Impact of Environmental Regulation on Regional Innovation in China from the Perspective of Heterogeneous Regulatory Tools and Pollution Reduction

Abstract

The relationship between environmental regulation and regional innovation has been disputed. This paper analyzes data at the provincial level in China between 2009 and 2020 using a fixed-effects model to investigate the relationship between environmental regulation and innovation. The baseline regression results suggest that market-based environmental regulatory instruments effectively promote regional innovation, while command-and-control environmental regulatory instruments hinder regional innovation. However, the impact of environmental regulation exhibits heterogeneity and non-linearity. The implementation of overly strict command-and-control environmental regulatory instruments hinders innovation, while the implementation of low-intensity command-and-control environmental regulatory instruments instead promotes innovation. In economically developed provinces, market-based environmental regulation promotes innovation, while in less economically developed provinces, market-based environmental regulation inhibits innovation instead. Further analysis from a pollution reduction perspective shows that environmental regulations that mitigate air pollution significantly promote regional innovation levels. This study not only enriches theoretical discourse but also offers practical policy recommendations for balancing environmental governance and innovation development in China.

1. Introduction

China’s economy has been expanding quickly since it joined the World Trade Organization in 2001, and the world’s manufacturing industry has been shifting to China on a large scale. However, behind this prosperity, there are also many problems. Although China’s manufacturing industry is the largest in the world, its technological strength remains weak. Most enterprises are still concentrated on low-end manufacturing, with weak technological innovation capacity [1]. In contrast, high-end manufacturing remains primarily in the hands of developed countries. Especially after the slowdown of China’s economic growth in recent years, improving China’s manufacturing industry’s innovation capacity has become a priority in China’s industrialization process. The lack of cutting-edge technology has become a key factor restricting China’s economic growth. In addition, with the development of the economy and the advancement of industrialization and urbanization, China’s ecological environment has been seriously damaged [2]. Problems such as air pollution, soil erosion, and ecological damage seriously affect people’s daily lives and physical and mental health and negatively impact sustainable development [3]. In order to solve the environmental pollution problems brought about by the traditional industrial structure, China has adopted the basic national policy of saving resources and protecting the environment. With the growing prominence of environmental problems and society’s urgent need for sustainable development, environmental regulation (ER) in China has been increasing in strength and breadth [4].

In general terms, China’s environmental policy is often characterized as a government-led, top-down approach: the implementation of China’s environmental policy relies on strong administrative means, and China can achieve significant results in environmental governance in the short term through administrative orders [5]. In Europe and the United States, environmental policies are more likely to be based on democratic consultation and law-led approaches. Policies often go through a complex legislative process and multi-interest discussions. While this model may be relatively slow in implementation, the laws and policies have a higher degree of transparency and public participation [6]. Although China has gradually introduced market-based mechanisms into its environmental policies in recent years, such as the carbon emissions trading market and environmental protection taxes, overall, China’s environmental governance has relied more on direct control measures of the administrative order type, such as emission limits and technical standards [7]. The Environmental Protection Tax Law, implemented in 2018, imposed taxes on various forms of pollution, encouraging industries to adopt cleaner technologies and reduce their environmental footprint. Furthermore, China introduced a national Emissions Trading System after years of pilot programs in select regions. These regulations, together with government support for innovation, have laid the foundation for environmentally sustainable development [8].

In recent years, China has made significant strides in environmental innovation, particularly in the renewable energy and electric vehicle sectors [9]. For example, China has become a major global force in solar photovoltaic panel production, significantly expanding its domestic capacity to meet increasing energy needs. Similarly, the wind energy sector experienced rapid development, making China one of the leading nations in renewable energy. The electric vehicle (EV) industry also witnessed substantial growth, with China becoming a prominent player in global EV production. Innovations in pollution control technologies, such as carbon capture and storage and advanced filtration systems, played a critical role in reducing emissions from industrial areas, particularly in regions heavily affected by pollution like Beijing and Hebei. These developments underscore China’s commitment to addressing environmental challenges through technological advancements and the implementation of clean energy solutions.

Innovation is a decisive factor in realizing the “win-win” objectives of environmental protection and economic development [10]. New industries can emerge as a result of innovation, which can also encourage the modern, green, and rational industrial structure. Additionally, innovation can promote technological progress and breakthroughs, making production methods more efficient, cleaner, and sustainable. Innovation therefore has the potential to realize a virtuous circle of economic growth and environmental protection [4]. Strengthening environmental regulations and promoting technological innovation serve as essential strategies for China to move towards a development model that emphasizes sustainability, quality, and efficiency. By enforcing stricter environmental policies, China can mitigate pollution and resource depletion, laying the groundwork for long-term economic resilience. At the same time, fostering innovation helps drive the development of cleaner, more efficient technologies, which not only contribute to economic growth but also enable the country to meet global environmental standards. Together, these initiatives create a pathway for China to achieve balanced and sustainable progress in its pursuit of a high-quality, modernized economy. Environmental regulation, as an important means to protect the ecological environment and social public interest, has an increasingly prominent impact on innovation [11,12]. Whether environmental regulation will have a positive or negative impact on innovation in China is therefore a matter of great concern and needs to be studied in depth. How to stimulate regional innovation and realize a “win-win” relationship between regional innovation and environmental improvement in the context of China’s reality? These are the key questions that are addressed in this paper. This paper analyses the effects of diverse environmental regulatory tools on innovation at the macro level, based on the macroenvironment of China’s deepening economic reform. It then describes how environmental regulation can achieve the “win-win” scenario between environmental protection and regional competitiveness. This study not only enriches theoretical discourse but also offers practical policy recommendations for balancing environmental governance and Chinese development. This study provides empirical evidence to guide policymakers in designing efficient, region-specific, and innovation-friendly environmental policies, ultimately promoting sustainable development in China and beyond.

The following summarizes this paper’s contributions. Initially, this paper will examine the relationship between environmental regulations and technological innovation from two angles: market-based and command-and-control. Through comparative analysis and using the threshold model, this paper aims to reveal the efficiency and effectiveness of different environmental regulatory tools on innovation incentives and provide policymakers with strategic recommendations on how to design efficient environmental policies to promote technological innovation, enriching the existing literature. Second, the existing literature often discusses the impact of environmental regulation on innovation only in terms of the Porter’s hypothesis, that is, environmental regulation “pushes” innovation, while ignoring the impact of the abatement effect of environmental regulation on innovation [13,14,15,16]. The ultimate goal of environmental regulation is to protect the environment and reduce pollution. This paper investigates whether reducing air pollution—a key objective of environmental regulation—also stimulates innovation. This further sheds light on why different environmental policy instruments have different effects in promoting innovation. By linking environmental quality improvements with economic and technological progress, this paper offers a broader perspective on how to select environmental policies.

2. Theoretical Analysis and Literature Review

According to conventional wisdom, environmental regulation (ER) makes it more expensive for businesses to comply institutionally since they force them to use more eco-friendly technologies and production techniques, which could raise business expenses [17,18]. Such rising costs may eat up the funds that firms spend on R&D, crowding out the resources that firms use for innovation [19]. The fact that innovation requires significant resource investment from businesses and that its benefits take time to materialize in the form of reduced energy use, lower emissions, and enhanced performance puts managers under pressure to forgo high-risk, high-investment ideas due to the cost effect. This cost effect could lead to a decline in the level of regional innovation. Ramanathan et al. [20] found that in the short term, ER in the UK can have a negative impact on innovation in the industrial sector. Pelkmans and Renda found that more prescriptive EU environmental regulation tends to discourage innovative activity [21].

However, according to the Porter Hypothesis, effective ER can push businesses to innovate and create benefits that outweigh the costs of such regulations [22]. By innovating production technologies, firms are able to improve their competitiveness, effectively compensating for the costs of environmental regulation and thus contributing to the regional level of innovation [23]. That is, environmental regulation can promote innovation by “incentive effects”. Nicolli and Vona [24] found that market regulation and renewable energy policies in the EU promote renewable energy technology innovation activities. Using policy and patent data from a large sample of more than 100 countries and territories, covering the period 1990 to 2016, Hille et al. [25] find that renewable energy policies have increased patenting of solar and wind related technologies.

Market-based environmental regulation (MBER) and command-and-control environmental regulation (CACER) are the most commonly used categorization [26]. MBER is a financial incentive offered by the government to businesses to let them follow their own interests while achieving pollution control targets. In contrast to MBER, CACER mandates that polluters fulfill particular emission-reduction goals and frequently calls for the installation and operation of particular equipment to cut emissions. Coercion, which is based on administrative directives, is the primary characteristic of CACER [15]. Under the direction and oversight of government agencies, polluting businesses use output reduction or technological advancements to keep pollution emissions within allowed bounds.

As can be seen from the existing research, the impact of ER on innovation is the result of a trade-off between the “cost effect” and the “incentive effect”. In the face of the differential intensity of ER, enterprises will often make trade-offs according to a “cost–benefit balance” either increasing the expenditure on pollution control and reducing the investment of innovation resources or increasing innovation efforts and improving the production technology. Firms are the main agents for undertaking innovation and may bring about higher or lower levels of regional innovation.

This paper proposes Hypotheses 1a, 1b, 2a, and 2b:

H1a. 

MBER promotes regional innovation in China.

H1b. 

MBER does not promote regional innovation in China.

H2a. 

CACER promotes regional innovation in China.

H2b. 

CACER does not promote regional innovation in China.

Changes in external and internal conditions may also lead to changes in the magnitude of “incentive effect” and “cost effect”, thus affecting the relationship between environmental regulation and innovation. This paper provides an analysis focusing on two factors: economic development and the intensity of environmental regulation.

Firms are the main body of industry to undertake innovation, and their reactions and coping strategies need to be taken into account when analyzing the impact of different environmental regulatory tools on regional innovation. On the one hand, firms may adopt a conservative attitude, viewing environmental regulation as a burden and resisting technological innovation [27]. This attitude may be common among firms in lower income regions, which may lack awareness of technological innovation and view environmental inputs as costs rather than investments. This conservative attitude can limit firms’ incentives to innovate, causing them to lag behind in technological upgrading and environmental protection, which can lead to a decline in the level of regional innovation. On the other hand, some firms may see environmental regulation as an opportunity to respond positively and enhance their competitiveness through technological innovation [28]. These enterprises may take a series of measures, such as increasing R&D investment, introducing advanced technologies, and cultivating high-quality talents, to cope with environmental pressure. This positive attitude may be more common among firms in developed regions, which helps enterprises realize sustainable development and enhance their position in the field of technological innovation, while also increases the reputation of the firms [29]. In regions with a developed economy and strong market demand, enterprises may be more motivated to carry out technological innovation to meet market demand and gain competitive advantages. On the contrary, in regions with a depressed economy and low demand, firms may take a conservative approach to technological change in order to reduce costs and risks. In addition, more economically developed regions may have more resources and capacity to implement environmental regulations, as well as more policy and financial support to support the firms’ innovations [14]. Therefore, the impact of environmental regulation on technological innovation may vary in different regions and can be analyzed. Therefore, this paper proposes Hypotheses 3a and 3b:

H3a. 

The effect of ER on regional innovation can be influenced by economic development.

H3b. 

The effect of ER on regional innovation is not influenced by economic development.

The impact of ER on innovation may also be constrained by the stringency of environmental regulation. ER relies on the promulgation of laws, regulations, and standards, which are mandatory for enterprises [30]. If enterprises fail to comply with the regulatory requirements, they will incur illegal costs, and if the illegal cost exceeds the cost of innovation and transformation, enterprises will tend to innovate technologically in order to avoid the illegal cost of regulation. However, when faced with overly strict ER, firms may tend to upgrade equipment with existing technology rather than innovate. For example, CACER often specifies environmental standards and specific compliance measures that companies must meet [31]. CACER is usually accompanied by a clear deadline for compliance. Within a limited timeframe, it is easier for companies to quickly meet regulatory requirements by upgrading existing technology, while developing new technology may take longer. Existing technologies are already widely used in the marketplace and have proven their reliability and effectiveness. In contrast, new technologies are not yet mature, and there is uncertainty about their performance and effectiveness, a risk that firms are reluctant to take. Therefore, overly strict ER may lack incentives for innovation and more often leads firms to meet minimum compliance standards [32]. This paper therefore proposes Hypotheses 4a and 4b:

H4a. 

The impact of ER on innovation may be constrained by the intensity of environmental regulation.

H4b. 

The impact of ER on innovation is not constrained by the intensity of environmental regulation.

The ultimate goal of environmental regulation is to reduce pollutant emissions and protect the environment. However, some of the environmental management tools currently used in China have not effectively controlled pollution levels [33]. In addition to the cost effect and the innovation compensation effect, the pollution reduction effect may also account for the different impacts of different types of environmental regulation on innovation. When the government relaxes environmental regulation, firms are subject to less external pressure and pollutant emissions increase. Correspondingly, when the government increases ER, the strict requirements on enterprises will force them to reduce their own pollutant emissions. Thus, pollutant emissions are a good response to the strength of environmental regulation [34]. Pollution, especially air pollution, affects human health and reduces labor productivity and human capital. Reducing pollution is therefore an enabling factor for environmental regulation to promote innovation [21]. That is, in addition to “incentive effects”, pollution reduction is also a mechanism by which environmental regulation promotes innovation. The level of regional innovation can significantly increase if the increase in innovative human capital resulting from reduced pollution from environmental regulation compensates for the “cost effect”. Therefore, this paper proposes Hypotheses 5a and 5b:

H5a. 

Environmental regulation of reducing air pollution significantly promotes innovation.

H5b. 

Environmental regulation of reducing air pollution does not significantly promote innovation.

3. Materials and Methods

3.1. Empirical Model Specification

In order to examine the impact of environmental regulation (ER) on innovation level in provinces in China, this paper constructs the following fixed effect model:

INN_it = α₀ + α₁MBER_it + α₂X_it + γ + ε_it

(1)

INN_it = α₀ + α₁CACER_it + α₂X_it + γ + ε_it

(2)

where INN_it represents the innovation level of province i in year t. MBER_it is the intensity of market-based environmental regulation in province i in year t. CACER_it is the intensity of command-and-control environmental regulation in province i in year t. This paper also controls for a set of province characteristics control variables in the baseline regression model to mitigate omitted variable bias where possible, denoted by X_it. The γ represents fixed effects. Finally, ε_it is the error term.

The extant body of literature only concentrates on the linear correlation between ER and innovation [11,32], neglecting the potential non-linear relationship: the degree of ER influences innovation in different ways. In order to further analyze the non-linear impact and to test whether the impact of ER on innovation varies with changes in ER, based on Hansen [35], this paper develops the following threshold model to analyze the data and examine the relationships among the variables:

INN_it = β₀ + β₁SER_it × I (SER ≤ TV) + β₂SER_it × I (SER > TV) + β₃Control + ε_it

(3)

where SER represents the threshold variable: strength of MBER or CACER. I(.) is an indicative function, and the value in the corresponding brackets is set to one and the condition is not set to zero. TV is the threshold value. Control variables are consistent with Equations (1) and (2).

In addition to the intensity of environmental regulation, the level of economic development may have a similar threshold effect. In the following, the level of economic development is measured by GDP per capita (denoted by LED) and used as a threshold variable, and, referring to Hansen [35], the corresponding threshold regression model is

INN_it = β₀ + β₁SER_it × I (LED ≤ TV) + β₂SER_it × I (LED > TV) + β₃Control + ε_it

(4)

where SER represents the threshold variable: strength of MBER or CACER. I(.) is an indicative function, and the value in the corresponding brackets is set to one and the condition is not set to zero. TV is the threshold value. LED is the level of economic development. The level of economic development is measured by GDP per capita. Control variables are consistent with Equations (1) and (2).

Further analysis: this paper examines not only how various ER tools influence innovation but also how environmental regulation impacts innovation from a pollution control perspective. Based on Equations (1) and (2), this paper constructs the following threshold model:

INN_it = α + β₁APER_it = + β₂Contorl_it + γ + ε_it

(5)

APER represents the strength of ER of air pollution. Control variables are consistent with Equations (1) and (2). PM2.5 data are from the Atmospheric Composition Analysis Group, University of Washington.

3.2. Variables and Data

3.2.1. MBER and CACER

The literature now in publication offers numerous approaches to quantifying environmental regulation [36,37]. The primary goal of this essay is to compare the effects on the Chinese economy of various environmental regulatory tools. This paper divides China’s environmental regulatory tools into two categories: MBER and CACER, citing the methodology of Xie et al. [38].

Following Pan et al. [39] and Liu et al. [40], the measurement index of MBER is given by the following formula:

$$\mathrm{M}\mathrm{B}\mathrm{E}\mathrm{R}={\displaystyle \frac{\mathrm{P}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{d}\mathrm{i}\mathrm{s}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e} \mathrm{f}\mathrm{e}\mathrm{e}}{\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}}}$$

In 2018, China enacted an official environmental protection law on 1 January and instituted a pollution discharge fee reform scheme. Since the introduction of the environmental protection tax, it has replaced the pollutant discharge fee. In this article, the data on pollutant discharge fees from 2018 to 2020 have been replaced with corresponding environmental tax collections.

Based on the methods of Lu et al. [41], Pan et al. [39], the measurement index of CACER is given by the following formula:

$$\mathrm{C}\mathrm{A}\mathrm{C}\mathrm{E}\mathrm{R}={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{i}\mathrm{n} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}{\mathrm{I}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{d}\mathrm{d}\mathrm{e}\mathrm{d} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}}}$$

Table 1. CACER and MBER.

Aspect	Command-and-Control Regulation	Market-Based Regulation
Definition	Direct regulation by setting specific limits or mandates for pollution control, enforced by legal penalties.	Utilizes economic incentives, taxes, or market mechanisms to encourage pollution reduction.
Mechanism	Prescribes specific pollution limits or mandates the use of pollution control technologies (e.g., emission standards, technology requirements).	Provides flexibility, allowing firms to reduce pollution in cost-effective ways (e.g., emissions trading, carbon taxes).
Flexibility	Low flexibility: businesses must comply with set standards or technologies regardless of cost-effectiveness.	High flexibility: businesses can choose how to meet environmental targets at lower costs.
Cost-Effectiveness	May result in high costs due to a one-size-fits-all approach, which does not account for different firm capabilities.	Typically cost-effective, as it allows firms to seek the most affordable methods of compliance.
Administrative Complexity	Easy to implement but requires significant enforcement and monitoring to ensure compliance.	Complex to design (e.g., setting cap levels or tax rates) and requires robust administration to manage markets.
Examples	Emission limits Technology standards (e.g., mandatory filters or scrubbers)	Carbon taxes Emissions trading systems Environmental taxes

demonstrates the main differences between CACER and MBER.

3.2.2. Environmental Regulation of Reducing Air Pollution

The issue most closely related to human health is air pollution. Considering the completeness and availability of indicators, this paper uses 1/PM2.5 to measure environmental regulation of air pollution:

$$\mathrm{A}\mathrm{P}\mathrm{E}\mathrm{R}={\displaystyle \frac{1}{\mathrm{P}\mathrm{M}2.5}}$$

A higher value of 1/PM2.5 indicates stricter ER on air pollution. The ultimate goal of environmental regulation is to reduce pollutant emissions and protect the environment. Accordingly, when the government increases ER, the strict requirements on enterprises will force them to reduce their own pollutant emissions. Therefore, pollutant emissions reductions are positively responsive to the strength of environmental regulation [10,42].

The reason for using PM2.5 instead of other pollutants is that haze is the most serious air pollution problem in China, and PM2.5 is considered to be the “culprit” of hazy weather [43]. Haze has been a common occurrence in China lately. In 2013, it spread to over 100 large and medium-sized cities across 25 Chinese provinces. This had a notably adverse effect on both public health and the government’s reputation. Haze has therefore become the main target of China’s pollution control and China’s environmental accountability.

3.2.3. Innovation Indicators

The most commonly used innovation indicators are patent data, including patents granted and patent applications. However, China’s patent funding policy has incentivized provinces to excessively pursue patent growth, leading to a surge in low-quality patents and the formation of “patent bubbles” or “innovation illusions” [44]. Thus, the growth of patents does not really represent an increase in the level of innovation [45]. This paper refers to the practice of Huang et al. [46] and uses the innovation index derived from the China Regional Innovation Capacity Evaluation Report, a report on the evaluation of regional development in China. The China Regional Innovation Capacity Evaluation Report is jointly compiled by the China Strategic Research Group for Science and Technology Development and the Research Center for Innovation and Entrepreneurship Management at the University of Chinese Academy of Sciences. The primary evaluation technique established in the report is a four-level indication system. Each Chinese province’s total level and production of innovation may be seen in the innovation index.

3.2.4. Control Variables

Based on the practice of Yin et al. [13] and Chen et al. [47], this paper controls for a set of province characteristic control variables in the baseline regression model to mitigate omitted variable bias where possible. This group of variables consists of FDI (foreign direct investment), scientific and technological inputs, physical capital investment, and human capital.

FDI is represented as the ratio of real foreign direct investment to GDP. Scientific and technological inputs are measured by the ratio of S&T expenditures to GDP. Physical capital investment is indicated by the proportion of real fixed asset investments to GDP.

3.3. Source and Statistical Description of the Data

This paper uses panel data from 30 provinces in China from 2009 to 2020. Since the regional innovation index data from the China Regional Innovation Capacity Evaluation Report are at the provincial level in China, we analyzed data from all provinces in mainland China except Tibet, for a total of 30 provinces (include Anhui, Beijing, Chongqing, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Inner Mongolia, Jiangsu, Jiangxi, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanghai, Shanxi, Sichuan, Tianjin, Xinjiang, Yunnan, and Zhejiang). Tibet was excluded because of its high level of missing data. Additionally, the time period ends in 2020 owing to data limitations. China’s economic status after 2008 has changed dramatically from what it was before the global financial crisis that struck in 2008. The main data sources include the China Environmental Statistics Yearbook, the National Bureau of Statistics of China website, various provincial statistical yearbooks, and the China Regional Innovation Capacity Evaluation Report. The VIFs are all less than 10, according to testing, suggesting that multiple collinearity between the interpretive variables is not a severe problem.

Table 2. Statistical description of the data.

Variable	Symbol	Obs	Mean	Std. Dev.	Min	Max
Innovations	INN	360	29.114	10.563	15.78	62.14
Market-based environmental regulation (%)	MBER	360	0.076	0.054	0.007	0.422
Command-and-control environmental regulation (%)	CACER	360	0.257	0.224	0.005	2.035
FDI (%)	FDI	360	1.996	1.604	0.01	8.191
Scientific and technological inputs	ST	360	0.004	0.003	0.001	0.014
Physical capital investment	PCI	360	0.745	0.248	0.21	1.455
Human capital (log)	HC	360	13.432	0.809	10.687	14.729
Environmental regulation of air pollution	APER	360	0.030	0.014	0.012	0.101

provides a statistical description of the data derived from these sources.

4. Empirical Results and Analysis

4.1. Baseline Regression Results

Table 3. Baseline regression results.

Variables	(1)	(2)
MBER	9.384 **
	(2.25)
CACER		−1.520 **
		(−2.00)
FDI	0.334 **	0.345 **
	(2.43)	(2.50)
ST	342.827 ***	294.679 **
	(2.90)	(2.52)
PCI	0.209	0.607
	(0.22)	(0.63)
HC	5.668 ***	3.790 **
	(3.53)	(2.52)
Constant	−46.833 **	−20.564
	(−2.21)	(−1.04)
Observations	360	360
R-squared	0.277	0.274
Fixed effect	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05.)

reports the baseline regression results based on the fixed effects model. The first column reports regression results for MBER. The coefficient for MBER is significantly positive, indicating that MBER significantly promotes regional innovation. The second column reports the regression results for CACER. The coefficient on CACER is significantly negative, indicating that CACER significantly inhibits regional innovation.

This result supports Hypotheses 1a and 2b. MBER directs firms to reduce pollution through market mechanisms. It provides economic incentives for firms to meet environmental standards while at the same time having an incentive to find and adopt innovative technologies to reduce costs and increase profits [48]. This type of regulation encourages firms to innovate technologically in order to stay ahead of the competition, thus increasing the overall level of innovation in the region. In contrast, CACER directly dictates the environmental standards that firms must comply with and penalizes violations. While this approach can be effective in reducing pollution, it typically increases compliance costs for firms and reduces the resources and time that firms devote to innovation. In addition, such mandatory regulations may limit the autonomy and flexibility of firms and inhibit innovative activities, leading to lower levels of regional innovation.

4.2. Impact of Changes in the Intensity of Environmental Regulation

This paper has found that MBER promotes the level of regional innovation, while CACER inhibits it. So, does environmental regulation affect outcomes because of changes in intensity?

The threshold effect test results are presented in

Table 4. Results of the ER threshold effect test.

Type of Environmental Regulation	Threshold Type	F Stat Value	p Value	BS	Threshold Value
Market-based	Single	5.42	0.758	500	0.0447
Command-and-control	Single	20.63	0.016	500	0.0569

. The test findings indicate that while the MBER threshold test was failed, the CACER threshold test was passed at the 5% level of significance. According to the results of testing, there is no threshold effect for MBER, while there is a significant single threshold effect for CACER, with a threshold value of 0.0569. Based on this threshold, provinces can be categorized into low-intensity CACER (CACER ≤ 0.0569) and higher-intensity CACER (CACER > 0.0569).

Table 5. The threshold model of the CACER.

Variables	Innovation
CACER(CACER ≤ TV)	60.432 ***
	(4.20)
CACER(CACER > TV)	−1.441 *
	(−1.95)
Control variables	YES
Observations	360
R-squared	0.413
Number of provinces	30

(t-statistics in parentheses, *** p < 0.01, * p < 0.1.)

reports the results of the threshold model of the CACER. In provinces with low intensity of command-and-control environmental regulation (CACER ≤ 0.0569), the CACER coefficient is significantly positive, and CACER significantly promotes innovation. For provinces with high intensity of CACER (CACER > 0.0569), the CACER coefficient is significantly negative, and CACER significantly inhibits innovation. This result proves Hypothesis 4a.

When the intensity of CACER is low, firms face relatively fewer compliance requirements and lower regulatory costs. This moderate pressure can stimulate firms to innovate in order to meet environmental standards and gain a competitive advantage. At this point, firms usually adopt technological improvements and innovations to improve resource utilization efficiency and reduce pollution emissions in order to meet compliance requirements. For example, introducing new production processes or improving existing equipment. These measures not only contribute to compliance but also enhance the overall competitiveness and market position of the enterprise. This enhances the level of regional innovation. However, when the intensity of CACER increases to a certain level, the compliance costs and the burden on firms increase significantly. High-intensity CACER may require enterprises to undertake large-scale technological renovation and equipment upgrading, which requires substantial investment of capital and time. In this case, firms may devote more resources to meeting regulatory requirements than to independent innovation. High-intensity regulations may also lead to higher operating costs and narrower profit margins for firms, which in turn limits their investment in innovation. This results in a decline in the level of regional innovation.

4.3. Impact of the Level of Economic Development

In addition to the intensity of environmental regulation, the level of economic development may have a similar threshold effect.

Table 6. Threshold effect test.

Type of Environmental Regulation	Threshold Type	F Stat Value	p Value	BS	Threshold Value
Market-based	Single	20.08	0.054	500	72,807
Command-and-control	Single	4.99	0.774	500	28,800

reports the results of threshold effect test. The test results indicate that the MBER threshold test is significant at the 10% level, but the threshold test of CACER is not passed.

According to the results of testing, there is no threshold effect for CACER, while there is a significant single threshold effect for MBER, with a threshold value of 72,807. Based on this threshold, provinces can be categorized into low economic development provinces (PGDP ≤ 72,807) and high economic development provinces (PGDP > 72,807). The results of the threshold model for the MBER are presented in

Table 7. The threshold model of the MBER.

Variables	Innovation
MBER (LED ≤ TV)	3.126
	(0.72)
MBER (LED > TV)	39.022 ***
	(4.84)
Control variables	YES
Observations	360
R-squared	0.361

(t-statistics in parentheses, *** p < 0.01.)

:

Table 7 demonstrates that the MBER cannot foster innovation in provinces with low levels of economic development (LED ≤ 72,807). The MBER coefficient is significantly positive for provinces with a high level of economic development (LED > 72,807), and MBER strongly fosters innovation. This result proves Hypothesis 3a. Thus, in economically developed provinces, firms generally have more resources and capacity to respond to MBER [49]. With stronger R&D capabilities and financial strength, these firms are able to invest in new technologies and innovative projects to reduce pollution and increase productivity, thereby not only meeting MBER requirements but also gaining a competitive advantage in the marketplace. In this case, MBER further stimulates the innovation drive of firms through economic incentives and promotes the level of regional innovation. On the contrary, in economically underdeveloped provinces firms often have limited resources and lack sufficient capital and technological reserves to innovate. These firms may find the costs associated with complying with MBER too high to bear, thus inhibiting innovative activities. For example, in the face of cost pressures from pollution taxes and fees, firms may be more inclined to adopt short-term cost-control measures than to make long-term investments in technological innovation.

4.4. Regional Heterogeneity Analysis

China is a large nation with distinctly diverse natural environments. Each region’s social growth and economic activity may be influenced by variables including resource distribution and climate. The levels of economic development across China’s eastern, central, and western regions vary significantly from one another. The eastern and central regions exhibit much higher levels of economic development, industrialization, and urbanization rates in comparison to the western regions [50]. This difference leads to differences in the industrial structure, energy consumption, and technological base of each region, all of which may affect the effectiveness of environmental regulation and the way in which enterprises respond to regulation. Policy implementation and resource allocation also differ between the eastern, central, and western regions. Eastern and central regions typically have greater access to government support and external investment, better infrastructure, and higher government governance capacity, which may make market-based environmental regulation easier to implement in eastern and central regions. In contrast, the effectiveness of market-based instruments may be compromised in the western region due to infrastructure and regulatory capacity constraints. Firms in the east–central region are typically more technologically advanced and more open to adopting new technologies and conducting R&D activities and thus may be more effective in responding to the innovation incentives of environmental regulation [51]. Firms in the western regions, on the other hand, may show different patterns of response to environmental regulation due to technological and capital constraints, which may affect the design and implementation of environmental policies in these regions.

By analyzing the heterogeneity of China’s eastern, central, and western regions, a deeper understanding of the complex relationship between environmental regulation and regional economic, environmental, and technological innovations can be gained, which can help to formulate more precise and effective regional environmental policies.

Referring to the practice of Yang et al. [52] and according to the division found in the China Statistical Yearbook, there are 19 provinces in the eastern and central regions of China and 11 in the western regions of China.

The findings of regressions on regional heterogeneity in MBER are presented in

Table 8. Regional heterogeneity regression of the MBER.

	(1)	(2)
Variables	Eastern and Central	Western
MBER	24.042 ***	−15.446 ***
	(4.02)	(−3.06)
FDI	0.380 **	−0.314
	(2.33)	(−0.93)
ST	479.250 ***	221.185
	(3.06)	(1.12)
PCI	−1.503	2.729 **
	(−1.06)	(2.35)
HC	5.576 *	−0.422
	(1.70)	(−0.21)
Constant	−44.786	29.636
	(−1.01)	(1.14)
Observations	228	132
R-squared	0.358	0.310
Fixed effect	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.)

. In the east–central region, the MBER regression coefficients are significantly positive; in the western region, they are significantly negative. According to the regression results, MBER encourages regional innovation in the eastern and central regions but hinders it in the western region.

The findings of the regressions on regional heterogeneity in CACER are presented in

Table 9. Regional heterogeneity regression of the CACER.

Variables	Eastern and Central	Western
CACER	−0.497	−2.114 ***
	(−0.32)	(−2.83)
FDI	0.420 **	−0.281
	(2.45)	(−0.83)
ST	457.746 ***	249.970
	(2.81)	(1.27)
PCI	−0.969	2.702 **
	(−0.66)	(2.31)
HC	0.809	2.206
	(0.25)	(1.19)
Constant	21.908	−4.760
	(0.51)	(−0.21)
Observations	228	132
R-squared	0.304	0.302
Fixed effect	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05.)

. In the eastern and central China, the CACER coefficient is not significant, but in the western region, it is significantly negative. The findings of the regression show that whereas CACER greatly hinders regional innovation in the western region, it has no effect on it in the eastern and central regions.

There is regional heterogeneity in the impact of both MBER and CACER on innovation. In the western region, where the economy is relatively backward, both MBER and CACER regulations inhibit regional innovation. The likely reason is that firms in the western region usually have limited resources and face greater financial and technological pressures. The relatively immature market and financial environment in the western region makes it not easy for companies to obtain sufficient external financial support for innovation. In addition, the institutional and administrative environment also plays a critical role. In the western region, local governments often lack the expertise, resources, and enforcement capacity to implement environmental regulations in a manner that encourages innovation. Firms in this area receive less guidance, technical assistance, and policy support, making it more difficult for them to adapt to regulatory changes. As a result, environmental standards become an additional burden rather than a stimulus for creative problem-solving and technological advancement.

Therefore, the government could establish dedicated innovation funds to provide low-interest loans, grants, and tax incentives for companies in the western region. These financial tools would alleviate resource constraints and enable businesses to invest more in R&D and technology upgrades, improving their ability to meet environmental requirements. Regional technical support centers could be established in the western region, staffed with experts who provide guidance on environmental technologies and compliance processes. By enhancing the capacity of local governments to enforce regulations and support firms, companies would face lower costs and challenges in meeting environmental standards.

4.5. Robustness Check

To ensure the reliability of the empirical findings, this section conducts a series of robustness checks. Given the complexity of the relationship between environmental regulation and innovation, it is essential to verify whether the baseline results remain consistent under different conditions. Our robustness checks address potential concerns such as the influence of external shocks (e.g., COVID-19), alternative measures of innovation, and endogeneity issues. By applying partial sample regression analysis, substituting explanatory variables, and employing instrumental variable techniques, this section provides additional validation for the core findings, reinforcing the credibility of the study’s conclusions.

4.5.1. Partial Sample Regression

The economic situation in 2020 is distinct from previous years because of COVID-19. Restrictive measures taken to stop the epidemic’s spread had varied effects on different industries and sectors, which had an impact on the state of the economy as a whole. As a result, this research uses data from 2009 to 2019 to rerun the regression.

The regression results are shown in

Table 10. Partial sample regression.

Variables	2009–2019	2009–2019
MBER	12.064 ***
	(2.70)
CACER		−1.441 *
		(−1.94)
FDI	0.165	0.184
	(1.17)	(1.30)
ST	411.051 ***	355.137 ***
	(3.42)	(2.95)
PCI	0.937	1.370
	(0.93)	(1.37)
HC	5.751 ***	3.338 **
	(3.27)	(2.04)
Constants	−48.474 **	−14.878
	(−2.08)	(−0.69)
Observations	330	330
Fixed effects	YES	YES
R2	0.311	0.303

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.)

. The first and second columns show the regression results for the impact of MBER and CACER on innovation, respectively. The regression coefficients for MBER in the first column are significantly positive, while the regression coefficients for CACER in the second column are significantly negative, suggesting that MBER promotes regional innovation while CACER hinders it. The outcomes are unchanged from the baseline regression. More importantly, even after excluding the potential impact of the COVID-19 pandemic in 2020, the direction and significance of the key variables remain unchanged. This further validates the stability of our conclusions. In other words, although the pandemic had a profound economic impact and may have temporarily disrupted firms’ innovation activities, the fundamental mechanisms by which MBER fosters innovation and CACER inhibits it remain intact over the long run. This suggests that these factors have a persistent and structurally significant influence on regional innovation.

4.5.2. Substitution of Explanatory Variables

Innovation is a key driver of economic growth and regional development, and accurately measuring it is crucial for empirical analysis. Patents are still widely regarded as a reliable proxy as they reflect firms’ investment in research and development (R&D) and their ability to generate novel technological outputs. To ensure the robustness of the findings, referring to the some of the literature [25,53], this paper measures innovation in terms of patents to test for robustness. The number of patent grants (log) is used to represent innovation, and the data are obtained from the China Science and Technology Statistical Yearbook.

The results are listed in

Table 11. Substitution of explanatory variables.

Variables	(1)	(2)
MBER	1.099 ***
	(2.66)
CACER		−0.201 ***
		(−2.67)
FDI	0.026 *	0.028 **
	(1.92)	(2.02)
ST	51.174 ***	45.387 ***
	(4.37)	(3.91)
PCI	0.095	0.144
	(0.98)	(1.50)
HC	1.016 ***	0.789 ***
	(6.38)	(5.30)
Constant	−5.091 **	−1.910
	(−2.42)	(−0.98)
Observations	360	360
R-squared	0.923	0.923
Fixed effect	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.)

. The first and second columns show the regression results for MBER and CACER, respectively. The regression coefficients for MBER in the first column are significantly positive, while the regression coefficients for CACER in the second column are significantly negative, suggesting that MBER promotes patents growth while CACER hinders patents growth. The outcomes are unchanged from the baseline regression and the results are still robust: Whereas CACER stifles innovation, MBER fosters it. Whether the level of innovation is measured using the number of patents or the regional innovation index, the results remain consistent.

4.5.3. Endogeneity

Endogeneity is an issue to address when examining the relationship between environmental regulation and innovation. For example, there may be omitted variable bias or simultaneity, or the estimated coefficients may be biased, leading to misleading conclusions. To address this issue, this section employs two approaches: controlling for additional variables that may influence innovation and using a two-stage least squares (2SLS) estimation with instrumental variables. By implementing these methods, this section aims to ensure that the observed effects of MBER and CACER on innovation are not driven by unobserved factors, thereby enhancing the credibility and robustness of the study’s findings.

(i)

The issue of omission bias

In empirical research on innovation, failing to account for key factors that influence innovation performance may lead to omission bias, potentially distorting the estimated effects of explanatory variables. This research further controls the aspects of industrial structure and quantity of R&D workers that may affect innovation in the regression, taking into account the potential crucial roles of these factors in innovation and mitigating the potential “omission bias” issue. This paper selects “the ratio of value added of secondary sector to GDP” to measure industrial structure (IS), “number of R&D staff (logs)” to measure role of R&D staff (NRD).

The regression results are shown in

Table 12. The result of addressing the issue of omission bias.

Variables	(1)	(2)	(3)	(4)
MBER	10.496 **	10.962 ***
	(2.56)	(2.69)
CACER			−1.717 **	−1.547 **
			(−2.30)	(−2.06)
FDI	0.374 ***	0.332 **	0.386 ***	0.351 **
	(2.77)	(2.45)	(2.85)	(2.58)
ST	178.046	159.474	125.155	110.369
	(1.44)	(1.29)	(1.01)	(0.89)
PCI	−0.108	0.049	0.340	0.464
	(−0.11)	(0.05)	(0.36)	(0.49)
HC	4.966 ***	4.631 ***	2.865 *	2.591 *
	(3.14)	(2.93)	(1.92)	(1.73)
NRD	2.730 ***	2.042 **	2.713 ***	2.126 ***
	(3.69)	(2.56)	(3.66)	(2.64)
IS		11.275 **		9.327 *
		(2.26)		(1.85)
Constant	−66.366 ***	−59.855 ***	−36.800 *	−31.269
	(−3.09)	(−2.78)	(−1.85)	(−1.56)
Observations	360	360	360	360
Fixed effects	YES	YES	YES	YES
R2	0.205	0.215	0.202	0.208

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.)

. The first and second columns report the results of the MBER regression: the variables NRD and IS are added to the first and second columns in that order. The third and fourth columns report the results of the MBER regression: the variables NRD and IS are added to the third and fourth columns in turn. The regression coefficients for MBER in the first and second columns are significantly positive, while the regression coefficients for CACER in the third and fourth columns are significantly negative, suggesting that MBER promotes innovation while CACER hinders innovation. The estimation results have not changed significantly from the baseline results. These findings suggest that the results remain robust after excluding omitted variable bias.

(i)

Two-stage least squares estimation

Endogeneity problems may also arise because of simultaneity concerns. In order to further mitigate endogeneity issues, this study employs a 2SLS estimate method.

The year-province average of MBER (excluding the province’s own value) and the year-province average of CACER (excluding the province’s own value) are the two instrumental variables used in the two-stage least squares estimation. The two instrumental variables selected have passed the unidentifiable test and the weak instrumental variable test, showing their effectiveness.

The regression results of the two-stage least squares estimation are shown in

Table 13. The regression results of 2SLS.

Variables	(1)	(2)
MBER	9.384 **
	(2.18)
CACER		−1.520 ***
		(−2.66)
FDI	0.334 **	0.345 **
	(2.12)	(2.18)
ST	342.828 ***	294.679 **
	(2.61)	(2.18)
PCI	0.209	0.610
	(0.23)	(0.69)
HC	5.668 ***	3.790 ***
	(4.18)	(2.72)
Constant	−26.566	−0.332
	(−1.46)	(−0.02)
Observations	360	360
Fixed effects	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05.)

. The first and second columns report the results of the MBER and CACER regressions, respectively. The regression coefficients for MBER in the first column are significantly positive, while the regression coefficients for CACER in the second column are significantly negative. After taking endogeneity concerns into consideration, the coefficient estimate for MBER is still significantly positive, indicating that MBER has generally aided in China’s innovation. Even after taking endogeneity concerns into consideration, the coefficient estimate for CACER is still significantly negative, indicating that China’s CACER inhibits innovation.

4.5.4. Summary

The robustness check results confirm the reliability of the study’s findings. First, a partial sample regression, excluding data from 2020 to account for the economic disruptions caused by COVID-19, yields results consistent with the baseline analysis—MBER promotes innovation, while CACER inhibits it. Second, substituting innovation indicators by using patent grants as an alternative measure also supports the main conclusions. Finally, addressing potential endogeneity through mitigating the potential “omission bias” issue and two-stage least squares (2SLS) estimation further reinforces the findings. The estimated coefficients remain stable and statistically significant after controlling for omitted variable bias and potential simultaneity concerns. These robustness tests collectively demonstrate that the conclusions drawn from the empirical analysis are robust and reliable.

4.6. Further Analysis: From the Perspective of Pollution Reduction

The influence of ER’s abatement effect on innovation has not been examined in the majority of the existing literature which examines the relationship between environmental regulation and innovation primarily from the standpoint of cost or Porter’s hypothesis [13,39]. Pollution affects human health and reduces labor productivity and human capital. Thus, from this perspective, protecting the environment promotes long-term innovation. Thus, in addition to the cost effect and the innovation compensation effect, the pollution reduction effect may also account for the different impacts of different types of environmental regulation on innovation.

This research explores the effects of different environmental control measures on innovation. However, some of the environmental management tools currently used in China have not effectively controlled pollution levels [33]. As a result, this paper examines not only how various ER tools influence innovation but also how environmental regulation impacts innovation from a pollution control perspective. Therefore, synthesizing these two aspects of research can provide more comprehensive theoretical and empirical support for the development of more effective environmental policies and the promotion of regional innovation.

Based on Equation (5), the regression results of the impact of ER of air pollution on innovation are shown in

Table 14. Impact of ER of air pollution on innovation.

Variables	FE	FE
APER	149.585 ***	116.886 ***
	(3.72)	(2.95)
FDI		0.337 **
		(2.47)
ST		267.647 **
		(2.29)
PCI		0.040
		(0.04)
HC		4.235 ***
		(2.88)
Constant	27.310 ***	−29.616
	(24.09)	(−1.54)
Observations	360	360
Fixed effects	YES	YES

(t-statistics in parentheses, *** p < 0.01, ** p < 0.05.)

. The results of regressions with fixed effects and no control variables are shown in the first column. The second column reports the results of regressions based on fixed effects without control variables. The coefficients on environmental regulation in both the first and second columns are significantly positive, indicating that environmental regulation of reducing air pollution does promote regional innovation in China. This result proves Hypothesis 5a. The result also illustrates that in addition to “incentive effects”, pollution reduction is also a mechanism by which environmental regulation promotes innovation.

5. Discussion

This paper’s similarities and differences with existing studies are as follows: Referring the methodology of Xie et al. [38] and Pan et al. [39], this paper divides China’s environmental regulatory tools into two categories—MBER and CACER—and explores the impact of both regulational tools on innovation. Unlike the approaches of Pan et al. [39] and Liu et al. [40], which primarily rely on linear models or direct comparisons between different types of environmental regulations, this paper adopts a threshold model to examine the differentiated impacts of various environmental regulations on innovation under different conditions. The threshold model allows for the consideration of non-linear relationships and the possibility that the effects of environmental regulations may vary depending on certain key factors such as the level of regulatory stringency or the region’s economic development level. This approach provides a more nuanced understanding of how different regulatory frameworks might stimulate or hinder innovation in various contexts, offering empirical evidence on the optimal conditions for choosing the most effective environmental regulation to foster innovation. Additionally, while the existing literature focuses on the trade-off between regulation costs and innovation benefits, this study incorporates the abatement effect, demonstrating that pollution reduction itself can serve as a catalyst for technological advancement. This further sheds light on why different environmental policy instruments have different effects in promoting innovation. By linking environmental quality improvements with economic and technological progress, this paper offers a broader perspective on how to select environmental policies.

It is important to mention the limitations of this study. For the study of the impact of environmental regulation on innovation from a pollution reduction perspective, there are many types of environmental pollutants, which are proxied in this paper and may not adequately reflect the complexity of the real world. Future research could further explore how environmental regulations targeting air pollution and other pollutants influence innovation. The second limitation of this study is the relatively short time span over which environmental regulation has been assessed and the coarse scale of the study at the provincial level, both of which are due to data limitations. Future research could further extend the research period, where data are available, to include longer-term data to more comprehensively assess the dynamic impacts of environmental policies on innovation, as well as adopting finer spatial scales, making use of city-level data where it exists, to improve the applicability of the study.

6. Conclusions

The relationship between ER and regional innovation levels has been controversial. In this paper, this analyzes data at the provincial level in China between 2009 and 2020 using a fixed-effects model to investigate this relationship. The results suggest that MBER effectively promotes regional innovation, while CACER hinders it. After accounting for endogeneity, the conclusions remain robust. However, the impact of ER is characterized by heterogeneity and non-linearity. The implementation of overly strict CACER does hinder innovation whilst the implementation of low-intensity CACER promotes innovation. In economically developed provinces, MBER does promote innovation, while in economically underdeveloped provinces, MBER inhibits innovation instead. Further analysis from a pollution reduction perspective shows that ERs that mitigate air pollution significantly promote regional innovation.

These findings offer important practical contributions to the literature on environmental regulation and innovation. By establishing a causal link between ER and regional innovation, this study provides empirical evidence that can guide policymakers in designing scientifically informed and innovation-friendly environmental policies. Rather than adopting a one-size-fits-all approach, governments should tailor ER policies to regional economic conditions and environmental needs, ensuring that regulations drive innovation rather than suppress it. Based on the above conclusions, the study makes the following policy recommendations. First, governments should promote and strengthen the application of MBER, especially in economically developed regions, which will help to improve regional innovation capacity and overall competitiveness. Secondly, CACER should be moderately adjusted. Whilst strict CACER may inhibit innovation, moderate low-intensity CACER may instead help innovation. Therefore, it is suggested that the government implement CACER with moderate adjustments according to specific circumstances and regional characteristics to avoid the negative impact of overly strict regulations on innovation. Finally, in economically developed regions, the use of MBER should be strengthened, while in less economically developed regions, the intensity of MBER should be appropriately reduced to avoid damaging impacts on regional innovation. Finally, while this study is based on China, its findings can offer valuable guidance for policymakers worldwide. To effectively stimulate innovation, governments should prioritize market-based environmental regulations, such as environmental taxes and fees, which provide economic incentives for businesses to adopt sustainable practices. In contrast, the use of command-and-control measures, such as rigid administrative mandates, should be limited to avoid stifling flexibility and innovation. By adopting this balanced approach, countries can create an enabling environment that encourages technological advancements while achieving environmental objectives. Governments should focus on air pollution and prioritize regulations that are effective in reducing air pollution as they can significantly enhance regional innovation.