Market-Oriented Environmental Regulation and Its Impact

Market-oriented environmental regulation is a form of induced regulation that pressures organizations while providing economic and non-economic incentives for voluntary compliance with specific conditions. It does not directly dictate what firms should do or not do. The literature on the impact of market-oriented environmental regulations on firm behavior is extensive, focusing on regulatory measures such as SO₂ emission trading policies (Ren et al., Reference Ren, Liu, Li, Wang and Chen2020), green credit policies (Lu et al., Reference Lu, Gao, Zhang and Wang2022), and carbon emission trading policies (Chen, Li, et al., Reference Chen, Li and Liu2024). SO₂ emission trading policies not only reduce SO₂ emission intensity but also incentivize polluters to engage in effective innovation (Dagestani et al., Reference Dagestani, Shang, Schneider, Cifuentes-Faura and Zhao2023), thereby enhancing firms’ total factor productivity. However, the productivity boost typically diminishes over time. Some scholars have highlighted the limitations of these policies in terms of emission reduction, primarily because lenient emission permit quotas may hinder the effective implementation of emission trading (Ji, Wang, Wang, & Tang, Reference Ji, Wang, Wang and Tang2024). Under green credit regulation, the policy exerts both punitive and incentivizing effects on high-pollution firms. It increases financing costs for high-pollution firms, while offering preferential interest rates to environmentally conscious industries (Lu et al., Reference Lu, Gao, Zhang and Wang2022). This dual effect encourages firms to enhance technological innovation and improve productivity, thus steering their development toward a green trajectory (Zhang, Wu, et al., Reference Zhang, Wu, He and Hao2022).

CETP is a system that enables firms to trade carbon allowances autonomously within an overall cap. The government allocates initial emission reduction quotas based on reduction targets, then optimizes firms’ production behaviors through carbon market prices and quota constraints. Carbon emissions trading incorporates both market and policy attributes. From a market perspective, it functions through a market-based mechanism driven by trading systems and price signals, thereby using economic incentives to guide firms to reduce greenhouse gas emissions. From a policy perspective, CETP serves as an effective tool for environmental protection. However, it inevitably pressures high-energy-consuming and high-emission firms, potentially leading to negative effects on short-term economic growth (Chen, Chen, et al., Reference Chen, Chen and Zhang2024).

Existing literature on the impact of CETP mainly focuses on areas such as pollution control, green innovation, and resource allocation. At the macro level, CETP has been found to reduce regional carbon emissions (Feng, Zhao, & Yan, Reference Feng, Zhao and Yan2024) and increase energy utilization efficiency (Zhang, Zhang, & Yu, Reference Zhang, Zhang and Yu2019) by enhancing technological capabilities. The number of firms participating in the carbon market is crucial for effective carbon emission reduction, and the longer the CETP is implemented, the greater its emission reduction effect (Shi et al., Reference Shi, Li and Gao2022). At the micro level, carbon markets have demonstrated significant emission reduction effects among firms in pilot regions (Chen, Chen, et al., Reference Chen, Chen and Zhang2024). However, the policy’s effects vary across different property rights and industries. Numerous scholars have found that CETP increases the total factor productivity of pilot firms (Bai, Liu, Zhang, & Feng, Reference Bai, Liu, Zhang and Feng2023) and promotes green innovation outputs (Chen, Li, et al., Reference Chen, Li and Liu2024), particularly in green invention patents, thereby supporting Porter’s hypothesis. Despite these findings, the conclusions of related studies remain inconsistent. For instance, Rogge, Schneider, and Hoffmann (Reference Rogge, Schneider and Hoffmann2011) found that the EU Emissions Trading System (ETS) had little substantial impact on green transformation and even had negative effects in certain industries. The above literature is summarized in Table A1.

Influencing Factors of Green Transformation

Research on the factors influencing firms’ green transformation has focused on both external and internal aspects. Externally, Zhou, Song, and Huang (Reference Zhou, Song and Huang2023) found that environmental regulation promotes firms’ green innovation, especially in regions with stricter regulatory intensity and industries with higher pollution levels. Regarding market-oriented environmental regulations, green credit policies facilitate green transformation by adjusting the size of bank loans and increasing firms’ environmental expenditures (Lu et al., Reference Lu, Gao, Zhang and Wang2022; Zhang, Wang, Li, Zhang, & Balezentis, Reference Zhang, Wang, Li, Zhang and Balezentis2024). Environmental tax policies promote green mergers and acquisitions, thereby reducing illegal emissions and encouraging shifts to green industries (Hu, Fang, & Wu, Reference Hu, Fang and Wu2023; Lu, Li, & Wang, Reference Lu, Li and Wang2023). However, when environmental costs are high, these regulations may hinder green transformation, forcing firms to exit the market or relocate (Lena et al., Reference Lena, Pasurka and Cucculelli2022). Uncertainty in the market environment can also deter firms from pursuing green development (Arena, Michelon, & Trojanowski, Reference Arena, Michelon and Trojanowski2018).

Internally, heightened awareness of carbon risk is essential for firms developing low-carbon strategies and green transformation, and it can even help reduce the carbon footprint across the supply chain (Deng et al., Reference Deng, Zhang and Guo2024). Executives, as key decision-makers, play a pivotal role in this process. Green-minded entrepreneurs can drive green transformation successfully if they effectively communicate, build trust, and generate profits (Brandt & Svendsen, Reference Brandt and Svendsen2016). Conversely, executives who prioritize short-term returns are less willing to engage in green transformation due to its high costs and risks (Flammer, Hong, & Minor, Reference Flammer, Hong and Minor2019). CEOs with overconfident personalities may overestimate the likelihood of success in green transformation (Arena et al., Reference Arena, Michelon and Trojanowski2018).

Research Gaps

Based on the analysis above, existing literature presents the following issues. First, although some studies have examined firms’ green transformation from a policy perspective, research on the multidimensional impacts of market-oriented environmental regulations remains limited. Green transformation is not merely the outcome of green practices (Deng et al., Reference Deng, Zhang and Guo2024), but a comprehensive plan from strategy to action. Current theoretical frameworks fail to fully explore the relationship between market-oriented environmental regulation and green transformation, thus hindering accurate assessments of policy effectiveness. Second, analyses of existing studies reveal that the relationship between market-oriented environmental regulations and green transformation often focuses on their impacts on green innovation and productivity, with significant differences in conclusions. Only analyzing green actions may underestimate the environmental governance effects of the CETP, leading to biased conclusions both theoretically and statistically. Third, prior studies have concentrated solely on the effects of formal market-oriented environmental regulations, such as the CETP, without considering the influence of external institutional environments and internal resources. This omission limits the understanding of the direct interactions between the regulations, external environments, and internal resources. Finally, much of the literature focuses only on the positive effects of policies, without deeply investigating their potential negative impacts.

Institutional Characteristics of Market-Oriented Environmental Regulation: Pressure and Incentive Coexist

Environmental regulations are designed to accomplish two things: reducing the adverse impact of businesses on the environment and fostering an atmosphere conducive to sustainable environmental practices (Dagestani et al., Reference Dagestani, Shang, Schneider, Cifuentes-Faura and Zhao2023). To achieve these goals, governments use command-and-control regulations or market-oriented environmental regulations that rely on economic instruments. Command-and-control environmental regulations are authoritatively enacted and enforced by the government, featuring strict penalty mechanisms (Xie, Li, Ye, & Jiang, Reference Xie, Li, Ye and Jiang2021). Due to the high costs of complying with environmental regulations, these regulatory tools are often ineffective in meeting most pollution control objectives, and they fail to provide sufficient incentives for green transformation (Dagestani et al., Reference Dagestani, Shang, Schneider, Cifuentes-Faura and Zhao2023). Therefore, many governments have turned to market-oriented alternatives to provide more economical means of addressing pollution issues. Based on market mechanisms, environmental regulations create pressures and incentives for emission reduction through cap-and-trade. Caps are primarily reflected in the government allocating initial emission quotas that are set below the total amount required for business production, thus limiting the emission rights available to regulated firms and creating pressure to reduce emissions (Shi et al., Reference Shi, Li and Gao2022). Trade means that although total emissions are controlled, firms have the autonomy to meet these targets in flexible ways, as market-oriented trading mechanisms provide economic incentives for emission reduction (Zhang & Xu, Reference Zhang and Xu2013).

The advantages of market-oriented environmental regulations over command-and-control approaches include the following: First, market-oriented environmental regulations provide economic incentives or penalties through fiscal measures such as tax preferences, subsidies, and emissions trading, encouraging firms to adopt greener behaviors. For example, the carbon trading market allows firms that meet emission reduction requirements to sell their surplus emission quotas to other emitters (Chen, Chen, et al., Reference Chen, Chen and Zhang2024). Second, they allow for greater flexibility, enabling firms to reduce costs while meeting environmental goals (Hu et al., Reference Hu, Fang and Wu2023). Third, by leveraging economic incentives, market-oriented environmental regulations can stimulate investment in new technologies aimed at reducing pollution and optimizing resource use (Lu et al., Reference Lu, Gao, Zhang and Wang2022). Fourth, these regulations can be dynamically adjusted according to market conditions and policy objectives. For instance, the government can adjust the total emissions cap in response to actual environmental improvement. Fifth, they can create a relatively fair competitive environment among different polluters. Each polluter faces the same marginal cost of reduction. These characteristics make market-oriented environmental regulations a flexible and effective tool for addressing a wide range of environmental issues.

Market-Oriented Environmental Regulation and Green Transformation

The carbon market represents a typical application of market-oriented environmental regulation in addressing environmental pollution. This system follows market dynamics of supply and demand, instead of imposing mandatory constraints on firm carbon emissions. Under cap-and-control measures, carbon emission rights become tradable commodities. This approach assigns a market price to the negative externalities of environmental pollution caused by firm activities, directly incorporating them into firms’ operating costs and investment opportunity costs. As firms continually strive for economic optimization, they need to balance rising environmental protection costs, short-term profitability, and long-term sustainability (Dey et al., Reference Dey, Malesios, De, Chowdhury and Abdelaziz2020). In this regulatory environment, firms typically have three behavioral options: one option is to maintain current production levels and purchase excess emission rights on the carbon market; another is to reduce output to meet total emission control requirements; a third option is to optimize resource allocation or innovate technologically to reduce carbon intensity per unit of output through green transformation, thereby achieving emission reduction goals. High costs of carbon emissions may also lead to factory relocation, production restrictions, or even market exit.

Based on the logic of the first two types of firm behavior, it is believed that an increase in environmental costs could undermine firms’ competitive advantage (Jorgenson & Wilcoxen, Reference Jorgenson and Wilcoxen1990). As profit-seeking organizations, firms are unlikely to comply passively and are more likely to adopt the third type of behavior. Driven by institutional pressures, firms in pilot areas evolve towards consistency in behavior (DiMaggio & Powell, Reference DiMaggio and Powell1983). Similar to command-and-control environmental regulations, firms face pressure to reduce their emissions. The CETP requires firms to follow carbon emission quotas. When a firm’s emissions exceed the quotas allocated to it, the firm must either buy additional allowances on the carbon market or face administrative penalties. Additionally, firms participating in carbon trading need to transparently record and report their carbon emissions data, which increases their operational burden (Chai, Zhou, Ji, Liu, Liu, & Chu, Reference Chai, Zhou, Ji, Liu, Liu and Chu2025). Therefore, driven by the need for legitimacy and operational pressures, firms tend to optimize resource allocation or upgrade technology, thereby fostering green transformation (Fabrizi, Guarini, & Meliciani, Reference Fabrizi, Guarini and Meliciani2018).

On the other hand, compared to command-and-control environmental regulations, the CETP’s emission reduction benefits incentivize firms to pursue green transformation. The demand for excess carbon rights in secondary markets motivates firms to pursue green transformation (Shi et al., Reference Shi, Li and Gao2022). Proactive green transformation not only helps firms meet total emission control targets but enables them to sell surplus rights for additional benefits (Clarkson, Li, Pinnuck, & Richardson, Reference Clarkson, Li, Pinnuck and Richardson2015), thus better matching the costs and benefits of emission reduction. In addition to direct economic incentives, carbon emission rights, as an asset, have given rise to various forms of green financial products, providing low-cost and diversified financing options for firms to support green transformation. Moreover, the implementation of CETP fosters a new green and low-carbon trend in society. It influences consumer preferences by sending clear signals about products. Environmentally compliant firms may be favored by eco-conscious consumers because of their ‘green image’ and better reputation (Kammerer, Reference Kammerer2009; Wang & Zhang, Reference Wang and Zhang2020). These firms are also more likely to attract financial support from green investors. Overall, the carbon emissions trading mechanism continually shifts the preferences among policymakers, consumers, and investors toward green and low-carbon firms. These economic and non-economic incentives make environmental behavior attractive. Based on the above discussions, we propose the following hypothesis:

Moderating Effect of External Institutional Environments

The external institutional environment, comprising policy structures, legal frameworks, and cultural elements, varies across regions, thereby influencing the effectiveness of the CETP. Tolerance for environmental pollution reflects the environmental expectations of stakeholders such as community groups and consumers. Local public attention to environmental issues can influence institutional pressures, often manifesting as public penalties for firms that fail to meet institutional requirements, such as reputational damage and reduced demand for their products. Moreover, firms are especially sensitive to stakeholders with substantial influence and power. The high costs associated with achieving institutional compliance and implementing environmental protection measures mean that green transformation is often highly dependent on government subsidies. The extent of government support is a crucial incentive for firms to pursue green transformation.

Regional public attention to environmental issues plays a significant role in shaping public environmental preferences, thereby influencing the green strategic choices of both governments and firms (Blome, Hollos, & Paulraj, Reference Blome, Hollos and Paulraj2014). It serves as a crucial source of pressure for firms. As public awareness of environmental issues rises, local firms face increased social scrutiny and public opinion pressure. Firms may encounter heightened external pressures in response to CETP. For example, the public may become less tolerant of polluting emissions or even boycott or criticize firms that fail to meet environmental standards (Chai et al., Reference Chai, Zhou, Ji, Liu, Liu and Chu2025). This social oversight compels firms to prioritize green transformation in order to mitigate public dissatisfaction and reputational risks. Furthermore, regional public attention to environmental issues acts as an important complement to government monitoring (Mao & Wang, Reference Mao and Wang2019). Increased public focus on environmental concerns can attract government attention, prompting more stringent regulatory actions against pollution. It can also generate public opinion pressure, particularly through online platforms, to prevent potential collusion between governments and firms over pollution activities (Gaganis, Papadimitri, Pasiouras, & Ventouri, Reference Gaganis, Papadimitri, Pasiouras and Ventouri2021). In response, firms are more inclined to adopt proactive measures to improve their environmental performance. Conversely, limited public attention may diminish the pressure from CETP, resulting in reduced motivation for green initiatives.

For listed firms in China, managing political costs is critical to their survival (Wang & Zhang, Reference Wang and Zhang2020). Achieving independent and proactive green transformation is challenging for firms, as it requires substantial investments, and the benefits of emission reduction are uncertain. Simply pursuing environmental goals without considering economic incentives makes it difficult for firms to achieve long-term sustainability and threatens their market competitiveness (Schaltegger & Hörisch, Reference Schaltegger and Hörisch2017). In this context, government subsidies serve as a crucial external incentive for firms. Government subsidies can offset emission costs, alleviate the financial constraints and share the risks associated with green transformation. These subsidies also play a policy-guiding role, encouraging firms to enhance their sense of social responsibility and actively engage in green practices (Wang & Zhang, Reference Wang and Zhang2020), thereby bridging the gap between the social and corporate benefits of green transformation. Additionally, government subsidies have signaling properties (Kleer, Reference Kleer2010), indicating the government’s support for green transformation. This signaling can create a ‘certification effect’ within the market and industry that highlights the investment value and market potential of firms’ green projects (Kleer, Reference Kleer2010). Receiving government subsidies signals positive messages to investors, reducing their moral hazard. This, in turn, facilitates firms in attracting external financing, which further accelerates their green transformation. Based on the above discussions, we propose the following hypotheses:

Moderating Effect of Internal Resources

Internal resources such as capital, talent, and management systems are crucial for firms to meet corresponding external institutional demands. A successful green transformation depends on effective financial management and strong internal capabilities. Firms are more likely to excel in environmental performance when their internal resources align with external institutional pressures. Additionally, managers, as primary decision-makers, possess cognitive abilities and values that significantly shape their perceptions of the business environment, thereby influencing strategic decisions (Hambrick & Mason, Reference Hambrick and Mason1984). Consequently, the characteristics of executives’ work experience can affect the success of green transformation under CETP (Bamber, John, & Wang, Reference Bamber, John and Wang2010).

Resource slack refers to the excess resources that firms possess beyond what is necessary for their daily operations (Wang, Choi, Wan, & Dong, Reference Wang, Choi, Wan and Dong2016). Green transformation requires strategic allocation of resources to effectively respond to external institutional pressures. For firms with resource slack, environmental labels provide limited benefits because they are less vulnerable to legitimacy deficits (Wang, Cui, & Liang, Reference Wang, Cui and Liang2015). Despite having vast resources available to initiate green efforts, the transition towards sustainability is a prolonged process, demanding substantial investments with uncertain returns. Managers often shy away from risky investments, preferring instead to focus on initiatives that enhance their personal reputations or improve personal well-being through superficial projects (Paeleman & Vanacker, Reference Paeleman and Vanacker2015), rather than committing to sustainable development for competitive advantage. This resource management approach compromises efficiency and hinders the achievement of green transformation goals. In contrast, firms with limited resources face greater constraints due to carbon emissions costs, increasing their urgency to embrace green transformation. Scarce resources also help align the objectives of directors and managers, fostering collaboration that supports long-term sustainability through green transformation.

Organizational strategic decisions often reflect the values and beliefs of executives. Executives with long-term experience in a particular area may develop selective cognition, resulting in decisions based on cognitive biases shaped by their past experiences (Zhang, Xu, & Chen, Reference Zhang, Xu and Chen2022). Executives with experience in the environmental field are inclined to view external CETP as development opportunities for firms. They proactively assume environmental responsibilities and guide green development by fostering a green culture and reallocating resources. These actions enable firms to gain a sustainable competitive advantage. According to Martin, Muûls, de Preux, and Wagner (Reference Martin, Muûls, de Preux and Wagner2012), firms are more likely to adopt environmentally friendly technologies and equipment for production activities when executives prioritize environmental protection. Conversely, executives with limited environmental awareness may prefer to maintain stability in existing production and operation systems, adopting only minimal green measures to comply with environmental policies. They may perceive the uncertainty associated with green transformation as a threat to their managerial roles and the firm’s performance. This reduces their enthusiasm for advancing green transformation strategies. Based on the above discussions, we propose the following hypotheses:

Figure 1 displays the theoretical framework of this study.

Figure 1.

The theoretical framework of this study

Data

This study utilizes data from Chinese A-share manufacturing firms listed on the Shanghai and Shenzhen Stock Exchanges, spanning the period from 2010 to 2020. After excluding firms labeled as ST and ST* states and removing entities with incomplete data, the final sample includes 346 listed manufacturing firms, yielding 1442 observations. The primary data sources include the China Juchao Information Website (http://www.cninfo.com.cn/), officially designated by the China Securities Regulatory Commission to disclose information on listed firms, which provides data related to firms’ green transformation. Data on regional public environmental attention are collected from the Baidu index website (http://index.baidu.com/), while data on executives’ environmental experience are sourced from the Sina Finance website (https://finance.sina.com.cn/). Other data are obtained from reliable databases including the China Stock Market & Accounting Research (CSMAR) database and the Chinese Research Data Services (CNRDS) platform.

Model Specification

To analyze the policy impact of the CETP on firms’ green transformation, this study employs a multi-period propensity score matching difference-in-difference (PSM-DID) model for several reasons. Since the selection of pilot areas is non-random and influenced by factors such as local economic development, pollutant emission intensity, and environmental management capacity, this approach helps mitigate sample selection and heterogeneity bias, thereby improving the robustness of the estimations. Moreover, considering the policy was gradually implemented across seven pilot regionsFootnote ¹ in 2013 and 2014, and acknowledging the fluctuating number of firms affected during different observational periods, a single-period DID method is insufficient to accurately evaluate the policy’s impact. Consequently, applying a multi-period PSM-DID design is essential.

The sample is divided into two categories: a treatment group including firms situated in the seven pilot regions, and a control group constituting firms from other regions. To construct the control group, firms within the treatment group are matched annually with control samples using the Propensity Score Matching (PSM) method, which estimates propensity scores based on firms’ participation in the carbon trading market. Significant variables influencing firms’ green transformation, including firm size, firm age, independent directors, shareholding concentration, asset liability ratio, main operating income, staff numbers, executive age, female executives and executives’ R&D backgrounds (elaborated in measurements of control variables), are employed as matching variables to pair samples across the two groups. The Logit model facilitates the estimation of propensity scores, adopting a one-to-one nearest neighbor matching technique to align samples with similar scores from the control group to the treatment group, while ensuring that samples meet the common support hypothesis. Furthermore, since the policy was implemented in Beijing, Tianjin, Shanghai, Guangdong (including Shenzhen) in 2013, and Hubei and Chongqing in 2014, the explanatory variable $Are{a_j} \times Pos{t_t}$is defined according to the actual policy implementation time. If a firm is located in Beijing, Tianjin, Shanghai or Guangdong and the year t $ \geqslant $2013, $Are{a_j} \times Pos{t_t}$ is 1; otherwise 0. If a firm is located in Hubei or Chongqing and the year t $ \geqslant $2014, $Are{a_j} \times Pos{t_t}$ is 1; otherwise 0.

We construct a two-way fixed effects panel data DID model based on the above settings, as follows:

(1)\begin{equation}GreenTransformatio{n_{it}} = {\beta _0} + {\beta _1}Are{a_j} \times Pos{t_{_t}} + {\beta _2}{X_{it}} + {\mu _t} + {\gamma _j} + {\varepsilon _{it}}\end{equation}

where $GreenTransformatio{n_{it}}$ represents the green transformation of firm i in year t; $Are{a_j} \times Pos{t_t}$ represents whether the firm is located in a pilot region after the policy launch. $Are{a_j} \times Pos{t_t}$equals 1 if the firm is located in a pilot region after the policy launch and 0 otherwise. The coefficient ${\beta _1}$captures the net policy effect on green transformation. ${X_{it}}$is a series of control variables to capture other factors that may influence green transformation, including firm size, firm age, independent directors, shareholding concentration, asset liability ratio, main operating income, staff numbers, executive age, female executives and executives’ R&D backgrounds. ${\mu _t}$is year-fixed effects that control for aggregate time-varying shocks common to all firms, and ${\gamma _j}$ is industry-fixed effects that capture time-invariant industry features. ${\varepsilon _{it}}$denotes the random error term.

Variables and Measurements

Measurement of green transformation

Existing literature typically measures green transformation using two indicators: green innovation level (Lu, Gao, Zhang, & Wang, Reference Lu, Gao, Zhang and Wang2022) and green total factor productivity (Zhang, Yu, et al., Reference Zhang, Yu and Sun2022). A major drawback of this approach is that relying on a single indicator fails to reflect the comprehensive nature of green transformation, which involves more than just enhancements in productivity or increases in green innovation (Xu et al., Reference Xu, Zhu and Wang2023). Although green total factor productivity integrates pollutant emissions into its analysis, it primarily focuses on output changes while overlooking the impacts of green strategies and institutional directives. It also fails to identify the drivers of changes in pollutant emissions, such as renewable energy utilization or firms’ pollutant monitoring efforts. Furthermore, although green patents are the product of the application of firms’ R&D activities to production and sales, they fall short in assessing the effectiveness of green innovations, management practices, and strategies. There is also a risk that firms may acquire low-quality green patents, thereby exaggerating the extent of their green transformation (Rong, Wu, & Boeing, Reference Rong, Wu and Boeing2017).

In this study, we evaluate green transformation by combining text mining techniques with a structural equation model, leveraging word frequency analysis of corporate social responsibility reports. These reports serve as bridges between firms and external stakeholders, portraying how firms balance profit maximization and social responsibility. To build a favorable image and enhance their social reputation, firms often highlight their green transformation initiatives at both strategic and practical levels. Thus, word frequency emerges as a meaningful indicator for assessing the extent of green transformation. The structural equation model enables a more nuanced analysis of word frequency as it accounts for the interrelationships between latent variables during the estimation phase. This approach facilitates a more precise calculation of correlation coefficients for each latent variable, thereby improving the measurement of green transformation (Anderson & Gerbing, Reference Anderson and Gerbing1988). By evaluating the overall fit of the model, we can identify paths that are most closely aligned with actual firm behavior.

Guided by works such as Loeser, Recker, Brocke, Molla, and Zarnekow (Reference Loeser, Recker, Brocke, Molla and Zarnekow2017), Zhou, Shu, Jiang, and Gao (Reference Zhou, Shu, Jiang and Gao2019), and relevant policiesFootnote ², this study categorizes green transformation into two distinct dimensions: green strategy transformation and green practice transformation. Green strategy transformation refers to firms recognizing green development as a strategic direction and developing green-oriented strategies that provide both institutional and theoretical foundations for implementing green initiatives (Chan, He, Chan, & Wang, Reference Chan, He, Chan and Wang2012). This includes green awareness, green planning, and green institutional frameworks. Conversely, green practice transformation emphasizes practical actions including cleaner production technologies, waste management, recycling, and technological advancements (Chen et al., Reference Chen, Lai and Wen2006). Therefore, pollutant management, production technology enhancements, green emissions, and green energy reforms represent the key aspects of green practical transformation. Clearly, adopting a keyword lexicon approach to assess firms’ green transformation offers a more comprehensive perspective than relying on singular indicators. Details on the calculation of the green transformation variable are described in Appendix 2. We construct the green transformation variable based on the outcomes of the structural equation model, as shown inEq. (2):

(2)\begin{equation}GreenTransformatio{n_{st}} = \sum\limits_s {{\omega _s}} \sum\limits_t {{\omega _{st}}} {Z_{st}}_{_{}}\end{equation}

where ${Z_{st}}$ represents the word frequency of each indicator, ${\omega _{st}}$ represents the weight of the third-level indicators to the second-level indicators, and ${\omega _s}$ represents the weight of the second-level indicators to the first-level indicators.

Furthermore, to address the potential for greenwashing by firms, we construct our green transformation lexicon by excluding terms that lack substantive environmental efforts. To enhance the reliability of our empirical findings, we conduct several robustness checks. We narrow our focus to the relationship between CETP and green practice transformation, and use widely accepted metrics, such as green innovation outputs, as proxies for green transformation. Through these measures, we aim to validate the robustness and validity of the green transformation framework.

Baseline Results

Following Eq. (1), we utilize the multi-period PSM-DID model to examine the impact of CETP on the green transformation of manufacturing firms. Note that Models 1, 3, and 5 in Table 1 exclude control variables, while Models 2, 4, and 6 include them. All results account for both year- and industry-fixed effects. Models 1 and 2 show the overall impact of the policy on firms’ green transformation. The coefficients of Area*Post are significantly positive at the 5% level, indicating a facilitating effect on the firms’ green transformation.

Table 1.

Results of baseline regression

* Notes: t values are reported in parentheses; Significance: ***1%, **5% and 10%; Robust standard errors are clustered at the province level; Results are obtained by Stata 17.0.

Furthermore, Models 3 and 4 shed light on the impacts on green strategy and practical transformations respectively, revealing positive influences in both areas. A comparative analysis of the coefficients of Area*Post in columns (4) and (6) shows that CETP has a more pronounced influence on the green practical transformation than on strategy. This suggests that the policy’s facilitative effects go beyond mere strategic considerations. Indeed, implementing green practices can bring long-term substantial environmental and economic benefits to firms by adopting innovative green technologies and phasing out outdated production equipment. By articulating green practices through transparent information disclosure, firms can cultivate a positive public image and elevate their social acceptance, supporting H1.

Placebo Tests

To circumvent the potential confounding influences of simultaneous policies affecting both the treated and control groups during the study period, we conducted a series of placebo tests. Initially, we established a hypothetical policy implementation timeline. Given the staggered implementation of the policy across the seven pilot regions in 2013 and 2014, we adjusted the policy implementation timeline by advancing it by two years and then delaying it by the same duration. The results of this adjustment are depicted in Table 2, including Models 1 and 2.Footnote ⁵ These models present insignificant coefficients for Area*Post, suggesting that altering the policy initiation timeline does not significantly affect firms’ green transformation.

Table 2.

Results of placebo tests

** Notes: t values are reported in parentheses; Significance: 5%; Robust standard errors are clustered at the province level; Results are obtained by Stata 17.0.

Next, we conducted an anticipated effect analysis to determine whether firms predicted the policy before its actual enactment. We added Area*Post1 and Area*Post2 as separate variables in the baseline regression model. Here, Fore1 represents the year before the CETP launch, while Fore2 represents two years prior to the policy introduction.Footnote ⁶ Models 3 and 4 in Table 2 indicate that, while the coefficients for Area*Post1 and Area*Post2 remain statistically insignificant, the coefficients for Area*Post exhibit a significant positive trend. This observation rules out any anticipated effects. Overall, these placebo test results confirm that the progress in firms’ green transformation can be exclusively attributed to the policy’s implementation in 2013 and 2014, emphasizing the causal relationship between the policy and the observed green transformation.

The results derived from the baseline regression may be influenced by certain latent factors. To address this, we conducted a placebo test using a randomized selection of samples. In accordance with Eq. (1), we extracted 500 estimated coefficients and t-values by simulating the equation 500 times at random. Figure 2 illustrates the probability density distribution of the obtained t-values. It is observed that the t-values predominantly cluster around 0, with only a small fraction exceeding 1.96, confirming that the facilitative effect of the policy on firms’ green transformation is not a mere coincidence.

Figure 2.

Results of placebo tests by randomly selecting samples 500 times

Robustness Checks

To validate the robustness of our findings, we conducted several tests, including: the balance test of Propensity Score Matching (PSM), alterations to the explanatory variable, mitigating the impact of other policies, and excluding certain samples. First, following the propensity score matching between the treated and control groups, we performed a balance test to assess the reliability of the sample pairing. Detailed results and discussions are presented in Appendix 5. Second, acknowledging the potential impact of less significant keywords of green transformation, we removed keywords with a frequency of occurrence below 10, and reformulated the structural equation model to assess the degree of green transformation.

Third, we took steps to eliminate the disturbance caused by other concurrent policies. One such policy that could introduce bias into our estimation is the SO₂ emissions trading scheme launched by China in 2007, which covered 11 pilot regions, including Tianjin, Hubei, and Chongqing. These regions are also included in the CETP regions. Given the interconnected nature of carbon and SO₂ emissions, the SO₂ emissions trading scheme could influence green transformation. As a result, we excluded these samples. Additionally, the enforcement of the Environmental Protection Supervision Program (trial phase) in 2015, followed by the first round of environmental inspections from January 4, 2016, to September 15, 2017, promoted high-polluting firms to reduce pollution. To mitigate these impacts, we excluded data from 2015 to 2017.

Next, we excluded specific samples. We refined our sample by narrowing the baseline regression period from 2010-2020 to 2012-2019. The low-carbon pilot policy was launched in 2010, and we excluded data from 2010 and 2011 to avoid capturing pre-policy responses of firms. We also excluded data from 2020 because China launched a national carbon emissions trading market that year. Additionally, we excluded firms with only one year of data to prevent skewed results. Data are winsorized at the 1% and 99% levels.

Lastly, to eliminate the potential tactical behaviors by firms, we used green practice transformation and green innovations as dependent variables to re-estimate Eq. (1). The count of green invention patent applications is used to present the green innovations, with data sourced from the Chinese Research Data Services (CNRDS) database. The results in Figure 3 demonstrate the robustness of the main conclusions.

Figure 3.

Robustness results (black dots represent the estimated coefficients, and red lines are confidence intervals at the 95% level)

Moderating Effects

Despite previous analyses substantiating the policy’s facilitative role in steering firms toward green transformation, it is necessary to explore whether these relationships are moderated by institutional environments and internal resources. The findings, presented in Table 3, address this concern. In Model (1), the coefficient of Area*Post*Attention is significantly positive. This suggests that heightened regional public environmental awareness can amplify the positive effects of the CETP on firms’ green transformation, affirming H2a. In these regions, the public can foster green transformation by closely monitoring firms’ environmental conduct (Mao & Wang, Reference Mao and Wang2019). Additionally, Model (2) reveals a significantly positive coefficient of Area*Post*Government subsidies, suggesting that government subsidies reinforce the favorable impact of CETP on green transformation and thus support H2b. Government subsidies incentivize firms to comply with environmental regulations and indirectly attract social investment, easing financial constraints on green transformation (Kleer, Reference Kleer2010).

Table 3.

Result of the moderating effects

* Notes: t values are reported in parentheses; Significance: ***1%, **5% and 10%; Robust standard errors are clustered at the province level; Results are obtained by Stata 17.0; Environmental experience in column (4) is measured as the ratio of executives with environmental experience to the total number of executives, and Environmental experience in column (5) is measured using a dummy variable.

Further, Model (3) reveals a significantly negative coefficient of Area*Post*Resource slack, indicating that resource slack can undermine the beneficial effects of the CETP on green transformation, lending support to H3a. Resource-abundant environments may lead firms to perceive fewer institutional pressures, thus hindering the momentum of green transformation. Managers may also divert surplus resources toward diversification or personal gain (Tan & Peng, Reference Tan and Peng2003), thereby sidelining green transformation goals. Finally, Model (4)-(5) reports a significantly positive coefficient of Area*Post*Environmental experience, implying that environmental experience of executives can augment the positive impact of CETP on green transformation, thus confirming H3b. Executives with environmental experience are better positioned to leverage green information and resources to reduce strategic environmental risks and green transformation costs, mitigate conflicts between environmental and financial performance, and encourage firms to pursue sustainability (Chen, Chen, et al., Reference Chen, Chen and Zhang2024).

Regional Spillover Effects

This section aims to analyze the regional spillover dynamics induced by the CETP. The policy may generate both positive and negative impacts on firms’ green transformation in neighboring regions. Positively, firms in pilot regions, required to enhance energy efficiency and reduce emissions, may foster significant green innovations. These advancements could spread to neighboring areas through industrial partnerships and economic collaborations. Negatively, increased production and pollution control costs might prompt firms to relocate high-emission operations to neighboring provinces to reduce expenses and avoid market penalties. Such actions could hinder green transformation efforts in surrounding regions.

To assess the regional spillover effects, we selected adjacent regions based on the principle of geographical proximity, including seven provinces: Hebei, Henan, Shandong, Jiangsu, Zhejiang, Hunan and Anhui. The time variable for firms in non-pilot regions was aligned with the CETP launch year in the neighboring pilot provinces. If a firm is located in Hebei, Jiangsu, Zhejiang or Anhui and time ≥ 2013, or located in Henan or Hunan and time ≥ 2014, Area*Post = 1; otherwise, Area*Post = 0. Model 1 in Table 4 reveals a significant negative coefficient of Area*Post at the 5% level. Models 2 and 3 further corroborate this trend, showing a substantial negative influence on both green strategy and practice transformations. Specifically, the CETP hinders green transformation efforts in neighboring regions, potentially turning them into havens for pollution-intensive operations and further exacerbating emission reduction challenges. This trajectory also signals a weakened drive toward green transformation among firms in non-pilot regions due to the lack of environmental regulations. These findings highlight the urgent need to expand cross-regional environmental regulations and advance a nationwide CETP to foster progressive green transformation.

Table 4.

Results of regional spillover effects and economic consequences

** Notes: t values are reported in parentheses; Significance: 5%; Robust standard errors are clustered at the province level; Results are obtained by Stata 17.0.

Economic Consequences

Building on the CETP’s potential to drive green practices, it is crucial to examine the subsequent impact of these transformations on economic performance. To gauge economic performance, we use firms’ profitability as an indicator, calculated as the ratio of operating profit (revenue minus cost) to operating revenue (Rahmana, Rodríguez-Serranob, & Lambkin, Reference Rahmana, Rodríguez-Serranob and Lambkin2020). The analysis presented in Model 4 of Table 4 shows a significant positive coefficient of Area*Post, suggesting that the CETP can significantly improve firms’ economic viability. It’s important to note that carbon quotas are tangible assets, as they can be monetized or transferred. Firms holding substantial carbon quotas can generate cash flows and profits by selling them or passing emission costs to consumers.

Model 5 adds an interaction between Green transformation and Area*Post, presenting a significant negative coefficient on the interaction term. This suggests that green transformation may have a short-term adverse impact on economic performance. One possible explanation is that large capital allocations toward green initiatives may reduce investment in other revenue-generating areas and lead to short-term economic slowdowns (Weche, Reference Weche2019). However, from a sustainability perspective, maximizing profit should not be the only objective. Instead, firms must embrace broader societal responsibilities. While empirical evidence highlights temporary financial pressures from green transformation, it is expected that these efforts will yield long-term benefits and pave the way for sustainable corporate growth. It is important to acknowledge that this study is limited by data availability, which restricts a more detailed exploration of the long-term policy implications.