2.1.1 Environmental regulations.

In the economically challenged 1980s, China designated environmental protection as a cornerstone of its national policy. The landmark legislation, "Environmental Protection Law of the People’s Republic of China," was enacted in 1989, outlining regulations for environmental conservation, oversight, pollution control, and legal accountability. However, economic growth is the primary goal of the traditional development perspective; if this view does not change, environmental issues will continue to worsen. A revised edition of the law was introduced in 2015, expanding the legal framework to include 29 environmental statutes, such as the "Water Pollution Prevention Law" and the "Atmospheric Pollution Prevention Law," among others [15]. The natural environment, being the foundation for all human activities, including economic endeavors, has a profound and varied influence that has drawn the interest of scholars across various disciplines. Researchers such as Ambec et al. [16] have dissected the interplay between environmental regulation and the Porter Hypothesis through the lens of policy reform, establishing a research framework for contemporary studies in this domain. Rubashkina [17] delved into the nexus between environmental regulation and corporate innovation and productivity, uncovering a significant catalytic effect on innovation, albeit with limited impact on productivity. Yenipazarli [18] utilized the Stackelberg game theory to examine the influence of emission tax policies on manufacturers’ optimal production and pricing strategies, highlighting how strategic business planning under environmental regulation can curb pollution while boosting profits. López-Gamero [19] explored the dynamic between environmental regulation and proactive environmental management, revealing that while mandatory regulations have a muted effect, voluntary measures significantly enhance management practices and, in turn, corporate competitiveness. This esteemed body of literature, with a focus on enterprises and employing a blend of empirical and normative research methodologies, underscores that environmental regulation has complex impacts on the economy, society, and nature. Furthermore, it illustrates the interdisciplinary nature of environmental regulation, offering a rich tapestry for future academic inquiry.

2.1.2 High-quality development.

In 2017, the concept of high-quality development was first articulated in China, and since then, it has gained prominence in scholarly and policy discourse. This term is often employed to denote an economic growth paradigm that transcends the mere pursuit of pace and magnitude, focusing instead on the quality and efficacy of growth. It underscores the importance of refining economic structures, bolstering innovation as a driving force, optimizing the allocation of resources, ensuring the sustainability of environmental conservation, and significantly elevating social welfare. Despite the concept’s apparent simplicity, its precise delineation, assessment of advancement, and practical implementation present a tapestry of intricate theoretical and practical challenges.

Chinese scholars, in their papers published in Chinese, have extensively focused on the background, connotations, characteristics, and implementation pathways of high-quality development. Jin [20] posits that high-quality development possesses notable dynamic and multidimensional attributes. The new concepts of development that emphasize innovation, coordination, green initiatives, openness, and sharing serve as the criteria for evaluating high-quality development. A modern governance system is required to guide the realization of high-quality development. The assessment of high-quality development must exhibit systemic characteristics; the goals and approaches of high-quality development are dynamic, and the process of achieving it is characterized by long-term features [21, 22]. High-quality economic development can address structural imbalances in China’s economy and enhance total factor productivity [23]; the realization of high-quality development necessitates impetus from the perspectives of systemic reform and mechanism transformation [24]; equitable income distribution [25], financial services [26], a smart economy [27], and the enhancement of human capital [28] are vital avenues for achieving high-quality development.

Despite an abundance of scholarly work on the high-quality economic development, a cohesive theoretical framework and index system for its quantification remain elusive. The literature can be primarily divided into two categories: single-indicator and composite-indicator approaches. The single-indicator method evaluates economic development quality through one metric, typically total factor productivity [23] or GDP per capita [29]. Conversely, the composite-indicator approach constructs an index system grounded in the five key developmental concepts [30, 31]. Considering the potential for a myopic view and resultant biases inherent in the single-indicator method, the majority of the current literature favors the more holistic composite-indicator methodology.

2.1.3 Relationship between environmental regulation and high-quality development.

Within the academic sphere, the discourse on the interplay between environmental regulation and economic development is both expansive and multifaceted. It spans a vast array of disciplines and subjects, encompassing macroeconomic strategies and microeconomic corporate operations, as well as specific industry analyses and cross-regional comparisons. These scholarly endeavors have not only examined the immediate effects of environmental regulation on economic growth but have also provided in-depth analyses of its broader implications for corporate innovation, the restructuring of industrial sectors, and the enhancement of social welfare. Consequently, the conclusions are varied, mirroring the intricate and multifaceted nature of the subject [32–34]. When it comes to the nexus between environmental regulation and the high-quality development of the economy, academia is divided into three predominant viewpoints:

1. Some scholars believe that environmental regulation can stimulate the innovation capability of enterprises, that is, stimulate the "innovation effect", thereby improving production efficiency and promoting economic growth, which is known as the "Porter Hypothesis" [35].By establishing stringent pollution emission standards and requirements, environmental regulation compels businesses to seek more efficient and eco-friendly production methods. This external pressure often catalyzes a company’s innovative potential, encouraging investment in research and development (R&D) to create new technologies and products that meet regulatory demands [36]. This process not only fosters an increase in corporate production efficiency but also, through innovation, leads to the development of new products and services [37] that can break into markets and create new growth opportunities [38]. Moreover, environmental regulation can enhance resource utilization efficiency, reduce long-term operational costs, and strengthen a company’s market competitiveness. As corporate innovation capabilities and production efficiencies improve, the entire industry and even the national economy can reap benefits, achieving sustainable development and economic growth [35]. The Porter Hypothesis suggests that appropriate environmental regulation can motivate companies to engage in technological innovation, thereby gaining a competitive edge [39]. Jaffe and Palmer expanded on the Porter Hypothesis, delineating the narrow, weak, and strong Porter Hypotheses [40]. Numerous scholars have conducted a series of tests around this hypothesis. Early studies, such as those by Henriques et al. [41], proposed that formal environmental regulations can positively promote the efficiency of corporate green innovation. Recent research, including studies by Li et al. [42] and Santis et al. [43], also supports the Porter Hypothesis, asserting that environmental regulation drives technological innovation, which in turn benefits economic development.
2. A different group of scholars argues that environmental regulation increases the costs for businesses, leading to a decrease in production efficiency and thus adversely affecting economic development [44, 45], which belongs to the "cost effect" school of thought [46]. These costs encompass, but are not limited to, investments in pollution control technologies, the purchase and maintenance of environmental protection equipment, and the acquisition of pollution emission rights. Such additional expenditures may compress the profit margins of businesses, affecting their ability to reinvest and expand [40]. The "compliance cost theory" in neoclassical economics posits that these costs from environmental regulation will lead to a decline in business production efficiency, as companies must balance meeting regulatory requirements with maintaining competitiveness. This perspective holds that if environmental regulations are too stringent, they may suppress business innovation and expansion, thereby negatively impacting overall economic development. Furthermore, for smaller businesses with weaker competitiveness, environmental regulations could become a barrier to their growth, potentially causing some businesses to exit the market. Therefore, finding a balance between protecting the environment and promoting economic development is a critical issue that policymakers need to consider carefully.
3. A third perspective posits that the relationship between environmental regulation and high-quality economic development is not linear but exhibits a certain curvilinear pattern. In the early stages of environmental regulation implementation, the "suppression effect" of costs predominates. However, once the intensity of regulation surpasses a certain threshold, the compensatory effect of innovation begins to take over, creating an U-shaped relationship. Researchers have delved into the multiple impacts of environmental regulation on the economy and environment through different datasets and models. For instance, Wang et al. [47], by analyzing panel data from the industrial sectors of OECD member countries and using an extended SBM-DDF method to measure the stringency of environmental regulation, found that within a certain range of policy enforcement strictness, environmental policies can actively promote the growth of green productivity. However, when the strictness of policy exceeds a certain threshold, the increase in compliance costs begins to outweigh the offsetting effects of innovation, leading to a negative impact on the growth of green productivity. This finding supports the strong version of the Porter Hypothesis. Song’s [48] research re-examined the relationship between the intensity of environmental regulation and the rate of economic growth. Based on panel data from 31 provinces and cities in China from 2004 to 2017, Song found a nonlinear "inverted U-shaped" relationship between environmental regulation and the rate of economic growth, where the rate of economic growth initially rises with the increase in the intensity of environmental regulation and then turns to decline, indicating that the internal impact pathways of environmental regulation vary with different types of industries. Furthermore, Zhang et al. [49] constructed a dynamic spatial panel model for China’s provinces from 2000 to 2017 to evaluate the nonlinear and spatial effects of environmental regulation on economic growth and carbon dioxide emissions. Their research results reveal that at the national level and in the eastern and central regions, the impact of environmental regulation on economic growth shows a significant "N-shaped" effect, while the impact on carbon dioxide emissions is an "inverted N-shaped." In contrast, both economic growth and carbon dioxide emissions in the western region show "N-shaped" effects. Additionally, the intensification of regional environmental regulation has a positive spillover effect on the reduction of economic growth and carbon dioxide emissions in neighboring provinces, but in the western provinces, this spatial effect shows an opposite trend. Therefore, technological innovation does not guarantee a synchronized enhancement of economic development and environmental quality [50]. Technological innovation has the potential to both increase and decrease pollution, thus necessitating a coordination of technological innovation and economic development through environmental regulation [51, 52].

Reviewing the academic discourse on the relationship between environmental regulation and green economic development, the research indicates a complex, nonlinear relationship between environmental regulation and high-quality economic development. Heterogeneity is observed across different industries, stages, and regions, potentially related to different stages of the Porter Hypothesis, which affects the efficacy of environmental regulation. Discrepancies in measurement methods also lead to divergent results. Research must take into account the characteristics of economic entities, such as green development, technological advancement, and industrial upgrading in the Yellow River Basin. The wide-ranging conclusions necessitate further in-depth study. Most existing research is based on spatial heterogeneity, neglecting the issue of pollution heterogeneity. Starting from the perspective of pollution heterogeneity can more comprehensively reflect geographical attributes, enhancing the scientific and practical significance of the research, aiding in a deeper understanding of the relationship between environmental regulation and high-quality economic development, and providing theoretical support for policy-making.

2.2.1 Linear relationship.

The government acts as the principal enforcer of environmental regulation policies, typically enhancing the stringency of these policies by increasing the environmental costs for businesses—such as through the imposition of environmental taxes. From the perspective of innovation incentives, well-crafted environmental regulations can stimulate technological advancement and industrial upgrading. The Porter Hypothesis suggests that stringent yet appropriate environmental regulations can spur corporate innovation, with businesses developing new technologies and improving production processes to increase resource efficiency and reduce pollution emissions. As an external pressure, environmental regulations compel companies to seek innovative solutions to meet environmental standards, thereby enhancing their market competitiveness and economic benefits. This innovation-driven mechanism not only aids businesses in breaking through with environmentally friendly technologies but also fosters the upgrading of the entire industry chain and the optimization of economic structures, ultimately promoting high-quality economic development.

From the perspective of market effects, appropriate environmental regulation can guide the allocation of market resources towards a green economy and sustainable development. By internalizing external costs, environmental regulations make businesses bear the true cost of their environmental impact, encouraging the market to favor environmentally friendly enterprises and industries. This optimization of resource allocation helps to develop a low-carbon economy and green industries, reducing environmental pollution and resource waste. Under the guidance of suitable environmental regulations, market mechanisms can create demand for green products and technologies, promoting the development of the environmental protection industry and enhancing the quality of economic growth. In the long run, this market effect will continue to drive the economy towards sustainable and high-quality development.

From the perspective of social benefits, appropriate environmental regulation can promote the improvement of social welfare and public health, thereby indirectly promoting high-quality economic development. Environmental regulations enhance environmental quality and living standards by controlling pollution and protecting natural resources, reducing the incidence of environment-related diseases and public health expenditure. This enhancement of social benefits not only improves the health and productivity of the workforce but also reduces social medical costs and raises the overall level of social welfare. As social welfare improves, the quality and sustainability of economic development will also be strengthened. Therefore, appropriate environmental regulation indirectly promotes high-quality economic development by enhancing social benefits. Based on the above theoretical analysis, Hypothesis 1 is proposed:

1. H1: Appropriate environmental regulation has a promoting effect on high-quality economic development.

2.2.2 Nonlinear relationship.

Enterprises are the microeconomic agents responsible for achieving high-quality development. For businesses with varying levels of pollution discharge, the costs of pollution control and the impact of environmental regulation differ. There exists a dynamic equilibrium between the high initial investment costs and the subsequent benefits of innovation. When environmental regulations are first implemented, companies must invest substantial resources to comply with new standards, including research and development, equipment upgrades, and operational adjustments, which may lead to increased production costs and a decline in economic benefits in the short term. However, over time, these initial investments gradually translate into innovative outcomes. By developing new technologies and optimizing production processes, companies enhance their efficiency and market competitiveness, thereby achieving greater economic benefits and quality development. Thus, the impact of environmental regulation on economic development may exhibit a nonlinear characteristic that initially suppresses and then stimulates growth. Extending this analysis to the regional level suggests that the macroeconomic impact of environmental regulation in areas with heterogeneous pollution levels also has nonlinear characteristics.

Based on practical experience, there is a positive correlation between a company’s pollution control costs and the intensity of environmental regulation; as the stringency of environmental regulation increases, so do the pollution control costs for businesses. The cost for a company to reduce each unit of pollutant emissions tends to rise, indicating an increasing marginal cost of pollution control. This is both the source of rising environmental regulation costs and the basis for the nonlinear impact of environmental regulation on high-quality economic development. According to market economy theory, in the early stages of environmental regulation implementation, strict regulations may lead to some highly polluting, high-energy-consuming companies exiting the market, causing a short-term negative impact on economic growth. However, this market adjustment helps to phase out outdated production capacities and guide resources towards technologically advanced, environmentally efficient businesses and industries, promoting the optimization and upgrading of industrial structures. As market mechanisms adjust and resources are reallocated, the environmental protection industry and green economy will develop, propelling the economy into a new stage of high-quality development.

Additionally, initial environmental regulations may provoke resistance and opposition from some businesses and the public, primarily due to perceived loss of interests and increased adaptation costs. Such societal reactions could lead to obstacles in policy implementation and a weakening of its effects. However, as public awareness of environmental protection grows and the benefits of environmental regulation are recognized, societal acceptance will gradually increase, and the execution and efficacy of policies will be strengthened. Concurrently, governments and policymakers, based on initial feedback, continuously refine and adjust regulatory measures to make them more scientific and appropriate, ultimately achieving a win-win situation for environmental protection and economic development. The nonlinear interaction between societal response and policy optimization also reflects the nonlinear impact of environmental regulation on the transition to high-quality development. Based on the above theoretical analysis, Hypothesis 2 is proposed:

1. H2: Environmental regulation has a nonlinear impact on high-quality development.

2.2.3 Heterogeneity.

In severely polluted areas, where environmental issues are acute, the government and businesses have an increased urgency for technological innovation. Stringent environmental regulations encourage companies to increase their investment in research and development of environmental protection technologies and clean production processes, thereby achieving technological breakthroughs and industrial upgrading. Compared to areas with lighter pollution, regions with heavy pollution are more likely to generate a concentrated effect of technological innovation in the face of environmental regulation. This effect not only improves environmental quality but also enhances the quality and sustainability of economic growth. Therefore, the role of environmental regulation in promoting technological innovation in heavily polluted areas is more pronounced, which in turn promotes their high-quality development.

From the perspective of resource reallocation, heavily polluted areas often accumulate a plethora of inefficient and highly polluting enterprises. These businesses face significant compliance costs under stringent environmental regulations, which may lead to their shutdown or relocation. While this process can have a negative impact on economic growth in the short term, it is beneficial in the long run as it helps optimize resource allocation, phase out outdated production capacities, and foster the development of high-efficiency, low-pollution businesses. The economic structural adjustments and technological upgrades brought about by resource reallocation make the achievement of high-quality development in heavily polluted areas more pronounced under the impetus of environmental regulation.

From the point of societal and policy responses, in regions with severe pollution, there is heightened public concern for environmental issues and increased governmental pressure for environmental protection. This has driven the introduction and enforcement of stricter and more effective environmental regulation policies. Liu [53] believes that as China faces increasingly severe environmental issues, the public’s awareness and demand for environmental protection are also continuously growing; the high level of public concern about environmental issues significantly promotes the green development of cities; with the increased public attention to the environment, government financial intervention has also been strengthened, which further promotes the development of cities towards a greener and more sustainable direction. Governments implement more rigorous environmental measures and incentive policies in heavily polluted areas, such as environmental tax incentives and green subsidies, to encourage businesses to undertake environmental renovations and technological upgrades. The combined effect of these policy responses and societal pressures enables regions with significant pollution to achieve high-quality economic development more rapidly and markedly under environmental regulation. Based on the aforementioned theoretical analysis, Hypothesis 3 is proposed:

1. H3: The impact of environmental regulation on high-quality economic development varies across regions with different levels of pollution, with a more pronounced effect in heavily polluted areas.

3.1.1 Model setting and variable description.

This paper primarily investigates the impact of environmental regulation on the high-quality economic development of the Yellow River Basin, employing panel data regression methods for empirical testing. The level of high-quality development in the Yellow River Basin serves as the dependent variable in the model, while the environmental regulation indicators constitute the core independent variables. Additionally, other factors that affect high-quality development are introduced in the form of control variables. To minimize the adverse effects of heteroscedasticity and multicollinearity, each variable is log-transformed following existing literature. The specific baseline model is constructed as shown in Eq (1): (1)

In the Eq (1), i represents the province (i = 1,2,⋯,9), t represents the year (t = 2011,2012, …., 2022), Hd_it denotes the level of high−quality development for each province in the Yellow River Basin for each year, Er_it denotes the intensity of environmental regulation for each province in the Yellow River Basin for each year, Cons_it represents the control variables, and ε_it is the stochastic disturbance term.

1. High-quality Economic Development (Hd_it). There is no unified standard in academia for measuring the high-quality development of the economy. In the early stages, some scholars used a single Total Factor Productivity (TFP) indicator to measure the high-quality growth of specific regions, which could not fully reflect the connotations of high-quality development and could not meet the demands for quality. This paper, referring to existing research [54] and in line with China’s new development concept strategy—namely innovation, coordination, green, openness, and sharing—along with the evaluation indicators in "Made in China 2025", constructs a high-quality development evaluation index system that includes 29 basic indicators, as shown in Table 1.
2. Environmental Regulation Intensity (Er). The academic community’s various methods for measuring environmental regulation can be summarized as single-indicator measurement methods and multidimensional comprehensive methods. Both methods have their characteristics and applicability: the single-indicator measurement method is relatively convenient in data collection, especially showing its convenience when assessing the impact of environmental regulation on specific enterprises or industries, but relying on a single indicator may lead to an imprecise assessment of the strength of environmental regulation. For instance, Low and Yeats [55] proposed in 1992 to assess the intensity of environmental regulation based on the number of regulations issued by the government. Cole and Elliott [56] in 2003 used the emission density of different pollutants as a measurement criterion. In their 2003 study, Aiken and Pasurka [57] took the ratio of operational costs for industry pollution control to industrial output as an indicator of measurement. In contrast, the multidimensional comprehensive method integrates multiple indicators, thereby improving the accuracy of the assessment. Given that the research subjects of this paper are provincial-level samples, it is more appropriate to choose the multidimensional comprehensive method for analysis. At the same time, for the convenience of the study, drawing on existing research [58, 59], this research uses the reciprocal of the pollution comprehensive index as the standard for measuring environmental regulation, with the specific formulas as shown in Eq (2) and Eq (3). The level of this indicator reflects the degree of government effort in pollution control; the higher the index, the greater the control effort, and vice versa, indicating insufficient effort.
3. Control Variables. The process of high-quality economic development is also influenced by factors such as the economic development level of each province, human capital, the degree of marketization, and the urbanization rate. Therefore, the control variables respectively select per capita regional gross domestic product (Pgp) to measure the level of economic development, average number of students enrolled in colleges and universities per 100,000 population (Huc), the proportion of actual foreign investment in the current year’s industrial fixed assets to measure the degree of marketization (Dom), and the proportion of urban population (Urb) to measure the level of urbanization.

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Table 1. Comprehensive index for high-quality economic development in the Yellow River Basin.

https://doi.org/10.1371/journal.pone.0312816.t001

(2)

(3)

In Eqs (2) and (3), e_ipt refers to the total emissions of a specific pollutant (Pth), y_it refers to the value added of industrial activity, and Er_it is the indicator used to measure the intensity of environmental regulation in each province.

The Environmental Kuznets Curve suggests that as economies and living standards grow, the demand for a healthy environment can exceed that for economic growth. China’s strategy for ecological civilization has made ecological construction a key performance metric for officials, driving a desire for high-quality development and stronger environmental regulations. This can lead to endogeneity issues in the relationship between environmental regulation and economic development, conflicting with OLS estimation assumptions. To address endogeneity and multicollinearity, this paper uses an instrumental variable least squares regression model, following established research methods, to ensure more consistent and reliable results.

(4)

(5)

In the model, IV_it represents the instrumental variable, and e_it is the stochastic disturbance term, with all other variables being the same as in Eq (1). Since rainfall (Rf) is significantly negatively correlated with the pollution levels of each province—meaning that areas with higher rainfall tend to have lower pollution levels and are more inclined to adopt less stringent environmental regulations—rainfall is often used as an instrumental variable, drawing from existing research [60]. Rainfall is clearly exogenous to high-quality economic development and affects it only through environmental regulation. Rainfall data is collected from the China Meteorological Administration’s website.

3.1.2 Heterogeneity of pollution levels in the Yellow River Basin.

To examine the differences in the impact of environmental regulation on high-quality economic development under the condition of regional pollution heterogeneity, this study draws on existing research and employs the Entropy-Weighted TOPSIS method to construct the pollution index for the Yellow River Basin as shown in Table 2. It uses this index to categorize the pollution heterogeneity of the nine sample provinces in the Yellow River Basin. The study selects the average emission levels of the three types of industrial waste (including industrial wastewater, SO₂, and dust emissions, with weights of 1/3 each) from each province to calculate the pollution index of the Yellow River Basin. To eliminate the influence of regional industrial scale, the emission amount of pollutants per unit of industrial value added is used to measure the average value of pollution levels for the sample years. This index reflects the amount of pollutant emissions per unit of industrial value added, making it a negative indicator—higher values indicate greater levels of regional pollution.

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Table 2. Indicators for heterogeneity division of pollution areas in the Yellow River Basin.

https://doi.org/10.1371/journal.pone.0312816.t002

3.1.3 Data sources.

The data used in this paper mainly comes from the China Economic Network Statistical Database, China Statistical Yearbook, China Energy Statistical Yearbook, China High-tech Industry Statistical Yearbook, China Environmental Statistical Yearbook, National Economic and Social Development Statistical Bulletin, and National Educational Development Statistical Bulletin. This study takes the data from 2011 to 2021 of the 9 provinces in the Yellow River Basin of China as the research sample. Missing data is supplemented by linear interpolation, and value data is deflated to the base period of 2011. Descriptive statistics of the sample are shown in Table 3.

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Table 3. Descriptive statistics of concerning variables.

https://doi.org/10.1371/journal.pone.0312816.t003

it can be observed that during the sample period, the mean value of the high-quality economic development index (lnHd) for the sample provinces is -0.8247, with a standard deviation of -2.3177, a minimum value of -1.2823, and a maximum value of -0.3905. This indicates that there is a significant difference in the high-quality economic development index among the provinces during the research period. The explanatory variable, environmental regulation (lnEr), also exhibits similar characteristics, which meet the statistical basis for subsequent model regression.

3.2.1 High-quality development evaluation.

Based on the calculated data, the high-quality development assessment results of the nine provinces in the Yellow River Basin from 2011 to 2021 show a positive growth trend. The high-quality development index of all provinces has been increasing year by year, indicating that the development quality in economic, social, and environmental aspects of these areas has been continuously improving. Ningxia, Gansu, Qinghai, Shanxi, and Inner Mongolia have shown relatively stable growth trends, with the index values increasing slightly year by year, demonstrating a steady pace on the path of high-quality development in these regions. Especially in Inner Mongolia, there was a significant improvement from 0.2968 in 2011 to 0.3375 in 2021, reflecting that the region may have achieved important accomplishments in environmental governance and industrial upgrading.

Shaanxi and Sichuan have slightly faster growth rates, with Sichuan in particular seeing its high-quality development index rise from 0.3556 in 2011 to 0.3882 in 2021, which may imply that the province has made significant progress in innovation-driven development and industrial structure optimization. Henan and Shandong have a significantly higher high-quality development index than other provinces, especially Shandong, which has continued to lead with an index increase from 0.6360 in 2011 to 0.6767 in 2021, possibly reflecting its outstanding performance in economic structural adjustment, technological innovation, and the construction of ecological civilization.

Overall, these data reflect that the provinces in the Yellow River Basin have achieved positive effects in pursuing high-quality development. Each region, based on its own characteristics and advantages, has promoted the optimization and upgrading of economic structure and the enhancement of sustainable development capabilities. In the future, these provinces should continue to deepen reform and opening up, strengthen regional collaboration, and promote green transformation to achieve more balanced, inclusive, and sustainable high-quality development.

3.2.2 Pollution level assessment.

Based on the assessment results of pollution levels, an average was taken of the pollution levels for the sample years in the Yellow River Basin. The heterogeneity division results of pollution levels for the 9 sample provinces are as follows: (1) Light Pollution: Qinghai Province, Ningxia Hui Autonomous Region, Gansu Province. (2) Moderate Pollution: Shandong Province, Shaanxi Province, Sichuan Province. (3) Severe Pollution: Shanxi Province, Inner Mongolia Autonomous Region, Henan Province.

3.2.3 Benchmark regression analysis.

Prior to the panel data regression analysis, a statistical test was conducted to select the appropriate model. The Hausman test statistic revealed that the fixed effects model is more effective than the random effects model. Consequently, the fixed effects model was selected to estimate the impact of environmental regulation on the high-quality economic development in the Yellow River Basin. The regression results are presented in Table 4. Result (1) displays the results for the entire sample of the Yellow River Basin. Results (2), (3), and (4) correspond to the regression outcomes for the light pollution group, moderate pollution group, and severe pollution group, respectively. The findings indicate that environmental regulation exerts a positive influence on the process of high-quality economic development in the Yellow River Basin. The estimated coefficient for environmental regulation is 0.1072, which is statistically significant at the 1% level. This suggests that a 1% increase in the stringency of environmental regulation can effectively lead to a 0.1072% increase in high-quality economic development. This finding aligns with the Porter Hypothesis scenario. Enterprises, in an effort to comply with regulations and reduce environmental costs, adopt advanced environmental technologies, thereby positively influencing the transition to high-quality development, thus confirming H1.

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Table 4. Baseline regression results.

https://doi.org/10.1371/journal.pone.0312816.t004

Table 4’s last three columns present the regression results for the Yellow River Basin, grouped by province according to the heterogeneity of pollution levels. In terms of sign, the relationship between environmental regulation and high-quality economic development shows a positive correlation across all groups. However, the regression coefficient is only significant at the 1% level for the high pollution group, indicating that for provinces with severe pollution levels, a 1% increase in the intensity of environmental regulation can lead to a 0.1182% increase in the level of high-quality economic development. Quantitatively, the impact of environmental regulation becomes more pronounced as the level of pollution increases. These regression results are in line with observed reality, where the relationship between environmental regulation and the level of high-quality development varies depending on the degree of pollution. These findings also validate H1. Lanoie et al. [61], using the manufacturing sector in Quebec as an example, demonstrated that sectors with more severe pollution and more intense international competition are more in line with the Porter Hypothesis, which is similar to H1 in this paper.

The regression results for the control variables are interpreted as follows:

Firstly, the regression results for both the overall sample and the subgroup samples indicate that the level of economic development has a significant positive impact on the high-quality economic development of the Yellow River Basin. The regression results for the light pollution group are greater than those for the heavy pollution group, while the moderate pollution group is not significant. Relatively speaking, the economic development level of the light pollution group has a greater driving effect on high-quality economic development. Talent is the key to driving high-quality development. In areas with severe pollution, to mitigate environmental impacts, businesses and governments tend to invest in clean technologies, which increases the demand for professionals with specialized skills and enhances the importance of human capital. Governments may strengthen environmental regulations and improve emission standards in response to high pollution levels, encouraging enterprises to adopt advanced technologies and management strategies, which relies on talents who have received quality education. Public awareness of environmental issues is deeper in areas with severe pollution, which may influence their educational and career choices, inclining them to select fields that contribute to environmental protection and sustainable development, thereby affecting the distribution of human capital.

Secondly, the regression results for both the overall sample and the subgroup samples indicate that the level of human capital has a significant positive impact on the high-quality economic development of the Yellow River Basin. In terms of the significance of the regression coefficients, only the impact of human capital level on high-quality economic development in the light pollution group is not significant. In terms of the magnitude of the regression coefficients, the impact coefficient of human capital level on high-quality economic development in the moderate pollution group is lower than that in the heavy pollution group. The possible reason is that the heavy pollution group pays more attention to human capital investment, and the investment effect has a significant promoting effect on high-quality economic development. As the level of pollution improves, the dependence of high-quality economic development on human capital will decrease.

Thirdly, the regression results for both the overall sample and the subgroup samples indicate that the degree of marketization has a significant positive impact on the high-quality economic development of the Yellow River Basin. In terms of the significance of the regression coefficients, similar to human capital, only the light pollution group is not significant. On the one hand, in areas with moderate and heavy pollution, the improvement of market openness may promote technological innovation and industrial upgrading. The opening of the market introduces international competition, forcing local enterprises to improve production efficiency and product quality, while absorbing and adopting advanced technology and management experience, thereby promoting the transformation of the economy towards high-quality development. On the other hand, market opening may be accompanied by stricter environmental regulation, prompting enterprises to adopt more environmentally friendly production methods and reduce pollution emissions. This is in line with the concept of green development and helps to achieve a win-win situation for economic development and environmental protection.

Fourthly, unlike the impact of other control variables, the urbanization rate generally has a negative impact on the high-quality economic development of the Yellow River Basin. With the acceleration of urbanization, a large influx of people into cities may exert significant pressure on urban resources and the environment, such as water shortages, increased energy consumption, air pollution, and other issues, which may restrict the high-quality development of the economy. In addition, the regression results also show that the promotion of the urbanization rate in provinces with lower pollution intensity has a greater negative impact on high-quality economic development. The possible reason is that provinces with lower pollution levels may have more economic structures dominated by the service industry or high-tech industries, which have higher requirements for environmental quality. If environmental protection is neglected during the urbanization process, it may affect the competitiveness of these industries.

3.2.4 Endogeneity treatment in the model.

Table 5 presents the instrumental variable regression results, where result (1) represents the benchmark regression results, and results (2) and (4) are the two-stage least squares regression results. The regression analysis of the first stage shows that the calculation result using rainfall as an instrumental variable is statistically significant at the 5% significance level, effectively ruling out the potential problem of weak correlation that might exist with the instrumental variable. In the first stage of estimation, the coefficient for rainfall is -0.1033. This statistical indicator reveals a significant negative correlation between rainfall and environmental regulation. This implies that in areas with higher rainfall, due to the ease with which pollutants can naturally disperse and decompose, these areas may implement more lenient environmental regulatory measures. Conversely, in areas with less rainfall, due to the lack of natural purification effects, there may be a tendency to adopt stricter environmental regulatory measures to compensate for the deficiency in natural purification capabilities, which aligns with theoretical expectations. The second-stage estimation results show that the estimated coefficient for environmental regulation is 0.0741 and passes the significance test at the 5% level. Compared with the benchmark regression model, the significance of the coefficient for environmental regulation in the second-stage regression is consistent, although the value of the coefficient is lower, it does not change the robustness of the basic regression results. This eliminates the impact of endogeneity, and the model setting and the choice of instrumental variables meet the requirements.

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Table 5. Regression results based on instrumental variables.

https://doi.org/10.1371/journal.pone.0312816.t005

Table 6 shows the results of the re-regression using instrumental variables based on pollution heterogeneity. Results (1), (4), and (7) are the benchmark regression results, while Results (2), (5), and (8) represent the first-stage regression results. Results (3), (6), and (9) are the second-stage regression results. The first-stage regression results indicate that the heterogeneity characteristics of pollution levels do not affect the negative impact of the instrumental variable on environmental regulation, which is in line with the actual situation. Compared with the benchmark regression, the second-stage regression results show that the positive impact of environmental regulation in the light pollution group changes from insignificant to significant, and the coefficient decreases from 0.2056 to 0.0894. This suggests that there is an endogeneity issue in the light pollution group samples, and the positive effect of environmental regulation may have been overestimated, which is consistent with the actual situation. In areas with light pollution, there is usually better ecological resilience, a greater number of high-tech enterprises and research institutions, and stronger technological innovation capabilities, making the high-quality development model more likely to gain social and governmental recognition and support. Furthermore, the significance of the regression results for the moderate and severe pollution groups does not change, and the change in the coefficients is small, indicating that the basic regression results are robust and free from endogeneity influence.

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Table 6. Instrumental variable estimation regression results considering heterogeneity of pollution levels.

https://doi.org/10.1371/journal.pone.0312816.t006

3.2.5 Non-linear regression analysis.

The relationship between environmental regulation and economic development is often complex and not simply linear. Different regions with varying pollution levels may respond differently to environmental regulations. Areas with light pollution may more readily adapt to and leverage environmental regulations to promote high-quality development, while areas with heavy pollution may face greater pressure to transform. By examining non-linear effects, we can more accurately assess the effectiveness of environmental regulation policies, providing a basis for policy adjustment and optimization. To this end, the quadratic term of environmental regulation is added to Eq (1). The Hausman test result indicates that the fixed effects model is optimal. Results (1)-(4) in Table 7 show the regression results for the overall Yellow River Basin sample group, light pollution group, moderate pollution group, and heavy pollution group, respectively.

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Table 7. Regression results of nonlinear effects under heterogeneous pollution levels.

https://doi.org/10.1371/journal.pone.0312816.t007

The model estimation results show that the estimated coefficients for the quadratic term of environmental regulation are positive and significant for all sample groups, thus validating Hypothesis 2. There is a considerable variation in the estimated coefficients of the environmental regulation quadratic term across the sample groups, indicating that the non-linear impact of environmental regulation on high-quality economic development also varies significantly depending on the level of pollution in different regions. In areas with higher levels of pollution, the positive effect of environmental regulation on the process of high-quality economic development has the largest coefficient.

3.2.6 Threshold effect test.

The previous section has demonstrated that the impact of high-quality economic development on environmental regulation has non-linear characteristics. To further analyze the specific thresholds of non-linear effects, as well as differences between provinces with heterogeneous pollution levels, a panel threshold model is constructed by introducing environmental regulation as a threshold variable. Before estimating the threshold model, a panel threshold existence test was first conducted based on the method of Hansen (1999) [62]. After using the "bootstrap" method with repeated sampling 3000 times, the results indicate that the impact of environmental regulation on high-quality economic development for the overall sample group, light pollution group, moderate pollution group, and heavy pollution group all significantly passed the single threshold test and did not pass the double threshold and triple threshold tests, as shown in Table 8. On this basis, a single threshold model with environmental regulation as the threshold variable was set up, as shown in Eq (6), and the regression results were obtained as shown in Table 9.

(6)

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Table 8. Environmental regulation panel threshold model test.

https://doi.org/10.1371/journal.pone.0312816.t008

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Table 9. Regression results of panel threshold model.

https://doi.org/10.1371/journal.pone.0312816.t009

In the Eq 6, contrs_it represents the set of control variables, I(*) is the indicator function, τ₁ is the threshold value, and ε_it is the stochastic disturbance term.

The threshold regression outcomes are displayed in Table 9. For the comprehensive sample of the Yellow River Basin, environmental regulation is categorized into two ranges based on the threshold value: lnER ≤ 0.2334 and lnER > 0.2334. At lnER ≤ 0.2334, the coefficient for environmental regulation is 0.1543, significantly passing the test at the 1% level, signifying a positive and promoting impact of environmental regulation on high-quality economic development within the initial threshold range. Beyond lnER > 0.261, the coefficient escalates to 0.5109, which is notably significant at the 10% level, suggesting that heightened environmental regulation intensity is beneficial in fostering high-quality economic development.

For the light pollution sample of the Yellow River Basin, environmental regulation is divided into two intervals based on the threshold value: lnER ≤ 0.3993 and lnER > 0.3993. When lnER ≤ 0.3993, the effect of environmental regulation on promoting high-quality economic development is negative, though not significant. This implies that the light pollution group is not suited to an excessively low intensity of environmental regulation, as it could hinder high-quality economic development to some extent. When lnER > 0.3993, the estimated coefficient for environmental regulation turns positive from negative, and, while not passing the significance test, it still indicates that once the intensity of environmental regulation reaches a certain level, it can have a positive effect on promoting high-quality economic development in the light pollution group.

For the moderately polluted sample of the Yellow River Basin, environmental regulation is divided into two intervals based on the threshold value: lnER ≤ 0.3077 and lnER > 0.3077. When lnER ≤ 0.3077, the effect of environmental regulation on promoting high-quality economic development is negative and significant at the 5% level. This indicates that for the moderately polluted group, an excessively low intensity of environmental regulation will hinder high-quality economic development to a certain extent. When lnER >0.3077, the estimated coefficient for environmental regulation turns positive from negative and passes the significance test at the 5% level. This suggests that once the intensity of environmental regulation reaches a certain threshold, it can positively promote high-quality economic development in the moderately polluted group.

For the severely polluted sample group of the Yellow River Basin, environmental regulation is divided into two intervals based on the threshold value: lnER ≤ 0.1362 and lnER > 0.1362. When lnER ≤ 0.1362, the effect of environmental regulation on high-quality economic development shows a significant negative effect; when lnER > 0.1362, the impact shifts to a significant positive effect, with a substantial increase in the coefficient. This indicates that a higher intensity of environmental regulation can more effectively promote high-quality economic development in severely polluted areas.

Integrating the aforementioned regression results, it can also be observed that for samples with lower pollution levels, a stronger environmental regulation is superior to a lower intensity of environmental regulation. Among the three subgroups, as the intensity of environmental regulation increases, there is a point at which the impact of environmental regulation on high-quality economic development shifts from negative to positive, dividing its effect on high-quality economic development into two parts, akin to a "V-shaped" relationship. Comparatively, in the regression results of the overall Yellow River Basin sample, the impact of environmental regulation on high-quality economic development has always been positive, and as the intensity of environmental regulation increases, its influence on high-quality economic development also becomes greater.

Integrating the analysis of existing literature, there is a significant variation in the non-linear relationship between environmental regulation and high-quality development. The reason may primarily be due to the differences in the selection of research samples and time periods. The theoretical analysis of H1 suggests that only an "appropriate" level of environmental regulation can promote high-quality economic development, while the promotion of high-quality economic development by environmental regulations that are too strong or too weak needs to be proven through testing.