Front. Environ. Sci. Frontiers in Environmental Science Front. Environ. Sci. 2296-665X Frontiers Media S.A. 1746090 10.3389/fenvs.2026.1746090 Original Research How does government regulation promote environmental performance: a configurational analysis from the “politics-administration-rule of law” perspective Han 10.3389/fenvs.2026.1746090 Han Xiao * Data curation Methodology Software Conceptualization Validation Investigation Writing - review and editing Supervision Resources Visualization Writing - original draft Formal Analysis Project administration Funding acquisition School of Human Resources, Guangdong University of Finance and Economics, Guangzhou, China Correspondence: Xiao Han, xiaohang@gdufe.edu.cn 19 02 2026 2026 14 1746090 14 11 2025 08 01 2026 12 01 2026 Copyright © 2026 Han. 2026 Han https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Against the backdrop of environmental crises, countries worldwide are striving to curb ecosystem degradation. China faces unbalanced ecological development, hindering its overall green transition. Based on balanced panel data of 30 Chinese provinces from 2010 to 2020, this study employed a comprehensive methodological framework integrating the entropy weight method, dynamic qualitative comparative analysis, one-way analysis of variance, and Kruskal-Wallis test to explore the interaction mechanisms of diverse policy instruments and the differentiated pathways of environmental governance. Results indicate that leaders’ awareness of technological innovation constitutes a necessary condition for high environmental performance. Political, administrative, and legal factors can be combined into seven synergistic policy instrument pathways enhancing environmental performance, which exhibit strong explanatory power across periods, confirming their effectiveness and stability. Moreover, distinct regions exhibit clear preferences for specific environmental regulation pathways. This study deepens the understanding of how government regulation shapes environmental performance from the perspective of configurational theory, elucidates the interaction mechanisms among environmental policy instruments, and provides insights for transnational and cross-regional entities to implement differentiated environmental governance and advance holistic green development.

 differentiated environmental governance dynamic qualitative comparative analysis policy instrument synergy regional heterogeneity time-varying efficacy The author(s) declared that financial support was received for this work and/or its publication. This research was funded by Humanities and Social Sciences Youth Foundation, Ministry of Education in China, grant number 24YJC630066. section-at-acceptance Environmental Policy and Governance Introduction

Eco-governance and green development have emerged as a pivotal issue attracting worldwide attention. Over-industrialization, unhealthy lifestyles, and inappropriate applications of technology have caused great damage to the environment, which not only threatens socioeconomic systems but may also trigger a climate crisis sooner than previously thought (Tang, 2019). Countries globally are exploring and trying different ways to deal with environmental problems (Li et al., 2023; Paauw et al., 2022; Hillis et al., 2020; Miller et al., 2020). China has also been actively integrating into the global eco-governance process. By introducing a series of environmental regulations (Hu Y. et al., 2023), policy pilots (Liu et al., 2024; Zeng et al., 2023)and innovations in governance tools (Hu S. et al., 2023), it has continuously intensified its efforts in environmental governance, leading to a remarkable improvement in the quality of the ecological environment.

Notably, China’s vast territorial span gives rise to pronounced heterogeneity in ecological development (Wang and Yang, 2024; Xie et al., 2024; Wang et al., 2019), which could constrain the overall progress of the nation’s green transition. A parallel scenario of disparities in green development is observed across other countries and regions worldwide (Li and Wang, 2014; Moutinho et al., 2017). In this context, identifying tailored environmental governance strategies for diverse regions represents a critical and pressing research agenda. This endeavor can not only narrow the gaps in green development within China but also mitigate the impediments posed by cross-country heterogeneity to the global advancement of green development.

Relevant studies have already acknowledged the existence of disparities in environmental performance. Within the Chinese context, such disparities are typically manifested as significant gaps between the more developed eastern and southern coastal regions and the central and western inland regions (Geng et al., 2023; Tao et al., 2016). Similar patterns of spatial differentiation have also been verified in the European Union and other countries (Li and Wang, 2014; Moutinho et al., 2017). To improve environmental performance, scholars have explored pathways to facilitate green development from technological, economic, and social dimensions. With the development of a new generation of technological revolutions, the impact of innovations represented by digital technologies on environmental performance has received wider attention, and many studies have confirmed that green technology can substantially improve environmental performance (Li et al., 2020; Zhu et al., 2021). In particular, blockchain and artificial intelligence technologies have emerged as new drivers for ecological improvement. Babaei et al. (2025) explored the challenges and feasibility associated with the application of blockchain technology in renewable energy supply chains. Smart manufacturing technologies such as artificial intelligence and industrial robots contribute to the growth of industrial green total factor productivity (Yang and Shen, 2023). A subset of studies has examined green development from economic and industrial perspectives. Ali, (2022) investigated the key enablers adopted by manufacturers to embrace green practices, revealing that economic constraints and regulatory frameworks exert substantial driving forces. In addition, economic growth, foreign direct investment (Ong et al., 2021), and industrial development (Zhang et al., 2020) are closely related to environmental quality. Other scholars have analyzed the influencing factors of environmental performance from a social perspective. Sociocultural backgrounds (Dangelico et al., 2020) and cultural levels of individual citizens (Jin et al., 2019) can directly or indirectly affect environmental performance. Environmental and political-legal regulations, coupled with efficient corporate social responsibility execution, constitute primary contributors to low-carbon performance (Ali et al., 2021).

Although these studies provide important insights into environmental performance, notable research gaps remain. First, while existing studies have identified the characteristics of environmental performance disparities, they have failed to further explore how to implement differentiated governance strategies. Second, most studies have focused on the net effects of individual factors, ignoring the fact that environmental governance constitutes a systematic project involving multi-dimensional elements, which makes it difficult to reveal the interaction logic and synergistic effects among multiple policy instruments. Third, existing literature has paid greater attention to the impacts of market and social mechanisms on improving ecological quality. However, the effectiveness of market and social mechanisms is highly dependent on the institutional foundation and incentive-constraint frameworks provided by government regulation, whereas systematic research focusing on how government regulation can improve environmental outcomes remains relatively insufficient.

Overall, study on how multiple governmental regulatory factors jointly shape environmental performance remains scarce. Given the intricate interplay among diverse elements within environmental-governance systems and the heterogeneity of regional contexts, devising differentiated governance pathways that match local environmental needs constitutes both an academic puzzle and a practical challenge for policymakers. To address this gap, we investigate the configurational effects of political, administrative, and legal factors on environmental performance. Specifically, we identify equifinal policy configurations that enhance environmental performance, evaluate the temporal stability of these pathways over the sample period, and discriminate the spatial heterogeneity of environmental-governance pathways across regions. The contributions of this paper are as follows: First, while the mainstream literature emphasizes the influence of external economic factors and technological innovation on environmental performance, we argue that the government is the most direct and capable of solving major environmental problems and focus on examining the impact of internal factors on environmental performance. Second, while most studies focus only on the independent net effect of a factor on environmental performance, we focus on the linkage effect of multiple factors. By studying the environmental driving mechanism based on political, administrative and rule-of-law factors, we are able to open the complex black box of environmental governance within the executive branch, which is conducive to understand more thoroughly the process of generating environmental performance. Third, existing literature tends to focus on deducing universal and singular principles of environmental governance. However, the generalizability of such conclusions is limited, as theoretical frameworks may easily become inapplicable once the situational context changes. In contrast, this study addresses the heterogeneity of environmental governance contexts by exploring diversified pathways for environmental governance. It aims to provide tailored approaches suited to governance contexts with distinct characteristics, varying backgrounds, and different resource foundations. Therefore, the findings hold potential transferability to other regions and diverse economic and sectoral contexts.

The second section of the paper summarizes the relevant literature on environmental regulation enhancement and constructs an analytical framework by combining important environmental policies. The third section introduces the research methodology, specific indicators, and data sources, and preprocesses the raw data. The fourth part analyzes the performance level and driving paths of environmental regulation in detail and performs a dynamic analysis of each driving path from the spatio-temporal dimension. The last two sections present the conclusions and propose policy recommendations.

 Literature review and research framework Literature review Environmental performance

Currently, there is no agreement on the meaning of environmental performance. In a broad sense, we generally consider environmental performance as the outputs and outcomes of environmental governance (Klassen and Whybark, 1999; Molina-Azorín et al., 2009; Schultze and Trommer, 2012), which can be activities (Nassani et al., 2022) carried out to protect the environment or environmentally friendly green products, and these outcomes should be measurable (Perotto et al., 2008). Environmental performance is a composite concept that has been expanding to become a multi-dimensional construct (Albertini, 2017) as the object and scope of environmental governance have changed. Initially, environmental regulation regarded a clean environment and pollution reduction as the primary goal; thus, environmental protection actions, pollutant emissions, and pollutant concentration (Falqi et al., 2020) were usually taken as the measurements of environmental performance. When the problem of industrial pollution intensified with the development of the industrial economy, people gradually realized that environmental regulation of industrial enterprises and polluting economic models was an important task for ecological protection. Many scholars have focused on the connection and mechanism between environmental regulation and economic development (Wang and Zhang, 2022; Ma and Xu, 2022), especially the causal mechanisms between environmental regulation and industrial (Chen H. et al., 2022) and manufacturing (Chen W. et al., 2022) firms. Therefore, the connotation of environmental performance extends from ecological pollution to green economic development. With the advancement of the modern process of ecological civilization, people are longing for clean and healthy living environments, paying more attention to social propaganda and education of ecological culture in society and schools, becoming more active in participating in environmental regulation, and having a stronger awareness of supervision responsibilities and rights. In this context, discussions on the legitimacy, accountability (Dryzek and Stevenson, 2011), democracy, and effectiveness (Zografos and Howarth, 2010) of regulations have begun to increase. Thus, environmental regulation can produce certain effects in the fields of social welfare, cultural awareness, and political responsibility; therefore, the connotation of environmental performance should also involve social, cultural, and political dimensions. However, existing literature still focuses on environmental and economic indicators when measuring environmental performance, and rarely explores performance indicators related to social, cultural, and political dimensions. This approach omits many crucial connotations of environmental performance in the context of this new era of development. Based on this shortcoming, this study measured environmental performance more comprehensively from five dimensions–ecological, economic, cultural, social, and political–to reflect the real level of environmental performance more completely.

 Driving mechanisms for environmental performance

The environmental performance is affected by multiple factors. The literature has examined the driving mechanisms mainly in terms of corporate management, economic development, technological innovation, and social culture pathways. Enterprise management factors such as organizational structure, values, governance, and human resources are vital forces driving environmental performance. Some scholars have used fixed-effects models to confirm that board independence, board diversity, and the existence of an environmental management committee are significantly associated with improved environmental performance (Abedin et al., 2023), revealing a close relationship between top-level corporate organizational structure and environmental performance. In terms of values, when companies have a strong sense of social responsibility, they not only directly positively affect environmental performance, Huynh (2020) but also indirectly promote environmental performance through the mediating effect of environmental strategy and green innovation (Kraus et al., 2020). In addition, specific management practices, such as cooperation between public and private companies (Sijing, 2022), information disclosure (Yun et al., 2023; Alsayegh et al., 2020), performance appraisal (Portocarrero et al., 2023), and employee satisfaction (Paillé et al., 2020) have a critical impact on environmental performance.

Second, the driving role of economic factors is a popular area of research on environmental performance, and the existing literature has investigated the mechanism of economic growth and industrial development on environmental regulation. Ong et al. studied various economic indicators in Malaysia and found a positive correlation between foreign direct investment (FDI), agricultural value-added (AVA), exported goods and services, and environmental performance, whereas the growth of gross domestic product (GDP) and population can hinder environmental performance (Ong et al., 2021). Other scholars used economic and environmental data from 88 countries to investigate the connection between economic complexity and environmental performance, indicating that the higher the economic complexity index, the higher the level of environmental performance (Boleti et al., 2021). The driving effect of industrial development on environmental performance is reflected in green supplier integration (Zhang et al., 2020) and green logistics management practices (Agyabeng-Mensah et al., 2020). Especially green supplier integration can promote environmental performance through the intermediary role of social capital accumulation.

Third, with the development of a new generation of technological revolutions, the impact of technological innovation has received widespread attention and data-driven environmental governance has received support worldwide (Nost, 2022). One study analyzed environmental efficiency and influencing factors in the Asia-Pacific region and found an inverted U-shaped relationship between technological innovation and environmental efficiency and that technological innovation can synergize with the use of renewable energy to promote environmental efficiency (Twum et al., 2021). Ying et al. specifically examined the impact of digital technology on environmental performance, and the results showed that digital technology could indirectly affect environmental performance through the mediating role of digital supply chain platforms (Li et al., 2020). Zhu et al. (2021) further focused on the transportation sector, where pollution emissions are high, and confirmed that technological innovation had a significant positive impact on energy and environmental efficiency, which may also lead to the improvement of energy and environmental efficiency in neighboring regions.

Fourth, the influence of cultural and social factors on environmental performance cannot be ignored. One study specifically examined the impact of various cultural factors, such as power distance, individualism, and masculinity, on environmental performance and found that, except for power distance, all other cultural factors could indirectly affect environmental performance through the mediation of socioeconomic factors (Dangelico et al., 2020). Other studies examining organizational management and environmental performance have found that environmental ethics not only directly affect environmental performance but also indirectly affect environmental performance through the mediation of employee training (Singh et al., 2019). Jin et al. (2019) confirmed the influence of residents’ literacy on green total factor efficiency through empirical research. In terms of social factors, environmental awareness can promote environmental performance through the mediating effect of public participation variables (Niu et al., 2022), whereas the level of urbanization development can hinder environmental performance (Yasmeen et al., 2020).

To summarize, existing studies have focused on the impact of economic development and corporate factors on environmental performance, whereas technology-driven studies are increasing. More specifically, scholars have paid more attention to the impact of external environmental factors, such as the market and society, on environmental performance, while lacking attention to the role of internal factors, such as decision-making, implementation, and regulation within government departments. In addition, existing literature focuses on the net effect of influencing factors or the mediating effect, which contains fewer factors and simpler influence mechanisms; thus, the conclusion is not applicable in practice. To address the gap, this study examines the role of internal governmental factors—such as political, administrative, and legal institutions—in driving environmental performance. It further pays attention to the synergistic and interactive effects. By focusing on the configuration effects among these factors, this study makes the research findings more applicable to complex real-world contexts and enhances the broader relevance of the conclusions across different regions and economic sectors.

 Research framework construction Theoretical foundation

In the work Public Administration: Understanding Management, Politics, and Law in the Public Sector, David H. Rosenbloom transcended the limitations of the single-perspective approach in traditional public administration research and proposed a systematic and inclusive analytical framework of management, politics, and law for public administration. This framework provides core theoretical underpinnings for deciphering the operational logic and internal mechanisms of public sectors (Rosenbloom et al., 2022). At its heart, the framework holds that public administration is not a one-dimensional managerial activity, but a sophisticated process integrating the three dimensions of management, politics, and law—dimensions that are inextricably intertwined, mutually complementary.

Specifically, Rosenbloom deconstructs public administration into three perspectives: the managerial perspective emphasizes intra-organizational management, division of labor, process optimization, and administrative efficiency; the political perspective foregrounds public values, democratic norms, responsiveness, representativeness, and equity, treating administrative processes as an extension of politics; the legal perspective stresses adherence to constitutional, statutory, and regulatory requirements. These three perspectives form an integrated whole: the managerial perspective underpins the efficiency foundation of public administration, the political perspective clarifies its value orientation, and the legal perspective consolidates its legitimacy basis. Collectively, they sustain the effective operation of public administration.

 Adaptability of theory to research question

The core research question of this study is to explore how the interactions among various elements within the government environmental governance system affect environmental performance. Rosenbloom’s three-dimensional analytical framework of management, politics, and law exhibits a high degree of compatibility with this research question, thereby providing a valuable theoretical perspective for addressing it.

First, both theoretical connotation and research question are inherently grounded in public administration. The government environmental governance focused on in this study falls within the scope of public administration, whereas Rosenbloom’s analytical framework is specifically constructed for the deconstruction and analysis of the public administration process. Thus, the operational logic of public sectors concerned by the theory is fully aligned with the practical essence of government environmental governance.

Second, the theoretical dimensions are highly matched with the core elements of the research question. This study aims to clarify how the interactions among elements in the government environmental governance system impact environmental performance, and the three-dimensional perspective of Rosenbloom’s framework corresponds precisely to the types of core elements in the government environmental governance system. The managerial, political, and legal dimensions comprehensively cover all elements of the government environmental governance system concerned in this study, providing clear analytical dimensions for deciphering the interaction mechanisms of these elements.

Therefore, it is theoretically justified for us to draw on this framework to deconstruct the government environmental governance system and construct a configuration model of government regulation driving environmental performance.

 Analytical framework

In environmental governance, process optimization from the managerial dimension may require value guidance at the political dimension as well as legal empowerment and oversight; the responsiveness to public demands from the political dimension may be achieved through resource allocation at the managerial dimension and institutional safeguards at the legal dimension; and legal oversight, in turn, restricts and standardizes the approaches to materializing managerial processes and political demands. By synthesizing existing literature, we extended and refined this framework and constructed a configuration model of environmental performance that integrates the political, administrative, and legal dimensions.

Political factors. Political leadership serves as the core driver of environmental governance (Wang, 2021), exerting influence via two dimensions: decision values and innovation awareness. Decision values reflect leaders’ prioritization of environmental issues; as a scarce resource (Bentzen et al., 2011), leaders’ attention significantly shapes intra-organizational management (Abebe, 2012) and inter-organizational joint governance (Mu et al., 2018), and their ecological preferences can integrate into policies to mobilize resources for better environmental performance. Innovation awareness refers to policymakers’ recognition of green technology’s value. Given that technological innovation is critical for indicators like GDP emission efficiency (Benedetti et al., 2020), ecological efficiency (Niu et al., 2022), industrial energy conservation (Miao et al., 2020) and power industry environmental efficiency (Sun et al., 2020), supportive policymakers can allocate more resources to green innovation to enhance environmental performance.

Administrative factors. Environmental regulation affects environmental performance via three pathways: command, incentive, and voluntary regulation. Command regulation uses laws and mandatory directives to curb environmental damage; its authority effectively restrains pollution and improves environmental quality, with literature validating its role in advancing emission-reduction technologies (Zhou et al., 2020), technical efficiency (Cheng and Kong, 2022), and environmental benefits (Blackman et al., 2018). Incentive regulation motivates market entities to cut emissions through taxes, subsidies, permits, and trades; tools including natural resource rents (Alfalih and Hadj, 2022), environmental licensing (Osmundsen et al., 2022), emissions trading (Gu et al., 2022), and government subsidies (Chen and Li, 2021) all facilitate energy conservation and sustainable development. Voluntary regulation, initiated by governments, industrial bodies, or third parties, is proven conducive to environmental sustainability by studies on public participation (Chu et al., 2022), autonomous regulation (Ji et al., 2022), voluntary certification (Leiringer, 2020), and environmental information disclosure (Jiang et al., 2020). It mobilizes firms’ initiative via flexible, incentive-based mechanisms, offsetting the limitations of mandatory enforcement alone.

Rule-of-law factors. The environmental rule-of-law system, a systematic constraint mechanism consisting of ecological supervision, evaluation feedback, and accountability mechanisms, impacts environmental performance through project supervision and subject regulation. Project supervision requires construction entities to assess a project’s environmental impacts and propose mitigation measures, which helps reduce potential environmental harm (Jay et al., 2007) and aligns supervision with economic and environmental sustainability. Subject regulation targets environmental officials and managers; corruption restricts environmental policy implementation (Kotlán et al., 2021) and green development (Zhou and Li, 2021), while regulation via rectification, interviews, accountability, and fines (Wen, 2023) can address policy implementation inefficiencies and accountability gaps.

The environmental regulatory system is constituted by political, administrative, and legal processes. The interactions among these three elements are transmitted through the chain of environmental regulatory decision-making, implementation, and supervision, collectively driving environmental performance. The theoretical framework of government regulation driving environmental performance is shown in Figure 1.

Configuration model.

Diagram showing four interconnected components around a central yellow oval labeled "Configuration match." Top: "Political support" includes "Decision Value" and "Innovation awareness." Right: "Environmental performance" includes "High performance" and "Non-high performance." Bottom: "Rule-of-law supervision" includes "Project supervision" and "Subject Regulation." Left: "Administrative implementation" includes "Command regulation," "Incentive regulation," and "Voluntary regulation." Arrows indicate relationships and directions. Methods and data Research methods

The entropy weight method (EWM) is an objective evaluation method used to calculate the weight of indicators. Compared with the Delphi method, expert survey method and hierarchical analysis method, the entropy weight method can make full use of data utility and reduce the interference of subjective experience and ensure the objectivity and uniqueness of the weights (Qin and Luo, 2014). Therefore, we used this method to assign weights to environmental performance indicators. The basic principle of the entropy method is to determine the objective weights of indicators based on their magnitude of variability. The greater the numerical variability of an indicator sample, the greater the weight assigned to the indicator. The dataset, comprising standardized non-negative quantitative values without any missing data, meets all criteria for applying the entropy weight method. The specific steps of the entropy weighting method for calculating environmental regulation performance are as follows.

Step 1: Data standardization. Suppose a problem consisting of m samples with n indicators is required for a comprehensive evaluation. Because the evaluation index system contains several indicators with different attributes and the scale and order of magnitude vary among different indicators, direct analysis of the original data will affect the accuracy of the evaluation results. To ensure the reasonableness and reliability of the data analysis results, the original data are standardized using the polar difference method to remove the scale.

For the standardization method of positive indicators in Equation 1, X i j ′ = X i j − min X j / max X j − min X j and for the standardization method of negative indicators in Equation 2, X i j ′ = max X j − X i j / max X j − min X j where Xij denotes the value of the jth evaluation index of the ith sample, X’ij is the standardized value of the jth evaluation index of the ith sample, min{Xj} denotes the sample value with the smallest value among the jth evaluation indices, and max{Xj} denotes the sample value with the largest value among the jth evaluation indices. Finally, a standardized raw data matrix R=(Xij)m*n for m samples and n indicators is formed.

Step 2: Equation 3 calculates the weight yij of the ith sample value under the jth indicator, from which the weight matrix Y of the data can be obtained in Equation 4:

y i j = X i j ′ ∑ i = 1 m X i j ′ 0 ≤ y i j ≤ 1 Y = y i j m × n

Step 3: Calculate the information entropy value of the jth indicator:

 e j = − K ∑ i = 1 m y i j ⁡ ln ⁡ y i j K = 1 ln ⁡ m In Equations 5, 6, K is the Boltzmann constant, and ej takes values in the range [0, 1].

Step 4: The information utility value of the jth indicator is calculated. The information utility value dj of the indicator is calculated according to the information entropy value ej of the indicator in Equation 7, and the larger the information utility value of a certain indicator, the stronger the determination of the outcome indicator:

 d j = 1 − e j , j = 1 , 2 , . . . , n

Step 5: Equation 8 calculates the weight of the jth indicator:

 w j = d j ∑ i = 1 m d j , j = 1 , 2 , . . . , n

Step 6: In Equation 9, the composite score of the ith sample is calculated. A larger Si value indicates a better effect on the ith sample:

 S i = ∑ j = 1 n y i j w j , i = 1 , 2 , . . . , m

Dynamic QCA (Garcia-Castro and Ariño, 2016) analysis 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. In contrast to traditional regression analysis, which analyzes the net effect of a single variable from an “atomic perspective,” dynamic QCA is good at analyzing the group effect of multiple variables simultaneously from a holistic perspective. It can analyze both small and large samples at the aggregate level. Moreover, dynamic QCA can analyze the configuration of outcome generation and outcome disappearance to address causality asymmetry and explain problematic causality more scientifically. Meanwhile, compared to traditional QCA, dynamic QCA is capable of tracking the group effects of multiple variables dynamically across both temporal and spatial dimensions. Therefore, we employ the dynamic QCA method to investigate the driving mechanisms of environmental performance. This approach not only allows for analyzing the configurational effects of multiple factors, but also captures the dynamic trajectories of different driving pathways across both temporal and spatial dimensions. The dataset is complete, with variables constructed as fuzzy sets, a moderate sample size, and an absence of multicollinearity among the conditional variables, thereby satisfying the data quality requirements for dynamic QCA.

This study treats political, administrative, and legal factors as configurational conditions and identifies sufficient combinations that generate high environmental performance, while comparing their temporal stability and spatial heterogeneity. First, all raw indicators are calibrated into fuzzy-set membership scores by assigning the thresholds of full membership, the crossover point, and full non-membership. Second, necessity analysis is performed to detect any condition that is consistently present in high-performance cases. A truth table is then constructed with consistency, frequency, and PRI (proportional reduction in inconsistency) thresholds to derive multiple configurational paths leading to superior environmental performance, and to trace their evolution across time and space. Finally, one-way ANOVA and Kruskal–Wallis tests are employed to ascertain whether regional preferences for specific configurational paths are statistically significant.

 Indicator and data

To analyze the configuration effect of government environmental governance factors and their impact on environmental performance, this study draws on the public administration analytical framework proposed by Rosenbloom, identifying three core dimensions—politics, administration, and law—as the primary analytical elements.

Specifically, the political dimension focuses on the government-led value orientation and resource allocation logic of environmental governance, encompassing two sub-elements: decision value and innovation awareness. The administrative dimension centers on enhancing the efficiency of environmental governance processes, including three sub-elements: command regulation, incentive regulation, and voluntary regulation. The legal dimension emphasizes institutional constraints and legitimacy guarantees for environmental governance, involving two sub-elements: project supervision and subject regulation. Collectively, these three dimensions integrate seven interwoven elements that jointly exert an influence on environmental performance. In addition, this study constructs a multi-dimensional evaluation system for environmental performance and employs the entropy weight method to quantitatively measure environmental performance levels.

To operationalize the analytical elements and test their impact on environmental performance, this study selects corresponding influencing indicators based on literature related to environmental governance and the availability of data. Detailed information on the indicators and their measurement methods is presented in Table 1.

Operationalization of analytical elements.

Variables Indicators Measurement
Independent variables
Political factors Decision value Investment in industrial pollution control per unit of output value
Innovation awareness R&D expenditure
Administrative factors Command regulation Number of laws and regulations promulgated in the year
Incentive regulation Ratio of sewage charges to the number of local enterprises
Voluntary regulation Number of information disclosure requests
Rule-of-law factors Project supervision Implementation of environmental impact assessment
Subject regulation Implementation of environmental supervision and inspection
Dependent variable
Environmental performance Eco-environmental performance Chemical oxygen demand emissions
SO2 emissions
General industrial solid waste generation
Eco-economic performance Coal consumption per unit of gross domestic product (GDP)
Per capita gross regional product
Ratio of tertiary to secondary industries
Eco-social performance Parkland area per capita
Urban sewage treatment rate
Number of sanitation technicians per 1000 people
Eco-cultural performance Number of patents granted
Number of public buses per 10,000 people
Greening rate of higher education campuses
Eco-political performance Number of environmental emergencies
National People’s congress (NPC) proposals
Chinese People’s political consultative conference (CPC) proposals

The study sample covered 30 provinces in China from 2010 to 2020. The original data were obtained from national statistical yearbooks—including the China Statistical Yearbook, China Environmental Statistics Yearbook, China Educational Statistics Yearbook, and China Energy Statistics Yearbook—and supplemented with official releases published on central and local government portals, the Ministry of Ecology and Environment, and provincial statistical bureaus. For a small number of missing values, linear interpolation or mean imputation was applied according to the characteristics of the indicators, ensuring data completeness. Continuous variables were standardized to eliminate differences in units and scales across variables and to prevent potential bias in analytical results due to variations in measurement units and value ranges.

 Calibration

The sample satisfies the requirements of case independence and limited diversity. In terms of the unit of analysis, China’s provinces possess substantial autonomy in key areas such as policy formulation, fiscal budgeting, and economic development planning, providing a sound theoretical basis for treating them as largely independent cases. This study draws on 330 case, which, relative to the seven core conditions included, offer ample empirical diversity. The observed configurations cover approximately 70% of the outcome instances, this indicates that the data capture the principal empirical patterns leading to the outcome.

According to the characteristics of the outcome and condition variables, we chose the fuzzy set qualitative comparative analysis (fs-QCA) for configuration analysis. This method would transform raw data into truth tables through calibration which is the process of assigning set affiliations to cases and ultimately transforming variables into sets. In the absence of an externally validated theoretical standard for demarcating set membership, we followed methodological convention and employed the direct-calibration procedure, anchoring the fuzzy-set thresholds at the 95th, 50th, and 5th percentiles of the empirical distribution to denote full membership, the crossover point, and full non-membership, respectively. After re-specifying the calibration anchors from the 95th/5th/50th to the 90th/10th/50th percentiles, solution consistency, solution coverage and the parsimonious solution paths remained identical, confirming the robustness of the configurational findings to perturbations in threshold values. Moreover, after lowering the consistency threshold from 0.80 to 0.75 and re-estimating the models, the core solution—especially the combinations of core conditions that constitute sufficient paths—remains highly stable in both composition and explanatory power. The calibration anchors for the conditional and outcome variables of environmental performance in the period of 2010–2020 are shown in Table 2 below.

Calibration of condition and outcome variable.

Variables Calibration
Full affiliate Intersection Full unaffiliated
Outcome variable Environmental performance 0.63004484 0.26855075 0.17297637
Condition variables Political factors Decision value 29.32328532 8.043569847 1.903468064
Innovation awareness 17210050.25 2845293 180,546.75
Administrative factors Command regulation 7 1 0
Incentive regulation 6230.93101 1549.969577 398.9643905
Voluntary regulation 12.62471717 1.679139726 0.276662357
Rule-of-law factors Project supervision 1 0 0
Subject regulation 3 1 0
Results Environmental performance results

Based on the established environmental performance evaluation system, we measured the environmental performance of 30 provincial-level administrative regions in China from 2010 to 2020. Figure 2 illustrates the trends in performance development in each region. There was a clear and rapid decline in environmental performance during 2010–2012. In particular, according to the 2012 China Environmental Status Bulletin, the country’s water environment quality was not optimistic, the air quality of more than half of the cities did not meet the standards, and inappropriate measures taken in the process of industrialization, urbanization, and modernization of agriculture had also given rise to serious environmental problems in rural areas. This was due to the failure to properly guide and restrict the development of polluting industries and projects while pursuing high economic growth, resulting in a greater environmental cost. Subsequently, overall, environmental performance in the period 2013–2018 achieved a relatively steady increase, owing to the governance values and principles of “ecological priority” development and environmental regulatory measures such as policy innovations, increased financial support, and strengthened regulatory efforts. In terms of economy, the principle of “ecological priority” defines the environmental bottom line for economic development and takes environmental performance as the primary standard to weigh the rationality of economic development. In the field of social and people’s livelihoods, ecology refers to people’s livelihoods, and solving prominent ecological and environmental problems should be a priority. In 2019-2020, the performance level exhibited a relatively large downward trend owing to the impact of the COVID-19 pandemic. On the one hand, to focus on fighting the pandemic, a large amount of funds and resources was invested in the fields of emergency management, medicine, and health, increasing the fiscal deficit and decreasing resources for ecological governance. However, on the other hand, the use of disposable masks, personal protective equipment, and medical supplies soared during the pandemic, and the lack of timely disposal of these wastes further increased the environmental burden.

Environmental performance levels.

Area chart depicting performance and growth rate from 2010 to 2020. The performance is shown in red, while the growth rate appears in blue. Performance remains mostly above zero, with fluctuations, peaking around 2011 and 2018. Growth rate dips below zero in 2012 and 2014, showing instability between 2010 and 2020.

In terms of spatial distribution (Figure 3), environmental performance was higher in the eastern coastal regions than in the inland regions in the center and west and higher in the southern regions than in the northern regions. This is a result of differences in regional industrial structures and economic development. Specifically, differences in industrial structure imply gaps in industrial pollution emissions. The eastern and southern coastal regions are dominated by high-tech, service, and light industries, resulting in lower pollution emissions, whereas the northern and inland regions have heavy industrial industries that provide important economic support, resulting in higher pollution emissions. In addition, the level of economic development determines local ecological governance capacity to some extent. Comparatively speaking, coastal and southern regions have higher levels of economic development and can provide more funds, social forces, green technologies, and other governance resources to improve the ecological environment. In summary, the southeastern coastal region has achieved good environmental performance owing to its low-pollution industrial structure and abundant governance resources.

Spatial distribution of environmental performance.

Map of China showing color-coded regions based on data from 2010-2020. Colors range from yellow (0.18-0.21) to dark blue (0.40-0.74) with white areas not counted. A scale indicates distance in kilometers. Necessity analysis of conditions

The necessity analysis of conditional variables is a key step after data calibration, intended to test whether the outcome variable depends on a certain variable. Traditional QCA usually considers the consistency level as a necessity judgment indicator. When the consistency level is greater than 0.9, it can be regarded as a necessary condition (Schneider et al., 2010). This means that it is an indispensable condition for the emergence of the outcome variable and must be regarded as an important explanatory variable that should not be excluded from the analysis of the configuration. In dynamic QCA, we additionally need to judge the necessity of the condition variable based on the consistency distance between and within the configurations. The consistency distance judgment standard is 0.2 (Garcia-Castro and Ariño, 2016); when it is greater than 0.2, then we need observe time distribution of consistency and coverage of this condition variable to specifically analyze its necessity.

Table 3 presents the results of the necessity test for the conditional variables. The consistency distances for the conditional variable decision value, command regulation, incentive regulation, voluntary regulation, project supervision, and subject regulation were all less than 0.2, whereas the pooled consistency levels were all below 0.9, suggesting that these factors are not necessary to drive outcomes that produce high or non-high environmental performance. Importantly, the conditional variable of non-high innovation awareness has a pooled consistency greater than 0.9, although the consistency distance is less than 0.2, suggesting that a non-high level of innovation awareness is a necessary condition for the outcome variable of non-high environmental performance. This implies that innovation awareness, as an important driver of environmental performance, should be given special attention in configuration analyses.

Analysis of necessary conditions.

Condition variables High environmental performance Non-high environmental performance
Pooled consistency Pooled coverage BECONS distance WICONS distance Pooled consistency Pooled coverage BECONS distance WICONS distance
Decision value 0.543 0.547 0.074 0.071 0.694 0.799 0.063 0.049
∼Decision value 0.801 0.696 0.037 0.034 0.607 0.603 0.071 0.069
Innovation awareness 0.825 0.889 0.037 0.055 0.419 0.516 0.046 0.118
∼Innovation awareness 0.55 0.453 0.051 0.081 0.91 0.856 0.02 0.035
Command regulation 0.664 0.655 0.058 0.045 0.585 0.66 0.062 0.055
∼Command regulation 0.655 0.58 0.057 0.05 0.693 0.703 0.047 0.037
Incentive regulation 0.491 0.502 0.081 0.084 0.729 0.852 0.074 0.045
∼Incentive regulation 0.855 0.734 0.038 0.031 0.574 0.564 0.095 0.076
Voluntary regulation 0.697 0.763 0.039 0.054 0.502 0.629 0.021 0.086
∼Voluntary regulation 0.661 0.538 0.025 0.061 0.811 0.754 0.028 0.04
Project supervision 0.863 0.606 0.039 0.021 0.802 0.644 0.053 0.026
∼Project supervision 0.493 0.685 0.186 0.037 0.509 0.809 0.181 0.054
Subject regulation 0.717 0.614 0.157 0.028 0.638 0.624 0.168 0.028
∼Subject regulation 0.561 0.575 0.175 0.035 0.606 0.71 0.17 0.041
Sufficiency analysis of configurations

Configuration analysis focuses on whether a set consisting of multiple conditions is a sufficiently conditional configuration for the emergence of the outcome variable. Referring to related studies, we set the case threshold to 1, consistency threshold to 0.8, and PRI threshold to 0.7 in the truth table construction process (Du et al. (2020). The intermediate solution has stronger interpretability and credibility than complex and parsimonious solutions. Therefore, we considered the intermediate solution the main basis of analysis, while referring to the parsimonious solution.

This study showed that seven pathways can produce high environmental performance results (Table 4). The pooled consistency was 0.924, which is much higher than the acceptable consistency threshold criterion of 0.8, indicating that the seven configurations could sufficiently explain the generation of high environmental performance. The overall coverage was 0.692, indicating that the seven configurations could explain more than 69.2% of the reasons for high environmental performance. In addition, four pathways triggered non-high environmental performance results. The overall consistency level was 0.925, which is much higher than the acceptable consistency threshold criterion of 0.8, indicating that the four configurations could sufficiently explain the generation of non-high environmental performance. The overall coverage level was 0.729, which indicates that the four pathways could explain more than 72.9% of the reasons for non-high environmental performance. Overall, the configurations that drive environmental performance have good explanatory strength, and according to the constitutive conditions, the configurations that can produce high environmental performance can be further subdivided into three models: the political-dominance-rule/of/law-driven model, the political-dominance-administration-rule/of/law model, and the systemic linkage model.

Configuration analysis of environmental performance.

Conditions High performance Non-high performance
H1 H2 H3 H4 H5 H6 H7 NH1 NH2 NH3 NH4
Decision value ​ ⊗ ⊗ ⊗ ​ ​ ● ​ ● ​ ●
Innovation awareness ● ● ● ● ● ● ● ⊗ ⊗ ⊗ ⊗
Command regulation ​ ● ● ​ ● ● ⊗ ​ ​ ​ ⊗
Incentive regulation ⊗ ⊗ ​ ⊗ ⊗ ​ ⊗ ● ​ ​ ​
Voluntary regulation ​ ​ ​ ● ● ● ⊗ ​ ⊗ ⊗ ​
Project supervision ● ● ● ● ● ● ● ● ● ● ●
Subject regulation ● ​ ● ​ ​ ● ​ ​ ​ ● ●
Consistency 0.930 0.960 0.962 0.964 0.975 0.970 0.953 0.957 0.957 0.945 0.967
Raw coverage 0.579 0.449 0.392 0.509 0.426 0.381 0.315 0.576 0.504 0.519 0.354
BECONS distance 0.019 0.016 0.012 0.011 0.009 0.009 0.013 0.010 0.012 0.013 0.006
WICONS distance 0.023 0.014 0.016 0.002 0.014 0.014 0.002 0.033 0.027 0.024 0.029
Unique coverage 0.044 0.011 0.009 0.007 0.001 0.003 0.008 0.062 0.007 0.088 0.004
Solution consistency 0.924 0.925
Solution coverage 0.692 0.729
Political dominance: rule/of/law-driven modes

In the political dominance-rule/of/law-driven mode, political and rule-of-law factors jointly influence the level of environmental performance; however, political factors dominate over rule-of-law factors. Configuration H1 has a high level of innovation awareness and a lack of incentive regulation as the core conditions and project supervision and subject regulation as the marginal conditions. This implies that when environmental regulation lacks incentive-based policy measures, sufficient innovation support from policymakers and strict rule-of-law regulations of environmental projects and governance subjects can drive the realization of high-level environmental performance. As demonstrated by configuration H7, when the administrative enforcement system, including command-based, incentive-based, and voluntary regulation, is at a low level of operational efficiency, if policymakers can attach great importance to environmental issues and technological innovation while paying close attention to the environmental impacts of construction projects and actively supervising and rectifying them, they are also able to achieve a high level of environmental performance. Overall, the political-dominance-rule/of/law-driven model relies more on the support of leaders at the environmental decision-making stage and tracking supervision but less on the administrative enforcement system. Therefore, this model is particularly suitable for improving environmental performance in areas with scarce administrative resources and weak operational systems.

 Political dominance-administration-rule/of/law-driven mode

In the political dominance-administration-rule/of/law-driven mode, political, administrative, and rule-of-law factors constitute a configuration that jointly affects the level of environmental performance but focuses more on the leadership and authority advantages of political factors than administrative and rule-of-law factors. Configuration H2 is driven by the core conditions of a high level of innovation and lack of incentive regulation and the peripheral conditions of lack of decision-making value, command regulation, and project supervision. This implies that even if policymakers do not prioritize solving environmental problems and lack sufficient incentivized environmental policies, high levels of environmental performance can be achieved with adequate support for technological innovation, command regulation, and program supervision. Noteworthy, configuration H4 is formed by replacing the command regulation condition of configuration 2 with voluntary regulation and it produces the same performance results. Thus, a potential substitution relationship exists between the H2 and H4 configurations. Configuration H5 suggests that the combined effects of policymakers’ sense of innovation, command regulation, voluntary regulation, and program supervision can offset the impediments of inadequate incentives and achieve high performance level. In short, this regulation mode is dominated by political factors, with administrative and rule-of-law factors driving environmental performance. Hence, it is suitable for regions with relatively well-developed environmental regulatory systems.

 System linkage-driven mode

In the system linkage mode, political, administrative, and rule-of-law factors jointly affect the level of environmental performance, and the effects of various factors show a balanced state. More specifically, the configuration is not dominated by a certain dimension, but all types of factors are core conditions of the linkage. Configuration H3 shows that when leaders do not pay enough attention to solving environmental problems, the core conditions of innovation awareness at the political level, command regulation at the administrative level, and subject supervision at the rule of law level, with project regulation as a peripheral condition, can eliminate the negative impacts caused by leaders’ insufficient attention and promote performance improvement. According to configuration H6, the linkage of command regulation and program supervision with innovation awareness as the core condition at the decision-making level, voluntary regulation at the administrative level, and subject supervision at the rule-of-law level can also drive the performance of environmental regulation. Overall, the systemic linkage model does not rely on one type of element as the core support but on the balanced power of political, administrative, and rule-of-law factors to synergize and drive environmental development. Compared to the first two models dominated by a single political element, the systemic linkage model can equally utilize the governance advantages of political, administrative, and rule-of-law elements and has better coordination and stability, which is more conducive to promoting high-quality development of the ecological environment.

The study also found four configurations producing non-high environmental performance. According to the core conditions, configuration NH1 is a politics-driven mode, in which the lack of innovation awareness among policymakers and insufficient green innovation are the dominant factors leading to low environmental performance. Even with good environmental incentives and program supervision, environmental performance is limited by insufficient technological innovations. Configurations NH2, NH3, and NH4 are politics-administration-driven modes; that is, political and administrative factors jointly constrain environmental development. Among these, there is a potential substitution relationship between configuration NH2 and NH3. When the lack of innovation consciousness and voluntary regulation are the core conditions and project supervision is the marginal condition, decision value, and subject regulation as the core conditions can be replaced with each other to produce the same performance results. Grouping NH4 uses the absence of innovation awareness, decision value, and command regulation as core conditions and the two marginal conditions of project supervision and subject regulation, which together drive the formation of non-high environmental performance. This suggests that even when ecological governance issues have strong political support at the decision-making level and are subject to strict rule of law regulations, it remains difficult to realize high environmental performance because they lack of technological innovation and command regulation.

Overall, the core conditions of the high environmental performance-driven path involve two or three types of elements, indicating that independent environmental governance measures cannot promote the improvement of environmental performance. Hence, government departments must take a systemic perspective to build an environmental regulatory system and strive to consider the whole process of political decision-making, administrative management, and rule of law regulation to improve the performance of environmental regulation. Second, innovation awareness exists as a core condition in all pathways that generate high environmental performance, suggesting that leaders’ and policymakers’ attention to technological innovation can strongly promote the transformation of technological resources into environmental performance. This fully reveals the critical driving effect of political factors on environmental performance. Therefore, in the process of ecological governance, we should focus on strengthening the innovation consciousness of decision-makers and promoting the realization of high environmental performance by continuously improving the transformation channels of green technology and the transformation efficiency of green achievements.

 Time effect and evolution trend of configurations

Effective environmental governance programs must be compatible with the society’s temporal context and development stages; the same governance program may produce diametrically opposite effects at different times. Therefore, we further analyzed the temporal evolution trend of the explanatory strength of the configurations that generate high environmental performance from the time dimension to determine their applicability and stability in different periods. The results of the analysis show that none of the between-consistency distances of the configurations that generate high environmental performance are greater than 0.2 (Table 3), indicating that there is no significant time effect on the explanatory strength of the seven configurations. Thus, the effectiveness of the seven pathways did not differ significantly over time. On this basis, we further investigated the promotional effect of the seven pathways on environmental performance in different years to determine the changing trend of their explanatory strength. According to Figure 4, the consistency level of all the configurations was above 0.75 in the period 2010-2020 and above 0.9 in most years, suggesting that they have good explanatory power in different developmental periods, reflecting their reliability and stability in driving the formation of high environmental performance. Specifically, the consistency levels of all configurations collectively decreased in 2012, with relatively large decreases, particularly for configurations H2 and H7. This may be because the air pollution situation in 2012 was particularly severe, which triggered widespread concerns from state leaders and society, especially regarding PM2.5, which became a hot topic in the two sessions that year. Consequently, a series of atmospheric governance work was carried out nationwide at the national level. This national campaign focused on environmental governance became the core element to promote environmental performance, which inevitably reduced the explanatory power of other factors. From 2014 to 2018, all seven configurations maintained a steady development at a higher consistency level and showed a small fluctuating upward trend. With the continuous improvement of the environmental regulatory system and regulatory capacity, the various types of regulatory pathways also show strong stability and can continue to promote environmental performance in different periods and environments. Notably, the consistency level of the seven configurations exhibited a collective decline in 2019. In 2020, configuration H7 showed a rebound trend, but in contrast, the remaining groupings continued to decline. This is because the new COVID-19 crisis caused heavy damage to social and economic development. To upgrade medical technology level and stabilize social order, the corresponding technological innovation and institutional resources and social and regulatory forces were preferentially supplied to the medical and health fields, leading to a lack of environmental regulatory resources and a certain degree of failure of the governance system. Configuration H7 could rebound rapidly in 2020 because it was not constrained by administrative implementation elements compared with the other configurations and fully utilized the political support of the leader to transmit pressure by increasing the attention of decision-makers to environmental issues and technological innovation while linking monitoring mechanisms to jointly promote environmental performance. Thus, in the face of crises, configurations with stronger political attributes contribute more effectively to environmental performance.

Trends in within-consistency of configurations.

Line graph showing the consistency of seven models (H1 to H7) from 2010 to 2020. Consistency values range from 0.75 to 1.00. All models show a dip around 2012 and varying trends up to 2020. Spatial differences and distribution characteristics of configurations

Due to differences in value preferences, governance resources, and social and cultural factors, regions have diverse preferences for environmental governance programs. In other words, the same governance plan may produce different results in different regions. Therefore, we further examined whether there were regional differences in the explanatory strength of the configurations producing high environmental performance from the spatial dimension and the spatial distribution characteristics of the cases that the configuration could explain.

As shown in Table 3, the within-consistency distances of the seven configurations with high environmental performance were not greater than 0.2, indicating that there was no significant difference between provinces in the interpretation strength of each configuration. On this basis, we further investigated the coverage difference of each configuration from the spatial dimension and evaluated the spatial distribution of configuration application cases and the configuration preferences in different regions by analyzing the promotion effect of configurations on generating high-level environmental performance in different regions.

We used the Kruskal–Wallis test and one-way ANOVA to explore the regional differences in each configuration. First, we employed the normality test method to investigate whether the coverage of the seven configurations in different regions obeyed a normal distribution. We selected the Shapiro-Wilk test results for the normality test according to the sample size. The results (Table 5) showed that the test coefficients of configuration H1 in the central and western regions were greater than 0.05 and followed a normal distribution; however, those in the eastern region were less than 0.05 and did not follow a normal distribution. Therefore, the Kruskal–Wallis test was used to analyze the difference in the degree of regional coverage in configuration 1. The test coefficients of the other six configurations in the eastern, central, and western regions were all greater than 0.05; hence, they were normally distributed. Therefore, one-way ANOVA was used to analyze the differences in the degree of regional coverage.

Results of normality test.

Configuration East Central West
H1 0.001 0.868 0.786
H2 0.722 0.799 0.612
H3 0.858 0.436 0.307
H4 0.076 0.244 0.597
H5 0.754 0.952 0.572
H6 0.445 0.654 0.546
H7 0.167 0.882 0.841

The results of this study (Tables 6, 7) show that the distribution of cases explained by configurations H3, H4, H5, and H6 do not show significant spatial differences. Among them, configurations H4 and H5 are politics-dominated-administrative-rule/of/law modes, with a high degree of similarity in the cases that can be explained, including Henan, Guangdong, and Gansu provinces. As a representative example, Henan Province stimulates the vitality of market players by reducing taxes and fees, promoting consumption, and expanding high-level open areas under the premise of strongly supporting the development of green technological innovation. In addition, it promotes the green transformation and industries upgrading by constructing technological innovation platforms and building advanced manufacturing systems, and strengthening the market and society. In addition, they eliminated the construction of highly polluting and noncompliant projects by establishing a closed-loop regulatory mechanism to identify, refer to, supervise, and rectify problems. Configurations H3 and H6 are system linkage modes, and the cases explained are highly similar, including Jilin, Liaoning, and Inner Mongolia provinces. As a representative example, Jilin, aiming at building a strong ecological province, has made numerous efforts to create an innovative province by supporting scientific and technological enterprises and promoting the transformation of achievements, focusing on the improvement of several environmental management systems such as the system of compensation for environmental damages, carbon emissions trading, and participation of social actors. Regarding the rule of law and supervision, it has promoted the rectification of central ecological and environmental protection inspection issues and the handling of public complaints and reported cases at a high standard.

Kruskal–Wallis test results.

Configuration Mean SD Chi-square Sig
H1 0.58943 0.172400 13.410 0.001***

***, **, * respectively represent significant values at 1%, 5% and 10% levels, the same as in the following table.

One-way analysis of variance results.

Configuration Mean SD F Sig
H2 0.46073 0.163820 3.017 0.066*
H3 0.42930 0.160717 2.498 0.101
H4 0.50200 0.184379 1.522 0.236
H5 0.43800 0.174316 1.190 0.320
H6 0.41693 0.170227 1.712 0.200
H7 0.40060 0.202296 7.618 0.002***

Unlike the above four configurations, the spatial distributions of the cases explained by configurations H1, H2, and H7 exhibit significant regional differences. Figure 5 shows that the region with the highest coverage rate for configuration H1 is in the central region. Although the coverage rates in the eastern and western regions were significantly lower than those in the central region, the actual coverage rates were all higher than 52%. Therefore, we argue that configuration H1 has good explanatory strength in the eastern and western regions. The explanation cases corresponding to configuration H7 were mainly concentrated in the central and western regions, whereas the coverage rate in the eastern region was much lower This indicates a large difference in the preference for configuration H7 in different regions. Both configurations H1 and H7 belong to the politics-dominated–Rule of Law mode, with political factors as the core driving condition, and the representative provinces include Shanxi, Jilin, and Shaanxi. Taking Shanxi Province as an example, Shanxi, as a typical coal province, strongly supports green technology innovation in terms of policy and promotes the green transformation of the energy industry by building a base for the green development and utilization of coal and creating a world-class base for the transformation of coal-based scientific and technological innovation results. In terms of the rule of law and supervision, it actively promotes the implementation of the coordinated supervision mechanism for ecological protection and the ecological environmental protection inspection system to realize unified supervision for owners, developers, and regulators of construction projects. The cases explained by configuration H2 are mainly concentrated in the central region, whereas the coverage in the central and western regions is relatively low, with Hubei and Anhui as representative provinces. All these provinces attach great importance to the value of technological innovation for environmental development, fully supporting and developing green technologies and green industries while continuously strengthening the environmental management system, regulating the environmental effects of construction projects, and realizing environmental performance through a politically oriented multi-dimensional driving mode.

Configuration coverage in different regions.

Horizontal bar chart displaying coverage for different categories labeled from East_H1 to West_H7. Bars are color-coded, transitioning from yellow at the top to dark purple at the bottom, indicating varying levels of coverage across categories. The x-axis represents coverage values from 0.0 to 0.8.

Overall, there are some differences in configuration preferences among the different regions. Some central and western regions tend to prefer a politics-dominated environmental regulation model, whereas the eastern region is less dependent on political driving. Political support is a subjective condition that local governments can control to effectively overcome the limitations of insufficient objective resources such as administrative factors, funds, talents, and infrastructure. Therefore, some regions with relatively limited administrative and legal resources can strive to reinforce ecological civilization politics and drive local environmental performance by strengthening the ecological value and green innovation awareness of decision-makers. At the same time, we should also be vigilant about this regulatory mode becoming the norm. Because the politics-dominated driving model weakens the role of other resources and elements, it is unbalanced and unsustainable. If this trend continues, it will not only lead to rent-seeking and environmental corruption but also affect the improvement of the environmental regulation system, eventually resulting in regulation failure. Therefore, a politically oriented regulation mode is only suitable for the transitional period. It is crucial to adhere to the comprehensive improvement and strengthening environmental regulation system, fully utilizing the multiple driving forces of politics, administration and the rule of law, and facilitating high-quality and sustainable ecological development.

 Discussion

In the field of environmental performance research, deficiencies remain in existing studies regarding the exploration of influencing mechanisms. A strand of literature has adopted traditional regression approaches to examine the impacts of factors such as corporate social responsibility, green innovation, and gross domestic product on environmental performance (Kraus et al., 2020; Ong et al., 2021). The limitation of such studies lies in their failure to capture the configuration effects of multiple factors, making it difficult to interpret the complex scenarios of environmental governance practices. Another strand of research has employed the conventional Qualitative Comparative Analysis (QCA) method to analyze static cross-sectional data. The core limitation of this method is its inability to dynamically depict the temporal evolution and regional distribution of different driving pathways. Although Mu et al. (2018) applied the QCA method to explore the driving conditions for inter-organizational collaboration in environmental governance, their study neither clarified the temporal effects of configurations nor verified the regional differences in configurations.

To address the aforementioned research gaps, this study introduces the dynamic QCA method, combined with one-way analysis of variance (ANOVA) and the Kruskal–Wallis test, to investigate the interaction mechanisms and environmental effects among government regulation factors. This study overcomes the limitation of regression analysis, which is constrained by net effects, and the constraint of conventional QCA method, which is restricted to static cross-sectional data. By deeply dissecting the dynamic driving configurations of environmental performance from both spatial and temporal perspectives, this study provides a novel analytical framework for advancing relevant research.

As the core driving force for economic development, technological innovation has prompted a growing number of scholars to explore the interaction mechanisms between technological innovation and the ecological environment from a green economy perspective. Zhu et al. (2021) conducted an empirical study on the environmental effects of technological innovation in the transportation sector, revealing that technological innovation can not only significantly improve energy and environmental efficiency but may generate spatial spillover effects, which drive the coordinated improvement of energy efficiency levels in surrounding regions.

Through necessary condition analysis, this study found that policymakers’ lack of awareness of technological innovation constitutes a necessary condition for low environmental performance. This conclusion corroborates the significance of technological innovation from a reverse perspective, which is consistent with the findings of Zhu et al. (2021). These mutually verified results indicate that green transformation driven by technological innovation has become an inevitable trend, and it can be predicted that the boosting effect of technological innovation on the green economy will continue to strengthen in the future.

Notably, compared with the study of Zhu et al. (2021), the innovation of this study lies in its further observation of how policymakers’ innovation awareness interacts with other factors to jointly affect environmental performance from both spatial and temporal dimensions. This study expands the research boundary of the relationship between technological innovation and environmental performance from the perspective of policy tool coordination.

Our research still has limitations. In research content, we mainly analyzed the synergistic effects of political, administrative, and rule-of-law factors, which are all internal control factors of the government. However, it lacks discussion of external environmental factors such as sociocultural attributes, global crises, and the international economy. Future research will incorporate a broader range of variables to refine the theoretical framework, thereby enabling the findings to more effectively explain and address the practical challenges of differentiated environmental governance. Additionally, this study primarily relies on provincial panel data, which limits its ability to capture the mechanisms of differentiated environmental governance across different countries and cities. Subsequent research will adopt multi-level data or case-based approaches to explore differentiated environmental regulation pathways across national, urban, institutional, and cultural contexts. Finally, environmental project supervision constitutes a complex and crucial system. In this study, project supervision was coded as a binary improvement indicator to better analyze the impact of the institutional embeddedness of environmental project supervision on environmental governance performance, without accounting for more factors within the environmental project supervision system. Future research will focus on exploring the effects of more complex elements within the environmental project supervision mechanism—such as operational intensity and implementation modes—on environmental governance outcomes.

 Conclusion and policy recommendations

The results and analysis demonstrated that seven distinct interaction mechanisms among political, administrative, and legal factors could drive environmental performance. Among these factors, lack of innovation awareness among leaders acted as a necessary condition for low environmental performance. Notably, the explanatory power of these seven pathways did not exhibit an obvious temporal effect; instead, they had maintained robust explanatory power consistently throughout the study period. However, the research confirmed the existence of significant regional disparities: central and western regions tended to adopt a politics-driven environmental governance model, whereas eastern regions showed a relatively low dependence on political drivers.

From a dynamic perspective, this study clarified the multi-dimensional driving mechanisms of environmental governance, which is of great significance for narrowing the development gaps between regions and overcoming the obstacles posed by regional imbalances to the overall green development process. Particularly for environmental authorities and managers, the findings provide a basis for selecting tailored environmental regulation pathways according to the resource advantages and shortcomings of specific regions. In this way, regions with different endowments can access a broader set of governance options and implement differentiated management strategies to improve local environmental performance more effectively. Specifically, the conclusions of this study offer the following implications for government departments engaged in environmental supervision:

First, the study found that leader’s lack of innovative awareness was a necessary condition for non-high environmental performance. Hence, improving environmental performance necessitates prioritizing the cultivation of innovative awareness among leaders and decision-makers.

Second, this study found that high-level environmental performance relied on the synergistic interaction of multiple factors rather than a single one. Therefore, the mindset of relying on a single governance tool should be abandoned, and instead, a systematic mindset featuring the coordination of multiple tools should be adopted.

Third, this study revealed significant interregional differences in configuration preferences: compared with eastern regions, central and western regions tend to rely more on politically driven paths. Hence, differentiated environmental governance strategies should be formulated based on the specific conditions of each region.

 Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

 Author contributions

XH: Data curation, Methodology, Software, Conceptualization, Validation, Investigation, Writing – review and editing, Supervision, Resources, Visualization, Writing – original draft, Formal Analysis, Project administration, Funding acquisition.

 Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Edited by: Sadia Samar Ali, King Abdulaziz University, Saudi Arabia

Reviewed by: Otilia Manta, Romanian Academy, Romania

Sulaman Muhammad, Brandenburg University of Technology Cottbus-Senftenberg, Germany