\) regression coefficient remains significant at the 1% level. Second, to address estimation biases caused by data lags and consider the timeliness of patent authorization, this study conducts robustness testing by lagging green innovation performance by one period (GP-1). The regression results in Model 2 of Table 4 show a significant coefficient for the interaction term. Third, excluding the impact of nonpilot-affiliated enterprises (GP2), samples with controlled affiliated enterprises belonging to listed companies located in nonpilot provinces are excluded. The regression results in Model 3 of Table 4 indicate that neither the parameter estimates nor the significance have changed significantly.

Table 4 Robustness test for green innovation performance in industrial firms.
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Other robustness tests

In addition to the abovementioned robustness tests, this study applies the propensity score matching and difference-in-differences (PSM-DID) method to alleviate potential selection biases in the existing sample and conducts additional robustness tests. This study employed a 1:4 nearest-neighbor matching method to pair control group companies. When studying the impact of the ETS pilot policy on government subsidy income for industrial enterprises, the selected covariates include the revenue growth rate (Gro), long-term debt ratio (Ld), fixed asset investment ratio (Rar), net cash flow from operating activities (Opencf), company size (Size), number of employees (Noe), and company age (Age). When investigating the impact of the ETS pilot policy on the green innovation performance of industrial enterprises, the chosen covariates include the logarithm of total assets (Assert), revenue growth rate (Gro), long-term debt ratio (Ld), fixed asset investment ratio (Rar), company size (Size), number of employees (Noe), and company age (Age). Table 5 displays the results of the PSM balance test. After matching, the standard deviations of all the variables significantly decrease, and the majority of the postmatching mean deviations are noticeably smaller than the prematching standard deviations. The results indicate that the matching meets the requirement of standard deviations being less than 10%, signifying that there are no significant differences in covariates between the experimental and control groups, and the PSM balance test is passed. On this basis, this study estimates the impact of the ETS pilot policy on government subsidies and the green innovation performance of industrial enterprises via a matched sample. The regression results are shown in Table 6. Whether utilizing the sample that satisfies the common support assumption or the sample that employs weighted sample regression, the \({Treat}\times {Time}\) coefficient consistently remains consistently significant, and its direction aligns with the baseline regression results, confirming the robustness of the baseline regression results.

Table 5 PSM balance tests.
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Table 6 PSM-DID tests.
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Placebo test

Owing to the arbitrary selection of the experimental and control groups and the artificial setting of the experimental time, there may be selection bias in the sample. Following the approach of (Cai et al. 2016), a placebo test is conducted by randomly assigning the ETS pilot provinces and cities. Specifically, six provinces and cities are randomly selected from the sample coverage as the treatment group, and the remaining provinces and cities automatically serve as the control group for permutation testing. This process is repeated 500 times, randomly sampling to construct the pseudopolicy shock variable interaction term \({{Treat}}^{{false}}\times {Time}\). Thus, a distribution plot of the estimated results of the newly constructed 500 interaction terms is obtained. In Fig. 2, the left side displays the placebo test for government subsidies, and the right side shows the placebo test for green innovation performance. The vertical dashed line indicates the impact coefficients of policy exit on government subsidies and green innovation performance for enterprises during the current policy year. When policy years are randomly generated, the mean impact of the placebo test interaction term on government subsidies and green innovation performance for enterprises is close to 0. The actual estimated values significantly deviate from the estimated coefficients of the placebo test, indicating the robustness of the baseline regression results.

Fig. 2
figure 2

Diagram of the placebo test.

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Mechanism analysis

To explore the transmission mechanism of the impact of the ETS pilot policy on the green innovation performance of industrial enterprises (H3), this study primarily employs a mediation effect model to investigate the role of enterprise operating costs (Cost) and green research and development investment (Gest). Building on Eq. (2), this study further develops Eq. (3) with the intermediate variable as the dependent variable and Eq. (4) incorporating the intermediate variable to quantify the explanatory power of the aforementioned mechanism. The specific formulas are as follows:

$${M}_{{ijkt}}={\beta }_{2}+{\alpha }_{2}{Treat}\times {Time}+{\delta }_{2}{X}_{2}+{{{Firm}}_{k}+{{Pro}}_{j}\times {{Year}}_{t}+{{Ind}}_{i}\times {{Year}}_{t}+\varepsilon }_{{ijkt}}$$
(3)
$$\begin{array}{l}{{GP}}_{{ijkt}}={\beta }_{3}+{\alpha }_{3}{Treat}\times {Time}+{\rm{C}} {M}_{{ijkt}}+{\delta }_{3}{X}_{3}+{{Firm}}_{k}\\\qquad\quad\;\;\,+\,{{Pro}}_{j}\times {{Year}}_{t}+{{Ind}}_{i}\times {{Year}}_{t}+\varepsilon _{{ijkt}}\end{array}$$
(4)

where \({M}_{{ijkt}}\) represent the two intermediate variables of enterprise operating costs and green research and development investment, respectively.

Model 1 of Table 7 indicates that \({Treat}\times {Time}\) has a significantly positive effect on enterprise operating costs. Model 2 shows that \({Treat}\times {Time}\) and enterprise operating costs have a significant effect on the green innovation performance of enterprises. However, the former has a positive impact, whereas the latter has a negative impact. This suggests a masking effect of enterprise operating costs on the impact of the ETS pilot policy on the green innovation performance of enterprises. The indirect effect is 0.0195, constituting 38.21% of the total effect. Model 3 indicates that \({Treat}\times {Time}\) has a significantly positive effect on the green research and development investment of enterprises. Model 4 shows that \({Treat}\times {Time}\) and green research and development investments have a significantly positive effects on the green innovation performance of enterprises. Combining the estimated results in Table 2, including the variable of green research and development investment, \({Treat}\times {Time}\) significantly affects the green innovation performance of enterprises. This finding indicates that green research and development investment plays a partial mediating role between the ETS pilot policy and the green innovation performance of enterprises. The indirect effect is 0.0237, constituting 54.96% of the total effect. Under the influence of the ETS pilot policy, the impact of green research and development investment on enterprises exceeds that of enterprise operating costs. Therefore, the impact of the ETS pilot policy on the green innovation performance of enterprises is characterized primarily by “top-by-top competition”. This conclusion aligns with previous research findings, primarily due to the promotion of enterprises by the ETS to increase investments in research and development personnel and funds. The integration of energy-saving and emission-reduction technologies through research and development initiatives has mitigated carbon emissions and bolstered the eco-friendly innovation efforts of enterprises (Yu et al. 2023). Consequently, it has driven enterprises to allocate greater financial resources toward environmentally optimizing projects, compelling their involvement in green innovation activities and enhancing the efficiency of their green innovation (Xu and Liu, 2023).

Table 7 Mechanism tests.
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Heterogeneity analysis

Classified by different industries

Under the regulatory framework of market-oriented environmental regulation, various industries exhibit substantial disparities in their inherent incentives for engaging in green innovation. The level of marketization in industrial enterprises’ production and operations impacts the incentive effect. When a particular industry receives increased government subsidies, the incentive effect of this approach is limited. Conversely, when there are more government subsidy withdrawals, the incentive effect for green innovation in that industry becomes stronger (Wang, 2023; Yang et al. 2023; Zou and Zhang, 2022). Furthermore, industries with weaker transferability and lower costs of green innovation have stronger inherent incentive effects (Nguyen and Vu, 2024). For example, the power industry possesses a strong ability to transfer carbon emission costs, which diminishes the intrinsic incentive for industrial enterprises to increase their green innovation performance.

In this study, sample companies are classified into 7 industries on the basis of the coverage of the ETS pilot policyFootnote 1. Part A of Table 8 reports the impact of the ETS pilot policy on government subsidies for industrial enterprises. Except for the mining industry, the \({Treat}\times {Time}\) coefficients are significantly negative, with the petrochemical and power industries exhibiting the most significant decreases in government subsidy income. Part B presents the regression results regarding the impact of the ETS pilot policy on the green innovation performance of industrial enterprises. The results indicate that upstream industry enterprises receive greater internal incentives from environmental regulation than do downstream enterprises. For example, industries facing overcapacity pressure, such as high-sulfur petroleum coke, steel, and nonferrous metal smelting, and those dealing with sustained market-driven carbon regulation policies tend to exhibit a stronger motivation for engaging in technological upgrading, energy conservation, and emission reduction practices. These industries often adopt proactive environmental protection strategies to consolidate their market positions. Therefore, under the ETS pilot policy, upstream enterprises are more likely to exhibit the characteristic of “top-by-top competition” in green innovation. This phenomenon may be attributed to the fact that heavy industries typically exhibit high carbon intensity, and their expansion is frequently associated with elevated consumption and emissions (Liu et al. 2021). Nonetheless, companies operating in sectors such as steel and petroleum face similar constraints due to environmental regulations, and they possess ample motivation to pursue eco-friendly innovation. Furthermore, they are capable of managing the escalating expenses linked to pollution abatement, which emerge with the tightening of governmental environmental regulations, aimed at facilitating the reduction of carbon intensity (Xiaobao et al. 2024).

Table 8 Heterogeneity analysis (1).
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Classification by Different Ownership

State-owned enterprises (SOEs) not only pursue short-term economic interests but also shoulder more social responsibilities; thus, they give greater attention to the comprehensive benefits of environmental and social impacts. In contrast, private enterprises often prioritize economic benefits to maximize short-term profits, and they are frequently more susceptible to various environmental policies. This study categorizes sample companies into SOEs and private enterprises (PEs), identifies heterogeneity through grouped regression tests, and conducts intergroup coefficient difference tests via the Fisher combination test. Table 9 reports the heterogeneous regression results based on the sample divided by enterprise ownership. The findings reveal that, compared with SOEs, the ETS pilot policy has a stronger effect on reducing government subsidy income and promoting green innovation performance for private enterprises. SOEs often have close relationships with the government and bear more policy burdens. Since the ETS pilot policy mainly regulates high-energy-consuming industries in the pilot provinces and cities without making mandatory provisions for withdrawal subsidies from high-energy-consuming enterprises, the withdrawal of government subsidies for SOEs is relatively weaker than that for PEs. When facing sustained market incentives and carbon regulatory policies, the PEs in the ETS pilot policy area face both challenges and opportunities, and demonstrate a stronger capacity for green innovation. Under the ETS pilot policy, PEs are more likely to exhibit the characteristic of “top-by-top competition” in green innovation. This contrasts with the conclusions of previous research (Hao et al. 2022). This is likely because private enterprises, in contrast to state-owned enterprises, frequently possess more adaptable market response mechanisms. When encountering policy changes and market opportunities, private enterprises can swiftly adjust their strategies and capitalize on the opportunity for green innovation.

Table 9 Heterogeneity analysis (2) and (3).
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Categorization by external financing dependency

Innovation activities in enterprises require substantial financial investment, and internal financing alone cannot guarantee sufficient support for innovation activities (Hall and Lerner, 2010). This is especially true for green technology innovation, which demands substantial and sustained funding due to longer innovation cycles. As a result, enterprises rely more on external funding support when engaging in green innovation (Qiu et al. 2020; Xiang et al. 2022) Following (Rajan and Zingales, 1995), the approach of, this study calculates the extent of external financing dependence by dividing the variance between capital expenditures and adjusted operating cash flow by capital expenditures. The sample is divided into two groups on the basis of the median: high external financing dependence (High) and low external financing dependence (Low). Grouped regression tests identify heterogeneity between these groups, and Fisher’s combined tests examine differences in coefficients. As shown in Models 3 and 4 of Table 9, the results indicate that the ETS pilot policy has an overall inhibitory effect on government subsidies for enterprises with both high and low external financing dependence. However, this inhibitory effect is notably more pronounced in enterprises with high external financing dependence. This is due to the higher economic activity in regions where enterprises with high external financing dependence are located. Pilot enterprises in these regions can quickly introduce technology and capital for innovative research and development, thereby better realizing the green transformation dividends brought about by the ETS pilot policy. Enterprises with high external financing demonstrate a greater positive impact on green innovation performance than do those with low external financing. This suggests that an increase in external financing contributes to the “top-by-top competition” in green innovation driven by the ETS pilot policy. This finding aligns with previous research. In addition to providing financial support, obtaining external financing frequently indicates market acknowledgment of a company’s potential in regard to green innovation and development, thus further fostering increased investment in this area. Moreover, the enhancement of external financing also facilitates the industry’s shift toward a greener, lower-carbon, and environmentally friendly trajectory, consequently propelling the upgrading and transformation of the entire industry value chain (Xu, 2023).

Further analyses

Before the ETS pilot policy was formally implemented, the capacity utilization rate of traditional high-energy-consuming industries in China, such as steel and cement, was generally less than 70%, indicating severe overcapacity. This led to a widespread decline in industry profits and corporate operating income, accompanied by an increase in nonperforming assets. Furthermore, local government policy support and relatively lenient environmental regulations exacerbated regional overcapacity issues, resulting in significant industry losses and making establishing a favorable development pattern led by high-quality enterprisesFootnote 2 challenging. The implementation of the ETS pilot policy provided an opportunity to expedite the phase-out of inefficient capacity and enhance the market positions of high-quality enterprises. In the previous text, we confirmed the “top-by-top competition of green innovation” feature of the ETS pilot policy. Next, we further examine how the ETS pilot policy accelerates the phase-out of inefficient capacity and promotes the growth of high-quality enterprises in the industry. “top-by-top competition” in green innovation may be influenced by various factors, such as enterprise cost constraints, industry chain pressure, and competitive advantages (Triebswetter and Wackerbauer, 2008). Enterprises with varying levels of competitiveness face distinct environmental innovation costs and pressures for pollution control. Some scholars discuss the heterogeneity of environmental regulatory costs from the perspective of enterprise size and suggest that larger enterprises have lower compliance costs (Hilke, 1986). However, for traditional high-energy-consuming enterprises, a larger size may imply greater excess capacity, which leads to higher environmental innovation costs and pollution control costs. This situation is not conducive to the further expansion of high-quality enterprises. Therefore, it is more advantageous to start from market competitiveness to identify the realization path of “top-by-top competition of green innovation” for traditional high-energy-consuming enterprises.

Drawing on studies by (Fresard, 2010; Rhoades, 1985) and others, we select the fixed asset investment expansion rate (far) and the market share of the enterprise within the industry (fms)Footnote 3 as measures. Operating cash flow/net total liabilities (cfod) serves as a proxy variable to measure the market competitiveness of enterprises. Simultaneously, we introduce \({Treat}\times {Time}\) and the interaction term \({Treat}\times {Time}\times {EMC}\), where EMC represents enterprise market competitiveness, to measure the indicator of corporate market competitiveness. In general, more competitive companies face lower environmental innovation costs. When confronted with market-driven carbon regulations, these companies are more inclined to embrace proactive technological innovation and organizational management and expand their operations to capitalize on market opportunities. Enterprises with higher market competitiveness often demonstrate stronger bargaining power and better debt-paying capabilities. Table 10 presents the regression results. Model 1 suggests that the ETS pilot policy has a more pronounced incentive effect on the green innovation performance of enterprises with high fixed asset investment expansion rates. In other words, the contraction of the fixed asset investment rate weakens the incentive effect of the ETS pilot policy on the green innovation performance of enterprises. Model 2 demonstrates that the greater a company’s market share within the industry is, the greater the incentive effect of the ETS pilot policy on the company’s green innovation performance. Model 3 demonstrates that for every 1% increase in the net cash flow from operating activities/total liabilities, compared with nonpilot enterprises, pilot enterprises experience a 1.641% increase in green innovation performance. Enterprises with negative net cash flows from operating activities specifically experience a reduced incentive effect on green innovation performance. In summary, for enterprises with stronger market competitiveness, the inherent incentive effect of the ETS pilot policy on the green innovation performance of industrial enterprises is more pronounced. This makes it more likely to induce “top-by-top competition” in green innovation. This is because enterprises with strong market competitiveness generally possess greater resources and capital, allowing them to allocate more funds toward green technology innovation and the development of environmental facilities. As a result, they secure favorable positions within the market. This resource advantage enhances their capacity to adapt and respond effectively to pilot policies under the ETS, enabling them to capitalize on the opportunities presented by these policies (Qi et al. 2023).

Table 10 The impact of the ETS on the green innovation performance of enterprises with different market competitiveness levels.
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Conclusion and discussion

Conclusion

This paper considers the ETS pilot policy as a quasinatural experiment and uses PSM-DID to investigate the effects of the ETS pilot policy on government subsidies and the improvement of green innovation performance in industrial enterprises and the mechanisms through which these effects occur. The research conclusions are as follows: First, the ETS pilot policy significantly reduces government subsidies for industrial enterprises in pilot areas but increases the level of green innovation performance. Second, the impact of the ETS pilot policy on green innovation performance mainly stems from two mechanisms: operating costs and green R&D investment. The induced green innovation competition arises from increased operating costs and investment in green research and development. Heterogeneity analysis indicates that the incentive effects of government subsidy withdrawal and green innovation performance enhancement are stronger for upstream enterprises, private enterprises, and enterprises with high external financing dependence. Third, further analysis indicates that the ETS pilot policy demonstrates a “survival of the fittest” effect in green competition. Enterprises with higher fixed asset investment expansion rates (far), higher market shares within the industry (fms), and higher net cash flows from operating activities/total liabilities (cfod) experience stronger incentive effects on green innovation performance. This makes it easier to induce “top-by-top competition” in green innovation, enhancing the competitiveness of advantaged enterprises and eliminating backward capacity.

Discussion

Current research has not yet reached a consensus regarding the emission reduction effects of ETSs, whereas improving the efficiency of green innovation in industrial enterprises stands out as one of the most effective methods for ensuring environmental sustainability. The introduction of government subsidies incentivizes enterprises to mitigate environmental costs through the adoption of green innovation. Importantly, however, government subsidies are subject to change. As these subsidies are gradually phased out, what the future landscape of green innovation in enterprises will be becomes a pertinent question.

Our primary contribution is the examination of the influence of the ETS on government subsidies and green innovation in enterprises, as well as the empirical testing of the ETS’s potential to promote green innovation in enterprises through the “green leapfrogging” competition effect. No consensus on the emission reduction effects of ETSs has been reached in the literature (Deng et al. 2023; Kruse-Andersen and Sørensen, 2024). Enterprises are often encouraged by government R&D subsidies during the implementation of environmental regulatory policies, leading to a positive stimulus for engaging in green innovation in these enterprises (Zhang et al. 2024). The literature does not offer a clear explanation as to whether the promotion of green innovation is attributable to the ETS itself or to government R&D subsidies. Our focus, therefore, centers on evaluating the impact of the ETS on government subsidies and green innovation in enterprises. Our research demonstrates that the ETS can reduce government subsidies while simultaneously enhancing green innovation in enterprises, thus creating a win‒win scenario, which has not been previously discussed in the literature. In contrast to previous studies, this paper delves into the promotion of green innovation from the standpoint of the interaction between the government and enterprises, ultimately contributing to an overall reduction in policy costs for carbon reduction and incentivizing enterprises to fulfill carbon reduction targets in a more market-oriented manner.

The relationship between the ETS and enterprise green innovation is affected by various factors, resulting in significant uncertainty. On the one hand, green innovation requires substantial initial capital investment, entails long profit cycles, and involves unpredictable risks, which can lead to substantial market failures, including challenging external environments, reliance on government subsidies, and imperfect markets (Lin and Zhang, 2023). On the other hand, the carbon trading system necessitates a significant number of low-carbon technology experts. The establishment of the carbon market calls for professionals in finance, regulation, law, and other specialized fields to integrate multiple disciplines and specialties, such as environmental science, chemistry, management, finance, law, energy, and materials. Consequently, we analyze the intermediary effects from the perspectives of operating costs and green R&D investment. The study reveals that the impact of the green R&D investment mechanism outweighs the impact of the enterprise’s operating cost mechanism, which is consistent with the “cost‒benefit” theory, thus uncovering the “black box” of the ETS driving enterprises’ “green innovation” competitive leapfrogging, in contrast to the research by Hao et al. (2022). Our findings suggest that the implementation of ETS policies has accelerated the phasing out of obsolete production capacity in traditional industries, facilitated industrial upgrading, and bolstered the market positions of high-quality enterprises, extending the literature.

The existing research on the relationship between the ETS and green innovation in industrial enterprises has largely ignored factors such as the uneven development of enterprises. Furthermore, policy-making departments struggle to identify the policy focus from the perspective of enterprise attributes. In this study, we investigated the heterogeneity of the ETS in terms of industry attributes and enterprise characteristics. Our findings reveal that traditional industries such as petrochemicals and power, upstream enterprises, private enterprises, and highly leveraged enterprises experience a higher green innovation effect than do other industries and enterprises. This may be attributed to the advanced resource endowment and technological research and development conditions of these industries, as well as their stronger innovation capabilities and rapid response to the influence of the ETS. Consequently, following the comprehensive initiation of the ETS, the government should prioritize encouraging these enterprises to withdraw from government subsidies and incentivize these industries or enterprises to achieve carbon reduction goals through green R&D. For example, within the power industry, increasing investment in clean energy sources such as wind and solar power, reducing reliance on traditional fuels such as coal, and increasing the competitiveness of clean energy through technological innovation and cost reduction are crucial (Qi et al. 2020). Similarly, in the chemical industry, collaboration within the industry chain should be strengthened while promoting the development of a circular economy and actively adopting green production technologies. Additionally, there is a need to intensify technological innovation and research and development investment (Chung et al. 2023).

In conclusion, employing empirical methods to examine whether the ETS achieves mutually beneficial goals and elucidating its operational mechanisms addresses the gap in the literature regarding simultaneous reductions in carbon emissions and government subsidies.

Policy recommendations

On the basis of the research conclusions above, this study proposes the following policy recommendations. First, the ETS pilot policy, as a market-oriented environmental regulation strategy, can effectively replace command-type environmental regulation strategies that are primarily based on government subsidies. This fully implements an environmental governance model with market mechanisms as the mainstay, thereby reducing the overall policy costs for national carbon reduction. It encourages relevant enterprises to engage in healthy competition in a more market-oriented manner. Second, the ETS pilot policy has accelerated the exit of outdated capacity in traditional industries and enhanced the market competitiveness of high-quality enterprises. Therefore, China should accelerate the promotion and construction of the ETS policy in regions with traditional high-energy-consuming industries, improve collaborative mechanisms for green R&D innovation in enterprises, and gradually form a market development pattern of “survival of the fittest” enterprises. Finally, during the comprehensive promotion stage following the ETS policy pilot, the government should initially encourage traditional industries and upstream enterprises, such as petrochemicals and electricity, to phase out government subsidies. This will incentivize these traditional industries to increase R&D innovation, optimize industry structure, enhance market competitiveness, and achieve carbon reduction goals. In pilot regions, it is essential to introduce diversified financing channels to increase the accessibility of green R&D funds for enterprises, alleviate financial constraints, and provide substantial financial support for green innovation within enterprises.

Research prospects

This paper empirically investigates the impact and mechanisms of the pilot ETS policy on government subsidy income and the performance of green innovation in industrial enterprises. However, the study has certain limitations due to various conditions. Future research could consider the following aspects.

First, alternative methods should be used to measure enterprise green innovation and examine the influence of the ETS on green innovation within enterprises. Most current studies measure innovation via green patents, and this study is no exception. However, green innovation may include other forms of technological advancements or energy efficiency efforts that are not reflected in patent applications. Therefore, in future research, credible alternative methods for measuring green innovation should be employed to broaden the scope of this study. For example, green supply chain management, green brand building, and so on.

Second, in mechanism analysis, it is crucial to consider not only operating costs and green R&D investment but also other potential factors from the government’s perspective, such as government human capital and technological support. Further research is needed to examine whether other factors mediate the impact of the ETS on enterprise green innovation.

Third, it is important to note that our study is solely based on data from Chinese enterprises. Consequently, future research should conduct comparative studies based on data from multiple countries. Since climate issues are global in nature, conducting research from a global perspective can provide a more comprehensive assessment of the strengths and weaknesses of ETS policies.