Research on the Green Investment of Traditional Energy Enterprises and Its Effectiveness Under Environmental Regulation

Abstract

Using data from 188 traditional energy companies listed on China’s A-share market from 2012 to 2023, this study adopts the double-difference method to assess the changes in the green investment of traditional energy companies before and after the implementation of environmental regulation policies in depth and further explores the actual effects and potential risks of green investment on the green transformation of traditional energy companies. This study shows that the environmental regulation policies significantly increased the green investment of traditional energy enterprises, but this increase did not effectively promote the overall green transformation of the enterprises; rather, they became more focused on meeting the current compliance requirements while ignoring the fundamental green technological innovation and production mode change. Further analysis reveals that, as environmental standards continue to rise, the increasing green investment expenditure of traditional energy companies for meeting the standards will continue to outpace the growth in capital inflows of traditional energy companies, resulting in a threat to the financial soundness of the companies in the short term and a significant increase in financial pressure. In the long run, the lack of sustained and stable external financial support for a long period of time will not only constrain the green transformation process of enterprises but may also pose a serious threat to the survival foundation of enterprises and even lead to the elimination of enterprises in fierce market competition.

1. Introduction

At present, global energy development is facing profound changes. The trends of cleaner fossil energy, cleaner energy scale, multi-energy complementary synthesis, and end-use energy intelligence accelerate the evolution of the energy technology revolution, and industrial change has become the world’s most active field of innovation [1]. On 29 August, the State Council Information Office issued a white paper on China’s Energy Transformation, pointing out that China’s future energy structure will remain coal-based for a long time; the consumption of coal and other fossil energy still occupies the dominant position in China’s energy consumption, and resource and environmental constraints will exist for a long time. In order to cope with these difficult challenges, it is fundamental to rely on energy transformation. However, in the case that new energy has not yet completely replaced traditional energy, traditional energy still needs to play a supporting and underwriting role. Therefore, China should comply with global energy development trends, and while vigorously developing new energy sources, it should also continue to rationally use and gradually transform traditional energy sources in order to achieve optimization of energy structure and sustainable development [2]. In February, in the twelfth collective study of the Political Bureau of the Communist Party of China (CPC) Central Committee, General Secretary Xi Jinping emphasized that energy security has a bearing on overall economic and social development and that it is necessary to ‘cultivate energy technology and its related industries into a new growth point to drive China’s industrial upgrading’. Traditional energy enterprises are the main force for ensuring China’s energy security, not only because they can ensure the security of the energy supply but also because they can be used to reforge survival and development advantages so as to achieve industrial change and green transformation.

Green investment is an important way of promoting the green transformation of traditional energy enterprises and the construction of ecological civilization, and it is a key component of the sustainable development of traditional energy enterprises [3]. Traditional energy enterprises should actively increase their green investment and enhance environmental protection [4]. However, as profit-oriented organizations, traditional energy enterprises are more inclined to use their limited operating funds for production and manufacturing, lacking the willingness and action to proactively engage in green investment [5], which may lead to insufficient investment. Therefore, market self-regulation alone cannot solve environmental problems; it is necessary for the government to introduce various environmental policies for regulation [6]. Then, can environmental regulations promote green investment through traditional energy enterprises? What are the micro-mechanisms and impact mechanisms? What are the heterogeneous manifestations of the impact of environmental regulations on enterprises’ green investment? What are the actual effects of green investment? Can it promote green transformation with certainty? These issues urgently need in-depth research.

2. Literature Review

Domestic and foreign scholars have not reached a unified conclusion regarding the effect of environmental regulation on enterprises’ green investment. At present, the main theories are the inhibition theory, promotion theory, and nonlinear relationship theory.

The first viewpoint holds that environmental regulations will restrain enterprises’ green investment. Due to the relatively low environmental protection standards and lax supervision in developing countries, when funds are limited, enterprises will see a reduction in production scale and profits due to green investment. To lower operating costs, they tend to cut environmental protection expenditure and prefer to pay fines instead [7]. Liu [8] found that, considering the high cost and significant uncertainty involved in achieving long-term growth, managers, for their own interests, would choose to cut pollution control expenditure to reduce costs and meet profit expectations. Cortez [9], taking Chinese manufacturing enterprises as the research subject, empirically found that environmental regulations would suppress enterprises’ green investment due to financing constraints. Hu [10] divided the uncertainty of environmental regulation policy measures into policy content uncertainty (PCU) and policy implementation uncertainty (PEU) and explored the impact of environmental policy uncertainty on enterprises’ green investment based on survey data from Zhejiang Province. The results showed that, in both cases, the uncertainty of environmental policies would significantly suppress enterprises’ green investment. Chen [11] pointed out that the competition effect caused by the occupation of production resources by environmental regulation’s pollution control resources has a certain inhibitory effect on the efficiency of enterprises’ green investment. Command-and-control-type environmental regulation tools lack flexibility, and the environmental standards that they stipulate do not change according to enterprises’ production structures or emission reduction capabilities, so enterprises can only passively accept them [12], which imposes certain cost burdens on enterprises’ operations, reduces their working capital, and makes it difficult for them to increase green investment [13]. Farooq [14] used a GMM system model and found that the carbon tax rate had a significant negative impact on enterprises’ investment decisions. Huang [15] and Du [16] suggested that under the pressure of emission reduction, enterprises would face risks such as cash flow fluctuations and a decline in profit levels, which would suppress green investment expenditure.

The second perspective starts from the “Porter Hypothesis”, which holds that environmental regulations can promote enterprises’ green investment. According to the Porter Hypothesis, appropriate environmental regulations will force enterprises to carry out technological innovation to enhance production efficiency. Green technological innovation requires financial support, which, in turn, will promote an increase in enterprises’ green investment [17]. Tian [18] suggests that command-and-control environmental regulations, market incentive environmental regulations, and voluntary environmental regulations can all, to a certain extent, stimulate enterprises’ enthusiasm for green investment, and all can show a lag effect [19] and are influenced by factors such as media supervision [20], public attention [21], political connections [22], and financing constraints [9]. Command-and-control environmental regulations have a more obvious promoting effect [23]. Li [24] holds that market incentive environmental regulations can significantly exert their incentive effect and promote enterprises’ green investment, and their incentive effect is influenced by factors such as local government competition [25] and the degree of organizational green learning [26]. Zhou [27] found that environmental regulations significantly promote the financial support of green finance for enterprises’ green investments. Feng [28] studied whether the relationship between environmental regulations and environmental quality has a positive or negative effect. The results show that the government’s environmental regulations have a significant positive correlation with improving environmental quality and enterprises’ green performance. Enterprises’ green performance is achieved through green investment, and environmental regulations have a promoting effect on green investment. Ge [29] took listed companies of real enterprises as samples to study whether environmental regulations have a positive promoting effect on real enterprises’ green investment. The research results show that negative environmental regulation tools have a promoting effect on real enterprises’ green investment. Zhong [30] verified through panel data regression that the intensity of environmental regulations has a significant positive promoting effect on the high-quality development of regional economies, and enterprises in economically developed areas tend to choose to carry out green investment.

The third perspective holds that there exists a nonlinear relationship between environmental regulations and enterprises’ green investment. Liu [31] and Petroni [32] found that the intensity of environmental regulations and enterprises’ environmental protection investment present a “U-shaped” relationship. That is, before the intensity of environmental regulations reaches a certain threshold, the regulations may suppress the effect of enterprises’ environmental protection investment by crowding out productive capital. However, once the intensity of environmental regulations exceeds this threshold, they will promote technological innovation and production efficiency, thereby enhancing the effect of enterprises’ environmental protection investment. In contrast, Chai [33] and Wang [34] found that the relationship between environmental regulations and enterprises’ environmental protection investment is an “inverted U-shape”.

To sum up, scholars at home and abroad have made certain progress in their research on environmental regulations and green investment. However, the existing research results still have some deficiencies. The innovations of this study mainly lie in three aspects.

(1)

Insufficient research on the green investment behavior of specific industries under environmental regulations

The existing literature mostly focuses on the impact of environmental regulations at the macro level. For specific industries, the research only delves into manufacturing or heavily polluting industries, ignoring the different responses and coping strategies of various industries under environmental regulations. China has long used a specific energy supply structure dominated by coal, which requires traditional energy enterprises to ensure the stability of the energy supply while meeting increasingly strict environmental protection requirements when facing environmental regulation policies. The challenges faced by traditional energy enterprises are more complex and unique compared with other manufacturing or heavily polluting industries. Therefore, it is necessary to specifically focus on the green investment behavior of traditional energy enterprises under environmental regulations and solve their practical problems from the policy level.

(2)

Environmental regulation and corporate green investment: complexity of relationship and research disagreements

By reviewing the literature on the impact of environmental regulation on corporate green investment, it is found that there is no consensus among existing studies on the relationship between environmental regulation and corporate green investment. The relationship between the two can mainly be summarized into three types: positive correlation, negative correlation, and nonlinear correlation. Based on the existing research results, it is found that the intensity of environmental regulation, financing constraints, enterprise scale, financial status, industry attributes, social responsibility, public supervision, etc., affect the decision making of enterprises regarding investment in environmental protection. Therefore, the impact of environmental regulation policies on the green investment behavior of traditional energy enterprises cannot be simply summarized based on existing research results. This also depends on the rationality of policy design, the resource endowment of enterprises themselves, and the degree of market mechanism improvement. Therefore, whether environmental regulation policies will strengthen the green investment preferences of traditional energy enterprises or inhibit their green investment behavior remains to be further studied.

(3)

Attention and assessment research on the actual effectiveness of green investment are still insufficient

Most existing studies have focused on exploring the direct effect of “environmental regulations on green investment”, while relatively few have paid attention to the actual effects brought by enterprises’ green investment. A possible reason is that, in previous studies, some viewpoints hold that environmental regulations may inhibit green investment. Therefore, if there is not enough green investment expenditure, there is no need to talk about its effect. In contrast, the argument supporting the idea that “environmental regulations promote green investment” is often rooted in the Porter Hypothesis, which holds that environmental regulations can stimulate technological innovation and, thereby, improve production efficiency. The positive impact of green investment is obvious. This study expands on this basis and deeply analyzes the actual effect of green investment for the following considerations. The green investment of traditional energy enterprises is not spontaneously generated by the market mechanism but is a product of responding to policy requirements. Whether such green investment has truly achieved the expected goals—that is, whether it not only meets compliance requirements but also brings about substantive effects such as improved production efficiency, effective cost control, or environmental performance improvement—cannot be directly determined through theoretical analysis but requires further empirical research. In short, merely analyzing the direct impact of environmental regulations on the scale of green investment is not sufficient; it is more necessary to deeply explore whether these investments have been transformed into actual positive benefits or merely increased the operating costs of enterprises without generating corresponding value returns.

At the same time, the limitations of this study are as follows.

(1)

The measurement indicators of green investment still need to be further improved. Due to the differences in the academic community’s definition of green investment, including related concepts such as “environmental investment”, “environmental protection input”, and “environmental protection expenditure”, there are differences in the measurement scope of green investment. Therefore, the measurement of green investment needs to be further refined and supplemented.

(2)

The data volume in the early stage was small and not standardized, and possible errors could not be avoided. It is hoped that, in future research, more updated and comprehensive data can be used to improve accuracy or more complete measurement indicators can be adopted.

(3)

China’s environmental regulation measures are divided into “command-type environmental regulation”, “market-type environmental regulation”, and “public participation-type environmental regulation”. Therefore, in the future, analysis of the impact and mechanism of other environmental regulation methods and contents on enterprises’ production, operation, and investment activities can be explored.

3. Theoretical Analysis and Research Hypotheses

3.1. Environmental Regulation and the Green Investment Expenditure of Traditional Energy Enterprises

According to the “compliance cost theory”, environmental regulation policies, as an important means for the government to intervene in the market and guide the behavior of enterprises [35], impose direct environmental protection pressure on traditional energy enterprises by setting clear emission standards and establishing strict enforcement mechanisms. This policy orientation forces enterprises to reallocate resources for investment in environmental protection technologies and pollution control equipment in order to meet the legal pollution discharge requirements and avoid potential fines and reputation losses [36]. This mainly includes the following aspects.

(1)

Investment in pollution control equipment: Enterprises need to purchase and install new pollution control equipment, such as desulfurization, denitrification, and dust removal devices, to meet lower emission standards.

(2)

Transformation of production processes: To reduce pollutant emissions, enterprises may need to transform or upgrade existing production lines and introduce cleaner production processes.

(3)

Operating and maintenance costs: New environmental protection technologies and equipment require regular maintenance and updates to ensure the normal operation of facilities and environmental protection effects, which also increases investment expenditure to a certain extent.

Intuitively, people tend to believe that the strengthening of environmental regulation intensity will directly prompt traditional energy enterprises to increase green investment to cope with higher environmental protection requirements. However, whether this seemingly intuitive causal relationship holds true still needs to be verified through rigorous empirical research.

Based on the above analysis, we put forward the following hypothesis:

H1. 

The implementation of environmental regulation policies will lead to a significant increase in the intensity of green investment expenditure of traditional energy enterprises.

3.2. Green Investment Expenditure of Traditional Energy Enterprises and Their Green Transformation

The original intention of green investment is to promote the green transformation and sustainable development of enterprises. The actual effect should be the core issue of concern [37]. From the perspective of economic rationality, as profit seekers, enterprises must conduct strict benefit evaluations on any additional cost items, including green investment. If these investments fail to achieve the expected green transformation effects, not only will it be impossible to achieve a win–win situation for environmental responsibility and economic benefits for the enterprise, but it may also impose a burden on the enterprise’s operation due to rising costs [38], affecting its market competitiveness.

Green investment mainly includes two forms: one is preventive green investment, which involves investing funds in the production process at the source and introducing new processes and tools to reduce energy consumption and increase the proportion of clean energy, thereby reducing the generation of pollutants; the other is remedial green investment, which involves investing funds in the end-of-pipe treatment process and reducing pollutant emissions through pollutant transfer and waste recycling. Preventive green investment has the characteristics of requiring large investments and a long cycle, and it has higher requirements for the funds and technology of enterprises [39]. The benefits obtained cannot be realized immediately, and there is a problem of lag in “innovation compensation”. Mantovani [40] pointed out that, due to the constraints of environmental technology standards and market competition, enterprises that need to meet the discharge standards in the short term are more inclined to increase remedial green investment.

In traditional energy enterprises, although environmental regulations may prompt them to increase investment in environmental protection technologies and equipment, such investments may mainly focus on meeting current compliance requirements rather than promoting fundamental green transformation. This is because traditional energy enterprises may encounter internal and external obstacles such as technological path dependence, market inertia, and financial constraints [41], which may limit their ability to achieve green transformation. However, whether this is truly the case requires empirical research methods for verification.

Based on the above analysis, we propose the following hypothesis:

H2. 

Although environmental regulation policies can encourage traditional energy enterprises to develop certain technological innovations and increase their investments, these measures are not sufficient to promote their comprehensive green transformation.

3.3. Financial Inflows of Growth and Green Investment Spending Imbalance

In their daily operation, enterprises always strive to find a balance between cost minimization and benefit maximization, which is based on the guiding principle of cost–benefit theory [42]. However, when the external environment, especially environmental regulation policies, changes, this balance may be broken. To adapt to stricter environmental standards, companies must make a series of investments, such as updating their environmental equipment and improving their production processes and pollution treatment capacity [43]. This investment expenditure is not optional but necessary for companies to meet regulatory requirements.

However, these necessary environmental protection investments will not quickly translate into economic benefits. On the one hand, it takes time for new equipment and technologies to demonstrate their cost-saving and efficiency-enhancing advantages [44]; on the other hand, these environmental measures may not directly contribute to an increase in sales or profits. The delayed or uncertain return on such investments poses a challenge to the financial situation of enterprises.

Therefore, when the growth rate of green investment expenditure caused by environmental regulations exceeds the growth of a company’s financial inflows, the company’s financial stability will be threatened. Cash flow may become tight, the debt ratio may rise, and the debt repayment ability may also decline. Although environmental regulation policies are important external forces driving enterprises to achieve sustainable development, in the short term, they may also bring considerable financial burdens to enterprises.

Based on the above analysis, we propose the following hypothesis:

H3. 

In the context of increasingly strict environmental regulations, the financial inflow index of enterprises may not be enough to offset the growth of green investment expenditure in response to environmental requirements, which may challenge the financial robustness of enterprises.

4. Research Design

4.1. Data Sources and Preprocessing

China has long used an energy supply system dominated by coal, which makes traditional energy enterprises such as coal and thermal power enterprises play a crucial role in ensuring national energy security [45]. When facing strict environmental regulations, traditional energy enterprises, unlike other high-emission enterprises, cannot be simply shut down or phased out just because of their high emissions. Instead, they must ensure the stability of the energy supply while meeting increasingly strict environmental protection requirements [46]. Their investment behaviors, their transformation strategies, and the challenges they face are significantly different from those of general enterprises, demonstrating particularity. Therefore, this study selects traditional energy enterprises as the research object and further classifies them into coal mining and washing, power, heat production and supply, and gas production and supply industries.

Data sources: The data sources for this study were selected from traditional energy enterprises listed on the A-share market in China. The enterprise-level characteristic variables and related variables involved were obtained from the CSMAR database of Guotai An; the official websites of some listed companies; the “China Energy Statistical Yearbook”; the social responsibility reports, environmental reports, and sustainable development reports disclosed by companies, etc. After data cleaning, a total of 2821 samples from 188 listed traditional energy enterprises from 2012 to 2023 were obtained, and unbalanced panel data were collated accordingly.

Data processing: Firstly, to avoid analytical bias caused by incomplete data, we excluded enterprise statistics with incomplete accounting periods and data of enterprises that had been listed for less than one year during the observation period. Secondly, to eliminate the influence of enterprises with abnormal operating conditions on the research results, we excluded the sample companies marked with ST and ST*, as these companies usually face significant financial risks or operational difficulties and do not align with the focus of this study on normally operating enterprises. Additionally, to better reveal the relationships among variables and reduce data heteroscedasticity, we also performed logarithmic transformation on these continuous variables.

4.2. Model Specification

Taking the “Plan for Improving the Dual Control System of Energy Consumption Intensity and Total Volume” in 2021 as a quasi-natural experiment, a difference-in-differences model was adopted to examine the relative differences in green investment expenditure behavior between the control group and the treatment group enterprises before and after the implementation of this policy. The benchmark model is constructed as follows:

$$G{I}_{it}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1{\mathrm{D}}_{\mathrm{t}}{\ast \mathrm{Treat}}_{\mathrm{i}}+{\mathrm{contros}}_{\mathrm{it}}+{\mathrm{\mu }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$

Here, ${\mathrm{GI}}_{\mathrm{it}}$ represents the green investment expenditure of enterprise i at time t; ${\mathrm{\alpha }}_0$ is a constant item; ${\mathrm{D}}_{\mathrm{t}}$ is a virtual variable during the policy implementation period, and if the yearly data are from before the implementation of the policy, its value is 1; ${\mathrm{Treat}}_{\mathrm{i}}$ represents whether enterprise i was affected by environmental regulation policies (treatment group: 1; control group: 0); ${\mathrm{D}}_{\mathrm{t}}{\ast \mathrm{Treat}}_{\mathrm{i}}$ represents the interaction of the treatment and time variables, and the coefficient ${\mathrm{\alpha }}_1$ reflects the net impact of the environmental regulation policy on the enterprise’s green investment expenditure; ${\mathrm{\mu }}_{\mathrm{i}}$ is the individual fixed effect; ${\mathrm{\lambda }}_{\mathrm{t}}$ is the time-fixed effect, which is used to more accurately capture inherent differences between businesses, as well as general trends over time, to ensure an accurate estimation of the impact of environmental regulatory policies; ${\mathrm{contros}}_{\mathrm{it}}$ represents the control variable of enterprise i at time t; ${\mathrm{\epsilon }}_{\mathrm{it}}$ represents the random disturbance term.

4.3. Variable Definitions

(1)

Dependent variable: Green investment

As there is no unified standard for measuring green investment at present, this study follows the approach of Zhang [47] for collecting green investment data through a company’s disclosed social responsibility reports, environmental reports, sustainable development reports, etc. However, since environmental information disclosure is not mandatory, most enterprises do not disclose green investment information on their websites. To further improve the green investment data, the projects that meet the definition of green investment were selected through keyword screening in the “Construction in Progress” and other items in companies’ annual reports for supplementation, and logarithmic processing was carried out simultaneously.

(2)

Explanatory variable: Environmental regulation

Since China explicitly proposed the “dual carbon” goals in 2020, the country has given unprecedented attention to the optimization and adjustment of its energy structure. In 2021, to further promote the energy revolution and facilitate a comprehensive green transformation of economic and social development, China released the “Plan for Improving the Dual Control System of Energy Consumption Intensity and Total Volume”. This plan has put forward more stringent and detailed requirements for the traditional energy sector from multiple dimensions, aiming to effectively control the total volume of energy consumption, enhance energy utilization efficiency, and reduce carbon emission intensity through institutional innovation and policy guidance. Given the significant role of this plan in promoting the transformation of the energy structure and achieving the “dual carbon” goals, as well as its comprehensiveness and specificity at the policy level, this study selects the “Plan for Improving the Dual Control System of Energy Consumption Intensity and Total Volume” as the reference policy for in-depth research. This study takes 2021 as the demarcation point. For example, ${\mathrm{D}}_{\mathrm{t}}$ is 0 before the policy implementation year and 1 after the policy implementation year. ${\mathrm{Treat}}_{\mathrm{i}}$ indicates whether enterprise i is affected by environmental regulation policies (assigning 1 in the treatment group and 0 in the control group). The interaction term of the time dummy variable and the group dummy variable (${\mathrm{D}}_{\mathrm{t}}{\ast \mathrm{Treat}}_{\mathrm{i}}$) is used as the core explanatory variable [48].

(3)

Control Variables

In order to avoid the potential impact of other factors on the green investment expenditure of traditional energy enterprises, the following control variables were imported into the model: profitability (ROA), which is an important indicator of the financial health of enterprises, as it directly affects the investment decisions of enterprises. Highly profitable companies may be more able to make green investments, including those that are needed to comply with environmental regulations. The next control variable is enterprise size (Size), which is usually related to an enterprise’s ability to acquire resources, its market influence, and its ability to respond to external shocks. When facing environmental regulation, large enterprises may be able to more easily adjust due to the effects of economies of scale. The next control variable is enterprise age (Age), which demonstrates that the age of an enterprise may affect its management style, technological innovation ability, and adaptability to external changes. Older companies may face more legacy problems, while emerging companies may be more flexible and willing to adopt new technologies to meet environmental requirements. The debt-to-asset ratio (Lev) reflects the financial leverage of an enterprise. Highly indebted companies may face greater financial constraints, limiting their ability to make large green investments. The proportion of independent directors (Bid) on the board of directors is an indicator of the quality of corporate governance. A higher proportion of independent directors usually implies a stronger supervision mechanism and a more objective decision-making process. The concept of dual roles (Dual) refers to a situation in which the CEO is also the chairman of the board of directors. This governance structure may affect the decision-making efficiency and risk-taking behavior of an enterprise and, thereby, affect enterprises’ coping strategies and green investments in response to environmental regulations.

The specific variables are defined in the following

Table 1. Variable definitions.

Variable Class	Variable Name	Variable Symbol	Variable Definition
Explained variable	Green investment	GI	See above for details.
Explanatory variable	Cross-section of time and grouped dummy variables	DID	See above for details.
Control variable	Profitability	ROA	Net profit/total assets at the end of the period.
	Enterprise scale	Size	Natural logarithm of the total assets.
	Enterprise age	Age	Number of years since the establishment of the enterprise.
	Asset–liability ratio	Lev	Total liabilities/total assets.
	Independent director ratio	Bind	Ratio of the number of independent directors to the number of directors.
	Dual roles	Dual	Whether the chairman and the general manager are the same person. 0: No; 1: Yes.

.

5. Empirical Analysis

5.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Observation	Mean	Standard Deviation	Minimum	Maximum
GI	2821	19.413	2.057	12.109	23.947
DID	2821	0.176	0.381	0	1
Size	2821	22.694	1.569	19.282	26.55
Age	2821	13.974	7.83	1	29
Dual	2821	0.199	0.399	0	1
Bind	2821	37.259	5.037	28.57	57.14
Lev	2821	0.469	0.214	0.045	1
ROA	2821	0.033	0.067	−0.309	0.185

reports the descriptive statistics for each variable. The mean value of green investment is 19.413, the standard deviation is 2.057, and the data range is between 12.109 and 23.947. This indicates that different enterprises have significantly different green investment expenditures. The mean value of the enterprise scale is 22.694, and the standard deviation is 1.569, showing that the enterprise scale distribution is relatively concentrated but that some differences remain, which helps us analyze the role of scale factors in the impact of policies. The large standard deviation of the enterprise age (7.83) means that the length of time varies, which may have an impact on the policy responses and adaptability of enterprises. The average asset–liability ratio is 0.469, indicating that some financial leverage generally exists in the sample enterprises. Although the mean profitability is low, the standard deviation is relatively large, reflecting the diversity of enterprise profitability levels. The statistics of the proportion of independent directors and the integration of two posts reveal differences in the corporate governance structures, which may have different effects on the impact of policy implementation.

5.2. Benchmark Regression Analysis

The results of the regression analysis are shown in

Table 3. Empirical results of the baseline regression.

Variable	GI	GI	GI	GI
DID	1.087 ***	0.128 **	0.329 **	0.122 *
	(0.102)	(0.053)	(0.134)	(0.072)
Controls	NO	YES	NO	YES
Individual FE	NO	YES	YES	YES
Year FE	NO	NO	YES	YES
Observation	2821	2459	2807	2432
R2	0.040	0.723	0.731	0.846

Note: *, **, and *** represent significance at the levels of 10%, 5%, and 1%, respectively.

. Column (1) explores the direct association between d (double-difference variable) and GI (green investment) without taking any control variables into consideration. The research results show that the regression coefficient of the DID is as high as 1.087 and highly significant. This finding preliminarily reveals that environmental regulation policies have a significant and positive role in promoting green investment expenditure.

Column (2) is an expansion of column (1) that introduces control variables to remove other potential factors from interfering with the findings. After the inclusion of control variables, the regression coefficient of the DID decreased slightly to 0.128, but its significance did not change. This result shows that after considering other influencing factors, the positive effect of an environmental regulatory policy on green investment expenditure still clearly exists, but the extent of its impact is adjusted.

Further, column (3) integrates individual time effects into the model to determine the impact of individual differences changing over time on green investment spending. The regression coefficient of the DID rebounded to 0.329, remaining significant, and this change highlights the importance of individual time effects in explaining changes in green investment spending.

Column (4) further incorporates time-fixed effects based on column (3) to fully control for the potential impact of time trends on the study results. At this point, the regression coefficient of the DID was fine-tuned to 0.122—a small change from column (3)—and remained significant. This finding shows that, after taking individual time effects and time-fixed effects into account, the model explains the impact of an environmental regulation policy on green investment expenditure more accurately and comprehensively. It also verifies that the positive effect of an environmental regulation policy has a certain stability and sustainability at different times and between individuals.

5.3. PSM Analysis

The Logit regression model was constructed according to the existing literature and the R² maximum rule. The control variables in the benchmark model were selected as covariates to estimate the propensity score of the sample enterprise. The processing and control groups were based on the nearest-neighbor matching method. To verify the reliability of the matching results, the balance of the variable score matching was tested (

Table 4. Comparison before and after variable matching.

Variable	Sample Matching	Mean		Standard Deviation	Magnitude of Standard Deviation Reduction	T-Test
		Processing Group	Control Group			t-Value	p-Value
Size	Pre-match	23.292	21.999	99.3	80.4	22.19	0.000
	Post-match	23.125	22.871	19.5		5.05	0.000
Age	Pre-match	16.552	9.2309	103.8	81.1	24.75	0.000
	Post-match	15.875	17.256	−19.6		−5.28	0.000
Dual	Pre-match	0.09981	0.35219	−63.3	93.4	−16.05	0.000
	Post-match	0.10981	0.12638	−4.2		−1.38	0.167
Bind	Pre-match	36.67	38.301	−31.7	98.8	−7.84	0.000
	Post-match	36.789	36.77	0.4		0.11	0.913
Lev	Pre-match	0.54483	0.34301	111.4	95.1	26.38	0.000
	Post-match	0.53458	0.51467	5.5		1.53	0.126
ROA	Pre-match	0.2474	0.4461	−31.4	80.9	−7.74	0.000
	Post-match	0.2713	0.02334	6.0		1.46	0.145

). After matching, there was no significant difference between the treatment and control groups. Compared with before matching, the standard deviation of each variable of the matched treatment and control groups decreased significantly, indicating a good agreement between the distribution of the matched treatment and control groups, which satisfied the hypothesis of parallel trends in the PSM analysis.

The regression results based on the sample data described above are shown in

Table 5. Regression results of the PSM-DID model.

Variable	GI	GI	GI	GI
DID	0.678 ***	0.669 ***	0.267 *	0.270 *
	(0.188)	(0.188)	(0.152)	(0.152)
Controls	NO	YES	NO	YES
Individual FE	NO	YES	YES	YES
Year FE	NO	NO	YES	YES
Observation	2821	2459	2807	2432
R2	0.015	0.019	0.712	0.712

Note: * and *** represent significance at the levels of 10% and 1%, respectively.

.

The regression results in Table 5 show that the double-difference estimation results did not change significantly from before to after the propensity score matching, and the impact of the policy was still positively correlated with the green investment expenditure of enterprises, while the absolute value did not differ much. These findings were similar to the benchmark regression results, which further verified the robustness of the empirical results.

5.4. Robustness Test

5.4.1. Regression Analysis of Tailed Variables with Balanced Panel Data

To retest the robustness of the results, a regression analysis was performed using the subsample. First, the continuous variables were reduced or increased by 1%, aiming to eliminate any possible outliers due to extreme cases in the dataset that had the potential to adversely affect the accuracy and reliability of the regression analysis results. For the results shown in

Table 6. Regression analysis of tailed variables with balanced panel data.

Variable	(1) 1 Percent Above and Below the Indented Variable	(2) Balanced Panel Data
DID	0.124 *	0.203 **
	(0.071)	(0.090)
Controls	YES	YES
Individual FE	YES	YES
Year FE	YES	YES
Observation	2432	1875
R2	0.849	0.846

Note: * and ** represent significance at the levels of 10% and 5%, respectively.

, column (1), the ${\mathrm{D}}_{\mathrm{t}}{\ast \mathrm{Treat}}_{\mathrm{i}}$ coefficient remains significantly positive. In addition, balanced panel data were selected for testing, aiming to more accurately capture the dynamic association between variables and effectively reduce the possible bias introduced by the missing data. The results are shown in column (2), and the ${\mathrm{D}}_{\mathrm{t}}{\ast \mathrm{Treat}}_{\mathrm{i}}$ coefficient is significantly positive, which again demonstrates a high degree of consistency with the benchmark regression results.

5.4.2. Parallel Trend Test

In order to verify that the green investment expenditure of enterprises is caused by environmental regulation policies rather than other factors that are difficult to observe, the pre-treatment trend test method was adopted to verify the rationality of the parallel trend assumption. The specific model is as follows:

$$G{I}_{it}={\mathrm{\alpha }}_0+{\sum }_{n=-6}^2{\mathrm{\gamma }}_{\mathrm{n}}{\mathrm{Treat}}_{\mathrm{it}}^{\mathrm{n}}+{\mathrm{contros}}_{\mathrm{it}}+{\mathrm{\mu }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$

Here, ${\mathrm{Treat}}_{\mathrm{it}}^{\mathrm{n}}$ is the relative annual policy variable, which is generated using the year of pilot policy implementation; ${\mathrm{Treat}}_{\mathrm{it}}^{\mathrm{n}}$ = 1 for included pilot areas, and ${\mathrm{Treat}}_{\mathrm{it}}^{\mathrm{n}}$ = 0 for non-pilot areas. We set the year before the implementation of the pilot policy as the benchmark year for event analysis. ${\mathrm{\gamma }}_{\mathrm{n}}$ is the regression coefficient of the relative benchmark year; if ${\mathrm{\gamma }}_{\mathrm{n}}$ is not significantly different from 0, this indicates that the parallel trend assumption is met.

In order to more intuitively observe the impact of the policy after implementation, Figure 1 presents an illustration of the dynamic effect test results.

As can be seen from the trend chart, the ordinate represents the relative change in green investment by traditional energy enterprises at different time points before and after the implementation of the policy, and 0 is the policy implementation point. Based on the figure, in the period before the policy, namely, at −6 to −1, the relative change in the traditional energy enterprises’ green investments exhibits small fluctuations, but the overall trend remains stable with no obvious upward or downward trend. This confirms the parallel trend hypothesis that before the policy implementation, the enterprise’s green investments are not impacted by other unknown factors.

In particular, before the policy implementation point, namely, at −2 and −1, the relative change in green investment expenditure is more stable without obvious abnormal fluctuations, which further verifies the establishment of the parallel trend. However, in the time after the implementation of the policy, namely, at 0 and later, the relative change in green investment spending rose significantly, especially at 1. The regression results are significant, which shows that, within a year after the implementation of an environmental regulation policy, its positive effect on the green investment spending of traditional energy enterprises can be seen, which verifies hypothesis H1.

5.5. Placebo Test

5.5.1. Change in the Policy Occurrence Time

In order to further test the parallel trend, we excluded the systematic differences between the treatment and control groups before the implementation of the policy and ensured that the increase in enterprise green investment expenditure was due to the impact of environmental regulation policies rather than other exogenous shocks. A placebo test was conducted using the counterfactual method by ending the environmental regulation policy after two years. The results shown in

Table 7. Results of changing the policy duration.

Variable	(1) Random Advance Policy for Two Years	(2) Random Advance Policy for Three Years
DID	0.094	0.078
	(0.070)	(0.072)
Controls	YES	YES
Individual FE	YES	YES
Year FE	YES	YES
Observation	2432	2432
R2	0.849	0.849

demonstrate that the coefficient of the interaction term is not significant, indicating that the significant increase in green investment was, indeed, caused by environmental regulation policies and not a prior trend.

5.5.2. Random Generation of Experimental Groups

In order to further confirm the authenticity of the impact of the implementation of environmental regulation policies on the green investment of enterprises, 600 enterprises were randomly set as the experimental group 500 times, and the policy effect was re-estimated. A scatter chart of the p-values is shown in Figure 2.

It can be established based on the figure that the scatter is concentrated around 0, and most of the points are distributed above the dotted line, which indicates that the randomized experimental group samples cannot pass the significance level of 10%; that is, except for the policy impact of environmental regulation, there are no unobserved factors with a significant impact on the green investment of enterprises. In summary, the impact of environmental regulation policies on enterprises’ green investments is real, and the research conclusions are robust.

5.6. Heterogeneity Test

Enterprise Characteristics

(1)

Capital intensity

The capital intensity reflects the entry threshold of the industry. Capital-intensive industries tend to raise the barriers to entry to reduce competition. For an enterprise itself, the capital intensity index reflects the enterprise’s strategic transformation and ability to resist risks. In the face of the challenges brought by environmental regulation policies, enterprises with high capital intensity may be better able to upgrade their technology and invest in environmental protection due to their capital advantages in order to adapt to the new policy environment. To explore this problem in depth, we took the ratio of the total assets of an enterprise to the total number of employees as the standard for measuring the capital intensity of enterprises and conducted a group regression analysis according to this standard.

(2)

Property of ownership

Enterprise ownership reflects the difference in an enterprise’s governance structure and management strategy. In the context of environmental regulation policies, this difference may significantly affect the green investment response of enterprises. Specifically, state-owned enterprises and non-state-owned enterprises may make different capital adjustment decisions when facing the same environmental policies due to their different ownerships. Generally speaking, state-owned enterprises, which assume more social responsibilities and are subject to stricter institutional constraints, may be more inclined to meet the policy requirements by increasing their environmental-protection-related capital expenditure when environmental regulations are strengthened. Non-state-owned enterprises, which also need to comply with environmental policies, may be more flexible in their capital expenditure adjustments. In addition, the difference in ownership may affect the resource acquisition ability and risk tolerance of enterprises and thereby produce different capital expenditure responses under the same environmental regulation policies. Therefore, we grouped these differences according to the nature of enterprise ownership.

The regression results are shown in

Table 8. Classification of results based on enterprise characteristics.

Variable	(1) High Capital Intensity	(2) Low Capital Intensity	(3) State-Owned Businesses	(4) Non-State-Owned Enterprises
GI	0.154 **	0.445	0.337 ***	−0.178
	(0.075)	(0.384)	(0.119)	(0.128)
Controls	YES	YES	YES	YES
Individual FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observation	2057	375	1366	1062
R2	0.851	0.845	0.581	0.777

Note: ** and *** represent significance at the levels of 5% and 1%, respectively.

. With the implementation of environmental regulation policies, state-owned enterprises and non-state-owned enterprises show significant differences in their green investment strategies. Specifically, state-owned enterprises show positive policy responses, and their green investments increase significantly and with statistical significance, with a coefficient of 0.337, which clearly shows that environmental regulation policies play an effective role in promoting the capital investment of state-owned enterprises. Relatively speaking, when facing the same policy environment, non-state-owned enterprises’ green investment responses are generally negative. Although it shows a certain degree of reduction, the coefficient is −0.178 and does not reach a significant level, indicating that the direct impact of environmental regulation policies on the green investment of non-state-owned enterprises is relatively limited.

5.7. Regional Characteristics

5.7.1. Characteristics of Resource-Based Areas

When faced with environmental regulation policies, traditional energy enterprises in resource-based areas and non-resource-based areas need to adopt different operational strategies to adapt to new environmental protection standards. The impact of these adjustments on green investments varies significantly between these two kinds of places.

In resource-based areas, traditional energy companies often have large infrastructures and fixed supply chains because their economies are highly dependent on specific natural resources. Therefore, under the pressure of an environmental policy, these enterprises not only have to update their equipment to reduce pollution and emissions but also improve their mining and processing procedures to improve the efficiency of resource utilization. However, due to the high cost of technology renewal and process improvement, and because it may be difficult to fully replace these in the short term, the increase in green investment by traditional energy enterprises in resource-based areas may be relatively limited following environmental regulation.

In contrast, the economic structure of non-resource-based areas is more diversified, which means that traditional energy enterprises in non-resource-based areas have greater flexibility and space for innovation when facing environmental regulation policies. To meet the new environmental standards, these companies may need to invest in the development and promotion of clean energy technologies as well as update their equipment to reduce energy consumption and emissions and improve their processes to increase production efficiency. These adjustments usually require a large amount of capital investment, so traditional energy companies in non-resource-based areas may see a more significant increase in green investment spending under environmental regulation.

This difference was verified in the data analysis. The empirical results in

Table 9. Results classified based on regional characteristics.

Variable	(1) Non-Resource-Based Areas	(2) Resource-Based Areas	(3) Eastern	(4) Central	(5) Western
GI	0.154 **	0.445	0.034	0.227	0.331 **
	(0.075)	(0.384)	(0.092)	(0.177)	(0.166)
Controls	YES	YES	YES	YES	YES
Individual FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observation	2057	375	1393	490	549
R2	0.851	0.845	0.865	0.805	0.832

Note: ** represents significance at the levels of 5%.

, columns (1) and (2), show that the GI coefficient of non-resource-based areas is significant and positive, indicating that environmental regulation policies have a significant impact on the green investment expenditure of traditional energy enterprises in non-resource-based areas.

5.7.2. Geographical Differences

Due to the obvious differences in the geographical location, resource availability, and openness to the world outside of China, as well as the impact of political policies stemming from long-term historical dynamics, the environmental conditions and policy direction are also different. Generally speaking, the scale, scale efficiency, and development level of enterprises in the eastern region of China are ahead of those in the central and western regions. Compared with the eastern and central regions, there is a significant gap in the number, strength, economic benefits, and other aspects, but the specific situation of traditional energy enterprises needs to be further analyzed.

As can be seen in Table 9, columns (3–5), the empirical results show that the GI regression coefficient in the western region is the highest and statistically significant, which indicates that environmental regulation policies have a significant impact on the green investment expenditure of traditional energy enterprises in the western region. In contrast, although the regression coefficients in the eastern and central regions are positive, they are not statistically significant, indicating that the green investment expenditure of enterprises in these two regions under environmental regulation policies is less obvious than that in the western regions.

This result may be related to the economic, social, and environmental characteristics of the western region. The western region may be more dependent on traditional energy industries, so the changes in environmental regulation policies will have a more direct and significant impact on the green investment spending of these companies. In addition, the western region may face more severe pressure in terms of environmental protection, which also makes the government and enterprises pay more attention to environmental protection investments, thus affecting green investment spending.

6. Further Analysis

6.1. Research on the Actual Effects of Green Investment

According to our review of the existing literature and empirical test results, it can be seen that environmental regulation policies have a significant and positive effect on the green investment expenditure of traditional energy enterprises; that is, with a reasonable enhancement in the intensity of environmental regulation, traditional energy enterprises tend to increase their green investments to cope with the environmental compliance requirements. On this basis, this study further focuses on a more critical and deeper issue: Does the increased green investment driven by environmental regulations really and effectively promote the green transformation of traditional energy enterprises? In order to further analyze this core issue, we built a set of comprehensive green transformation index systems that consider energy consumption and resource utilization, pollution degree, green technology innovation, production efficiency, and other dimensions and aimed to explore the incremental green investments made by traditional energy enterprises to achieve a green transformation. The following model was constructed:

$$\ln \left({\displaystyle \frac{\mathrm{p}}{1-\mathrm{p}}}\right)={\mathrm{\beta }}_0+{\mathrm{\beta }}_{1}G{I}_{it}+{\mathrm{contros}}_{\mathrm{it}}+{\mathrm{\mu }}_{\mathrm{i}}+{\mathrm{\lambda }}_{\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$

Here, $\mathrm{p}$ is the probability of a successful green transformation of traditional energy enterprises, and ${\mathrm{\beta }}_1$ represents the increased investment expenditure (the marginal impact on the probability of successful transformation). If ${\mathrm{\beta }}_1$ is positive, this indicates that increasing investment spending will increase the probability of a successful transition.

The existing literature mainly measures the green transformation of enterprises using a single index, such as “emission reduction” and “efficiency increase”. However, to encompass the nuances of this process, the index should reflect various processes, such as intensive resource utilization, pollutant emission reduction, environmental impact reduction, and productivity improvement. This study constructs an index system for the evaluation of the green transformation degree of traditional energy enterprises and measures the quality and degree of this transformation based on four dimensions (see

Table 10. Green transformation index system for traditional energy enterprises.

Dimension	Measurement	Direction
Energy consumption and resource utilization	Overall energy consumption	-
	Water consumption	-
Pollution level	Sulfur dioxide emissions	-
	Carbon dioxide emissions	-
Green technology innovation	Number of green patent applications	+
Production efficiency	Total factor productivity	+

). The objective entropy method is used to determine the weight according to the information entropy value of each index. The data involved in the indicators are from the statistical yearbooks of various provinces, the Statistical Bulletin of Social Development, the China Energy Statistical Yearbook, the China Taian database, etc.

The results of the regression analysis are shown in

Table 11. Benchmark regression results.

Variable	Green Transformation	Green Transformation	Green Transformation
GI	−0.408 ***	−0.314 ***	−0.383 ***
	(0.030)	(0.048)	(0.124)
Controls	NO	YES	YES
Individual FE	NO	NO	YES
Year FE	NO	NO	YES
Observation	1810	1810	1425
R2	0.106	0.141	0.472

Note: *** represents significance at the levels of 1%.

. The key variable GI showed a significant negative coefficient in all models, with a specific value of −0.408 (p < 0.030). This finding shows that, after controlling for other potential influencing factors, the increased investment expenditure does not effectively promote the green transformation of traditional energy companies.

Through further analysis, we found that the interpretation strength of the model was significantly improved after including individual time effects and time-fixed effects (adjusted R² = 0.472). However, even after considering these effects, the negative effect of GI remained significant, indicating that these control variables, while improving the robustness of the model, did not directly promote the green transition. At the same time, individual time effects and time-fixed effects themselves are not statistically significant, indicating that their direct impact on the green transition is relatively limited. Hypothesis H2 was verified.

This research result points to several important problems: first, the specific allocation and use of investment expenditure may not be effectively focused on the development and application of green technology, which leads to a mismatch of resources; second, enterprises may face a limited technology absorption capacity, making it difficult to quickly convert new investments into the actual result of a green transformation; finally, the design and implementation of environmental regulation policies may have some deficiencies, failing to fully stimulate the motivation of enterprises to achieve a green transition.

Table 12. Industry classification results.

Variable	(1) Electricity and Heat Power Production and Supply Industry	(2) Coal Mining and Washing Industry	(3) Gas Production and Supply Industry
Green Transformation	−0.311 **	−0.145	−0.526
	(0.151)	(0.312)	(0.443)
Controls	YES	YES	YES
Individual FE	YES	YES	YES
Year FE	YES	YES	YES
Observation	940	229	220
R2	0.505	0.491	0.432

Note: ** represents significance at the levels of 5%.

presents the results of a heterogeneity test conducted on the industry types of traditional energy enterprises. The data in the table clearly show that the green transformation coefficients of the three major industries are all negative, indicating that the green investment in these industries has not effectively promoted the green transformation process of the enterprises themselves. The effects on the power, heat production, and supply industry are particularly significant.

The reason might lie in the inherent characteristics and operational models of the power and heat production and supply industry, which result in a relatively low conversion rate of green investment benefits. On the one hand, the production process is highly complex and deeply dependent on traditional energy sources. This makes the introduction and integration of green technologies more challenging, requiring a longer time and higher costs to achieve. On the other hand, due to the large scale and significant influence of the power and heat production and supply industry, its transformation is more difficult and complex, making it hard for the effects of green investment to be evident in the short term.

6.2. Research on Potential Financial Risks

So far, this study has so far revealed that although environmental regulation policies have significantly promoted the growth of green investments by traditional energy enterprises, this growth has not promoted the green transformation of enterprises as effectively as expected, indicating that there is no direct and inevitable positive connection between the increase in green investment and green transformation.

Given that the increase in green investment fails to effectively promote green transformation, enterprises may face a dilemma of an “input–output” imbalance; that is, high capital investment does not lead to a corresponding improvement in green benefits. In this context, with the continuous enhancement in environmental regulation intensity and the continuous improvement in environmental protection requirements, enterprises must continue increasing their green investments in order to meet new environmental protection standards, which undoubtedly increases the financial burden on enterprises. Therefore, the focus of this study turns to the severe financial challenges that enterprises may face in the context of increasingly strict environmental regulations. Specifically, with the continuous improvement in environmental protection requirements, the increasing levels of green investments made by enterprises to cope with these requirements may gradually exceed the carrying capacity of their financial inflow, leading to a serious threat to the financial robustness of enterprises. The model is as follows:

$${\mathrm{FT}}_{\mathrm{it}}={\mathrm{\lambda }}_0+{\mathrm{\lambda }}_1\Delta {\mathrm{R}}_{\mathrm{it}}\%+{\mathrm{\lambda }}_2\Delta {\mathrm{GGI}}_{\mathrm{it}}\%+{\mathrm{\lambda }}_3{\mathrm{contros}}_{\mathrm{it}}+{\mathrm{\epsilon }}_{\mathrm{it}}$$

Here, ${\mathrm{FT}}_{\mathrm{it}}$ represents the financial pressure index of enterprise i in period t, which is expressed by the Z-value to measure the economic condition of an enterprise. The function of the Ζ-value model is as follows:

$$\mathrm{Z}=1.2\ast {\mathrm{X}}_1+1.4\ast {\mathrm{X}}_2+3.3\ast {\mathrm{X}}_3+0.6\ast {\mathrm{X}}_4+0.999\ast {\mathrm{X}}_5$$

where ${\mathrm{X}}_1$ is the working capital/total assets, which reflects the company’s short-term debt solvency; ${\mathrm{X}}_2$ represents the retained earnings/total assets, i.e., the cumulative profitability of the company; ${\mathrm{X}}_3$ represents the EBIT/total assets; ${\mathrm{X}}_4$ represents the equity market value/total liabilities, as well as the financial structure and solvency of the enterprise; ${\mathrm{X}}_5$ represents sales revenue/total assets; $\Delta {\mathrm{R}}_{\mathrm{it}}\%$ represents the rate of financial inflow growth from period t − 1 to period t, expressed using the entropy method, and it embodies the change in corporate profitability; $\Delta {\mathrm{GGI}}_{\mathrm{it}}\%$ represents the growth rate of green investment from period t− 1 to period t, which reflects the increase and decrease in the investment activities of enterprises. The coefficient ${\mathrm{\lambda }}_1$ demonstrates the degree of the impact of financial inflow growth on the financial situation of enterprises, and the coefficient ${\mathrm{\lambda }}_2$ reflects the influence of green investment growth on the financial situation of enterprises. By comparing the size and direction of ${\mathrm{\lambda }}_1$ and ${\mathrm{\lambda }}_2$, the above equation provides a thorough analysis of the dynamics between financial inflow growth and green investment expenditure growth and how they work together to affect an enterprise’s financial situation.

Table 13. Financial inflows.

Variable Class	Variable Name	Variable Definition
Financial inflows	Cash inflows resulting from investment activities	Reflects the cash inflow of enterprises through investment activities (such as the sale of assets, recovery of investment, etc.).
	Cash inflows resulting from financing activities	Reflects the cash inflow of enterprises through financing activities (such as the issuance of stocks, bonds, borrowing, etc.).
	Cash inflows arising from operating activities	Reflects the cash inflow of enterprises through daily business activities (such as sales of goods, providing labor services, etc.).

reflects the financial inflows.

The results of the regression analysis are shown in

Table 14. Benchmark regression results.

Variable	FT	FT	FT	FT
∆R%	−0.146 ***	−0.036 ***
	(0.044)	(0.011)
∆GGI%			0.237 ***	0.043 ***
			(0.069)	(0.013)
Controls	NO	YES	NO	YES
Individual FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observation	1801	1801	1794	1794
R2	0.518	0.661	0.517	0.658

Note: *** represents significance at the levels of 1%.

, with a $\Delta \mathrm{R}\%$ coefficient of −0.146 and high statistical significance (p-value <0.001). This shows that there is a negative correlation between the financial inflow growth rate and the financial pressure index. Specifically, the financial stress index may increase when the growth rate of financial inflows decreases. This trend is particularly dangerous in the context of increasingly stringent environmental requirements because, even if corporate financial inflows increase, the increase is not likely to fully offset the additional green investment spending that must be carried out to comply with environmental requirements. This imbalance will further increase the financial pressure on enterprises and threaten their financial soundness.

The coefficient of $\Delta \mathrm{GGI}\%$ is positive and highly statistically significant. This shows that, as enterprises increase their green investment expenditure to comply with environmental standards, the increase in the growth rate of their green investment expenditure will directly lead to an increase in the financial pressure index. In other words, the increase in environmental protection requirements not only increases the operating costs of enterprises but also indirectly increases the financial pressure of enterprises by increasing their green investments. This double pressure means that companies face even greater challenges in maintaining their financial robustness.

After accounting for control variables, including individual time effects and time-fixed effects, the model fit improved significantly, with adjusted R² values ranging between 0.518 and 0.661. This result not only confirms the validity of the model in explaining the changes in FT but also further strengthens the conclusion that “$\Delta \mathrm{R}\%$” and “$\Delta \mathrm{GGI}\%$” significantly influence FT. Hypothesis H3 is thus verified.

6.3. Case Discussion

Huadian International is taken as an example. It is one of the largest comprehensive energy companies in China and holds a leading position in the power industry. However, driven by their nature of pursuing profit, enterprises often tend to concentrate their limited operating capital in their production and manufacturing links to maximize their economic benefits. Although the purchase of environmental protection equipment and investment in green technologies are in line with the trends of the times, due to their high capital costs, enterprises lack the willingness and do not take actual action to make green investments proactively in the absence of external incentives. Under the strict environmental regulations of the government, Huadian International has made multiple green investments with a total scale of over 10 billion yuan, making it a typical representative of enterprise green investment. However, the returns brought by such green investments are not certain and are influenced by various factors, including the maturity of technology, changes in market demand, and adjustments in policy orientation. This makes enterprises face significant uncertainties and risk challenges in the process of green investment.

7. Conclusions and Policy Implications

Traditional energy enterprises are not only the main carriers of economic development but also the primary sources of environmental pollution. They should assume more responsibility and be more proactive in environmental governance. However, as profit-oriented organizations, traditional energy enterprises are more inclined to allocate their limited operating funds to production and manufacturing, lacking the willingness and not taking action to make green investments voluntarily. Therefore, it is imperative to conduct in-depth research on whether environmental regulations can promote green investment by traditional energy enterprises, what the micro-mechanism and impact mechanism are, and whether green investment necessarily leads to green transformation.

This study selected 188 traditional energy enterprises listed on the A-share market in China from 2012 to 2023 as research samples and used the difference-in-differences model to analyze the changes in green investment expenditure of these enterprises before and after the implementation of environmental regulation policies. It further examined the actual effects of this expenditure on the green transformation of enterprises and the potential risks that enterprises may face in the process of promoting green transformation. The research showed that environmental regulation policies have significantly increased the green investment of traditional energy enterprises. However, this growth has not effectively promoted the comprehensive green transformation of enterprises but, rather, has been more focused on meeting current compliance requirements, neglecting fundamental green technological innovation and production mode transformation. A further analysis revealed that as environmental protection standards continue to rise, the increasing green investment expenditure of traditional energy enterprises for meeting these standards will continuously outpace the growth rate of capital inflows to traditional energy enterprises, posing a threat to the short-term financial stability of enterprises and significantly increasing their financial pressure. In the long term, if there is a lack of continuous and stable external financial support, traditional energy enterprises will face increasingly severe capital shortage problems. This predicament will continue to deteriorate, not only constraining the green transformation process of enterprises but also potentially posing a serious threat to the survival foundation of enterprises and even leading to their elimination due to fierce market competition. Traditional energy enterprises in China play a crucial role as the cornerstone of national energy security. If the development of these enterprises encounters difficulties, it will undoubtedly pose a severe challenge and potential threat to China’s energy security.

Based on the research conclusions of this study, the following policy implications are proposed:

(1)

Optimization of the design of environmental regulation policies to balance the costs and benefits of enterprises

①

Fine-tuning the intensity of environmental regulations to ensure a reasonable match between policies and the enterprises’ bearing capacity

In the formulation of environmental regulation policies, a core principle is to ensure that the intensity of regulations can promote the achievement of environmental protection goals without overly suppressing the economic activities and development potential of enterprises. This requires policymakers to conduct in-depth research on the actual operating conditions of enterprises, including but not limited to their cost structure, technological innovation capabilities, market competition positions, and potential for green transformation. By constructing a multi-dimensional assessment model and comprehensively considering enterprises’ economic bearing capacity, industry characteristics, and regional differences, the upper and lower limits of the regulation intensity should be scientifically defined. Specifically, a cost–benefit analysis method can be adopted to assess the additional costs that enterprises incur due to compliance with environmental standards and the expected environmental improvement benefits under different regulation intensities, striving to find the optimal balance point between the two. Additionally, enterprises should be encouraged to participate in the policymaking process through public consultations and opinion collection, making their voices an important reference for policy adjustments and, thereby, ensuring that regulation policies can effectively motivate enterprises to adopt green production methods while maintaining their financial stability, thus achieving a win–win situation for economic development and environmental protection.

• ②

  Establishment of a dynamic adjustment mechanism to ensure the timeliness and adaptability of environmental regulation standards

Environmental regulation standards should not remain static but should be flexibly adjusted in response to technological progress, industrial upgrading, and changes in market demand. To this end, it is crucial to build an efficient dynamic monitoring and evaluation system. This system should include the following key elements: Firstly, a comprehensive data collection and analysis platform should be established to track the pace of technological progress, industry best practices, and the actual compliance of enterprises with regulatory standards in real time, thus providing data support for the adjustment of regulatory standards. Secondly, a regular review mechanism, such as conducting a comprehensive assessment every two or three years, combining expert reviews, public participation, and third-party evaluations, should be set up to ensure the objectivity and comprehensiveness of the assessment results. During the review process, particular attention should be paid to whether the regulatory standards can still effectively promote the achievement of environmental goals while also assessing their impact on the competitiveness of enterprises to avoid “one-size-fits-all” policies that place unfair burdens on some enterprises. Finally, based on the assessment results, the regulatory standards should be adjusted in a timely manner. This should reflect the possibilities brought about by technological progress while also considering the period for enterprises to adapt to new regulations. By setting reasonable transition periods and providing subsidies for technological transformation or tax incentives and other incentive measures, this can help enterprises make a smooth transition and, ultimately, achieve a positive interaction between environmental regulation and enterprise development.

(2)

Optimization of the combination of policy tools to guide financial resources to support green transformation

①

Precisely implementing fiscal support and subsidy strategies to target key nodes and bottleneck breakthroughs in green transformation

To promote the green transformation of enterprises, the government needs to adopt more refined and targeted fiscal support measures to ensure the effective allocation and maximum utilization of resources. First, through in-depth industry analysis and enterprise research, key areas and weak links in the green transformation process, such as the research and development and innovation of green technologies, the renewal of efficient production equipment, and the wide application of energy-saving and emission reduction technologies, should be accurately identified. On this basis, differentiated fiscal support policies, including but not limited to direct subsidies, tax reductions, and additional deductions for research and development expenses, should be designed to alleviate the financial pressure on enterprises in these key areas. For example, for green technology research and development, the government can establish a special fund to support joint research and development projects between enterprises and research institutions and provide high rewards to enterprises that achieve major breakthroughs; for the upgrading of production equipment and energy-saving and emission reduction renovations, low-interest loans or interest subsidies can be provided to reduce the renovation costs of enterprises. At the same time, to ensure the transparency and efficiency of fund usage, a strict supervision and evaluation mechanism should be established, and regular audits of the implementation effects of supported projects should be conducted to ensure that fiscal support is truly transformed into an actual driving force for promoting the green transformation of enterprises, rather than disrupting their normal business operations.

• ②

  Establishment and continuous optimization of the green transformation guiding fund to build a new model of collaboration for the government and social capital

The establishment of the green transformation guiding fund is an important measure for the government to guide social capital towards green fields and promote the green transformation of enterprises. The construction of the fund should follow the principle of “government guidance, market operation, and controllable risks” to ensure the long-term stability and efficient utilization of funds. Specifically, the government should act as the initial investor, jointly raising fund capital with financial institutions, large enterprises, social donations, and other forces. In terms of fund management, professional investment management teams should be introduced, and a market-oriented investment decision-making mechanism should be adopted to ensure the scientificity, feasibility, and profitability of the fund’s investment projects. At the same time, a sound risk control system should be established, effectively controlling investment risks through diversified investment portfolios, phased investments, and the setting of stop-loss lines.

To further optimize the fund’s operation mechanism, the government should regularly assess the investment performance and social impact of the fund, adjusting investment strategies and capital flows based on the assessment results to ensure that the fund always focuses on the most cutting-edge areas of green transformation. Additionally, the government should actively build information exchange platforms to facilitate effective communication among fund managers, investors, and enterprises, enhancing the transparency and credibility of the fund and attracting more social capital to voluntarily join, forming a virtuous cycle of collaboration between the government and social capital to promote the green transformation of enterprises. Through the continuous improvement and expansion of the green transformation guiding fund, not only can the financial bottleneck of enterprises’ green transformation be effectively alleviated, but it can also drive a green investment trend throughout society, laying a solid foundation for the construction of a green and low-carbon economic development model.

(3)

Strengthening the synergistic effect of environmental regulations and financial policies and building a support system for green transformation

①

Close synergy in policy formulation and implementation to establish a deeply integrated system of environmental regulations and financial policies

During the planning and introduction of environmental regulation policies, it is essential to deeply understand their potential impact on the structure of the financial market, the operational strategies of financial institutions, and the flow of funds, ensuring that environmental regulation policies not only effectively promote the achievement of environmental protection goals but also form a positive interaction with financial policies to jointly drive the green transformation of enterprises. Specifically, the government should establish a cross-departmental policy coordination and dialogue mechanism, incorporating environmental regulation departments and financial regulatory authorities into the same decision-making framework. Through regular meetings, joint research, and policy consultations, information sharing and strategic coordination between the two departments should be enhanced. When formulating emission standards and environmental protection regulations, environmental regulation departments should fully consult the opinions of financial regulatory authorities to ensure that policy designs not only meet environmental protection requirements but also do not overly suppress the vitality of the financial market. Meanwhile, when adjusting credit policies and designing green financial products, financial regulatory authorities should actively incorporate suggestions from environmental regulation departments to ensure that financial policies can precisely meet the financing needs of green transformation.

• ②

  Establishment of an information sharing and risk sharing mechanism to promote the deep integration of environmental regulations and financial policies

To further enhance the synergy between environmental regulations and financial policies, the government should establish a comprehensive and efficient information sharing platform that covers the latest developments in environmental regulations, the operational status of the financial market, the financing needs and progress of green transformation projects, and other key information. Through this platform, the government, enterprises, and financial institutions can exchange information in real time, reduce information asymmetry, and improve the efficiency of policy implementation and resource allocation. In the construction of a risk sharing mechanism, the government should play a guiding role by setting up a green transformation risk compensation fund and providing government-backed financing guarantees to provide risk buffers for financial institutions’ participation in green transformation projects. At the same time, it is encouraged to establish risk sharing mechanisms among financial institutions, such as through syndicated loans and joint investments, to diversify the risks of individual projects or individual financial institutions. Through the improvement in information sharing and risk sharing mechanisms, the deep integration of environmental regulations and financial policies can be effectively promoted, providing strong policy and financial support for enterprises’ green transformation.