Temporal–Spatial Evolution, Influencing Factors, and Driving Mechanisms of Environmental Regulation Performance Disparities: Evidence from China

Abstract

Countries worldwide are facing ecological crises, and improving global ecological quality through environmental regulations is key to achieving sustainable development. As the largest developing country, China’s response to diverse ecological conflicts in different regions through environmental regulations considerably impacts global ecological conservation. Based on 2008–2020 panel data from 30 provinces, this study measures the spatial distribution patterns and time-series evolutionary trends in environmental regulation performance differences using the entropy weight method and the Theil index model. Quadratic assignment procedure and qualitative comparative analysis models were combined to explore the determinants and driving mechanisms of differences in environmental regulation performance. The results show that the prevalent uneven development of environmental regulations and disparities in regulation performance mainly originate from inter-regional differences. Political factors affecting performance differences include decision value and decision decentralization; administrative factors are command-based regulations; and rule-of-law factors include project, financial, and subject regulation. Furthermore, these three factor types can interact to form eight high environmental regulation performance paths and seven non-high environmental regulation paths, which together constitute the driving mechanism for performance differences. This study enriches the theoretical understanding of environmental regulation performance differences from the public management perspective, which can guide environmental regulation policy optimization and promote high-level, balanced environmental development.

1. Introduction

The Industrial Revolution has led to science and technological development and human progress; however, the corresponding high-energy-consuming and polluting mode of production have also resulted in many environmental problems, such as ecological degradation, biodiversity loss, increased desertification, and frequent extreme weather events. The conflict between ecological crises and economic and social development has become an important issue worldwide. Many countries are working to curb the rate of environmental degradation and improve the environment both domestically and through global agreements [1]. Because of the different approaches to environmental regulation and diverse regional ecological problems, the effectiveness of environmental regulation varies considerably from country to country and from region to region, ultimately leading to a serious imbalance in global ecological development. Indeed, countries and regions with less effective regulations restrict high-quality, sustainable ecological environmental development overall.

As the world’s largest developing country, China faces various environmental problems and inconsistencies in environmental regulation performance. China’s ecological protection began in the 1970s, and environmental regulation reform was carried out in parallel with that of the market economy system, fully reflecting the value of coordinated environmental–economic development. The 20th Party Congress clarified that the harmonious coexistence of humans and nature is an important factor of Chinese-style modernization. China’s environmental regulation reform measures have effectively improved ecological conflicts, but polarized environmental regulation performance levels and uneven ecological development are very prominent. This is because when environmental policies are implemented at the local level, the implementation process is influenced by different, context-dependent regulatory elements, meaning environmental regulations show significant regional differences in promoting economic and social development [2]. Furthermore, there are distinct requirements for regulatory approaches and policy standards for each distinct stage of economic and social progress [3], which leads to differences in the degree and effectiveness of policy feedback. Thus, an environmental regulatory reform or policy can improve environmental performance in some areas abut have a weaker effect in others or even a negative effect due to policy pressure.

Differences in environmental regulatory performance are not a problem unique to China; other countries and regions worldwide are also suffering from uneven environmental development. If this environmental regulation performance disparity cannot be solved, the ecological shortcomings of regions with lagging environmental regulation performance will not only affect the local ecological performance level, but they will also become a key obstacle to the high-quality environmental development of the country and the wider region. Therefore, elucidating the mechanism of environmental regulation performance differences and lessening uneven performance are necessary to comprehensively improve the overall ecological development of the country and region, which is important to promote the modernization of ecological civilization. Several studies explore and explain the mechanism of environmental regulation performance differences from economic, industrial, and technological perspectives. Hou et al. found that the level of economic development and industrial structure had a significant impact on the environmental regulatory performance in the Yangtze River Delta region [4]. Liu’s research on urban eco-efficiency found that import and export trade and information level had a significant impact on the environment [5]. Zhu confirmed that technological advancement had a significant impact on ecological performance [6]. In addition, scholars have tried to explore the solution path to improve the unbalanced environmental development phenomenon from different perspectives. We need to optimize the energy structure and industrial structure [7] and change the economic development mode from high pollution and high energy consumption to high output and low-pollution mode [8]. More importantly, it is necessary to prioritize the development of green technological innovation for future sustainable development [9] and to consider the green life cycle in the product-development process [10]. It can be seen that environmental regulation performance differences, as an important topic in the field of ecological protection, have received extensive attention and accumulated certain research results. However, the existing literature focuses on economic and technological approaches, but few studies have been conducted from the perspective of government regulation and public management. As a result, government departments, as the core body of environmental regulation, lack a direct theoretical basis and targeted policy programs in the process of improving the regulation and governance of the problem of unbalanced environmental development. Therefore, to improve the phenomenon of unbalanced ecological development in different countries and regions, it is urgent to analyze the mechanism of environmental regulation performance differences from the perspectives of government regulation and public policy to provide government departments with a reliable theoretical basis and professional and feasible solutions to improve the problem of environmental imbalance.

This study explores the internal logic and operating mechanism of environmental regulation performance differences from the government regulation approach. Based on clarifying the spatial distribution and temporal evolution of environmental regulatory performance differences, the influence of political, administrative, and rule-of-law measures taken by government departments on performance differences and the combined driving effect of multiple factors are discussed. This study contributes to the literature in three ways. First, compared with previous studies that measured the performance differences of environmental regulations from economic and ecological dimensions, this study focuses on the performance differences of environmental regulations in ecological, economic, social, cultural, and political fields from a more comprehensive perspective. Second, compared with previous studies that focused on the impact of economic and innovation factors on performance differences, this study examines the impact of political, administrative, and rule-of-law factors from the perspective of government regulation and public governance. Third, the existing literature focuses on the study of driving factors but ignores the driving mechanism. This study focuses on the driving mechanism of environmental regulatory performance differences formed by multiple variables through synergistic interaction.

The remainder of this paper is organized as follows. In Section 2, we review the literature on environmental regulation performance differences. Section 3 discusses the methods and data employed in this study, and Section 4 presents the results. Section 5 discusses the results, and Section 6 contains conclusions and policy recommendations.

2. Literature Review

2.1. Environmental Regulation Performance

Environmental regulation performance is the sum of the outcomes, efficiency, and effectiveness of the environmental regulation process. The established literature focuses on the performance generated by environmental regulations in the economic, environmental, and innovation domains, that is, eco-economic, eco-environmental, and eco-innovation performance.

The economic performance of environmental regulations focuses on regulations related to economic growth and enterprise development. Environmental regulation can facilitate high-quality economic development [11]; however, overly stringent regulations may inhibit economic growth because of the “cost compliance” effect between environmental regulations and economic development [12]. In addition, environmental regulations substantially impact the green development of industrial [13] and manufacturing firms [14]. In this regard, environmental regulations indirectly affect the economy by influencing firm development.

The ecological performance of environmental regulations involves pollution reduction, and relevant research has centered carbon emissions. Several studies have shown that environmental regulations can effectively improve efficiency and reduce carbon emissions’ intensity [15]. Specifically, environmental regulations reduce carbon emissions through the mediating role of green technological innovation [16]. In addition to carbon emissions, environmental regulations have ameliorative effects on industrial solid waste [17] and haze pollution [18]. Further, different types of environmental regulations have distinct effects on environmental pollution, with an inverted “U” curve between fee-based environmental regulations and environmental pollution and a “U” curve between investment-based environmental regulations and environmental pollution [19]. Nevertheless, some empirical studies have found that governmental environmental regulations do not significantly contribute to reduced environmental pollutants [20].

Environmental regulations’ innovation performance entails how environmental regulations promote green technological innovation and the technological upgrading of green industries, which varies depending on the regulation type. Voluntary [21] and informal environmental regulations significantly contribute to green technological innovation, whereas formal environmental regulations have a significant inhibitory effect [22]. In addition, it has been shown that environmental regulations’ effect on innovation performance is related to the degree of environmental decentralization, and regulations’ green innovation utility gradually decreases when environmental decentralization increases [23].

In summary, the economic, ecological, and innovation performance of environmental regulations has been empirically explored. However, as society and culture progress and citizens demand a higher quality of life, environmental regulation in the social, cultural, and political spheres has also received increasing attention. Compared with the existing literature, this study compares and analyzes regulations’ performance from the ecological, economic, social, cultural, and political perspectives to present a more comprehensive picture of the benefits of environmental regulations.

2.2. Disparities in Environmental Regulation Performance

Differences in environmental regulation performance reflect the uneven development of the ecological environment. Improving and reducing the performance disparities in less-developed regions is key to comprehensively improving overall environmental regulation performance and promoting a high level of ecological development. Accordingly, relevant studies have focused on the spatial distribution and time-series evolution of differences in environmental regulation performance.

Environmental regulation policies’ effects vary depending on region, and differences in environmental regulation performance is common. Studies on atmospheric environmental efficiency show that efficiency in China’s southeast coastal region is higher than in the northwest inland region [24]; furthermore, atmospheric environmental performance in the north is significantly higher than in the south [25]. Compared to eastern China, the atmospheric environmental performance of central cities has improved significantly but lacks momentum in the later stages, and the performance of western cities lags behind, owing to outdated technology and insufficient regulation [26]. Studies on agricultural environmental efficiency in Middle Eastern/North African countries have shown it is much higher in Jordan and Israel than in Egypt and Morocco [27], and a study on urban environmental governance efficiency in the Yangtze River Basin found extremely significant differences between cities [28]. Studies on total-factor eco-efficiency have similarly confirmed the problem of uneven ecological development [29].

Regarding trends, environmental regulation performance disparities do not show signs of substantially contracting; in some areas, they are expanding, and uneven environmental development has not been effectively mitigated. For example, Zhao et al. examined the manufacturing industry’s green transformation efficiency in the Yangtze River economic zone and found that the efficiency in the eastern region is higher than that in the central and western regions; this regional difference shows a trend of initial convergence and subsequent expansion [30]. In an empirical study of the environmental performance of urban agglomerations in the Beijing–Tianjin–Hebei region from 2003 to 2015, Xue et al. found fluctuating changes in performance differences between regions but a trend in expansion in the last two years [31]. Moreover, a study of the power industry’s environmental efficiency showed no significant contraction trend between regions [32]. In contrast, Wang and Wang examined the environmental regulation and tourism eco-efficiency of urban agglomerations in the Yangtze River Delta region using the entropy power method and the super-efficiency slacks-based measure (SBM) model. They showed that regional differences in environmental governance showed a continuous contraction trend, whereas regional differences in tourism eco-efficiency first expanded and then contracted [33].

In summary, disparities in environmental regulation performance remain widespread, and uneven ecological development have not been effectively addressed. Studies have analyzed environmental regulation performance differences, mainly from the perspectives of pollutant emissions and green production efficiency. In the current study, while we measure the economic and ecological performance differences of environmental regulations, we further analyze differences in the social, cultural, and political domains and provide a more comprehensive analysis of the spatial distribution and temporal evolution of environmental regulation performance differences.

2.3. Driving Mechanisms of Differences in Environmental Regulation Performance

Regarding more causes of environmental regulation performance differences, previous studies have mainly analyzed economic and industrial development, energy consumption, technological innovation, and management institutions. First, regarding economic factors, gross domestic product (GDP) significantly contributes to environmental efficiency [34], while the relationship between economic development factors and PM2.5 environmental efficiency resembles an inverted “U” shape [8]. In terms of industrial factors, the clustering of high-tech industries is conducive to enhancing green innovation performance [35]. In addition, industrial structure has a significant effect on environmental efficiency [34], especially the proportion of secondary industries that inhibit environmental efficiency [8].

Second, energy consumption can directly affect environmental performance because an increase in energy consumption significantly contributes to carbon emission levels and thus to environmental pollution [36]. By improving energy structure [37] and efficiency [7], polluters can be encouraged to emit less pollution, thereby improving ecological performance.

Third, technological innovation is an important driving force in improving environmental performance. The Fourth Industrial Revolution centers on technology, and the emergence of a large number of high- and fin-tech industries has significantly impacted environmental efficiency [38], demonstrating the close relationship between technological innovation and environmental performance. Studies on the development of high-tech industries in China have shown that the introduction of advanced technology makes a much greater contribution to environmental efficiency than foreign investment [39]. While industry can improve technological innovation and environmental performance, it can also further transform environmental performance into financial performance [40] to improve the financial and economic status of enterprises.

Fourth, democratic, leadership, and instrumental environmental regulation systems can cause differences in environmental regulation performance. Some studies have found that countries with increased citizen e-participation and lower levels of corruption are more likely to achieve good environmental performance [41], revealing the importance of a transparent and democratic institutional environment. Top managers’ green commitment also affects environmental performance through human resource management mechanisms [42], implying a profound impact of leaders’ awareness of environmental performance. Studies on heterogeneous regulatory policies have found that combined environmental regulatory policy instruments are more conducive to improving industrial total factor productivity than monolithic policy instruments [43]. In addition, corporate social responsibility can both directly contribute to corporate environmental performance and act as a mediating variable to enhance the impact of environmental resource conservation on performance [44], revealing the important influence of stakeholders besides government departments.

In summary, the literature has explored the causes of performance differences in environmental regulations using different research approaches. However, more studies have been conducted on the drivers of these differences than on the driving mechanisms, and studies on the effects of political, administrative, and rule-of-law factors on the governmental environmental regulation process are lacking. In practice, environmental regulation performance is the result of the synergistic effect of different factors rather than a single-factor effect. Therefore, this study analyzes the combined effect of political, administrative, and rule-of-law factors on environmental regulation performance from a group perspective to systematically reveal the driving mechanisms of performance differences.

3. Methods and Data

3.1. Entropy Method

To scientifically measure the performance of environmental regulation, it is necessary to obtain the performance result of environmental regulation through weighted calculation based on empowering the constructed performance indicator system of environmental regulation. The main methods of index weighting include the analytic hierarchy process (AHP), experts grading, and entropy methods. Compared with other methods, the advantage of the entropy method is that it can give full play to data utility and reduce the influence of subjective value preference. The disadvantage is that even if researchers have relevant prior knowledge, they cannot intervene in the data-processing process, and the analysis results may be distorted. As a highly representative objective evaluation method, the entropy method is widely used in performance evaluation in the field of social and economic research. Therefore, we used the entropy method to assign weights to environmental regulation performance indicators to improve the objectivity and credibility of the analysis results. The basic principle of the entropy method is to determine the objective weights of indicators based on their discreteness. The greater the discreteness of an indicator sample, the greater the weight assigned to the indicator [45].

3.2. Theil Model

The Theil index is an inequality coefficient based on information theory, which was originally used by economist Henri Theil in his economic study of income disparity [46]. With the continuous development of research methods, the Theil index has been widely used in the study of economic, environmental, and energy differences. The index can not only accurately and quantitatively describe the disparity between the entire study sample, but also further decompose the disparity structure to explore the disparity between and within regions, as well as their respective contributions to overall disparity. Therefore, we use the Theil index to further analyze the internal structure of environmental regulation performance differences and the main sources of these differences. In the specific application of the analysis, a smaller Theil index value indicates less unevenness in the data distribution and, conversely, a greater degree of variation between samples.

3.3. Quadratic Assignment Procedure Method

In general, quantitative analysis techniques require variables to be independent of each other; therefore, multiple collinearity tests are needed to ensure results’ validity. The dependent variable in this study is the difference in environmental regulation performance, that is, the magnitude of the difference in performance values between two samples, which is essentially a “relationship” between samples rather than an independent property of a single sample. If conventional regression tests are used, the variance and standard deviation of the parameter estimates increase, resulting in a loss of statistical significance in the correlation and regression analyses. A quadratic assignment procedure (QAP) is used to solve the problem of variable interdependence [47]. Because the QAP can effectively solve multicollinearity, the estimation results are more robust than those obtained using parametric methods [48]. Specifically, the QAP compares the values of each element in two (or more) square matrices [49]. It calculates the correlation coefficients between the two matrices by comparing the corresponding lattice values of each square matrix, simultaneously performing a nonparametric test on the coefficients to determine the relationship between the two matrices [50]. Accordingly, this paper uses QAP analysis to analyze the influence of political, administrative, and rule-of-law factors on the differences in environmental regulation performance from the perspective of “relational” variables to reveal the deeper root causes behind these differences.

3.4. Qualitative Comparative Analysis

Qualitative comparative analysis (QCA) is a comparative case-oriented research approach and a collection of techniques based on set theory and Boolean algebra that aims to combine the strengths of qualitative and quantitative research methods [51]. In contrast to traditional econometric analysis, which analyzes the net effect of a single variable from an “atomic perspective”, QCA is good at analyzing the group effect of multiple variables simultaneously from a holistic perspective [52]. QCA can analyze both small and large samples at the aggregate level [53]. Moreover, QCA can analyze the configuration of outcome generation and outcome disappearance to address causality asymmetry [54] and thus explain problematic causality more scientifically. Therefore, using QCA to explain environmental regulation performance differences can facilitate the analysis of the multiple histories that generate high and low performance levels, which not only helps to better explain the logic of environmental regulation performance differences, but also provides a more contextualized policy basis for environmental regulation policymaking. However, the 15 conditional variables in the four dimensions of political decision making, administrative implementation, rule-of-law regulation, and non-environmental regulation need to be prescreened before QCA analysis. This is because only the condition variables that can significantly affect the outcome variables in the combined path analysis must be included in QCA [55] and because a large sample of panel data tends to make group analysis too cumbersome, resulting in insufficient theoretical abstraction and generalization of the differences in environmental regulation performance. Thus, a fixed-effects regression model (FEM) is used to exclude the antecedents that significantly affect environmental regulation performance from the 15 variables, which are then included in the QCA model to ensure the results have good explanatory power while improving the generalization and simplicity of the explanatory theory.

3.5. Indicator and Data Source Description

The measurement system for environmental regulation performance differences includes 15 indicators in five dimensions. The indicators for measuring ecological environmental performance differences are chemical oxygen demand emissions from wastewater, SO2 emissions, and general industrial solid waste generation. Those for economic differences are coal consumption per unit of GDP, per capita gross regional product, and the ratio of tertiary to secondary industries. Those for social differences are parkland area per capita, urban sewage treatment rate, and number of sanitation technicians per 1000 people. Those for cultural differences are the number of patents granted, number of public buses per 10,000 people, and greening rate of higher-education campuses. Finally, those for political differences are the number of environmental emergencies, National People’s Congress (NPC) proposals, and Chinese People’s Political Consultative Conference (CPPCC) proposals.

Environmental regulation is also a performance-generation process. This study examines the effects of political, administrative, and rule-of-law factors on the differences in environmental regulation performance. The political factors of environmental regulation include decision-making value (decision makers’ preference for ecological governance), innovation consciousness (decision makers’ emphasis on green technological innovation), and decentralization (environmental departments’ voice in joint decision making), which are measured by investment in industrial pollution control per unit of output value, internal R&D expenditure, and the proportion of energy conservation and environmental protection expenditure in the general public budget. The administrative factors are resource input factors (including environmental infrastructure), green technology innovation, and environmental regulation tool-selection factors (including traditional command-based tools, incentive-based tools, and voluntary tools), which are measured by the scale of investment in environmental infrastructure construction, full-time equivalence of R&D personnel, number of laws and regulations promulgated in the year, resource taxes, and the number of information disclosure requests. The rule-of-law factors include project regulation (environmental impact evaluation of construction projects), fund regulation (budget performance evaluation of environmental funds), and subject regulation (environmental protection inspections of environmental department officials), which are measured by the improvement in environmental regulation impact evaluation, budget performance evaluation, and environmental protection inspector systems. The control variable indicators include the proportion of the urban population to the total regional population, marketization index, and foreign investment level.

The study sample covers 30 provinces in China during the period 2008–2020. The original data were mainly obtained from the China Statistical Yearbook, China Environmental Statistical Yearbook, China Environmental Yearbook, China Education Statistical Yearbook, China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, and provincial statistical yearbooks. Other data were obtained from central and local government portals, ecological/environmental department websites, and statistical department websites. To fill in a small amount of missing data, we selected the linear interpolation method or the mean value method according to the nature of the indicators and the actual situation of the missing data to ensure data integrity and analysis accuracy.

4. Results

4.1. Temporal and Spatial Characteristics of Environmental Regulatory Performance Differences

The results show that significant differences exist in environmental regulation performance and that the differences vary in terms of spatial and temporal distribution characteristics.

In general, Figure 1 shows the spatial distribution characteristics of performance differences. There is a decreasing pattern from east to west and from coast to inland shown. The performance level in the eastern region is higher than that in the central, western, and northeastern regions. This is closely related to the economic foundation and geographical advantages of the eastern region, which can obtain more environmental regulatory resources, such as capital, technology, and policies, to achieve a higher level of environmental performance. As shown in Figure 2, the trends in environmental regulation performance change were similar in the various regions; however, there were obvious performance differences between regions and no significant narrowing trend. This reflects the lack of environmental regulation resources in the central and western regions for a long time, resulting in the imbalance in environmental development between regions. As seen in Figure 1, the average value of environmental regulation performance in the eastern region exceeds the national average, reaching approximately twice the average performance value of the other regions, which is significantly higher. The average performance of the western region is slightly higher than that of the northeastern region; however, the performance levels of the two regions are relatively similar. The central region was at the same level as that of the western and northeastern regions between 2008 and 2012, but in 2013, its performance began to rise steadily, and in 2020, it was already slightly higher than the national level. Comparatively, the performance of the western and northeastern regions rises extremely slowly, and the performance gap between the central region and the western and northeastern regions increases. Overall, there is no significant trend in narrowing inter-regional performance differences.

In terms of eco-environmental performance, Figure 3 shows that the performance of the southern region is higher than that of the northern region. In particular, the problem of ecological degradation is more serious in the northeast than in the eastern and southern regions. This is because the northern region is the primary dense area of China’s heavy-industry distribution, and the phenomenon of environmental pollution and ecological damage is more serious here. In particular, the northeast region, as the earliest industrial region in China, has the most serious problems of resource consumption and ecological degradation. The temporal evolutionary trend in performance level changes is similar across regions, except for the northeastern region, where the performance level fluctuates more frequently. In Figure 4, the eastern region has the highest performance level, while the central region has a mean performance value slightly lower than the eastern region and higher than the national average, but the performance difference between the two regions increased between 2019 and 2020. Both the western and northeastern regions have average performance values below the national average, and there is a gap between their performance and that of the eastern and central regions. Further, the level of performance differences between the western and northeastern regions is relatively high owing to the northeastern region’s fluctuating performance level. Overall, regional differences in ecological environmental performance tend to widen and become unstable.

Regarding eco-economic performance (Figure 5), the performance differences show the spatial characteristics of high coastal and low inland, and the overall level of economic performance is lower than the performance levels of other dimensions. This is because the high-tech industry and new energy industry in the eastern region are relatively advanced, which is conducive to promoting energy conservation, emission reduction, and industrial upgrading, to achieve a higher level of green economic development. Figure 6 shows that the eastern region still has the highest performance level, whereas the central region, which is high in overall and eco-environmental performance dimensions, has the lowest performance level—approximately one-third of the eastern region’s average. The performance of the western region is slightly higher than that of the central region, and the gap between the two has decreased since 2016, with performance levels tending to be the same. It is worth mentioning that the northeastern region ranks the lowest in overall and eco-environmental performance but second in the eco-economic dimension, slightly below the national average level. This indicates that the northeastern region emphasizes scientific and technological innovation, transformation of polluting production technologies, and green technology development. However, the mean performance value in the northeastern region decreased from 2019 to 2020, narrowing the gap between the central and western regions and opening a larger gap with the eastern region. However, there is no significant contraction in ecological economic performance.

For eco-social performance (Figure 7), the spatial distribution characteristics show that the east is higher than the west and the north is higher than the south. This shows that the environmental regulations in the eastern and northern regions are more focused on providing a green and healthy living environment for social citizens. Further, there is an obvious degradation of eco-social performance in the east in recent years. With the continuous increase in population in the eastern region, the supply capacity of environmental functional departments in green living services has not been improved correspondingly, leading to this decline. Figure 8 shows that the eastern region has the highest temporal evolution. However, there were downward fluctuations in this region from 2012 to 2013 and a significant downward trend after 2017, which approached the national average. The performance of the central, western, and northeastern regions fluctuates similarly, and the average performance values were relatively close between 2008 and 2016. However, from 2017 to 2020, the western and central regions’ performance showed a slow upward trend, and that of the western region was higher than that of the central region. The mean performance of both is close to that of the eastern region year by year, while that of the northeastern region shows a fluctuating downward trend until 2020, when it rose sharply. Thus, according to the trend changes in performance difference, each region’s mean performance levels demonstrate an obvious contraction trend.

Concerning eco-cultural performance level (Figure 9), the spatial distribution characteristics show that the eastern region is higher than the western region and the southern region is higher than the northern region. This shows that environmental departments in the eastern and southern regions are better at cultivating national awareness of ecological environmental protection through media and educational institutions, as well as by forming the values of green travel, green consumption, and green production. Furthermore, the eco-cultural performance of the western region has deteriorated significantly in recent years. The key problem lies in the lack of publicity and education of green culture in the western region, which makes it difficult to form a common ecological culture concept in the local area. Figure 10 shows that the eastern region is still much higher than that of the other regions, with the average value of performance reaching four to five times that of the other regions. Not only do the performance levels of the central, western, and northeastern regions lag far behind those of the eastern regions, but there is also a large gap between them and the national average. The three regions were very close from 2008 to 2013, but after 2013, the growth rate of the central and western regions was slightly higher than that of the northeast, and this gap has widened since. Further, the performance gap between the central and western regions began to increase slowly in 2018. In addition, the central, western, and northeastern region performance levels increased extremely slowly, whereas mean performance in the eastern region rapidly increased in 2018 and maintained consistent slow growth, resulting in an overall widening gap between the regions after 2018.

In terms of eco-political performance, Figure 11 shows that spatial characteristics are high in the east and low in the west. This is because government environmental regulation in the eastern region is more open and democratic, and it is easier for society and citizens to participate in and supervise the environmental regulation process. Furthermore, the eco-political performance of the northeast and northwest regions has lagged behind in recent years. This reflects that the environmental regulation systems in the northeast and northwest regions are relatively close, and the social participation mechanism is not perfect. Figure 12 shows that the central region is the best performing, while the eastern region’s performance is slightly lower than the central region but higher than the national average. Average performance in the western region is slightly higher than that in the northeastern region, but both are lower than the national average. The fluctuation trends in performance levels in each region are similar, but the magnitude and frequency of fluctuations vary, leading to dissimilar characteristics of performance differences at distinct stages. Owing to the large fluctuations in the northeastern region’s performance level in the early stage, overall performance differences were high from 2008 to 2011. From 2011 to 2012, they contracted sharply, and each region’s performance levels were very close to each other. From 2013 to 2020, the fluctuation trend in each region was very similar; however, fluctuation magnitudes differ, resulting in growing disparities in performance.

4.2. Decomposition of Environmental Regulation Performance Differences

This study further explores the structure of the differences in environmental regulation performance and the sources of these differences using the Theil index. The results of this study (

Table 1. Decomposition results of the differences in the performance of integrated environmental regulation.

Year	Total	Within-Group Differences						Between-Group Differences
		D-V	C-R (%)	Contribution Rate of Different Regions (%)				D-V	C-R (%)	Inter-Regional Contribution Rate (%)
				E	C	W	Ne			E-C	E-W	E-Ne	C-W	C-Ne	W-Ne
2008	0.108	0.052	47.97	9.12	12.71	8.07	18.07	0.056	52.03	7.76	4.86	9.99	6.60	13.97	8.85
2009	0.108	0.052	47.85	9.11	12.64	7.97	18.13	0.056	52.15	7.81	4.88	9.86	6.60	14.05	8.95
2010	0.091	0.045	49.79	9.40	13.29	8.35	18.75	0.046	50.21	7.42	4.55	9.72	6.40	13.55	8.57
2011	0.097	0.040	41.10	7.76	10.90	6.87	15.57	0.057	58.90	8.70	5.47	11.44	7.47	15.82	10.00
2012	0.095	0.050	52.81	9.91	13.90	9.18	19.82	0.045	47.19	7.02	4.20	9.18	6.00	12.73	8.06
2013	0.091	0.035	38.23	7.19	10.16	6.42	14.46	0.056	61.77	9.18	5.72	11.97	7.83	16.58	10.49
2014	0.094	0.041	43.83	8.29	11.64	7.35	16.55	0.053	56.17	8.30	5.16	10.92	7.13	15.11	9.55
2015	0.095	0.042	43.93	8.29	11.68	7.38	16.58	0.053	56.07	8.27	5.16	10.90	7.12	15.09	9.53
2016	0.082	0.037	45.06	8.45	12.02	7.58	17.01	0.045	54.94	8.02	5.03	10.63	7.04	14.85	9.37
2017	0.084	0.033	39.41	7.36	10.48	6.61	14.96	0.051	60.59	8.78	5.60	11.78	7.77	16.37	10.29
2018	0.068	0.033	48.75	9.12	12.99	8.18	18.46	0.035	51.25	7.38	4.58	9.93	6.62	13.96	8.78
2019	0.066	0.027	40.75	7.59	10.95	6.78	15.43	0.039	59.25	8.48	5.27	11.57	7.62	16.19	10.12
2020	0.079	0.027	34.48	6.41	9.29	5.71	13.07	0.052	65.52	9.28	6.04	12.75	8.53	17.84	11.08

Note: D-V, C-R indicate difference values and contribution rate, respectively; E, C, W, and Ne indicate eastern, central, western, and northeastern, respectively; E-C, E-W, E-Ne, C-W, C-Ne, and W-Ne indicate eastern–central, eastern–western, eastern–northeastern, central–western, central–northeastern, and western–northeastern, respectively.

) show that the Theil index of China’s comprehensive environmental regulation performance decreased from 0.1083 in 2008 to 0.0658 in 2019, and then increased to 0.0789 in 2020. This indicates a decreasing trend between 2008 and 2020 for overall environmental regulation performance differences in China, whereas between 2019 and 2020, the differences showed an increasing trend. From the Theil index decomposition index, the contribution of within-group variation in environmental regulation performance to overall variation is smaller than that of between-group variation. The contribution of within-group differences decreased from 47.97% in 2008 to 34.48% in 2020, while that of between-group differences increased from 52.03% in 2008 to 65.52% in 2020; thus, the contribution of between-group differences is much higher than that of within-group differences. This implies that, compared to within-group differences, between-group differences are the main factor leading to differences in environmental regulation performance, and the effect of between-group differences on total differences increases over time, while that of within-group differences gradually decreases. In terms of within-group differences, the northeastern region has the highest contribution, the central region has the second highest, the western region has the lowest, and the eastern region has a slightly higher contribution than the western region but a lower contribution than the central region. Therefore, regarding within-group differences, the northeastern and central regions are the main influencing factors for total differences in environmental regulation performance, whereas the eastern and western regions contribute less. From a time-series perspective, the trends in each region’s contribution to differences in environmental regulation performance are generally similar; however, the intensity of the influence on total performance differences decreases in all regions. In terms of between-group differences, the largest between-group differences and the highest contribution rates are found between the central and northeastern regions, followed by the eastern–northeastern, western–northeastern, eastern–central, central–western, and eastern–western regions, with decreasing between-group differences and contribution rates to differences in this order. This indicates that within the category of intergroup differences, those of the central–northeastern, eastern–northeastern, and western–northeastern regions have a greater effect on total differences in performance, whereas those of the eastern–central, central–western, and eastern–western regions contribute less to total difference. The contribution of intergroup differences between regions shows a similar trend, and the degree of influence on performance differences increases slowly.

The results of the Theil index analysis of the environmental regulation performance disparities in various dimensions also differ. Those of overall ecological environmental performance differences and its decomposition (Figure 13) showed a fluctuating downward trend between 2008 and 2020, with performance differences widening each year from 2008 to 2014 and an inverted “N” shape (decline–rise–decline) between 2015 and 2020. The difference decomposition indicates that the intra-group differences in ecological environmental performance contribute more to overall differences than intergroup differences. From an economic perspective, the overall variance in ecological economic performance in China shows a rise–decline–rise “N”-shaped fluctuation, and the difference decomposition index demonstrates that the contribution rate of within-group differences to overall differences in ecological economic performance from 2008 to 2019 was smaller than that of between-group differences, but the within-group differences’ contribution rate to overall differences in 2020 was larger than the between-group difference. From a social perspective, the frequency of fluctuation in ecological social performance differences is high, and the contraction of performance differences was more obvious in earlier years, but the contraction trend was weaker in more recent years. The structural analysis of differences shows that the contribution of within-group differences to overall differences is greater than that of between-group differences. The overall trend in ecological cultural performance differences is shrinking, but the Theil index almost did not change from 2019 to 2020, implying that ecological cultural performance differences have leveled off in the past two years and that the contribution of within-group differences in ecological cultural performance to overall differences is smaller than that of between-group differences. The Theil index analysis of ecological political performance differences shows an increasing trend that is very unstable, and the difference decomposition index indicates the contribution rate of within-group differences to overall differences is larger than that of between-group differences.

4.3. Factors Influencing Environmental Regulation Performance Differences

To examine the factors influencing environmental regulation performance differences, the QAP method was applied to test the effects of political, administrative, and rule-of-law factors on these differences overall, as well as their five specific dimensions.

The QAP correlation results are presented in

Table 2. Quadratic assignment procedure correlation analysis of differences in environmental regulation performance.

Variable		Integral	Eco- Environmental	Eco- Economic	Eco- Social	Eco- Cultural	Eco- Political
Political factors	Value of decision making	−0.355 ***	−0.465 ***	−0.280 ***	−0.037	−0.297 ***	−0.212 ***
	Sense of innovation	0.179 ***	0.060	0.226 ***	0.170 ***	0.184 ***	−0.017
	Decentralization of decision making	−0.164 ***	−0.320 ***	−0.082 **	0.044	−0.164 ***	−0.150 ***
Administrative factors	Infrastructure inputs	0.039	0.142 ***	−0.016	−0.047	0.020	0.073 *
	Green technology inputs	0.126 ***	0.050	0.151 ***	0.106 **	0.128 ***	0.006
	Command-based regulation	0.124 **	0.097 **	0.083 *	0.017	0.130 **	0.073 *
	Incentive-based regulation	−0.156 ***	−0.309 ***	−0.131 ***	0.053	−0.065*	−0.169 ***
	Voluntary regulation	0.044	−0.044	0.048	0.082*	0.055	−0.019
Rule-of-law factors	Project supervision	0.085 *	−0.137 ***	0.390 ***	0.253 ***	0.066 *	−0.293 ***
	Financial supervision	0.226 ***	0.021	0.350 ***	0.280 ***	0.140 ***	0.002
	Subject supervision	0.332 ***	0.035	0.424 ***	0.330 ***	0.293 ***	0.002
Non-environmental regulatory factors	Urbanization	0.188 ***	0.002	0.207 ***	0.191 ***	0.166 ***	0.037
	Marketization	0.156 ***	0.005	0.148 ***	0.121 ***	0.143 ***	0.076 *
	Openness to the outside	−0.007	−0.033	−0.075 *	−0.030	0.017	0.060
	Regional population density	0.069 *	−0.034	0.058	0.070*	0.050	0.047

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

. The explanatory variables that influence differences in environmental regulation performance include political factors (e.g., decision value, innovation awareness, and decentralization of decision making), administrative factors (e.g., green technology input, command-based regulation, and incentive-based regulation), and rule-of-law factors (e.g., financial and subject regulation). Specifically, the factors influencing different types of environmental regulation performance vary. First, those influencing eco-environmental performance differences include decision value and decision decentralization in the political domain; infrastructure input, command-based regulation, and incentive-based regulation in the administrative domain; and project supervision in the rule-of-law domain. Second, the political factors influencing eco-economic performance differences include decision value, innovation consciousness, and decision decentralization; administrative factors include infrastructure input, green technology input, and incentive-based regulation; and rule-of-law factors include project supervision, funding supervision, and subject supervision. Third, the political factors influencing eco-social performance differences include innovation consciousness; administrative factors include green technology input; and rule-of-law factors include project, fund, and subject supervision. Fourth, in terms of eco-cultural performance, political factors including decision-making values, innovation awareness, and decentralization of decision making; administrative factors including green technology input and command-based regulation; and rule-of-law factors including financial supervision and subject supervision are important causes of performance differences. Fifth, regarding eco-political performance, decision value and decision decentralization factors in the political domain, incentive-based regulation factors in the administrative domain, and project-supervision factors in the rule-of-law domain are closely related to performance differences.

The QAP regression results for each urban agglomeration are shown in

Table 3. Quadratic assignment procedure regression analysis of differences in environmental regulation performance.

Variable		Integral	Eco- Environmental	Eco- Economic	Eco- Social	Eco- Cultural	Eco- Political
Political factors	Value of decision making	−0.333 **	−0.409 *	−0.284 **	−0.046	−0.304 **	−0.149 **
	Sense of innovation	0.120	0.155	0.137	0.203	0.291 **	−0.237 *
	Decentralization of decision making	−0.093 **	−0.240 ***	−0.099 **	0.040	−0.120 **	−0.018
Administrative factors	Infrastructure inputs	0.051	0.065 *	0.010	−0.016	0.028	0.072 *
	Green technology inputs	−0.112	−0.044	−0.026	−0.156	−0.229 *	0.048
	Command-based regulation	0.090 **	0.021	0.019	0.019	0.070 *	0.118 ***
	Incentive-based regulation	−0.004	−0.238 ***	−0.002	0.156 ***	0.055	−0.080 **
	Voluntary regulation	0.010	−0.039	0.019	0.040	0.019	−0.021
Rule-of-law factors	Project supervision	−0.465 ***	−0.332 ***	0.215	−0.087	−0.411 ***	−0.857 ***
	Financial supervision	−0.329 ***	−0.247 **	−0.242 **	0.010	−0.548 ***	0.089
	Subject supervision	0.953 ***	0.451 ***	0.424 ***	0.360 ***	1.048 ***	0.630 ***
Non-environmental regulatory factors	Urbanization	0.027	−0.016	0.030	0.088	0.045	−0.042
	Marketization	0.077	−0.038	−0.058	−0.026	0.059	0.220 ***
	Openness to the outside	0.001	−0.025	0.008	0.011	0.029	−0.027
	Regional population density	0.004	−0.014	0.015	−0.028	−0.061	0.064
Adj. R2		0.375	0.417	0.294	0.145	0.357	0.335

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

. The driving factors for the differences in environmental regulation performance include decision value, decision decentralization, command-based regulation, and project, financial, and subject supervision, all of which are significant at the 5% level. Among them, subject and project supervision have the strongest impact on environmental regulation performance differences, with standardized regression coefficients of 0.953 and −0.465, respectively, indicating that controlling for differences in subject and project supervision is a priority for promoting environmental regulation performance. The drivers of differences in eco-environmental performance include decision decentralization, incentive-based regulation, and project, financial, and subject supervision. In terms of standardized regression coefficient magnitude, subject supervision and decision value are the main drivers of differences. Drivers of eco-economic performance differences include decision value, decision decentralization, financial supervision, and subject regulation, with subject regulation having the strongest impact on eco-economic performance differences. The drivers of eco-social performance differences include incentive-based regulation and subject supervision, both of which are significant at the 1% level. The drivers of eco-cultural performance differences include decision value, innovation consciousness, decision decentralization, and project, funding, and subject supervision. Controlling for subject, funding, and project-supervision differences is important for promoting the balanced development of eco-cultural performance. Finally, the driving factors of eco-political performance differences include decision value, command-based regulation, incentive-based regulation, and project and subject supervision, the latter two being the dominant factors of differences.

Overall, the influencing factors and intensity of different types of environmental regulation performance differences vary, with the drivers of overall, eco-environmental, eco-cultural, and eco-political performance differences distributed across all segments of political decision making, administrative implementation, and rule-of-law regulation. The drivers of eco-economic performance differences are mainly concentrated in political decision making and rule-of-law regulation, while those of eco-social differences are mainly found in administrative implementation and rule-of-law regulation. Therefore, different types of environmental regulatory performance differences should be treated distinctly, and targeted policy measures should be taken depending on the different types of performance differences. It is worth emphasizing that subject-supervision differences have significant effects on both overall environmental regulation performance differences and those in each dimension, and the intensity of the effects is high, indicating that changes in subject-supervision differences cause changes in overall environmental regulation performance differences and those in each dimension. Therefore, subject regulation should be considered the primary policy measure to mitigate differences in environmental regulation performance and promote balanced development.

4.4. Driving Mechanisms of Environmental Regulation Performance Differences

To explore the driving mechanisms created by the synergy of multiple factors, a combination of the fixed-effects model (FEM) and QCA is used to examine the combined driving effects of political, administrative, and rule-of-law elements on the differences in environmental regulation performance.

We must screen the key influencing factors of environmental regulation performance before QCA analysis because only the condition variables that can significantly affect the outcome variables are necessary to be included in the QCA analysis. If all variables are included in the QCA model without strict screening, the analysis results will be complicated and redundant, which is inconsistent with the simplicity and scientific principles of the analysis conclusions. Therefore, Stata 14.0 is used to analyze and screen the factors with significant effects on environmental regulation performance before QCA to improve the conciseness and generalizability of the results. First, the augmented Dickey–Fuller (ADF) test is used to test the smoothness of the data series, and the panel data are transformed into a smooth series after second-order differencing for the presence of unit root variables. Second, the Hausman test is applied to select the regression model, which results in p < 0.05; thus, the original hypothesis of random effects is strongly rejected in favor of fixed effects. Finally, the F-test result is p = 0.0000, so the original hypothesis of mixed regression is rejected in favor of fixed effects. Therefore, the FEM was selected for regression analysis of the panel data.

According to

Table 4. Results of regression analysis of factors influencing environmental regulation performance.

Variable	Coef.	Std. Err.	t	p > |t|	95% Conf. Interval
Value of decision making	−0.000036	0.0002736	−0.13	0.895	−0.0005741	0.0005022
Sense of innovation	3.97 × 10−9 *	2.22 × 10−9	1.79	0.075	−3.97 × 10−10	8.34 × 10−9
Decentralization of decision making	0.011316 ***	0.0032977	3.43	0.001	0.0048299	0.0178021
Infrastructure input	3.77 × 10−6	0.000067	0.06	0.955	−0.0001279	0.0001355
Green technology inputs	−2.11 × 10−7 *	1.10 × 10−7	−1.91	0.056	−4.28 × 10−7	5.81 × 10−9
Command-based regulation	0.0002421	0.0007939	0.3	0.761	−0.0013194	0.0018035
Incentive-based regulation	4.55 × 10−8	4.98 × 10−8	0.91	0.362	−5.24 × 10−8	1.43 × 10−7
Voluntary regulation	0.0024201 ***	0.0006877	3.52	p < 0.001	0.0010676	0.0037727
Project supervision	0.0251753 ***	0.0079573	3.16	0.002	0.0095244	0.0408261
Financial supervision	−0.0286102 ***	0.0065112	−4.39	p < 0.001	−0.0414169	−0.0158035
Subject supervision	−0.0027016	0.0056018	−0.48	0.63	−0.0137196	0.0083164
Urbanization	0.0055518 ***	0.0012937	4.29	p < 0.001	0.0030074	0.0080962
Marketization	0.0046557	0.0042731	1.09	0.277	−0.003749	0.0130604
Openness to the outside	0.4377848 **	0.1984643	2.21	0.028	0.0474326	0.8281369
Regional population density	−0.0002059 ***	0.0000666	−3.09	0.002	−0.000337	−0.0000749
_cons	0.0349871	0.0740736	0.47	0.637	−0.1107056	0.1806798

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

, the political factors that significantly affect differences in environmental regulation performance include awareness of innovation and decision-making decentralization; administrative factors include green technology input and voluntary regulatory instruments; rule-of-law factors include project and financial regulation; and non-environmental regulation factors include urbanization level, degree of openness to the outside world, and regional population density. As the significance level (10%) of the effect of creation awareness and green technology input on environmental regulation performance is weak, they are not included as driving mechanisms. Finally, the factors of decision-making decentralization, voluntary regulatory instruments, project regulation, financial regulation, urbanization level, degree of openness to the outside world, and regional population density are included in the QCA model as antecedents for group analysis of environmental regulation performance.

This study uses fsQCA3.0 software to analyze the combined driving effects of political, administrative, and rule-of-law factors. According to

Table 5. Analysis of the groups that produce high and non-high environmental regulation performance.

Conditional Variables	High Environmental Regulation Performance Group								Non-High Environmental Regulation Performance Group
	H1	H2	H3	H4	H5	H6	H7	H8	NH1	NH2	NH3	NH4	NH5	NH6	NH7
Decentralization of decision making		□		○		■	■	○		●	○	●	□	●	●
Voluntary regulation	●		●		○		□	●		■		■	□	■	■
Project supervision		●	●	●	●	□	●	●	■	□	■		■	■	■
Financial supervision	○	○		○	○	■	○	○			○	○	●	□
Urbanization	●	○	●			●	○	○	□	□			■		○
Openness to the outside			○	○	○	○	●	●	■	■	■	■		○	○
Regional population density	●	●	●	●	●	●			■		■	□	■	■	■
Consistency	0.9 55	0.9 45	0.9 60	0.9 74	0.9 86	0.9 32	0.9 40	0.9 46	0.9 12	0.9 52	0.9 50	0.9 28	0.9 41	0.9 41	0.9 33
Raw coverage	0.4 15	0.1 89	0.3 30	0.1 58	0.1 76	0.3 41	0.1 26	0.1 43	0.4 77	0.3 25	0.1 87	0.3 24	0.2 05	0.2 51	0.1 91
Unique coverage	0.0 27	0.0 06	0.0 35	0.0 10	0.0 01	0.0 63	0.0 04	0.0 03	0.1 03	0.0 10	0.0 01	0.1 56	0.0 20	0.0 12	0.0 01
Solution consistency	0.923								0.900
Solution coverage	0.663								0.703

Note: ● indicates that the core condition is present; ○ indicates that the edge condition is present; ■indicates that the core condition is not present; □ indicates that the edge condition is not present; and a space indicates that the condition variable is both present and missing.

, the groupings that produce high environmental regulation performance include eight paths, whereas the groupings that produce low environmental regulation performance include one path.

4.4.1. Driving Mechanism of High Environmental Regulation Performance

According to the attributes and composition of the antecedent conditions contained in the driving paths, the paths that generate high environmental regulation performance can be classified into five types: rule-of-law-oriented unitary-driving paths; administrative/rule-of-law-oriented binary-driving paths; political/rule-of-law-oriented binary-driving paths; political/administrative/rule-of-law-oriented ternary-driving paths; and non-environmental-regulation-oriented driving paths. These can provide diverse paths to improve environmental regulation performance for regions with different ecological environmental foundations and governance levels.

The rule-of-law-oriented unitary-driving paths include H2 and H7. The H2 path illustrates that even when environmental officials have insufficient decentralization and a weak voice and initiative in regulatory decision making, high environmental regulatory performance can still be achieved by improving environmental project regulation, strengthening ecological funding regulation, accelerating urbanization, and controlling regional population density. The H7 path suggests that even with a low degree of decentralization in the environmental sector and insufficient use of voluntary environmental regulations, a combination of improved project and financial regulations, higher levels of urbanization, and greater openness to the outside world can contribute to high levels of environmental regulation performance. In conclusion, the rule-of-law-oriented unidirectional driving path considers regulation and supervision the core driving forces and enhances environmental regulation performance by continuously strengthening the standardization and effectiveness of environmental regulations.

Second, the administrative/rule-of-law-oriented binary-driving paths include H1, H3, and H5. The H1 path suggests that high environmental regulatory performance is possible with or without sufficient political support, provided voluntary environmental regulation tools are fully applied and environmental funds are strictly regulated while maintaining high levels of urbanization and moderate population density. The H3 path suggests high performance is possible via voluntary environmental regulation tools and strict implementation of project-regulation systems, while promoting openness to the outside world, urbanization, and controlling population density. Finally, the H5 path requires simultaneously implementing voluntary environmental regulation, environmental project and financial regulation, opening up to the outside world, and controlling population density. In short, the administrative/rule-of-law-oriented binary path relies on synergy between administrative enforcement and rule-of-law regulatory elements to achieve high environmental regulatory performance.

Third, the political/rule-of-law-oriented binary-driving path corresponds to H4, which shows that even if the level of administrative enforcement elements (e.g., voluntary environmental regulation tools) is relatively low, the synergy of decision-making decentralization, project and financial regulation, openness to the outside world, and population control can reduce negative effects on performance. This driving path relies mainly on the interaction between political decision making and rule-of-law regulatory elements to achieve a high level of environmental regulation performance.

Fourth, the political/administrative/rule-of-law-oriented ternary-driving path includes H8. According to this group, even if the level of other environmental governance factors is poor, a high degree of decision-making decentralization, sufficient voluntary environmental regulation, perfect regulation and supervision, a high level of urbanization, and openness to the outside world can effectively reduce impediments to achieve a high level of environmental regulation performance. This path emphasizes the synergistic effects of political decision making, administrative implementation, and rule-of-law regulation elements.

Fifth, the non-environmental-regulation-oriented driving path is H6, which shows that even though environmental authorities have insufficient power sharing, a weak voice and initiative in regulatory decision making, and low levels of project and financial supervision, they can still achieve a high level of environmental regulation performance by accelerating urbanization, encouraging openness to the outside world, and controlling regional population density. This path does not rely on the political, administrative, and rule-of-law elements of environmental regulation but rather achieves high levels of performance through the synergistic effects of regulatory elements in the economic and social spheres.

4.4.2. Non-High Environmental Regulation Performance-Driven Mechanisms

According to the antecedent condition attributes and composition of each driving path, the paths generating non-high environmental regulation performance can be divided into four condition groupings: rule-of-law-oriented unitary-driving paths; administrative-oriented unitary-driving paths; administrative/rule-of-law-oriented binary-driving paths; and political/administrative/rule-of-law-oriented ternary-driving paths. These groupings present the diverse environmental regulation performance dilemmas faced by different regions and reveal the specific reasons for lagging environmental regulation performance. This can help lagging regions reflect on ecological governance issues and optimize environmental regulation policies to avoid reform detours and potential risks in the process of catching up with environmental regulation performance.

First, the rule-of-law-oriented unitary-driving paths include groups NH1 and NH3. Group NH1 illustrates that outdated project supervision mechanisms, lower levels of urbanization, more closed market environments, and lower population density combine to impede the improvement in environmental regulation performance. Group NH3 shows that even under conditions of high decentralization and financial regulation, the multiple impediments of inadequate project supervision, low openness to the outside world, and lack of population resources still inhibit environmental regulation performance. In this type of path, inadequate rule-of-law regulation is the main driver of suboptimal environmental regulation performance.

Second, the administrative-oriented unitary-driving path is NH4. The results show that even if high political support and strict environmental supervision can be obtained, environmental regulation performance is inhibited as long as local social organizations and citizens lack the willingness to participate in ecological governance or poor participation channels lead to insufficient voluntary regulation, insufficient regional openness, and a relatively sparse population. On this path, inefficient administrative implementation of environmental regulations is the main driving cause of unsatisfactory environmental regulation performance.

Third, the administrative/rule-of-law-oriented binary-driving paths include the NH2, NH6, and NH7 groups. Group NH2 indicates that a combination of insufficient voluntary regulation, poor project supervision, low levels of urbanization, and insufficient openness to the outside world can inhibit environmental regulation performance. Group NH6 indicates that a combination of insufficient voluntary regulation, inadequate project and financial supervision, and a lack of population resources may result in a lower level of performance. Group NH7 suggests that lower levels of performance can occur when hindered by the absence of voluntary regulations, inadequate project-supervision mechanisms, or a lack of population resources. This type of path emphasizes that, if the creation of administrative implementation and rule-of-law regulation levels are neglected, it is difficult to achieve high levels of environmental regulation performance, regardless of how much political support is strengthened.

Fourth, the political/administrative/rule-of-law-oriented ternary-driving path corresponds to group NH5, which is the most complex group for non-high environmental regulation performance. The government regulation elements of political decision making, administrative implementation, and rule-of-law regulation together constitute the driving force of non-high performance. The findings suggest that even with a well-developed supervisory system for environmental funding and high efficiency of fund use, lower environmental regulation performance is highly likely if conditions such as less decentralized decision making, insufficient voluntary-type regulation, lax project supervision, lower urbanization, and sparse population size are present simultaneously. In this type of path, lower levels of political decision making, administrative enforcement, and rule-of-law regulatory elements together inhibit environmental regulatory performance.

5. Discussion

Compared to the existing literature, the findings from our study on environmental regulation performance differences and their driving mechanisms differ. First, most of the existing literature has concluded that the spatial distribution of environmental regulation performance differences is characterized by a decrease from east to west. Using multiple regional divisions, the current study finds that environmental regulation performance is lower in the northeast than in the west. This study further examines environmental regulation performance differences in five dimensions and finds that the western region has higher eco-environmental, eco-social, eco-cultural, and eco-political performance than the northeastern region, while the western region has higher eco-economic performance than the central region. These findings differ from the spatial characteristics of the performance differences, which decrease from east to west.

Second, no consistent trend in environmental regulation performance differences has been identified; most studies have concluded that performance differences tend to decrease, but others have found they tend to increase. This study finds that the overall performance differences show a fluctuating decreasing trend, which is consistent with mainstream research findings. However, further independent analysis of environmental regulation performance differences in five different dimensions revealed an increasing trend in eco-economic and eco-political differences.

Third, previous studies have confirmed the driving effects of economic, technological, and institutional factors on differences in environmental regulation performance, while few have explored those of the political, administrative, and rule-of-law actions of government departments from the public management perspective. In this study, QAP analysis confirmed that differences in political decision making, administrative implementation, and rule-of-law supervision by government departments in the environmental regulation process have significant effects on environmental regulation performance. In particular, subject-supervision differences in the rule-of-law regulation dimension are the most important core drivers of performance differences. Thus, this study enriches and extends the literature on environmental regulation performance from a public management perspective.

Finally, prior research on the causes of environmental regulation performance differences has focused on drivers instead of driving mechanisms. By examining the effects of the combination of drivers in the political, administrative, and rule-of-law dimensions, this study finds that different factors can be combined in diverse ways to form multiple driving paths that produce high and non-high environmental regulation performance outcomes, which together constitute the driving mechanism of performance differences.

The relevant results of influencing factors and driving mechanisms are important guidelines for solutions on how to solve environmental regulatory performance differences. The existing literature has mainly proposed solutions from the aspects of transforming the economic development model [8], optimizing the industrial structure [7], and promoting technological innovation [9]. These solutions are mainly economic regulations, but this study mainly proposes solutions from the perspective of environmental regulations. We found that the rule-of-law-supervision measures taken by government departments, especially the environmental-supervision system for environmental regulators, are the core factors affecting the differences in environmental regulation performance. Therefore, we believe that we should focus on strengthening the supervision of the main leaders of the environmental department and strengthening the environmental-supervision system to make managers pay attention to environmental protection work to effectively improve the scientificity of environmental regulation decision making, standardize the implementation order of environmental regulation, and reduce environmental corruption. In addition, we also found that the solution to environmental regulation performance differences cannot rely on a single action strategy, and the combined environmental regulation scheme has a better effect on promoting the balanced development of the environment. The combination of administration and rule of law in the application of environmental regulation should be emphasized, that is, the full use of voluntary environmental regulation tools and strict regulation of environmental financing, while maintaining a high level of urbanization and moderate population density, because this combination path has the best effect on environmental performance.

Despite the important results, this study has some limitations that should be addressed in future studies. Regarding scope, the study uses provincial data to examine the mechanisms of environmental regulation performance differences but does not sufficiently examine local-level data, such as cities and counties. This may mean that the study has insufficient explanatory power for environmental regulation performance differences at the grassroots level. There are prominent differences in environmental regulation at the local level in terms of performance outcomes and regulatory decision making, implementation, and supervision that are an important source of uneven ecological development nationwide. Therefore, collecting environmental data from different cities and counties can allow for a more accurate and comprehensive reflection of the current state of environmental regulation performance differences and enrich the research content and results. In terms of research methods, this study applies quantitative empirical analysis, but there are insufficient case studies on differences in environmental regulation performance. This may mean that the results cannot present a complete picture of environmental regulation practices nor explain the specific performance differences, problems, and mechanisms in local-level environmental governance. Combining a quantitative study with a single-case in-depth description or a multi-case tracking study would improve the analysis of the mechanism of environmental regulation performance differences and the reliability of the research conclusions.

6. Conclusions and Policy Recommendations

This study uses the entropy method and the Theil index to measure the spatial distribution and temporal evolution of environmental regulation performance differences. QAP and QCA are used to examine the drivers and mechanisms of these performance differences. Our main findings are as follows. First, environmental regulation performance is higher in the eastern coastal and central regions compared to the western and northeastern regions. The performance difference did not improve significantly from 2008 to 2020. Second, environmental regulation performance differences are mainly inter-regional, whereas the contribution of intraregional differences is relatively small. Specifically, inter-regional differences mainly originate from the east–northeast region, whereas the intraregional differences are from the northeast region. Third, political factors affecting the performance differences include decision-making value and decentralization; administrative factors include command-based regulation; and rule-of-law influences include project, financial, and subject-supervision factors. Fourth, in the practice of environmental regulation, political, administrative, and rule-of-law factors can interact in various combinations to form multiple driving paths that can produce different levels of performance.

This study explains the theoretical logic and generation mechanisms of environmental regulation performance differences from political, administrative, and rule-of-law perspectives; reveals the multiple regulation paths and systematic driving mechanisms; and enriches the theoretical study of environmental regulation performance differences from the perspective of public management, which has important implications for government environmental regulation reform and policy optimization. We propose the following policy recommendations based on our research findings. First, local government incentives should be reformed, and the weight of officials’ ecological appraisals should be increased to strengthen intrinsic incentives for environmental protection and motivate local governments to perform effectively in ecological governance. Second, inter-regional communication and cooperation in environmental regulation and ecological protection should be strengthened, especially to expand the scope of regional cooperation to promote the joint development of environmental regulations in different regions. Third, it is necessary to strengthen the environmental-regulation-supervision system and take note of the moderating role of subject supervision on the differences in environmental regulation performance. Fourth, it is of the utmost importance to make full use of regional environmental regulation resources, give full play to the synergistic effects of multiple environmental regulation elements, and explore individualized regional environmental regulation paths. Fifth, the improvement in environmental regulatory performance differences depends on the cooperation of environmental departments and other field-function departments. Therefore, we should simultaneously promote supporting reforms in key areas, such as economic and social regulation, while deepening environmental regulation reform.