Environmental Regulation, Green Innovation, and Corporate Brand Value

Abstract

Under the background of green development and brand building, this article aims to explore the relationship between environmental regulation on corporate brand value and the mediating effect of green innovation, which can help enterprises build a synergistic mechanism between brand building and green development, and step into a benign track of high-quality development. Based on institutional theory and resource-based theory, this paper takes the 106 listed companies on the 2018–2022 consecutive list of China’s 500 Most Valuable Brands as a sample and constructs a two-way fixed-effects model to test the impact of heterogeneous environmental regulations on green innovation and corporate brand value. The empirical results showed that: (1) Command-based environmental regulation exhibits an inverted U-shaped relationship with brand value, as it compels enterprises to adopt environmental governance in the short term but gradually erodes productive resources and triggers negative environmental and reputational effects over the long term; market-based environmental regulation demonstrates a U-shaped relationship with brand value: while it crowds out production funds and shifts costs to consumers in the short term, the government’s “resource compensation” effects ultimately outweigh “compliance cost” pressures in the long run; (2) green innovation plays a partial mediating role in the impact of command-based environmental regulation on corporate brand value; (3) the impact of environmental regulations on corporate brand value is heterogeneous in terms of the nature of corporate ownership, life cycle, and location. The above findings provide a useful reference for the government to use environmental regulation tools flexibly, optimally adjust the environmental regulatory mechanism, and promote corporate brand building and green development.

1. Introduction

Nowadays, while China’s rapid economic growth has created a “speed miracle”, the problems of resources and the environment have become increasingly prominent. The early development model of “high input, high consumption, high pollution and low value-added” led to frequent ecological problems such as haze, water pollution, and floods. The country is facing difficulties such as resource shortages, insufficient technological innovation ability, and environmental pollution. Therefore, seeking a balance between economic development and environmental protection is an urgent issue for us to consider at present [1]. Environmental regulation is an important means for the “active government” to achieve the win–win goal of environment and economy [2], which can encourage enterprises to adopt environmentally friendly production and operation methods [3], implement green technology innovation, and optimize internal resources [4], so as to enhance enterprises’ market competitiveness [5]. At the same time, with the rise of the era of the brand economy, brands have gradually become the core competitiveness of long-term and stable development of enterprises [6], and the government and enterprises pay more and more attention to brand value shaping. From “Outline for Building a Strong Quality Nation”, which specifies the goal of brand development, to the 2024 government work report, which emphasizes “creating more internationally influential Made-in-China brands”, the government is focusing more and more on brand value building.

Enterprises are not only the main body of brand building but also the direct implementers of environmental regulation policies. Exploring the impact of environmental regulation on enterprises from the perspective of brand value can help reveal their strategic adjustment and value reconstruction mechanism under the pressure of the system, promote the synergistic development of greening and branding of enterprises, and help realize the high-quality development of the economy and society. At the same time, green innovation is a key path to achieving environmental adaptation and value creation. As a deep-rooted change in technology, management, and products, green innovation not only reflects the sensitivity and ability to respond to environmental policies [7] but also plays a central role in rebranding and conveying the concept of sustainability [8], which significantly enhances the brand identity of consumers and investors. Therefore, this paper selects green innovation as a mediating variable to explore how environmental regulation can be transformed into brand value through the proactive strategic behavior of enterprises.

Existing researches mostly explore the connotation of brand value from multiple perspectives, such as financial perspective, consumer perspective, and enterprise perspective, pointing out that brand value is the added value of a product due to the brand name, the perceived utility of creating differentiation for consumers, and the customer relationship that brings additional benefits to the enterprise, and ultimately demonstrates the profitability in the economic behavior [9,10]. The enterprise brand value system is composed of two types of factors, namely internal factors and external factors. The former is mainly the internal product function value of the brand, including the characteristics and functions of the product itself; the latter is mainly the external emotional perceived value of the brand, such as consumer perception, brand association, stakeholder identity, and so on [11]. For the brand value driving mechanism, scholars have explored the impact of corporate innovation [12,13], management system [14], marketing [15], R&D investment [16], political affiliation [17], and other factors on corporate brand value.

Regarding environmental regulation, the existing literature mainly discusses the environmental performance it brings, which is mainly manifested in curbing carbon emissions [18,19], reducing air pollution [20,21], enhancing water utilization efficiency [22,23], and lowering energy consumption [24]. In addition, scholars also attach great importance to the economic performance brought by environmental regulation, mainly focusing on green technology innovation [25,26], industrial structure upgrading [27,28,29], total factor productivity enhancement [30], enterprise value enhancement [31,32], and so on. In addition, some scholars have also explored the impact of environmental regulation on enterprise value, including the view of facilitation where reasonable environmental regulation can motivate enterprises to use new production technologies and processes, improve production efficiency and competitiveness, and enhance enterprise value [33,34]; the view of restraining effect where the cost of regulatory compliance will only bring cost burden to the operation of enterprises, which is not conducive to the development of enterprises [35,36]. Some scholars also believe that there is a nonlinear relationship between environmental regulation and corporate value [31].

With the deepening of brand value and environmental regulation, scholars have found that only a single theoretical perspective can form a partial explanation, so they gradually turn to analyze it by combining multiple theoretical perspectives. For example, the establishment of the resource-based theory and stakeholder theory of the weighting framework [37], the combination of the resource-based theory and the dynamic capabilities theory of the system analysis [38]. The original institutional theory does not explore the issue of enterprise value in depth but focuses on the regulatory pressure to make enterprises take similar actions to narrow the legitimacy gap with competitors without considering the premise of financial performance [39], but it is easy to ignore the motivation of enterprises’ choice of legitimacy actions. Resource-based theory can make up for the shortcomings of institutional theory by explaining how firms can increase resource inputs and develop relevant capabilities to achieve value creation by making green technology innovations [40], but it cannot explain the dynamic effects of facing different pressures on resource mobilization and capability differences. Based on previous research [41,42], this paper combines institutional theory and resource-based theory to explore the driving mechanism of brand value by considering external environmental regulation as the condition for internal green innovation to affect brand value and internal green innovation as the complement of external regulation and the driving force of brand value.

In summary, the existing literature has not yet explored the mechanism of environmental regulation on corporate brand value, nor has it discussed the impact of heterogeneous environmental regulation tools on corporate green innovation and even brand value. Building on this foundation, this paper combines institutional theory and resource-based theory to explain the impact mechanism of heterogeneous environmental regulation on corporate green innovation and brand value from the perspective of green innovation. This paper tries to answer the following questions: (1) Among the existing means of environmental regulation, which way can better enhance brand value? (2) Through what “black box” mechanism do different regulatory tools promote corporate brand value? (3) What are the differences in the impact of environmental regulations on corporate brand value under different corporate characteristics?

The main contributions of this paper are as follows: (1) In terms of research perspective, previous studies have examined the impact of a single environmental regulation on micro-enterprises from the aspects of enterprise innovation, enterprise upgrading, enterprise value, and so on. However, this paper explores the impact of different environmental regulations on enterprise brand value from the perspective of double heterogeneity and further expands the research perspective related to environmental regulations. (2) In terms of the mechanism, different from the existing research mechanisms that focus on variables such as financing constraints and technological innovation, this paper uses the green innovation intermediary mechanism to closely combine the “environment” of environmental regulation and the “economy” of corporate brand value, and to a certain extent clarifies the path for environmental regulation to enhance corporate brand value. (3) In terms of practical significance, this paper not only examines the impact and mechanism of environmental regulation on corporate brand value but also further analyses the heterogeneous effects of brand value enhancement. The research conclusions provide policy references for formulating reasonable and effective environmental regulations and have important significance for achieving the win–win goal of brand building and green development.

2. Theoretical Mechanisms and Research Hypotheses

2.1. Environmental Regulation and Brand Value

Institutional theory holds that institutions, as the external norms that enterprises need to comply with, are important macro-factors influencing innovation choices and value enhancement [43,44]. Currently, China’s environmental regulation policies mainly include command-based environmental regulation and market-based environmental regulation, and there are significant differences between the two in the implementation subjects and action mechanisms, so the impact on brand value is also different.

2.1.1. The Impact of Command Environment Regulation on Brand Value

Command-based environmental regulation is mandatory by the government through the enactment of environmental protection regulations, the allocation of emission limits and other rigid control measures, and mandatory constraints on the behavior of economic agents affecting the environment so as to reduce pollution emissions [45,46]. For example, laws and regulations such as the Clean Air Act (1970) and the Clean Water Act (1972) in the United States, the Industrial Emissions Directive (2010) in the European Union, and China’s Environmental Protection Law (2015) are typical examples of command-based environmental regulation.

When the intensity of command-based environmental regulation is low, firms respond to the regulatory pressure with end-of-pipe pollution control [47] while reflecting on the shortcomings of environmental protection behaviors [48] and reducing organizational inertia to increase productivity and enhance brand value. With the gradual increase in regulatory intensity, environmental standards and supervision become more stringent, based on the motivation of long-term development, enterprises will turn the government’s regulatory pressure into the source power of environmental governance, which can help enterprises build green brands, improve corporate awareness, and enhance the brand value of the enterprise [49]. In addition, the “survival of the fittest effect” of environmental regulation will gradually appear, and enterprises that have not achieved results in environmental pollution control will face the risk of closure for rectification [50], making inefficient enterprises gradually withdraw from the market, and thus the market competitive advantage of advantageous enterprises will be enhanced [51], which is conducive to the increase in brand value. When the intensity of environmental regulation breaks through a certain threshold and is further enhanced, green norms have been formed in the industry, and the marginal effect of the enterprise’s green production factor inputs will gradually decrease, facing higher regulatory costs [52]. Under strict environmental regulations, on the one hand, the possibility of enterprises meeting regulatory requirements is reduced, and the negative reputation of environmental evaluation will affect the brand value of enterprises [53]; on the other hand, excessive regulatory costs will induce rent-seeking behavior of enterprises [54], which will crowd out the R&D investment of enterprises and restrict the appreciation of brand value. Based on the above analysis, the following hypothesis is proposed.

Hypothesis 1a (H1a). 

There is an inverted U-shaped relationship between command-based environmental regulation and corporate brand value.

2.1.2. The Impact of Market-Based Environmental Regulation on Brand Value

Market-based environmental regulation is full of flexibility and mainly refers to the government through the collection of sewage charges, environmental protection subsidies, and other flexible economic measures to give enterprises more autonomy of choice to achieve the maximum degree of pollution reduction purposes [45,46]. Examples include the European Union’s carbon emissions trading system and China’s emissions trading system and sewage charging system.

When the intensity of market-based environmental regulations is low, the level of pollution charges is low. At this time, enterprises will choose to pay pollution charges from the perspective of profit maximization, which will increase the opportunity cost of energy factors used by enterprises [55,56]. At the same time, in order to continue to operate, enterprises will transfer additional pollution control costs to product prices and ultimately pass them on to consumers [57]. The increase in product prices leads to a weakening of consumers’ willingness to pay, weakening the competitiveness of the enterprise’s products in the market for similar products, which is not conducive to the enhancement of brand value. When the intensity of environmental regulation breaks through a certain threshold and is further strengthened, based on regulatory pressure and public pressure, enterprises will take the initiative to disclose environmental information and adopt proactive environmental strategies [58], indicating a high level of management and environmental risk prevention and control capabilities, alleviating information asymmetry and establishing a relationship of trust, establishing a good brand image and corporate reputation, and enhancing brand value. At the same time, enterprises with a good environmental reputation will obtain policy support from government departments such as government subsidies, tax incentives, and publicity and promotion [59], which is more likely to win the trust of consumers and is conducive to the value-added brand value. Based on the above analysis, the hypothesis is proposed.

Hypothesis 1b (H1b). 

There is a U-shaped relationship between market-based environmental regulation and corporate brand value.

2.2. The Mediating Role of Green Innovation

According to the resource-based theory, the resources and capabilities accumulated by enterprises to meet the challenges of the natural environment and to achieve environmental sustainability will provide them with long-term brand competitive advantages [60,61]. Green innovation refers to new or improved technologies, techniques, processes, or products that help to reduce energy consumption, reduce pollution emissions, and improve the ecological environment [62]. Therefore, green innovation, as an important part of an enterprise’s sustainable development strategy, is a key intrinsic condition for enhancing the value of an enterprise’s brand.

First, green innovation strengthens the innovation and R&D capabilities of enterprises, enabling them to create and lead market demand. By developing environmentally friendly products, enterprises can provide differentiated experiences, meet the growing demand for green consumption, and adapt to the low-carbon economic environment. This reduces consumers’ decision-making costs, increases purchase intention, improves market recognition of products, extends brand life cycles, and ultimately realizes brand value-added [63]. Additionally, green innovation can promote enterprises to upgrade the green production process, improve production efficiency, reduce production costs, and generate excess profits, further contributing to brand value [64]. Second, green innovation enables enterprises to leverage market resources [65]. The development and application of green innovative technology indicates that the enterprise has long-term goal planning and future development potential, which is more likely to gain the favor of investors and attract excellent talents [66], and reserve high-quality capital and talent resources for enhancing the value of the brand.

Green innovation is characterized by double externalities [67], high costs, high risks, and long cycles [68], and the effects of different types of environmental regulations on green innovation are not always consistent.

There is an inverted U-shaped relationship between command-based environmental regulation and enterprise green innovation. When the intensity of command-based environmental regulation is low, the environmental compliance pressure faced by enterprises is relatively low. Considering the R&D time constraints and the uncertainty of green technological innovation, enterprises satisfy the minimum environmental standards by changing their pollution control decisions. At this time, the mandatory nature of regulatory policies can prompt enterprises to analyze the shortcomings of green development and form the willingness of green innovation [24]. For example, during the initial implementation of the Clean Air Act in the United States, automobile companies such as Ford and General Motors catered to the policy requirements by improving fuel technology and emission control equipment. With the gradual increase in the intensity of regulation, enterprises need to seek breakthroughs through green innovation in order to meet the established environmental performance standards. The increase in regulatory intensity will point out a clearer direction for the investment of innovation activities [69]. When the intensity of environmental regulation breaks through a certain threshold and further enhancement, enterprises will face high pollution control costs, it is more difficult to invest sufficient funds for green innovation, or the regulatory standards are too high to meet, resulting in a negative attitude towards pollution control, which is not conducive to the green innovation of enterprises.

There is a U-shaped relationship between market-based environmental regulation and enterprise green innovation. When the intensity of market-based environmental regulation is low, the cost of enterprise pollution control is much lower than the input cost of green innovation. From the perspective of a “rational economic man”, enterprises are more inclined to pay sewage charges, and the “follow-cost effect” dominates [70]. At this time, enterprises lack the motivation to pursue green innovation. With the gradual enhancement of market-based regulatory intensity due to the sewage charge levy, there are crowded enterprise production funds, R&D investment, and other policy failures [71]. This results in increased uncertainty in the supply of R&D resources, making the short-term economic transformation effect of patents difficult to achieve. Consequently, the risk of enterprise green innovation is large, inhibiting the enthusiasm of enterprises to carry out green innovation. When the intensity of market-based environmental regulation breaks through a certain threshold and is further enhanced, the cost of enterprise pollution control tends to be even higher than the input cost of green innovation, forcing enterprises to carry out green innovation activities [72]. Positive green innovation behavior can not only directly improve enterprise productivity and profitability but also partially offset the associated compliance costs. It also enables enterprises to indirectly access the government’s environmental subsidies, tax incentives, and other financial incentives to establish a good government–enterprise relationship, convey favorable signals to the outside world, and win the favor of investors, which can reduce enterprise financing costs [73]. Based on these points, the following hypotheses are proposed:

Hypothesis 2a (H2a). 

Green innovation mediates the relationship between command-based environmental regulation and brand value.

Hypothesis 2b (H2b). 

Green innovation mediates the relationship between market-based environmental regulation and brand value.

Based on the theoretical analysis above, this study constructs a conceptual model illustrating the relationships among environmental regulation, green innovation, and brand value, as depicted in Figure 1.

3. Research Design

3.1. Sample Selection and Data Sources

This paper selects data from 106 listed companies on the 2018–2022 consecutive list of China’s 500 Most Valuable Brands published by the World Brand Lab as a sample. The data sources are as follows: (1) brand value data from the World Brand Lab’s “China’s 500 Most Valuable Brands” list; (2) command-based and market-based environmental regulation data from China Environmental Yearbook, China Environmental Statistics Yearbook, and China Taxation Yearbook; (3) green innovation patents from the CNRDS database; (4) all other control variables from the CSMAR database. Following established practices in the prior literature [17,74], data were processed as follows: listed enterprises that appeared on the list for five consecutive years were selected; the samples of ST and ST* enterprises were excluded; financial enterprises were removed; listed companies with missing key variables were excluded; continuous variables were winsorized at the 1st and 99th percentiles to mitigate the impact of outliers. Final cross-sectional data are obtained from 106 sample companies, five observations per company, with a total of 530 samples. Statistical analysis was conducted using Stata 17.0 for empirical testing.

3.2. Variable Measurement

3.2.1. Dependent Variable

Brand Value (BV): Referring to the measurement approach of Zou et al. [75], brand value is measured using the natural logarithm of brand value data from the World Brand Lab’s China’s 500 Most Valuable Brands list. The brand value is calculated by multiplying the adjusted annual business earnings (E), the brand added value index (BI), and the brand strength coefficient (S). This metric comprehensively integrates multifactorial indices, including financial data, consumer behavior, and brand strength, providing an accurate reflection of a brand’s value within China’s competitive market environment.

3.2.2. Independent Variables

Command-based environmental regulation (ER1): Referring to prior studies [76,77], the entropy method is employed to calculate the ratio of industrial wastewater, exhaust gas, and solid waste emissions to industrial output. This constructs the pollutant emission intensity for each region, and the inverse of the integrated emission intensity is used to represent the intensity of command-based environmental regulation.

Market-based environmental regulation (ER2): Referring to the method of Sun et al. [78], using the amount of sewage charges unloaded into the household to indicate. The sewage charge levy has been implemented widely and for a long period of time, which is more representative compared with the policies of sewage rights trading and government subsidies. To mitigate the effects of heteroskedasticity, the variables are logarithmically transformed.

3.2.3. Mediating Variable

Green Innovation (GPG): Drawing on Liu et al.’s method [79], this paper uses the number of green patents granted for measurement. Compared with other indicators, green patents provide a more intuitive, timely, and quantifiable reflection of an enterprise’s green innovation level, with additional spillover effects both within and outside the industry. Considering the right-skewed distribution of patent data and avoiding the loss of observations due to zero patents, we take the logarithm of all patent data after adding 1, in line with the approach of Cornaggia et al. [80].

3.2.4. Control Variables

It has been pointed out in the literature that brand value is also affected by factors such as basic company characteristics, financial status, and governance traits [79,81]. Therefore, this paper establishes the following control variables with reference to previous research. For the basic characteristics of a company, this study selects two indicators: firm size (Size) and nature of property rights (State). The financial status of the company includes 3 variables: firm leverage (Debt), growth capacity (Growth), and return on total assets (Roa). For the governance traits of a company, the proportion of independent directors (Dlds), duality of roles (Dual), and shareholding concentration (Top) were selected. The measures of the specific variables are detailed in

Table 1. Variable description.

Variable	Name	Signal	Variable Description
Dependent variable	Brand Value	BV	ln (assessed value of enterprise-owned brands)
Independent variable	Command-based environmental regulation	ER1	1/pollutant emission intensity
	Market-based environmental regulation	ER2	ln (amount of sewage charges discharged to the bank account)
Mediating variable	Green Innovation	GPG	ln (number of green patents authorized by the enterprise in the year + 1)
Control variables	Firm size	Size	ln (total number of employees in the enterprise)
	Nature of property rights	State	State-owned enterprises are assigned a value of 1, and non-state-owned enterprises are assigned a value of 0.
	Firm leverage	Debt	Gearing ratio (total liabilities/total assets)
	Growth capacity	Growth	Operating income growth rate (current year’s operating income—previous year’s operating income) Previous year’s operating income
	Return on total assets	Roa	Net profit/total assets
	Proportion of Independent directors	Dlds	Number of independent directors/total number of board members
	Duality of roles	Dual	Dual-occupation assigns a value of, and non-dual-occupation assigns a value of 0
	Shareholding concentration	Top	Shareholding ratio of the largest shareholder
	Individual	Ind	Controlling for individual fixed effects
	Year	Year	Controlling for year-fixed effect

.

3.3. Model Design

This paper constructs the following model to test the research hypotheses.

$$B{V}_{i,j,t}={\alpha }_0+{\alpha }_{1}ER{\sigma }_{j,t}+{\alpha }_{2}ER{{\sigma }^2}_{j,t}+{\alpha }_3{X}_{i,t}+\sum Ind+\sum Year+{\epsilon }_{i,j,t}$$

(1)

Model (1) examines the impact of environmental regulation on enterprise brand value. Where i, j, t, represent firms, provinces, and cities where firms are located, and years, respectively, BV_i,j,t denotes the brand value of firm i in province and city j in year t. When $\sigma$ = 1, ER1_j,t denotes the intensity of command-based environmental regulation in province and city j in year t. When $\sigma$ = 2, ER2_j,t denotes the intensity of market-based environmental regulation in province and city j in year t. X denotes the control variables, α₀ is the intercept term, ∑Ind and ∑Year represent the individual and year fixed effect of the enterprise, respectively, ${\epsilon }_{i,j,t}$ is the random error term.

$$GP{G}_{i,j,t}={\alpha }_0+{\alpha }_{1}ER{\sigma }_{j,t}+{\alpha }_{2}ER{{\sigma }^2}_{j,t}+{\alpha }_3{X}_{i,t}+\sum Ind+\sum Year+{\epsilon }_{i,j,t}$$

(2)

$$B{V}_{i,j,t}={\alpha }_0+{\alpha }_{1}GP{G}_{i,j,t}+{\alpha }_2{X}_{i,t}+\sum Ind+\sum Year+{\epsilon }_{i,j,t}$$

(3)

$$B{V}_{i,j,t}={\alpha }_0+{\alpha }_{1}ER{\sigma }_{j,t}+{\alpha }_{2}ER{{\sigma }^2}_{j,t}+{\alpha }_{3}GP{G}_{i,j,t}+{\alpha }_4{X}_{i,t}+\sum Ind+\sum Year+{\epsilon }_{i,j,t}$$

(4)

Model (2) is constructed to examine the impact of environmental regulation on corporate green innovation, while Model (3) is designed to assess the impact of corporate green innovation on brand value. Model (4) simultaneously incorporates both environmental regulation and green innovation to evaluate their combined effects on corporate brand value. Following the three-step mediation test proposed by Baron and Reuben [66], the mediating role of green innovation between command-based environmental regulation, market-based environmental regulation, and corporate brand value is tested by integrating the results of Model (1), Model (2), and Model (4)

4. Empirical Analysis and Results

4.1. Descriptive Statistics

Table 2. Results of descriptive statistics.

	Variable	Mean	Median	Std.Dev	Min	Max
Dependent variable	BV	5.657	5.614	0.990	3.580	8.363
Independent variable	ER1	450.524	2.262	1241.418	0.387	5974.787
	ER2	11.149	11.059	0.718	9.507	12.791
Mediating variable	GPG	1.616	0.693	1.819	0.000	6.941
Control variables	Size	9.482	9.378	1.472	4.736	13.140
	state	0.604	1.000	0.490	0.000	1.000
	Debt	0.498	0.526	0.191	0.060	0.941
	Growth	0.108	0.087	0.233	−0.693	2.068
	Roa	0.055	0.041	0.053	−0.242	0.240
	Dlds	39.118	36.360	8.005	30.770	80.000
	Dual	0.194	0.000	0.396	0.000	1.000
	Top	40.492	38.216	16.330	9.274	86.006

reports the descriptive statistics of the main variables. Within the sample interval, the maximum and minimum values of the brand value of listed firms are 8.363 and 3.580, respectively, with a mean of 5.657 and a standard deviation of 0.990, which indicates that there is a large gap in the brand value of the sample firms. The median green patent authorization is 0.693, with a mean value of 1.616, indicating that the level of green innovation is insufficient in most enterprises. Among the control variables, except for the proportion of independent directors and equity concentration, which exhibit larger standard deviations, the remaining variables show relatively smaller standard deviations.

4.2. Correlation Analysis

The results of the correlation test are shown in

Table 3. Results of correlation analysis.

	BV	ER1	ER2	GPG	Size	State	Debt	Growth	Roa	Dlds	Dual	Top
BV	1
ER1	0.273 ***	1
ER2	−0.023 *	0.018	1
GPG	0.504 ***	0.225 ***	0.029	1
Size	0.636 ***	0.275 ***	−0.055	0.584 ***	1
State	0.317 ***	0.205 ***	−0.017	0.172 ***	0.232 ***	1
Debt	0.297 ***	0.050	0.100 **	0.434 ***	0.460 ***	0.181 ***	1
Growth	−0.043	−0.014	0.066	0.046	0.040	−0.009	0.108 **	1
Roa	−0.046 **	−0.136 ***	0.010	−0.216 ***	−0.121 ***	−0.194 ***	−0.468 ***	0.216 ***	1
Dlds	0.222 ***	0.107 **	−0.131 ***	0.193 ***	0.245 ***	0.232 ***	0.149 ***	−0.012	−0.089 **	1
Dual	0.036	−0.106 **	0.047	0.073 *	0.092 **	−0.148 ***	0.063	0.022	0.080 *	0.006	1
Top	0.161 ***	0.180 ***	−0.068	0.019	0.036	0.128 ***	−0.128 ***	0.012	0.044	0.222 ***	−0.086 **	1
VIF	—	1.200	1.060	1.640	1.880	1.180	1.910	1.120	1.530	1.180	1.070	1.130

Notes: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively.

: the strong correlation between environmental regulation and brand value indicates that the setting of the main effect has strong rationality and provides preliminary support for the subsequent regression analysis; the coefficients between the brand value and the control variables are mostly significant, which indicates that the selection of the control variables is more appropriate. The maximum value of VIF of each variable is 1.190, which is far less than 5, and Pearson’s correlation coefficient of each variable is basically less than 0.6; therefore, it can be judged that there is no serious covariance problem among the variables.

4.3. Baseline Regression Analysis

In order to unify these data’s statistical caliber, the variables were standardized before regression. To ensure that the results are robust, the results are adjusted for robust standard errors based on fixed effects.

From the results of commanded environmental regulation, it can be seen from Columns (1) and (2) of

Table 4. Regression results of the command-based environmental regulation model.

Variable	BV	BV	GPG	BV	BV
	(1)	(2)	(3)	(4)	(5)
ER1	0.315 ***	0.288 ***	0.151 **		0.250 ***
	(0.032)	(0.058)	(0.066)		(0.064)
ER12	−0.174 ***	−0.161 ***	−0.096 **		−0.137 ***
	(0.019)	(0.036)	(0.045)		(0.041)
GPG				0.268 ***	0.253 ***
				(0.052)	(0.052)
Size		0.579 ***	0.489 ***	0.449 **	0.456 **
		(0.220)	(0.153)	(0.221)	(0.218)
Debt		0.057	0.013	0.062	0.054
		(0.075)	(0.054)	(0.074)	(0.072)
Roa		0.007	−0.041	0.023	0.017
		(0.027)	(0.032)	(0.027)	(0.027)
top		−0.126	0.022	−0.183 **	−0.131 *
		(0.084)	(0.093)	(0.076)	(0.079)
state		−0.097	0.556 ***	−0.258	−0.238
		(0.313)	(0.041)	(0.293)	(0.303)
Dlds		0.055 **	−0.027	0.068 **	0.062 **
		(0.026)	(0.027)	(0.028)	(0.026)
Dual		−0.026	−0.106 **	0.015	0.000
		(0.078)	(0.044)	(0.073)	(0.073)
Growth		−0.083 ***	−0.041 **	−0.071 ***	−0.072 ***
		(0.014)	(0.017)	(0.013)	(0.014)
_cons	0.000	0.064	−0.315 ***	0.153	0.143
	(.)	(0.189)	(0.026)	(0.178)	(0.183)
Ind FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
N	530	530	530	530	530
R2	0.048	0.202	0.100	0.228	0.258

Notes: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively; t-statistics are in parentheses.

that the effect of command-based environmental regulation on brand value is significantly positive in the primary term and significantly negative in the secondary term, regardless of whether control variables are added or not. This indicates that there is an inverted U-shaped relationship between command-based environmental regulation and brand value, supporting Hypothesis H1a. This is because when a command-based environmental regulation is less than the critical value, with the gradual increase in regulatory intensity, regulatory pressure to stimulate corporate pollution management behavior, which will reduce the late environmental pollution penalties, help enterprises to obtain more market resources, thus enhancing the brand value of the enterprise. When the command-based environmental regulation is greater than the critical value, the strict system of norms will increase the pressure on the enterprise’s environmental protection, negatively impact corporate environmental evaluations, and stimulate the rent-seeking behavior of enterprises, which is not conducive to the appreciation of brand value.

From the results of market-based environmental regulation, it can be seen from Columns (1) and (2) of

Table 5. Regression results of the market-based environmental regulation model.

Variable	BV	BV	GPG	BV	BV
	(1)	(2)	(3)	(4)	(5)
ER2	−3.887 ***	−3.156 ***	−0.598		−3.006 ***
	(0.498)	(0.497)	(0.506)		(0.479)
ER22	3.898 ***	3.176 ***	0.589		3.027 ***
	(0.521)	(0.517)	(0.514)		(0.497)
GPG				0.268 ***	0.252 ***
				(0.052)	(0.054)
Size		0.451 **	0.458 ***	0.449 **	0.336
		(0.224)	(0.143)	(0.221)	(0.219)
Debt		0.080	0.019	0.062	0.076
		(0.067)	(0.054)	(0.074)	(0.063)
Roa		0.023	−0.036	0.023	0.032
		(0.026)	(0.032)	(0.027)	(0.026)
Top		−0.180 **	−0.001	−0.183 **	−0.180 **
		(0.080)	(0.097)	(0.076)	(0.074)
state		−0.045	0.563 ***	−0.258	−0.187
		(0.322)	(0.040)	(0.293)	(0.314)
Dlds		0.041	−0.031	0.068 **	0.048 *
		(0.027)	(0.027)	(0.028)	(0.027)
Dual		0.012	−0.091 **	0.015	0.035
		(0.071)	(0.043)	(0.073)	(0.066)
Growth		−0.073 ***	−0.039 **	−0.071 ***	−0.063 ***
		(0.012)	(0.017)	(0.013)	(0.012)
_cons	0.000	0.025	−0.322 ***	0.153	0.106
	(.)	(0.195)	(0.026)	(0.178)	(0.190)
Ind FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes
N	530	530	530	530	530
R2	0.120	0.236	0.096	0.228	0.292

Notes: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively; t-statistics are in parentheses.

that the effect of market-based environmental regulation on brand value is significantly negative in the primary term and significantly positive in the secondary term, regardless of whether control variables are added or not. This indicates that there is a U-shaped relationship between market-based environmental regulation and brand value, supporting Hypothesis H1b. This indicates that when the market-based environmental regulation is lower than the critical value, with the gradual increase in regulatory intensity, enterprises choose to pay sewage charges in order to maximize the benefits, increasing the cost pressure, limiting the competitive advantage of “economies of scale”, and transfers the cost expenditure to consumers, reducing consumers’ purchase willingness, which is not conducive to the improvement of brand value. When regulatory intensity exceeds the critical value, enterprises are forced to build a good environmental image through measures such as environmental information disclosure and fulfilling social responsibilities. These efforts improve brand awareness, reputation, and loyalty, ultimately enhancing brand value.

4.4. Mediator Effect Analysis

The above analysis confirms a significant nonlinear relationship between command-based environmental regulation, market-based environmental regulation, and corporate brand value. Next, the mediating effect of green innovation is according to Models (2) and (4). Columns (2), (3), and (5) of Table 4 analyze the mediating role of green innovation between command-based environmental regulation and corporate brand value. The results in Column (3) show that the coefficient of the primary term of commanded environmental regulation and green innovation is significantly positive, and the coefficient of the secondary term is significantly negative, showing an inverted U-shaped relationship. In Column (5), green innovation exhibits a significantly positive effect on corporate brand value. Additionally, the absolute values of the primary and secondary term coefficients of command-based environmental regulation are smaller than those in the main effect model, suggesting that green innovation partially mediates the relationship between command-based environmental regulation and corporate brand value. Thus, Hypothesis H2a is supported.

Columns (2), (3), and (5) of Table 5 analyze the mediating role of green innovation between market-based environmental regulation and corporate brand value. The result of Column (3) shows that the correlation between market-based environmental regulation and firms’ green innovation is not significant, indicating that market-based environmental regulation has no effect on green innovation. Consequently, Hypothesis H2b is not supported. The possible reason is that market-based environmental regulation in China is still in the development stage and is not perfect and sound enough. Sewage charges and penalties are low, and many incentives have not been effective in promoting green innovation. Some enterprises even use strategic innovation to cheat environmental protection subsidy resources; thus, the incentive-guided role of environmental governance is not significant. On this basis, this paper conducts a bootstrap test according to the mediation effect test program. It is found that the mediating effect of green innovation in market-based environmental regulation and brand value fails the test, meaning Hypothesis H2b is also not verified.

4.5. Robustness Tests

4.5.1. Considering the Lag Effect

Considering the possible time lag effect of environmental regulation implementation, the environmental regulation variables are regressed with one period lag. The results, presented in columns (1) and (2) of

Table 6. Robustness test: considering the lag effect and substituting the test methods.

Variable	Considering the Lag Effect		Substituting the Test Methods
	BV	BV	BV	GPG	BV	GPG
	(1)	(2)	(3)	(4)	(5)	(6)
ER1			0.288 ***	0.151 **
			(0.058)	(0.066)
ER12			−0.161 ***	−0.096 **
			(0.036)	(0.045)
ER2					−3.156 ***	−0.598
					(0.497)	(0.506)
ER22					3.176 ***	0.589
					(0.517)	(0.514)
L.ER1	0.207 ***
	(0.032)
L.ER12	−0.093 ***
	(0.021)
L.ER2		−2.693 ***
		(0.338)
L.ER22		2.716 ***
		(0.351)
_cons	0.148 ***	0.134 ***	0.064	−0.315 ***	0.025	−0.322 ***
	(0.021)	(0.023)	(0.189)	(0.026)	(0.195)	(0.026)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Ind FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	424	424	530	530	530	530
R2	0.183	0.195	0.202	0.100	0.236	0.096

Notes: *** and ** denote 1% and 5% significance levels, respectively; t-statistics are in parentheses.

, show that the coefficients for ER1, ER1², and ER2, ER2² retained their consistent directionality and statistical significance in line with the baseline regression, further validating the reliability of the conclusions drawn in this study.

4.5.2. Substituting the Test Methods

To ensure the reliability of the nonlinear conclusions, this study adopts the three-step test for U-shaped (or inverted U-shaped) relationships proposed by Lind et al. [82] to further validate and analyze the relationship between environmental regulation and corporate brand value.

To test whether the variables support a nonlinear U-shaped (or inverted U-shaped) relationship, three conditions must be satisfied:

① The coefficient of the primary term must be significantly negative (positive), and the coefficient of the quadratic term must be significantly positive (negative).

② The slope at the minimum value of the independent variable (left endpoint) must be negative (positive), and the slope at the maximum value of the independent variable (right endpoint) must be positive (negative).

③ The inflection point of the curve must fall within the value range of the independent variable.

Based on the regression results in Columns (3)–(6) of Table 6, it is evident that these conditions are met, confirming consistency with the findings of this paper.

In order to further validate the effect mechanism of command-based environmental regulation on corporate brand value through green innovation, this paper uses the Bootstrap model to further analyze the significance of intermediary effect based on benchmark regression. The results are shown in

Table 7. Bootstrap test.

	Observed Coef.	Bias	Bootstrap td.Err.	[90%Conf.Interval]
_bs_1	−0.02421539	−0.0012753	0.01470402	−0.0478746	−0.0018503	(BC)
_bs_2	−0.13715563	−0.000023	0.04997564	−0.204954	−0.0429049	(BC)

, and the confidence intervals are all on the same side of 0. The test is passed, indicating that the mediating effect of green innovation on the command environmental regulation and enterprise brand value has strong robustness.

4.5.3. Instrumental Variable Method

To mitigate potential endogeneity issues and bolster the robustness of our analysis, we adopt the instrumental variable methodology. Regarding the selection of instrumental variables, drawing on the research method of Ouyang et al. [83] and Hering et al. [84], this paper selects the natural logarithm of environmental word frequency in government work reports (IV1) as the instrumental variable for command-based environmental regulation, and the natural logarithm of air circulation coefficient (IV2) as the instrumental variable for market-based environmental regulation.

Table 8. Robustness test: instrumental variable method.

Variable		First			Second
	ER1	ER12	ER2	ER22	BV	BV
	(1)	(2)	(3)	(4)	(5)	(6)
IV1	10.782 ***	10.584 ***
	(8.960)	(8.910)
IV12	−10.615 ***	−10.398 ***
	(−8.715)	(−8.657)
IV2			1.596 ***	1.508 ***
			(3.287)	(3.059)
IV22			−1.720 ***	−1.641 ***
			(−5.712)	(−5.398)
ER1					9.091 **
					(2.120)
ER12					−9.401 **
					(−2.133)
ER2						−18.960 *
						(−1.774)
ER22						24.836 *
						(1.790)
_cons	0.047	0.055	−0.261 *	−0.245 *	−0.227 ***	−0.194
	(0.611)	(0.710)	(−1.945)	(−1.811)	(−3.678)	(−1.291)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Ind FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	530	530	530	530	530	530
F statistics	63.71	64.05	47.62	47.21
LM statistic					70.22	21.62
(p-value)					(0.000)	(0.000)
K-P Wald F statistic					52.80	14.02

Notes: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively; t-statistics are in parentheses.

presents the instrumental variables regression outcomes in two sections. Columns (1), (2), and (5) are the results of regressing IV1 and IV1² as instrumental variables, and Columns (3), (4), and (6) are the results of regressing IV2 and IV2² as instrumental variables. The first stage F-statistics are all greater than 10, indicating the absence of weak instrumental variables. The LM statistics and K-P Wald F-statistics for the second stage indicate that the test for non-identifiability and the test for weak instrumental variables are passed. The direction of the coefficients of ER1, ER1² and ER2, ER2² do not change significantly, which once again validates the results above.

4.6. Heterogeneity Analysis

4.6.1. Ownership Structure Heterogeneity

From the perspective of enterprise ownership heterogeneity, enterprises under different ownership structures exhibit significant differences in decision-making approaches, institutional frameworks, and resource conditions, leading to varied responses to environmental regulation. The sample enterprises are grouped by state-owned and non-state-owned enterprises, and the regression results of the grouping are shown in

Table 9. Regression results of ownership structure heterogeneity.

Variable	Command-Based Environmental Regulation		Market-Based Environmental Regulation
	BV	BV	BV	BV
	SOEs	Non-SOEs	SOEs	Non-SOEs
	(1)	(2)	(3)	(4)
ER1	0.294 ***	0.397 ***
	(0.065)	(0.133)
ER12	−0.164 ***	−0.220 ***
	(0.040)	(0.077)
ER2			−3.656 ***	−2.579 ***
			(0.737)	(0.633)
ER22			3.695 ***	2.646 ***
			(0.770)	(0.647)
_cons	−0.005	0.096 **	−0.001	0.078
	(0.059)	(0.041)	(0.059)	(0.052)
Inflexion point	0.896	0.902	0.495	0.487
Controls	Yes	Yes	Yes	Yes
Ind FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	320	210	320	210
R2	0.177	0.367	0.211	0.391

Notes: *** and ** denote 1% and 5% significance levels, respectively; t-statistics are in parentheses.

, which are consistent with the previous conclusions. Analyzing the inflection points of the impact of environmental regulation on brand value reveals that state-owned and non-state-owned enterprises have different tolerances for environmental regulation.

In the case of command-based environmental regulation, the inflection point of the brand value curve of state-owned enterprises (SOEs) occurs earlier than that of non-state-owned enterprises (non-SOEs), which indicates that state-owned enterprises are more responsive and less tolerant of command-based environmental regulation. This may be attributed to the fact that SOEs, with the government as their actual controller, actively comply with mandatory policies, whereas non-SOEs tend to prioritize short-term profit maximization.

In market-based environmental regulation, the inflection point of the brand value curve of non-SOEs arrives before SOEs, which suggests that market-based environmental regulation promotes the brand value of non-SOEs faster. This phenomenon may be attributed to the strong financial advantages of SOEs, which make them less sensitive to financial policies such as sewage charges or tax subsidies. In contrast, non-SOEs are more inclined to leverage government-led policies to offset regulatory costs and enhance their market reputation. At the same time, compared with state-owned enterprises, non-state-owned enterprises have more flexible factor flows and more efficient resource allocation.

4.6.2. Enterprise Life Cycle Heterogeneity

From the perspective of enterprise life cycle heterogeneity, firms in different growth stages exhibit distinct strategic tendencies, organizational structures, and corporate behaviors. Accordingly, this study employs indices such as sales revenue growth rate, retained earnings rate, capital expenditure rate, and enterprise age to categorize the sample into three life cycle stages: growth, maturity, and decline [85]. Regression analyses were conducted for each stage, and the results are presented in

Table 10. Regression results of enterprise life cycle heterogeneity.

Variable	Command-Based Environmental Regulation			Market-Based Environmental Regulation
	BV	BV	BV	BV	BV	BV
	Growth	Maturity	Decline	Growth	Maturity	Decline
	(1)	(2)	(3)	(4)	(5)	(6)
ER1	−0.124	0.328 ***	0.312 ***
	(0.136)	(0.060)	(0.112)
ER12	0.023	−0.188 ***	−0.193 **
	(0.124)	(0.042)	(0.078)
ER2				−2.150 ***	−1.499	−3.113 ***
				(0.384)	(1.593)	(0.514)
ER22				2.294 ***	1.526	3.104 ***
				(0.442)	(1.584)	(0.535)
_cons	0.055 ***	0.006	0.055 ***	0.041	0.134 ***	0.146 ***
	(0.007)	(0.005)	(0.007)	(0.027)	(0.023)	(0.023)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Ind FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	22	159	345	22	159	345
R2	0.989	0.329	0.116	0.998	0.280	0.202

Notes: *** and ** denote 1% and 5% significance levels, respectively; t-statistics are in parentheses.

.

From Columns (1)–(3), it is observed that, except for the growth stage where command-based environmental regulation does not significantly affect brand value, the effects of commanded environmental regulation on brand value in the other life cycle stages are consistent with previous findings. This may be because, during the early growth stage, firms face a large amount of capital needs and serious financing constraints. They are more inclined to implement relevant strategies that meet the minimum compliance requirements, utilize existing environmental protection technologies, reduce energy consumption and pollution through equipment upgrades, and minimize investments in uncertain projects. As a result, command-based environmental regulations do not significantly influence brand value during the growth stage.

From Columns (4)–(6), the impact of market-based environmental regulations on corporate brand value in other life cycle stages is consistent with the previous findings, except that market-based regulations in the maturity stage do not significantly affect brand value. This may be due to the fact that, during the maturity stage, with abundant capital and a standardized management system, these enterprises have relatively fewer financing constraints and are better positioned to comply with regulatory requirements. As a result, they may reduce R&D investments to avoid high-risk environmental projects, minimizing the uncertainty associated with innovation. At the same time, the innovation consciousness of enterprises in this period is weakened, which affects the production efficiency and enthusiasm of enterprises, so the influence of market-based environmental regulations on the brand value of enterprises in this stage may be weaker.

4.6.3. Geographical Regional Heterogeneity

From the perspective of regional heterogeneity, significant disparities exist in economic development levels and environmental regulation policies across China’s eastern, central, and western regions. Therefore, the sample enterprises are divided into three groups of eastern, central, and western regions according to the provinces they belong to for the heterogeneity regression, and the regression results are shown in

Table 11. Regression results of geographical regional heterogeneity.

Variable	Command-Based Environmental Regulation			Market-Based Environmental Regulation
	BV	BV	BV	BV	BV	BV
	Eastern Regions	Central Regions	Western Regions	Eastern Regions	Central Regions	Western Regions
	(1)	(2)	(3)	(4)	(5)	(6)
ER1	0.274 ***	−49.863	−19.353
	(0.062)	(41.995)	(41.777)
ER12	−0.152 ***	25,526.003	5174.535
	(0.038)	(15,499.571)	(6364.268)
ER2				−2.793 ***	−4.168 *	−6.740 **
				(0.526)	(2.046)	(2.754)
ER22				2.797 ***	4.202 *	6.920 **
				(0.543)	(2.121)	(2.970)
_cons	−0.177	0.075	−0.273 **	−0.071	−0.016	−0.098 *
	(0.107)	(0.051)	(0.128)	(0.049)	(0.023)	(0.051)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Ind FE	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes
N	380	90	60	380	90	60
R2	0.254	0.336	0.376	0.266	0.288	0.383

Notes: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively; t-statistics are in parentheses.

.

From Columns (1)–(3), it can be seen that the inverted u-shaped trend of the impact of command-based environmental regulation on corporate brand value is more obvious in the eastern region, while the central and western regions are not significant, which may be due to the fact that the eastern region has a well-developed economy and relatively more mature technology, and places more emphasis on green innovation and low-carbon development than the central and western regions. At the same time, the phenomenon of “pollution shelter” is prevalent in the eastern region, where high-pollution and high-energy-consuming enterprises are often relocated to backward areas. In contrast, the central and western regions, in their efforts to attract investment and promote development, may engage in a “race to the bottom” in environmental regulation, which can hinder the enhancement of enterprise brand value.

Columns (4)–(6) correspond to the effects of market-based environmental regulations on enterprise brand value in different regions. It is found that regardless of the region, there is a U-shaped relationship between market-based environmental regulation and enterprise brand value.

5. Conclusions and Implications of the Study

5.1. Conclusions

Based on institutional theory and resource-based theory, this paper takes the 106 listed companies on the 2018–2022 consecutive list of China’s 500 Most Valuable Brands as a sample and takes enterprise green innovation as an entry point to explore the relationship between environmental regulation, green innovation, and corporate brand value. The research results show that (1) command-based environmental regulation and brand value have a significant inverted U-shaped relationship, and market-based environmental regulation and brand value have a significant U-shaped relationship. Command-based environmental regulation is a mandatory constraint on the institutional norms, and when on the left side of the inflection point, firms convert government regulatory pressure into environmental awareness and behavior; across the inflection point after the formation of green norms, regulatory costs, negative environmental evaluation is not conducive to the enhancement of corporate brand value. Market-based environmental regulation is a guiding and regulating strategy; when located on the left side of the inflection point, enterprises choose to pay low-cost sewage charges and tend to actively cater to the government and the public after crossing the inflection point, establish a good brand image and corporate reputation, and promote the value of brand value appreciation. (2) Green innovation has a significant promotional effect on corporate brand value and plays a partly intermediary role in the impact of command-based environmental regulation on corporate brand value. (3) The impact of environmental regulations on enterprise brand value is heterogeneous in terms of the nature of enterprise property rights, life cycle, and location. Command-based environmental regulation is more significant for state-owned enterprises, eastern region enterprises, and mature and declining enterprises, while market-based environmental regulation is more significant for non-state-owned enterprises and growing and declining enterprises.

5.2. Policy Recommendations and Management Insights

5.2.1. Government Level

As the designer and regulator of environmental policies, the government should fully integrate the operational dynamics of industries, enterprises, and market mechanisms, consider the heterogeneity of environmental regulatory tools and regulatory capacity, and formulate rational environmental policies.

First of all, maintain moderate command-based environmental regulation. The government should establish a regional coordination mechanism for environmental regulation and a policy implementation coordination system to improve policy coordination and moderation. At the same time, various means such as digital technology tracking, unannounced inspection, and information disclosure should be adopted to improve the efficiency of government environmental regulation and the intensity of administrative punishment.

Second, improve market-based environmental regulation. The government needs to continue to deepen and strengthen relevant market reforms and regulatory efforts, such as emissions trading, environmental tax, and carbon neutralization, to improve the construction of a market-oriented green innovation system.

Finally, strengthen the comprehensive application of regulatory instruments. When implementing environmental regulation, it is necessary to take into full consideration the ownership of enterprises, the life cycle of enterprises, and the regions where enterprises are located and to formulate differentiated environmental regulation policies in a targeted manner.

5.2.2. Enterprise Level

From the perspective of long-term development, enterprises should assume the corresponding environmental governance responsibilities and actively participate in green investment activities.

First of all, establish green innovation and environmental protection concepts. Enterprises should take environmental regulations as opportunities and challenges, turning external regulatory pressure into internal business motivation, from organizational management, system construction, personnel training, and other aspects of in-depth implementation of the concept of green development.

Second, implement a green innovation development strategy. Enterprises should attach great importance to the crucial role of green innovation strategy to enhance the value of corporate brand, establish the concept of green innovation and enhance the green technology innovation capacity and management capacity. While investing in green innovation, enterprises should fulfill their social responsibility according to the constraints of environmental regulations and resource structure and effectively implement green innovation to enhance brand value.

Finally, build a green innovation cooperation system. Establish an active modern enterprise system featuring win–win cooperation between government and enterprise, take the lead in joining forces with universities, research institutes, intermediary organizations, and financial capital to participate in green innovation, obtain resource support from stakeholders, alleviate financing constraints, and promote the symbiotic development of the enterprise and the stakeholders to create comprehensive value covering the economy, society, and the environment, and thus enhance the value of the enterprise’s brand.

5.3. Research Limitations and Future Research Directions

First, there are some limitations in the selected sample. The sample collection method of this study, which relies on the 106 listed companies that are continuously listed in China’s 500 Most Valuable Brands from 2018 to 2022, limits the ability to generalize the findings obtained, and this geographic and market specificity may affect the extrapolation of the findings. Future research could collect data from companies in other countries and unlisted companies to verify the generalizability and limitations of the findings in this paper. Second, the research questions are not in-depth enough. This study only examines the mechanism of green innovation in the impact of environmental regulation on corporate brand value, and future research can further explore other mechanisms by which environmental regulation affects corporate brand value and explore the moderating mechanisms of other internal and external factors. Finally, the measurement of core variables still needs to be improved. This paper adopts the number of green patents to measure the level of green innovation, which is cross-industry comparable but may underestimate the non-technological green innovation in the service industry, and machine learning and text analysis can be used to portray green innovation in future research accurately.