Research on the “combination punch” effect of heterogeneous environmental regulation linkage on carbon productivity

Research on the “combination punch” effect of heterogeneous environmental regulation linkage on carbon productivity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the “combination punch” effect of heterogeneous environmental regulation linkage on carbon productivity Dongri Han, Xiaoli Lv, Su Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3838878/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 May, 2024 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted 7 You are reading this latest preprint version Abstract Building an effective environmental regulation “combination punch” to improve carbon productivity is of great significance for achieving the “dual carbon” goal. Taking 30 provinces (municipalities and autonomous regions) in my country as research cases, the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method is used to explore the impact of command-and-control, market incentive, voluntary and recessive environmental regulation linkages on carbon productivity. The research found that: ① A single type of environmental regulation antecedent condition does not constitute a necessary condition for the improvement of carbon productivity, and the linkage of heterogeneous environmental regulation has a significant “combination punch” effect on the improvement of carbon productivity. ②There are three configuration paths to achieve high carbon productivity, namely active environmental protection under the leadership of market incentive, government restraint and public supervision and promotion type I, and government restraint and public supervision and promotion type II. ③ There is a potential substitution relationship between the combination of robust market incentive and robust voluntary environmental regulation and a single robust command-and-control environmental regulation. The conclusions can provide policy suggestions and useful references for formulating an effective and diversified environmental regulation tool portfolio. Environmental Regulation Configuration Carbon Productivity fsQCA “Combination Punch” Effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Instruction In 2022, China's carbon emissions will still exceed 100 billion tons, accounting for 32.19% of the world's total. It can be seen that China's ecological environment situation is still grim, and it is facing multiple pressures such as post-epidemic economic recovery, air quality constraints, and the realization of the “double carbon” goal. It is a long way to go to realize the construction of a beautiful China (Guo and Li 2022). Different from developed countries in crossing the inflection point of the environmental Kuznets curve, China's current green high-quality development faces two strategic tasks at the same time: economic growth, carbon peaking and carbon neutrality. The requirement for low-carbon growth is further highlighted, and carbon productivity is the key characteristic index of the “dilemma” pattern of maintaining growth and promoting emission reduction (Wang et al. 2014). Improving carbon productivity has become an inevitable choice for the economic and social development of China to realize a comprehensive green transformation in the new stage of development (Sun and Liu 2021). From the perspective of economics, the environment is typically non-competitive and non-exclusive, and both regions and enterprises have the motivation of “free riding” behavior. The key to solving environmental problems is to formulate and implement reasonable and effective environmental regulation measures. Existing studies mainly start from the single environmental regulation policy of command type or market type, and explore the regulatory effects such as “Porter Hypothesis” (Ramanathan et al. 2015; Murty and Kumar 2003), “Innovation Compensation Effect” (Farooq et al. 2023; Li et al. 2020), “Compliance Cost Theory”(Shi and Zhao 2018; Qiao 2021). Due to the multi-domain and complexity of environmental regulation policies, a single end-treatment model has been unable to meet the needs of coordinated promotion of pollution control and green high-quality (Baker and Ayala-Orozco 2020), and the “combination” of regulation based on multiple policy coordination undoubtedly provides a “new answer” for the improvement of carbon productivity (Dzwigol et al. 2023). As a “combination” of traditional regulatory means and regulatory policies, the combination of environmental regulation can effectively avoid the regulatory deviation of a single policy, crack the drawbacks of different regulatory policies' mutual constraints and conflicts, and then effectively improve the regulatory effect. Based on the internal correlation between environmental regulation policy coordination, carbon emissions and economic green growth, it can be inferred that the “combination” of environmental regulation is bound to have a profound impact on carbon productivity. Therefore, it is of great practical significance to build a carbon productivity improvement model based on environmental regulation linkage. Based on the perspective of the “holistic” configuration of the element set, this paper uses the fuzzy set qualitative comparative analysis method to explore the “combination” effect of heterogeneous environmental regulation on carbon productivity by taking 30 provinces (autonomous regions and municipalities directly under the central government) in mainland China as a case study, in order to explore the causal relationship between different types of environmental regulation and carbon productivity and the potential relationship between different types of environmental regulation. The conclusion of this paper provides useful reference and practical guidance for constructing a multi-environmental regulation system and promoting environmental regulation behavior from “bottom-to-bottom competition” to “top-to-top competition”. 2 Literature Review 2.1 Heterogeneous environment regulation classification and coordination In the field of public policy, environmental regulation, as a typical environmental management policy tool, aims to achieve the goal of environmental protection through tangible or intangible forms, relying on the role of laws and regulations, and plays a crucial role in pollution reduction and economic green development. Since environmental regulation policies are different in terms of policy implementation subjects, regulation objects and function objectives, scholars have conducted relevant studies on environmental regulation heterogeneity from different perspectives. In the beginning, scholars mainly discussed the command-and-control environmental regulation led by the government and the market incentive environmental regulation with market-oriented economic incentives, and put forward the “dichotomy” of environmental regulation. On this basis, Zhang et al. (2021) and Ren et al. (2018) put forward the “three-part method”, arguing that importance should be attached to the role of voluntary environmental regulation. Bu et al. (2020) showed through empirical analysis that voluntary environmental regulation significantly improved the innovation performance of enterprises. Galinato and Chouinard (2018) paid attention to the environmental supervision power of the public, and proposed to take the recessive environmental regulation into consideration. In addition, Demirel et al. (2018) divided environmental regulation into formal regulation and informal regulation according to regulation forms. Nazir et al. (2023) divided environmental regulation into two types based on cost: expense-type regulation and investment-type regulation. Bashir et al. (2022) divided environmental regulation into five categories at the level of policy formulation: water pollution control, air pollution control and solid pollution control, etc. In recent years, some scholars have proposed that it is easy to make the mistake of “one-size-fits-all” to analyze environmental regulation from the overall level by using a single indicator (Song and Han 2022), and the mutual constraints and conflicts among policies will also lead to regulatory failures, so it is inevitable and necessary to explore effective policy combinations for environmental governance. The idea of enhancing the effectiveness of policies by constructing complementary and interdependent “policy package” has been widely recognized by scholars at home and abroad. Nissinen et al. (2015) proposed that Finland's “package” regulatory policies have significant pollution control effects, and it is expected that 4.5 million tons of carbon emissions will be reduced in 2020. According to the research of Gong and Sun (2021), the combination of diversified policy tools, such as normative policy texts and carbon emission trading, is an effective guarantee for achieving energy conservation and emission reduction targets in the central and western regions. Murshed et al. (2021) proposed the combination of market-based environmental regulation tools and command-and-control environmental regulation in industry, which is conducive to improving the flexibility and effectiveness of pollution control measures. It is worth noting that the regulatory policy combination does not simply inject the “new wine” into the “old bottle”, but adopts the “optimal” coupling model according to policy objectives, tool preferences, governance levels, etc., to crack the risks of the policy combination and ensure the effect of policy implementation (Testa ey al. 2011). 2.2 Environmental regulation and carbon productivity Based on the summary of the existing literature, the basic paths of environmental regulation on high-quality economic green development mainly include (1) direct action path. Due to the external diseconomy of environmental pollution, environmental regulation, as an important category of government social regulation, effectively regulates the production and operation activities of enterprises through administrative penalties, emission reduction constraints and other means, which helps to achieve the improvement of carbon productivity (Baloch and Danish 2022). (2)Industrial structure adjustment path. As an effective forcing mechanism, environmental regulation can promote the orderly withdrawal of energy-intensive industries, promote the upgrading and optimization of industrial structure, and then effectively coordinate sustainable economic development and environmental protection, and improve carbon productivity (Yuan and Stavropoulos et al. 2018). (3) Technological progress path. “Porter's hypothesis” points out that appropriate environmental regulation can maximize the role of “innovation compensation” to make up for the “compliance cost” of enterprises, further improve the level of technological innovation, promote the coordinated development of economic growth and environmental protection, and enhance carbon productivity (Fernando and Wah 2017). Shen and Liu (2012) pointed out that there are regional differences in the implementation of “Porter's hypothesis”, and it is not that the higher the intensity of regulation, the more effective it is in environmental pollution. In addition, some scholars have explored the low-carbon effect of environmental regulations based on the adjustment of energy structure (Bye and Klemetsen 2018) and the development of green finance (Ahmed et al. 2022), etc. However, some scholars have pointed out that due to the differences in the implementation of environmental standards among regions, polluting enterprises reduce the cost of environmental governance through relocation, resulting in the effect of pollution refuge, resulting in the inability of environmental regulation to effectively improve carbon productivity (Raff and Earnhart 2022). In summary, scholars have explored the feasibility and effectiveness of environmental regulation tools from various perspectives. While some scholars have recognized the necessity of combining multiple types of environmental regulations and demonstrated that the governance effectiveness of compound policies often surpasses that of singular policy (Pizer 2002), comprehensive studies on different configurations of environmental regulation tools are still lacking. Particularly, there is a dearth of empirical testing regarding the enhancing effects of environmental regulation combinations on carbon productivity. Therefore, this paper aims to clarify the roles of various environmental regulation tools, further uncover the “combination punch” effects of different types of environmental regulation tool configurations in enhancing carbon productivity in China. It seeks to reveal the potential relationships among environmental regulations and provide recommendations and references for the practical application of viable environmental regulations across regions, fostering green and high-quality development. 3 Analytical Framework In accordance with the “Fourfold Classification” of environmental regulation policy tools (Wang and Wei 2020), this paper unfolds its analysis across four dimensions: command-and-control, market incentive, voluntary environmental regulation, and recessive environmental regulation. To elaborate: Command-and-control environmental regulation is established on the foundation of government enforcement. It primarily regulates and constrains pollution emissions generated by enterprises to achieve the goals of pollution reduction. Policy tools in this category include laws and regulations, technical standards, intensity standards. Command-and-control tools possess strong binding force, are widely applied, and can effectively constrain pollution behavior within a region. Their implementation is swift, and the effects are determinable. However, they lack flexibility and incentives, leading to higher execution costs (Graafland 2019). Through the imposition of emission constraints, command-and-control environmental regulation compels enterprises to undergo a green “Butterfly transformation”, thereby enhancing carbon productivity. Market incentive environmental regulation, based on the principle of “polluter pays”, employs market mechanisms such as imposing emission fees and promoting carbon emission trading to reduce enterprise carbon emissions. In comparison to command-and-control environmental regulation, enterprises have greater autonomy, allowing them to make decisions based on their operational circumstances. Market incentive environmental regulation relies on the inherent regulatory mechanisms of the market. Its advantages lie in autonomy, strong flexibility, and the ability to stimulate innovation. However, it requires a higher degree of market mechanism completeness (Millimet and Roy 2016).By kindling enthusiasm for innovation within enterprises and guiding them toward “green” production, market incentive environmental regulation aims to enhance carbon productivity. Voluntary environmental regulation is grounded in diverse voluntary mechanisms involving public expressions of environmental concerns, social organizations supervising regional enterprises environmental departments, enterprises autonomously implementing energy conservation and emission reduction measures, etc. This gradual reduction in pollution emissions is achieved through policy tools like environmental certification systems, ecological labeling systems, and voluntary agreements, etc. Voluntary environmental regulation reduces the cost of environmental regulation and significantly stimulates the motivation of polluters to reduce emissions, offering high flexibility. While it has lower binding force and may exhibit a time lag (Rassier and Earnhart 2015). Taking ISO 14001 as an example, enterprises with environmental commitment signal their intentions to external stakeholders, prioritize environmental policies and emission standards, adjust environmental management practices, and consequently enhance carbon productivity. Recessive environmental regulation is a form of spontaneous collective environmental action undertaken by the public or non-governmental organizations based on their own environmental awareness and for their own interests. As societal environmental consciousness grows, and public awareness of environmental issues increases, recessive environmental regulation has become a new force in enhancing environmental governance capabilities and refining a “diversified co-governance” environmental governance system. It gradually propels government regulatory agencies to scientifically allocate regulatory resources, thereby improving regional environmental quality. Policy tools primarily include protest, complaint, and supervision, etc. Recessive environmental regulation boasts lower implementation costs, high flexibility, and grants enterprises significant autonomy and choice regarding pollution reduction measures. It demonstrates remarkable environmental improvement effects and strong timeliness (Danish et al. 2020). Under the supervision and guidance of recessive environmental regulation, enterprises actively respond to environmental pressures, relying on knowledge updates and green innovations to enhance carbon productivity. Based on the aforementioned, this paper incorporates command-and-control, market incentive, voluntary environmental regulation, and recessive environmental regulation into the same research framework, exploring the multifaceted and concurrent impact of different environmental regulation types on carbon productivity. 4 Research Methods and Data Processing 4.1 Research methods Qualitative Comparative Analysis (QCA) is a data analysis method developed based on set theory and Boolean algebra, specifically designed for analyzing small and medium-sized sample cases. Combining the strengths of qualitative and quantitative analysis, QCA explores complex causality between the outcome variable and multiple condition variables. It derives multiple complex driving paths of interactions among factors (Zhang et al. 2020). In essence, this method involves selecting relevant outcome and condition variables, logically combining and comparing cases, identifying logical relationships, and simplifying the results to derive configurations (Fiss 2011). The selection of Fuzzy Set Qualitative Comparative Analysis (fsQCA) for investigating the impact of heterogeneous environmental regulation on carbon productivity is primarily motivated by the following reasons: (1) Environmental regulation itself exhibits characteristics of multi-domain and complexity, and the classification of heterogeneous environmental regulations intensifies these complexities. Moreover, the enhancement of carbon productivity is not solely determined by a specific type of environmental regulation policy. The fsQCA method excels in exploring the interplay and matching of different types of factors, providing a strong explanatory power in this context. (2) Identifying the environmental regulation configuration pathways that can improve carbon productivity is a crucial issue addressed in this article. The fsQCA method, combining qualitative and quantitative approaches, emphasizes delving into the antecedent conditions complexity and causal asymmetry. It allows for a more profound interpretation of different driving pathways. (3) The various types of environmental regulations selected in this study all influence the improvement of carbon productivity. The fsQCA method holds a significant advantage in identifying the presence of complementary or substitution relationships. This facilitates a thorough analysis of whether complementary or substitution relationships exist among different types of environmental regulations in enhancing carbon productivity. 4.2 Data source and processing (1) Case Selection To fulfill the requirements of ample case representation and maximize heterogeneity among cases, and considering the suitability of fuzzy set qualitative comparative analysis for small to medium-sized samples, this study selects data from 30 provinces (autonomous regions and direct-administered municipalities) in China as research samples (excluding Tibet and Hong Kong, Macao, and Taiwan for data accessibility reasons) (Stokke 2007). The data is derived from the China Statistical Yearbook , China Environmental Database and provincial statistical yearbooks for the years 2017-2021. In instances where data for specific years or provinces are sparse, linear interpolation is employed for supplementation. (2) Variable Selection ① Outcome Variable Carbon productivity refers to the level of CO2 emissions per unit of GDP output. Since regional carbon emission data is currently not publicly available, it is estimated using the IPCC's CO2 calculation method. Following the methodology presented by Chen (2022), this study uses the rate of change in carbon productivity as the measurement for the outcome variable (Zhong and Zhao 2021). The carbon productivity change rate for the years 2017-2021 is calculated based on the carbon productivity data for 2017 and 2021, utilizing the formula for annual growth rate. From Figure 2, it is evident that the majority of provinces achieved an increase in carbon productivity during the sample period. This indicates that China's continuous efforts to promote intensive and economically efficient development, along with adjustments to industrial structures, contribute to the economy and the environment “Win-win”. Regionally, due to variations in economic development levels, eastern provinces such as Beijing, Shanghai, and Zhejiang exhibited higher growth rates in carbon productivity compared to other regions. This is attributed to the abundant resources in terms of talent, capital, and technology in the eastern regions, providing prerequisites for green and high-quality economic development. In contrast, resource-dependent regions like the northeastern provinces, Ningxia, and Shanxi faced challenges due to factors such as slower economic growth, lower levels of intensification, and the “resource curse”, appearing to be caught in a “carbon productivity poverty trap”. Additionally, it is noteworthy that despite the generally lower performance in many western regions, Sichuan and Chongqing demonstrated particularly remarkable carbon productivity. ② Condition Variables Command-and-control Environmental Regulation (CCER): This type involves government departments formulating laws, regulations, or policies to protect the ecological environment, with the government as the guiding entity. The government’s emphasis on pollution control determines the level of investment in pollution control. Following the approach by Song and Zhang (2022), this study employs the rate of change in the ratio of local government investment in pollution control to GDP from 2017 to 2021 as the measure of the intensity of command-and-control environmental regulation. Market Incentive Environmental Regulation (MIER): This type employs environmental economic measures to include environmental costs in the prices of a company's goods or services, effectively promoting resource utilization and emission reduction. Referring to the study by Ma and Zhang (2022), this paper measures the intensity of market incentive environmental regulation by whether a province possesses carbon emission trading rights. Binary coding is applied for quantification, assigning a value of 1 if the province has carbon emission trading rights and 0 otherwise. Voluntary Environmental Regulation (VER): The core idea of this type is that enterprises or other relevant institutions voluntarily provide public goods or suggestions to improve the environment based on their actual situations. Referring to the study by Luo and Wang (2022), this paper measures the intensity of voluntary environmental regulation using the number of proposals related to environmental protection in the People's Congress and Political Consultative Conference of each province from 2017 to 2021. Recessive Environmental Regulation (RER): This category encompasses the collective environmental awareness and concepts of the general public. The stronger the public’s educational attainment and income level, the higher their demand for environmental quality and the more pronounced their environmental protection intentions. Additionally, a higher proportion of young people correlates with a greater emphasis on environmental quality. Drawing insights from studies by Xia et al. (2017), Ma et al. (2022), this paper adopts four indicators—education level, age structure, income level, and population density—to comprehensively measure the intensity of recessive environmental regulation. Education Level: Measured by the proportion of the population with a college education or above in each province. Age Structure: Measured by the proportion of the population under 15 years old in each province. Income Level: Measured by the average wage of urban employees in each province. Population Density: Measured by the ratio of the total population to the total area of each province. To standardize these indicators for a comprehensive assessment of recessive environmental regulation intensity, this paper utilizes the ratio of provincial GDP to national GDP in 2021 as a weight coefficient. Each indicator is multiplied by its corresponding weight, summed, and then averaged to derive the final composite measure of recessive environmental regulation intensity for each province. 4.3 Variable calibration To adhere to the logical requirements of Boolean algebra, it is essential to calibrate each variable. Only after calibration, where raw data is transformed into fuzzy membership values, can empirical analysis be conducted. It's crucial to note that before calibrating the raw data, calibration thresholds for each variable must be established. This involves using quartiles as anchor points for complete non-membership, crossover, and complete membership. Considering the characteristics of the data, this paper adopts the 75%, 50%, and 25% percentiles as the anchor points for complete membership, crossover, and complete non-membership, respectively, for the variables CCER, VER, RER, and CP (Zhang et al 2019). As MIER has already undergone binary assignment, it does not require further calibration and does not appear in Table 1. Table1 Each variable aligns the anchor point Research Variable complete membership crossover complete non-membership CCER 0.24 0.02 -0.18 Condition Variable VER 0.39 0.19 0.04 RER 788.89 396.99 208.02 Outcome Variable CP 0.30 0.17 -0.02 5 Empirical Analysis Results 5.1 Necessity analysis Before the configurational analysis, it's crucial to assess the necessity of individual conditions, and the result is used as the necessary condition to judge whether a certain condition is a result. A consistency threshold of 0.9 is commonly used, conditions with consistency exceeding this value are considered necessary (Zhang et al. 2019). Examining Tables 2 and 3 reveals that the consistency for individual variables (CCER, MIER, VER, and RER) is consistently below 0.9. This suggests that none of these environmental regulations is individually necessary for the enhancement of carbon productivity. It means that there are “multiple concurrent causes and effects” in the improvement of carbon productivity, which is the result of the “combined punch” of various environmental regulations. Table 2 Necessity analysis results of each variable of high carbon productivity Variable consistency cover degree CCER 0.75 0.76 ～CCER 0.31 0.35 MIER 0.28 0.64 ～MIER 0.72 0.50 VER 0.50 0.54 ～VER 0.57 0.60 RER 0.69 0.73 ～RER 0.38 0.41 Note: "~" represents the "not" of the logical operation, that is, the condition does not exist. The following table is the same. Table3 Necessity analysis results of each variable of no-high carbon productivity Variable consistency cover degree CCER 0.33 0.30 ～CCER 0.74 0.71 MIER 0.18 0.36 ～MIER 0.82 0.50 VER 0.56 0.53 ～VER 0.51 0.47 RER 0.37 0.34 ～RER 0.71 0.66 5.2 Conditional Configuration Analysis Following the necessity analysis, a further examination of conditional configuration sufficiency will be conducted using truth tables. Parameters for the conditional configuration analysis should be set based on research requirements. Referring to studies by Du and Jia (2017), the original consistency threshold is set at 0.8. Considering the limited number of cases (30), an instance frequency threshold is set at 1 to avoid excluding too many practical situations. Additionally, to minimize the likelihood of contradictory configurations, the PRI consistency threshold is set at 0.7 (Greckhamer T et al. 2018). Since the complex solution fails to consider the cases that have not been included in the study, it has a certain one-sidedness, the analysis will focus on contrasting simple and intermediate solutions to identify conditional properties. Specifically, variables present in both simple and intermediate solutions will be identified as core variables, while those appearing solely in intermediate solutions will be deemed marginal conditions (Greckhamer 2016). The results are presented in Table 4. Table4 Environmental regulation configurations that produce high and non-high carbon productivity Condition Variable High carbon productivity No-high carbon productivity Configuration1 Configuration2 Configuration3 Configuration4 Configuration5 CCER ⊗ ● ● ⊗ ⊗ MIER ● ⊗ ● ⊗ VER ● ⊗ ⊗ ● RER • ● ● ⊗ ⊗ Consistency 0.97 0.93 0.89 0.86 0.95 Original coverage 0.07 0.36 0.29 0.05 0.29 Unique coverage 0.05 0.16 0.06 0.05 0.29 Overall consistency 0.92 0.94 Overall coverage 0.49 0.34 Note: ● is the core condition exists; ⊗ is the absence of core condition; • is edge conditions exist; ⊗ is the absence of edge condition; According to Table 4, there are three environmental regulatory configurations (Configuration 1, Configuration 2, Configuration 3) associated with high carbon productivity, and two configurations (Configuration 4, Configuration 5) associated with non-high carbon productivity. The consistency of each condition configuration and overall are higher than 0.75, demonstrating the sufficiency of every configuration in influencing the outcomes. Coverage indicates the explanatory power of configuration solutions for the results. The coverage rates for high carbon productivity and non-high carbon productivity in the table are 49% and 34%, respectively, both surpassing 30%, signifying the robust explanatory power of each configuration on the results. (1) Environmental Regulation Configuration Paths for Achieving High Carbon Productivity Configuration 1: ~CCER×MIER×VER×RER, that is, active environmental protection under the dominance of market incentive. This path indicates that robust market incentive environmental regulation, robust voluntary environmental regulation and non-robust command-and-control environmental regulation are taken as the core conditions, and complementary robust recessive environmental regulation is taken as the edge condition. Configuration 1 exhibits a consistency of 0.97 and an original coverage of 0.07, explaining 7% of the cases. The study reveals that a combination of market incentive, voluntary, and recessive environmental regulations, where the market signals environmental pressure, companies express environmental commitments, and the public effectively supervises environmental protection, can effectively enhance carbon productivity. A typical case of this configuration is observed in Guangdong Province. With robust economic development, Guangdong, amidst transitioning to new energy sources and the “strengthening foundation and consolidating basics” transformation in manufacturing, has witnessed a thriving carbon emission trading market. Accounting for over 35% of the national trading volume, it maintains a leading position among the seven pilot markets. Leveraging market forces to enhance carbon productivity becomes an inevitable choice. Moreover, as an economically influential province, Guangdong increasingly emphasizes the importance of sustainable development, wielding stronger discourse power in relevant proposals. Consequently, the following propositions can be derived: Proposition 1: The combination of robust market incentive and robust voluntary environmental regulations, supplemented by robust recessive environmental regulations, contributes to improving carbon productivity. Configuration 2: CCER×~MIER×RER, i.e., Type I Driven by Government Constraints and Public Supervision. In Configuration 2, robust command-and-control environmental regulation and robust recessive environmental regulation are taken as the core conditions, non-robust market incentive environmental regulation is taken as the edge condition. Configuration 2 exhibits a consistency of 0.93 and an original coverage of 0.36, explaining 36% of the cases. The study reveals that under a non-robust market incentive environmental regulation, when robust command-and-control environmental regulation and robust recessive environmental regulation simultaneously exert force, increasing pollution control investments and compensating for the shortcomings of voluntary environmental regulation through recessive environmental regulation can still yield high carbon productivity. Typical cases of this configuration are observed in Henan and Sichuan provinces. Both provinces, economically developed, have sufficient finances to ensure that they are not stingy in their investment in pollution control. With large populations, higher education levels, and per capita income surpassing the national average, these provinces exhibit a relatively strong awareness of green development. Configuration 3: CCER×~VER×RER, i.e., Type II Driven by Government Constraints and Public Supervision. In Configuration 3, robust command-and-control environmental regulation and robust recessive environmental regulation are taken as the core conditions, supplemented by non-robust voluntary environmental regulation. Configuration 3 exhibits a consistency of 0.89 and an original coverage of 0.29, explaining 29% of the cases. The study reveals that this configuration, similar to Configuration 2, though having non-robust voluntary environmental regulation, does not affect the efficacy of robust command-and-control and robust recessive environmental regulation as core conditions, yielding high carbon productivity. This emphasizes the greater effectiveness of current robust command-and-control environmental regulation. Typical cases of this configuration are observed in Hunan and Jiangsu provinces. Both provinces rank among the top ten economically in the country, especially Jiangsu consistently in the top three. The government holds stronger dominance in pollution control investments. From Configurations 2 and 3, the following proposition can be derived: Proposition 2: The combination of robust command-and-control and robust recessive environmental regulation contributes to improving carbon productivity. As illustrated in Figure 3, it presents typical explanatory cases of the above three configurations corresponding to high carbon productivity solutions. There are two configurations corresponding to non-high carbon productivity, each with core condition distributed in market incentive environmental regulation and voluntary environmental regulation respectively. Overall, these configurations lack a complete chain for achieving high carbon productivity, making it challenging to form configurations with significantly positive outcomes. (2) Environmental Regulation Configuration Pathways to Non-High Carbon Productivity Configuration 4: ~CCER×MIER×~VER×~RER, i.e., Market Incentive Type. In Configuration 4, robust market incentive environmental regulation and non-robust recessive environmental regulation are taken as the core conditions, complemented by non-robust command-and-control environmental regulation and non-robust voluntary environmental regulation as marginal conditions. Configuration 4 exhibits a consistency of 0.86 and an original coverage of 0.05, explaining 5% of the cases. The study reveals that in situations where pollution control investments are insufficient, public supervision is lacking, companies lacking environmental willingness struggle to achieve high carbon productivity solely relying on market incentives. Tianjin is a typical representative of this configuration. Despite being a direct-administered municipality and benefiting from its proximity to Beijing, Tianjin's favorable market environment has not translated into significant achievements in low-carbon transformation in recent years. Configuration 5: ~CCER×~MIER×VER×~RER, i.e., Voluntary Regulation Type. In Configuration 5, robust voluntary environmental regulation, non-robust command-and-control environmental regulation, and non-robust recessive environmental regulation act as core conditions, complemented by non-robust market incentive environmental regulation as a marginal condition. Configuration 5 exhibits a consistency of 0.95 and an original coverage of 0.29, explaining 29% of the cases. The study reveals that this configuration, in contrast to the market incentive environmental regulation and voluntary environmental regulation in Configuration 4, demonstrates that while strong voluntary environmental regulation can compensate for the shortcomings of non-strong recessive environmental regulation, it struggles to bridge the significant gap left by non-robust command-and-control environmental regulation and non-robust market incentive environmental regulation. This indirectly indicates that a singular environmental regulation is not a necessary condition for achieving high carbon productivity. Jilin and Inner Mongolia are typical representatives of this configuration. Despite introducing favorable policies, the absence of an open carbon emission trading market hinders strong market incentives. Additionally, financial constraints on the government make it challenging to undertake large-scale pollution control investments and high carbon productivity, emphasizing the importance of the combined effect of environmental regulations. Based on the above, the following proposition can be derived: Proposition 3: Solely focusing on market development or policy proposals is challenging to effectively improve carbon productivity. As illustrated in Figure 4, it presents typical explanatory cases for the two configurations corresponding to non-high carbon productivity solutions mentioned above. Looking at the overall situation of configurations corresponding to high and non-high carbon productivity, it's evident that the number of conditions in configurations associated with high carbon productivity is significantly higher than those for non-high carbon productivity. In configurations corresponding to high carbon productivity, there are 2-3 core or marginal conditions, whereas configurations for non-high carbon productivity have only one core condition. This suggests that a diversified combination of environmental regulations plays a positive “combination punch” role in enhancing carbon productivity. This aligns with recent policy discussions focusing on policy combinations (Yang et al. 2022), and the idea that policy coordination promotes high-quality development (Lu et al. 2022). Therefore, we can conclude that: Proposition 4: Diverse combinations of environmental regulations contribute to improving carbon productivity. Through the analysis of the five environmental regulation configurations mentioned above, it is evident that there is no symmetry between conditions and outcomes. In other words, the configurations leading to high carbon productivity are not the opposite direction of those leading to non-high carbon productivity. This indicates that merely adjusting the intensity of environmental regulations in the opposite direction cannot achieve a practical transformation from low carbon productivity to high carbon productivity. It also suggests that the “combination punch” of environmental regulations is not a simple accumulation of regulatory tools but should be based on the actual development of the economy and society, adjusting the combination of regulatory tools to effectively achieve the configurational effects of environmental regulation linkage for enhancing carbon productivity. 5.3 Potential relationships between conditions Through the analysis of configuration sets, configuration solutions generating high carbon productivity and non-high carbon productivity have been identified. While individual condition variables cannot independently produce outcomes, a horizontal comparison between configurations can better reveal the potential relationships between conditions. (1)There is a potential substitutive relationship between the combination of robust market incentive environmental regulation and robust voluntary environmental regulation and a single strong command-and-control environmental regulation. Comparing Configuration 1 with Configurations 2 and 3 reveals that in the presence of recessive environmental regulation, command-and-control environmental regulation does not coexist with market incentive environmental regulation and voluntary environmental regulation within a single configuration. However, configurations with either command-and-control environmental regulation or a combination of market incentive and voluntary environmental regulations can both lead to high carbon productivity under specific conditions, indicating a substitutive relationship. This is illustrated in Figure 5. By applying the “fuzzy or” operation in the fsQCA 3.0 software, it is found that the consistency and coverage results for the new combination of factors are both higher than the original results, confirming the existence of this substitutive relationship (refer to Table 5). Table5 Potential relationship analysis results Potential substitution relationships analysis Analysis result (Consistency, coverage) Original result (Consistency, coverage) Conclusion command-and-control and combination of market incentive and voluntary （0.75，0.76） （0.79，0.77） Potential substitution relationships exist (2) Robust command-and-control and robust recessive environmental regulations play a dominant role in the generation of high carbon productivity. The results of the condition configuration analysis indicate that both Configuration 2 and Configuration 3, leading to high carbon productivity, have core conditions of robust command-and-control and robust recessive environmental regulations. In contrast, Configuration 4, leading to non-high carbon productivity, has a core condition of non-robust recessive environmental regulation, and command-and-control environmental regulation is absent. Configuration 5, also leading to non-high carbon productivity, has core conditions of non-robust command-and-control and non-robust recessive environmental regulations. Additionally, Configurations 2 and 3 each have non-robust market incentive and non-robust voluntary environmental regulations as core conditions. However, in configurations corresponding to non-high carbon productivity, Configurations 4 and 5 have robust market incentive and robust voluntary environmental regulations as core conditions. Therefore, the inference drawn is that robust command-and-control and robust recessive environmental regulations play a dominant role in enhancing carbon productivity. 5.4 Robust test Following the approach employed by Zhang et al. (2019) in prior studies utilizing the fsQCA method, a robust test was conducted. Robust tests typically involve raising the original consistency threshold, increasing PRI consistency, or modifying the number of cases—commonly selecting one of these methods for examination. This study, referencing the work of Du et al. (2019), increased the original consistency threshold from 0.8 to 0.85. The test results indicate complete consistency between the new configuration and the original 0.8 consistency threshold. Consequently, this suggests that the findings related to the environmental regulation configuration exhibit strong robustness, as detailed in Table 6. Table6 Robustness analysis of environmental regulation configurations that produce high and non-high carbon productivity Conditional Variable High carbon productivity No-high carbon productivity Configuration1 Configuration2 Configuration3 Configuration4 Configuration5 CCER U ● ● V U MIER ● U ● V VER ● U V ● RER • ● ● U U Consistency 0.97 0.93 0.89 0.86 0.95 Original coverage 0.07 0.36 0.29 0.05 0.29 Unique coverage 0.05 0.16 0.06 0.05 0.29 Overall consistency 0.92 0.94 Overall coverage 0.49 0.34 6 Conclusion 6.1 Research findings This paper, adopting a configurational perspective on the “completeness” of the factor set, establishes a research and analysis framework for the impact of environmental regulation configurations on carbon productivity. Utilizing data from 30 provinces (municipalities, autonomous regions) in China, the fsQCA method is employed to explore, from a configurational perspective, the combined and concurrent effects of command-and-control, market incentive, voluntary, and recessive environmental regulations on carbon productivity. The study reveals a synergistic “combination punch” effect. The conclusions drawn from this analysis are as follows: Firstly, a single factor alone is not a necessary condition for improving carbon productivity, instead, the effective “combination punch” of environmental regulations proves to be the key measure driving enhancements. The inability of command-and-control, market incentive, voluntary, and recessive environmental regulations to individually increase carbon productivity supports this conclusion. It underscores that the pathway to achieve high carbon productivity lies in the complex combination of multiple conditions. This further emphasizes that regions should move away from the current emphasis on traditional singular policies. Optimal improvements in carbon productivity can only be achieved by judiciously selecting and combining types and intensities of environmental regulations based on local conditions. Secondly, there are three environmental regulation configuration pathways to achieve high carbon productivity: namely, the active environmental protection under the dominance of market incentive (Configuration 1), Type I driven by government constraints and public supervision (Configuration 2) and Type II (Configuration 3). According to the configuration analysis results, Configuration 2 is the most prevalent. This suggests that the robust combination of command-and-control and recessive environmental regulations is highly significant. By leveraging government leadership, increasing environmental governance investment, actively promoting green development concepts to the public, and practicing low-carbon lifestyles, carbon productivity can be effectively improved. On the other hand, two pathways leading to non-high carbon productivity are identified: the market incentive type (Configuration 4) and the voluntary regulation type (Configuration 5). This indicates that a singular reliance on market incentive and voluntary corporate environmental initiative cannot achieve high carbon productivity, emphasizing the necessity and imperative of combining various environmental regulatory tools. Thirdly, in the presence of recessive environmental regulation, robust command-and-control environmental regulation can potentially form a substitution relationship with robust market incentive and voluntary environmental regulations. As companies trend towards environmentally friendly transformation and the public exhibits a strong awareness of environmental protection, reducing the compulsory policy intensity by the government can also promote an increase in carbon productivity. Further analysis indicates that robust command-and-control and robust recessive environmental regulations play a central role in the driving pathways of high carbon productivity, serving as the primary conditions for achieving it. Regions can uniformly strengthen both command-and-control environmental regulation and robust rececssive environmental regulation, recognizing the government as both the leader in pollution control and the promoter of green development. 6.2 Research contributions This study makes three primary contributions: Firstly, from a methodological perspective, the introduction of the fsQCA method into the research on environmental regulation and carbon productivity enriches the study of carbon productivity driving mechanisms. The strength of fsQCA lies in its ability to integrate qualitative and quantitative methods, allowing for a thorough exploration of the complexity of antecedent conditions and causal asymmetry, aligning well with the practical complexities of improving carbon productivity Secondly, in terms of research content, this paper shifts the focus from a single environmental regulation driving carbon productivity to the synergistic effects of heterogeneous environmental regulation “combination punch”, expanding theoretical research on the “Porter Hypothesis”. It provides valuable insights for China in constructing a diversified combination of environmental regulation tools. Thirdly, in terms of research conclusions, this study contends that the interaction among heterogeneous environmental regulations, rather than independent effects, leads to different configurations of environmental regulations affecting high and non-high carbon productivity. Further analysis reveals the heterogeneous roles of different types of environmental regulations in various configurations, significantly enhancing the applicability and reliability of strategies for improving carbon productivity. 6.3 Policy recommendations To enhance carbon productivity across provinces (municipalities, autonomous regions) and achieve green, high-quality development, this study proposes the following recommendations: Firstly, Strengthen the use of environmental regulation tool “combination punch”: Local governments should design appropriate environmental regulation intensities and policies based on their actual conditions, considering the advantages and cross-effects of various environmental regulations. They should comprehensively leverage the strengths of different types of environmental regulations, avoiding overreliance on a single regulation. In the implementation of market incentive environmental regulation, consider adding suitable categories and implement precise strategies for different regions. For example, establish marine carbon sink trading markets for coastal provinces like Shandong, Fujian, Guangdong and implement environmental tax policies for regions with excessive carbon emissions, such as Shanxi, actively harnessing market incentive. Secondly, Establish a carbon reduction and emission reduction environment led by the government, responded by the market, voluntarily undertaken by enterprises, and participated by the public: In less economically developed central and western regions, where the willingness and intensity of recessive environmental regulation are weak, the government can build information platforms to guide public, non-profit organizations, and other entities to actively participate in ecological construction. This multi-dimensional collective effort can enhance the level of recessive environmental regulation. During the government-led process, attention should be given to strengthening the “innovation compensation effect” for enterprises, offsetting or reducing the costs incurred by enterprises in adapting to new environmental regulation policies. Thirdly, Adjust the environmental regulation combination in response to global energy shortages, economic downturn, and emission constraints: Enhancing carbon productivity is a crucial measure for achieving a green “Butterfly transformation” under multiple pressures. Governments should actively adjust the combination of environmental regulations, promote low-carbon transformation and green development, and establish a positive long-term mechanism to effectively boost carbon productivity. Particularly in economically underdeveloped areas, there should be a proactive response to the call for “green” actions. Governments can use measures like green finance, investments in key areas, and institutional innovation to drive post-pandemic economic green recovery, continuously addressing the weaknesses in China's green and high-quality development. Declarations Ethics approval Not applicable Consent for participate Not applicable Consent for publication Not applicable Conflict for publication Not applicable Conflict of interest We declared that we have no conflicts of interest in this work. Author Contributions Conceptualization: [Dongri Han, Xiaoli Lv]; Methodology: [Dongri Han, Su Yan]; Software: [Dongri Han]; Writing-original draft: [Xiaoli Lv, Su Yan]; Writing—review and editing: [Dongri Han, Xiaoli Lv, Su Yan]; Funding acquisition: Dongri Han; Resources: Dongri Han; Supervision: [Dongri Han]. Acknowledgments We are very grateful to editors and anonymous reviews for reviewing this paper. Funding Study on driving mechanism and path selection of synergistic effect of pollution reduction and carbon reduction in energy-rich areas under whole-process governance (23CGL041). Availability of data and materials All data can be downloaded from China's National Bureau of Statistics. References Ahmed Z, Ahmad M, Rjoub H et al (2022) Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustain Dev 30(4):595–605 Baker S, Ayala-Orozco B, Garcia-Frapolli E (2020) Hybrid, public and private environmental governance: the case of sustainable coastal zone management in Quintana Roo. Mexico Int J sustainable Dev world Ecol 27(7):625–637 Baloch MA, Danish (2022) CO2 emissions in BRICS countries: what role can environmental regulation and financial development play?[J]. 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Resour Industries 24(01):107–114 Song WF, Han XF (2022) Heterogeneous two-sided effects of different types of environmental regulations on carbon productivity in China. Sci Total Environ (841):156769 Stavropoulos S, Wall R, Xu YZ (2018) Environmental regulations and industrial competitiveness: evidence from China. Appl Econ 50(12):1378–1394 Stokke OS (2007) Qualitative comparative analysis, shaming, and international regime effectiveness. J Bus Res 60(5):501–511 Sun HB, Liu ZL (2021) Environmental regulation, clean-technology innovation and China’s industrial green transformation. Sci Res Manage 42(11):54–61 Testa F, Iraldo F, Frey M (2011) The effect of environmental regulation on firms' competitive performance: the case of the building & construction sector in some EU regions. J Environ Manage 92(9):2136–2144 Wang QW, Zhou P, Zhou DQ (2014) Heterogeneity of production technology, carbon dioxide emission and performance lose: An international comparison based on meta-frontier. Sci Res Manage 35(10):41–48 Wang SY, Wei ZR (2020) Spillover effect of environmental regulations and industrial carbon productivity: empirical study based on provincial panel data of China. Geogr Geo-Information Sci 36(03):83–89 Xia HX, Tan QM, Shang LY (2017) Structural adjustment effect of industry collaborative cluster informal environmental regulation——based on the empirical tests with the static Fama-Macbeth estimation and GMM model. Soft Sci 31(04):9–14 Yang W, Lao XY, Zhou Q, Zhang L (2022) The governance niche configurations for the resilience of regional digital innovation ecosystem. Stud Sci Sci 40(03):534–544 Zhang GX, Feng YC, Wang AL (2021) The heterogeneous effects of different types of environmental regulation on technological innovation of industrial enterprises. Manage Rev 33(01):92–102 Zhang M, Chen WH, Lan HL (2019) Why do Chinese enterprises completely acquire foreign high-tech enterprises——A fuzzy set qualitative comparative analysis (fsQCA) based on 94 cases. 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Cite Share Download PDF Status: Published Journal Publication published 03 May, 2024 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted Editorial decision: Revision requested 11 Feb, 2024 Reviews received at journal 01 Feb, 2024 Reviewers agreed at journal 22 Jan, 2024 Reviewers invited by journal 21 Jan, 2024 Editor assigned by journal 16 Jan, 2024 Submission checks completed at journal 06 Jan, 2024 First submitted to journal 06 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3838878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265564050,"identity":"53014d97-1efc-488b-9b81-5428aba9c2d9","order_by":0,"name":"Dongri Han","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongri","middleName":"","lastName":"Han","suffix":""},{"id":265564051,"identity":"04ea5f61-f41b-450d-9c4a-1c3344405b8f","order_by":1,"name":"Xiaoli Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIie3RsUrEQBCA4T0Wcs1o2jmE4AsIAwsRIeCD2GwQtlI4m8PiwISDpBGuzWMc+AJzDFy16COYe4MtFQQ9WyERO4v9qyn2K2ZWqVjsPzat632gz+xy3bK83Rc61Vr6UQIiBufaEPpSlHfTWZs4GiXoHELQ5UZZI5NGUnqBUxwTZ9UNIVJizhVbuWvciRFQpJbF1RDJ2RPNCbKLVc3SPRcmlyPu1c7dVkNk+0gWCY1itgwLd53LsaVJJcNEgBiIyortYUjk4WkFhKNkl5QVkC03ByLQiCb9G/FaNBKbWefttvNOoxyObMd2ed237+GDszRtJYTvr1yL9GFZDJKB7N+ex2KxWOxHXx7PZpQjERfnAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Lv","suffix":""},{"id":265564052,"identity":"6d122334-7175-444b-80fb-487128f5896f","order_by":2,"name":"Su Yan","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-01-06 05:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3838878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3838878/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10098-024-02834-x","type":"published","date":"2024-05-03T19:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49382428,"identity":"c2aba431-722c-42f4-a9b5-95472646453b","added_by":"auto","created_at":"2024-01-09 19:21:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58751,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Model\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/915e13fe02579c3a3aa7ab57.png"},{"id":49382432,"identity":"491796dc-7aaa-420b-9f10-6a84c55af76e","added_by":"auto","created_at":"2024-01-09 19:21:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29491,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon productivity in 2017 and 2021 and its rate of change between 2017 and 2021\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/4b5493a07a97624d617423c4.png"},{"id":49382930,"identity":"677e5731-ce22-4723-83cd-1ba075330658","added_by":"auto","created_at":"2024-01-09 19:37:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20644,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the case of high carbon productivity\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/9ff0e701d06f967091dec7ff.png"},{"id":49382600,"identity":"8274a3ab-295e-4adc-84ac-3b5febd1b457","added_by":"auto","created_at":"2024-01-09 19:29:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15925,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the case of no-high carbon productivity\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/65e49c60df14b3ac2ad59fd1.png"},{"id":49382431,"identity":"91a0f76d-a8e0-440f-8cc1-0fd844489271","added_by":"auto","created_at":"2024-01-09 19:21:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12298,"visible":true,"origin":"","legend":"\u003cp\u003ePotential substitutional relationships between command-and-control and combination of market incentive and voluntary\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/940f1aac8f973d11d0e0b83f.png"},{"id":56042912,"identity":"381f5477-e07a-4942-b150-384d26b9dc9f","added_by":"auto","created_at":"2024-05-07 20:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046803,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3838878/v1/023868fc-de84-4b0f-9d6b-e3c00433b724.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the “combination punch” effect of heterogeneous environmental regulation linkage on carbon productivity","fulltext":[{"header":"1 Instruction","content":"\u003cp\u003eIn 2022, China\u0026apos;s carbon emissions will still exceed 100 billion tons, accounting for 32.19% of the world\u0026apos;s total. It can be seen that China\u0026apos;s ecological environment situation is still grim, and it is facing multiple pressures such as post-epidemic economic recovery, air quality constraints, and the realization of the \u0026ldquo;double carbon\u0026rdquo; goal. It is a long way to go to realize the construction of a beautiful China (Guo and Li 2022). Different from developed countries in crossing the inflection point of the environmental Kuznets curve, China\u0026apos;s current green high-quality development faces two strategic tasks at the same time: economic growth, carbon peaking and carbon neutrality. The requirement for low-carbon growth is further highlighted, and carbon productivity is the key characteristic index of the \u0026ldquo;dilemma\u0026rdquo; pattern of maintaining growth and promoting emission reduction (Wang et al. 2014). Improving carbon productivity has become an inevitable choice for the economic and social development of China to realize a comprehensive green transformation in the new stage of development (Sun and Liu 2021).\u003c/p\u003e\n\u003cp\u003eFrom the perspective of economics, the environment is typically non-competitive and non-exclusive, and both regions and enterprises have the motivation of \u0026ldquo;free riding\u0026rdquo; behavior. The key to solving environmental problems is to formulate and implement reasonable and effective environmental regulation measures. Existing studies mainly start from the single environmental regulation policy of command type or market type, and explore the regulatory effects such as \u0026ldquo;Porter Hypothesis\u0026rdquo; (Ramanathan et al. 2015; Murty and Kumar 2003), \u0026ldquo;Innovation Compensation Effect\u0026rdquo; (Farooq et al. 2023; Li et al. 2020), \u0026ldquo;Compliance Cost Theory\u0026rdquo;(Shi and Zhao 2018; Qiao 2021). Due to the multi-domain and complexity of environmental regulation policies, a single end-treatment model has been unable to meet the needs of coordinated promotion of pollution control and green high-quality (Baker and Ayala-Orozco 2020), and the \u0026ldquo;combination\u0026rdquo; of regulation based on multiple policy coordination undoubtedly provides a \u0026ldquo;new answer\u0026rdquo; for the improvement of carbon productivity (Dzwigol et al. 2023). As a \u0026ldquo;combination\u0026rdquo; of traditional regulatory means and regulatory policies, the combination of environmental regulation can effectively avoid the regulatory deviation of a single policy, crack the drawbacks of different regulatory policies\u0026apos; mutual constraints and conflicts, and then effectively improve the regulatory effect. Based on the internal correlation between environmental regulation policy coordination, carbon emissions and economic green growth, it can be inferred that the \u0026ldquo;combination\u0026rdquo; of environmental regulation is bound to have a profound impact on carbon productivity. Therefore, it is of great practical significance to build a carbon productivity improvement model based on environmental regulation linkage.\u003c/p\u003e\n\u003cp\u003eBased on the perspective of the \u0026ldquo;holistic\u0026rdquo; configuration of the element set, this paper uses the fuzzy set qualitative comparative analysis method to explore the \u0026ldquo;combination\u0026rdquo; effect of heterogeneous environmental regulation on carbon productivity by taking 30 provinces (autonomous regions and municipalities directly under the central government) in mainland China as a case study, in order to explore the causal relationship between different types of environmental regulation and carbon productivity and the potential relationship between different types of environmental regulation. The conclusion of this paper provides useful reference and practical guidance for constructing a multi-environmental regulation system and promoting environmental regulation behavior from \u0026ldquo;bottom-to-bottom competition\u0026rdquo; to \u0026ldquo;top-to-top competition\u0026rdquo;.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Heterogeneous environment regulation classification and coordination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the field of public policy, environmental regulation, as a typical environmental management policy tool, aims to achieve the goal of environmental protection through tangible or intangible forms, relying on the role of laws and regulations, and plays a crucial role in pollution reduction and economic green development. Since environmental regulation policies are different in terms of policy implementation subjects, regulation objects and function objectives, scholars have conducted relevant studies on environmental regulation heterogeneity from different perspectives. In the beginning, scholars mainly discussed the command-and-control environmental regulation led by the government and the market incentive environmental regulation with market-oriented economic incentives, and put forward the \u0026ldquo;dichotomy\u0026rdquo; of environmental regulation. On this basis, Zhang et al. (2021) and Ren et al. (2018) put forward the \u0026ldquo;three-part method\u0026rdquo;, arguing that importance should be attached to the role of voluntary environmental regulation. Bu et al. (2020) showed through empirical analysis that voluntary environmental regulation significantly improved the innovation performance of enterprises.\u0026nbsp;Galinato and Chouinard\u0026nbsp;(2018) paid attention to the environmental supervision power of the public, and proposed to take the recessive environmental regulation into consideration. In addition,\u0026nbsp;Demirel\u0026nbsp;et al. (2018) divided environmental regulation into formal regulation and informal regulation according to regulation forms.\u0026nbsp;Nazir\u0026nbsp;et al. (2023) divided environmental regulation into two types based on cost: expense-type regulation and investment-type regulation.\u0026nbsp;Bashir\u0026nbsp;et al. (2022) divided environmental regulation into five categories at the level of policy formulation: water pollution control, air pollution control and solid pollution control, etc.\u003c/p\u003e\n\u003cp\u003eIn recent years, some scholars have proposed that it is easy to make the mistake of \u0026ldquo;one-size-fits-all\u0026rdquo; to analyze environmental regulation from the overall level by using a single indicator (Song and Han 2022), and the mutual constraints and conflicts among policies will also lead to regulatory failures, so it is inevitable and necessary to explore effective policy combinations for environmental governance. The idea of enhancing the effectiveness of policies by constructing complementary and interdependent \u0026ldquo;policy package\u0026rdquo; has been widely recognized by scholars at home and abroad. Nissinen et al. (2015) proposed that Finland\u0026apos;s \u0026ldquo;package\u0026rdquo; regulatory policies have significant pollution control effects, and it is expected that 4.5 million tons of carbon emissions will be reduced in 2020. According to the research of Gong and Sun (2021), the combination of diversified policy tools, such as normative policy texts and carbon emission trading, is an effective guarantee for achieving energy conservation and emission reduction targets in the central and western regions. Murshed et al. (2021) proposed the combination of market-based environmental regulation tools and command-and-control environmental regulation in industry, which is conducive to improving the flexibility and effectiveness of pollution control measures. It is worth noting that the regulatory policy combination does not simply inject the \u0026ldquo;new wine\u0026rdquo; into the \u0026ldquo;old bottle\u0026rdquo;, but adopts the \u0026ldquo;optimal\u0026rdquo; coupling model according to policy objectives, tool preferences, governance levels, etc., to crack the risks of the policy combination and ensure the effect of policy implementation (Testa ey al.\u0026nbsp;2011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Environmental regulation and carbon productivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the summary of the existing literature, the basic paths of environmental regulation on high-quality economic green development mainly include (1) direct action path. Due to the external diseconomy of environmental pollution, environmental regulation, as an important category of government social regulation, effectively regulates the production and operation activities of enterprises through administrative penalties, emission reduction constraints and other means, which helps to achieve the improvement of carbon productivity (Baloch\u0026nbsp;and\u0026nbsp;Danish\u0026nbsp;2022). (2)Industrial structure adjustment path. As an effective forcing mechanism, environmental regulation can promote the orderly withdrawal of energy-intensive industries, promote the upgrading and optimization of industrial structure, and then effectively coordinate sustainable economic development and environmental protection, and improve carbon productivity (Yuan and\u0026nbsp;Stavropoulos et al.\u0026nbsp;2018). (3) Technological progress path. \u0026ldquo;Porter\u0026apos;s hypothesis\u0026rdquo; points out that appropriate environmental regulation can maximize the role of \u0026ldquo;innovation compensation\u0026rdquo; to make up for the \u0026ldquo;compliance cost\u0026rdquo; of enterprises, further improve the level of technological innovation, promote the coordinated development of economic growth and environmental protection, and enhance carbon productivity (Fernando and Wah\u0026nbsp;2017). Shen and Liu (2012) pointed out that there are regional differences in the implementation of \u0026ldquo;Porter\u0026apos;s hypothesis\u0026rdquo;, and it is not that the higher the intensity of regulation, the more effective it is in environmental pollution. In addition, some scholars have explored the low-carbon effect of environmental regulations based on the adjustment of energy structure (Bye\u0026nbsp;and\u0026nbsp;Klemetsen\u0026nbsp;2018) and the development of green finance (Ahmed et al.\u0026nbsp;2022), etc. However, some scholars have pointed out that due to the differences in the implementation of environmental standards among regions, polluting enterprises reduce the cost of environmental governance through relocation, resulting in the effect of pollution refuge, resulting in the inability of environmental regulation to effectively improve carbon productivity (Raff and Earnhart\u0026nbsp;2022).\u003c/p\u003e\n\u003cp\u003eIn summary, scholars have explored the feasibility and effectiveness of environmental regulation tools from various perspectives. While some scholars have recognized the necessity of combining multiple types of environmental regulations and demonstrated that the governance effectiveness of compound policies often surpasses that of singular policy (Pizer 2002), comprehensive studies on different configurations of environmental regulation tools are still lacking. Particularly, there is a dearth of empirical testing regarding the enhancing effects of environmental regulation combinations on carbon productivity. Therefore, this paper aims to clarify the roles of various environmental regulation tools, further uncover the \u0026ldquo;combination punch\u0026rdquo; effects of different types of environmental regulation tool configurations in enhancing carbon productivity in China. It seeks to reveal the potential relationships among environmental regulations and provide recommendations and references for the practical application of viable environmental regulations across regions, fostering green and high-quality development.\u003c/p\u003e"},{"header":"3 Analytical Framework","content":"\u003cp\u003eIn accordance with the \u0026ldquo;Fourfold Classification\u0026rdquo; of environmental regulation policy tools (Wang and Wei 2020), this paper unfolds its analysis across four dimensions: command-and-control, market incentive, voluntary environmental regulation, and recessive environmental regulation. To elaborate:\u003c/p\u003e\n\u003cp\u003eCommand-and-control environmental regulation is established on the foundation of government enforcement. It primarily regulates and constrains pollution emissions generated by enterprises to achieve the goals of pollution reduction. Policy tools in this category include laws and regulations, technical standards, intensity standards. Command-and-control tools possess strong binding force, are widely applied, and can effectively constrain pollution behavior within a region. Their implementation is swift, and the effects are determinable. However, they lack flexibility and incentives, leading to higher execution costs (Graafland 2019). Through the imposition of emission constraints, command-and-control environmental regulation compels enterprises to undergo a green \u0026ldquo;Butterfly transformation\u0026rdquo;, thereby enhancing carbon productivity.\u003c/p\u003e\n\u003cp\u003eMarket incentive environmental regulation, based on the principle of \u0026ldquo;polluter pays\u0026rdquo;, employs market mechanisms such as imposing emission fees and promoting carbon emission trading to reduce enterprise carbon emissions. In comparison to command-and-control environmental regulation, enterprises have greater autonomy, allowing them to make decisions based on their operational circumstances. Market incentive environmental regulation relies on the inherent regulatory mechanisms of the market. Its advantages lie in autonomy, strong flexibility, and the ability to stimulate innovation. However, it requires a higher degree of market mechanism completeness (Millimet and Roy 2016).By kindling enthusiasm for innovation within enterprises and guiding them toward \u0026ldquo;green\u0026rdquo; production, market incentive environmental regulation aims to enhance carbon productivity.\u003c/p\u003e\n\u003cp\u003eVoluntary environmental regulation is grounded in diverse voluntary mechanisms involving public expressions of environmental concerns, social organizations supervising regional enterprises environmental departments, enterprises autonomously implementing energy conservation and emission reduction measures, etc. This gradual reduction in pollution emissions is achieved through policy tools like environmental certification systems, ecological labeling systems, and voluntary agreements, etc. Voluntary environmental regulation reduces the cost of environmental regulation and significantly stimulates the motivation of polluters to reduce emissions, offering high flexibility. While it has lower binding force and may exhibit a time lag (Rassier and Earnhart 2015). Taking ISO 14001 as an example, enterprises with environmental commitment signal their intentions to external stakeholders, prioritize environmental policies and emission standards, adjust environmental management practices, and consequently enhance carbon productivity.\u003c/p\u003e\n\u003cp\u003eRecessive environmental regulation is a form of spontaneous collective environmental action undertaken by the public or non-governmental organizations based on their own environmental awareness and for their own interests. As societal environmental consciousness grows, and public awareness of environmental issues increases, recessive environmental regulation has become a new force in enhancing environmental governance capabilities and refining a \u0026ldquo;diversified co-governance\u0026rdquo; environmental governance system. It gradually propels government regulatory agencies to scientifically allocate regulatory resources, thereby improving regional environmental quality. Policy tools primarily include protest, complaint, and supervision, etc. Recessive environmental regulation boasts lower implementation costs, high flexibility, and grants enterprises significant autonomy and choice regarding pollution reduction measures. It demonstrates remarkable environmental improvement effects and strong timeliness (Danish et al. 2020). Under the supervision and guidance of recessive environmental regulation, enterprises actively respond to environmental pressures, relying on knowledge updates and green innovations to enhance carbon productivity.\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned, this paper incorporates command-and-control, market incentive, voluntary environmental regulation, and recessive environmental regulation into the same research framework, exploring the multifaceted and concurrent impact of different environmental regulation types on carbon productivity.\u003c/p\u003e"},{"header":"4 Research Methods and Data Processing","content":"\u003cp\u003e\u003cstrong\u003e4.1 Research methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualitative Comparative Analysis (QCA) is a data analysis method developed based on set theory and Boolean algebra, specifically designed for analyzing small and medium-sized sample cases. Combining the strengths of qualitative and quantitative analysis, QCA explores complex causality between the outcome variable and multiple condition variables. It derives multiple complex driving paths of interactions among factors (Zhang et al. 2020). In essence, this method involves selecting relevant outcome and condition variables, logically combining and comparing cases, identifying logical relationships, and simplifying the results to derive configurations (Fiss 2011).\u003c/p\u003e\n\u003cp\u003eThe selection of Fuzzy Set Qualitative Comparative Analysis (fsQCA) for investigating the impact of heterogeneous environmental regulation on carbon productivity is primarily motivated by the following reasons:\u003c/p\u003e\n\u003cp\u003e(1) Environmental regulation itself exhibits characteristics of multi-domain and complexity, and the classification of heterogeneous environmental regulations intensifies these complexities. Moreover, the enhancement of carbon productivity is not solely determined by a specific type of environmental regulation policy. The fsQCA method excels in exploring the interplay and matching of different types of factors, providing a strong explanatory power in this context.\u003c/p\u003e\n\u003cp\u003e(2) Identifying the environmental regulation configuration pathways that can improve carbon productivity is a crucial issue addressed in this article. The fsQCA method, combining qualitative and quantitative approaches, emphasizes delving into the antecedent conditions complexity and causal asymmetry. It allows for a more profound interpretation of different driving pathways.\u003c/p\u003e\n\u003cp\u003e(3) The various types of environmental regulations selected in this study all influence the improvement of carbon productivity. The fsQCA method holds a significant advantage in identifying the presence of complementary or substitution relationships. This facilitates a thorough analysis of whether complementary or substitution relationships exist among different types of environmental regulations in enhancing carbon productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Data source and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Case Selection\u003c/p\u003e\n\u003cp\u003eTo fulfill the requirements of ample case representation and maximize heterogeneity among cases, and considering the suitability of fuzzy set qualitative comparative analysis for small to medium-sized samples, this study selects data from 30 provinces (autonomous regions and direct-administered municipalities) in China as research samples (excluding Tibet and Hong Kong, Macao, and Taiwan for data accessibility reasons) (Stokke 2007). The data is derived from the \u003cem\u003eChina Statistical Yearbook\u003c/em\u003e, \u003cem\u003eChina Environmental Database\u003c/em\u003e and provincial statistical yearbooks for the years 2017-2021. In instances where data for specific years or provinces are sparse, linear interpolation is employed for supplementation.\u003c/p\u003e\n\u003cp\u003e(2) Variable Selection\u003c/p\u003e\n\u003cp\u003e①\u0026nbsp;Outcome Variable\u003c/p\u003e\n\u003cp\u003eCarbon productivity refers to the level of CO2 emissions per unit of GDP output. Since regional carbon emission data is currently not publicly available, it is estimated using the IPCC\u0026apos;s CO2 calculation method. Following the methodology presented by Chen (2022), this study uses the rate of change in carbon productivity as the measurement for the outcome variable (Zhong and Zhao 2021). The carbon productivity change rate for the years 2017-2021 is calculated based on the carbon productivity data for 2017 and 2021, utilizing the formula for annual growth rate.\u003c/p\u003e\n\u003cp\u003eFrom Figure 2, it is evident that the majority of provinces achieved an increase in carbon productivity during the sample period. This indicates that China\u0026apos;s continuous efforts to promote intensive and economically efficient development, along with adjustments to industrial structures, contribute to the economy and the environment \u0026ldquo;Win-win\u0026rdquo;. Regionally, due to variations in economic development levels, eastern provinces such as Beijing, Shanghai, and Zhejiang exhibited higher growth rates in carbon productivity compared to other regions. This is attributed to the abundant resources in terms of talent, capital, and technology in the eastern regions, providing prerequisites for green and high-quality economic development. In contrast, resource-dependent regions like the northeastern provinces, Ningxia, and Shanxi faced challenges due to factors such as slower economic growth, lower levels of intensification, and the \u0026ldquo;resource curse\u0026rdquo;, appearing to be caught in a \u0026ldquo;carbon productivity poverty trap\u0026rdquo;. Additionally, it is noteworthy that despite the generally lower performance in many western regions, Sichuan and Chongqing demonstrated particularly remarkable carbon productivity.\u003c/p\u003e\n\u003cp\u003e②\u0026nbsp;Condition Variables\u003c/p\u003e\n\u003cp\u003eCommand-and-control Environmental Regulation (CCER): This type involves government departments formulating laws, regulations, or policies to protect the ecological environment, with the government as the guiding entity. The government\u0026rsquo;s emphasis on pollution control determines the level of investment in pollution control. Following the approach by Song and Zhang (2022), this study employs the rate of change in the ratio of local government investment in pollution control to GDP from 2017 to 2021 as the measure of the intensity of command-and-control environmental regulation.\u003c/p\u003e\n\u003cp\u003eMarket Incentive Environmental Regulation (MIER): This type employs environmental economic measures to include environmental costs in the prices of a company\u0026apos;s goods or services, effectively promoting resource utilization and emission reduction. Referring to the study by Ma and Zhang (2022), this paper measures the intensity of market incentive environmental regulation by whether a province possesses carbon emission trading rights. Binary coding is applied for quantification, assigning a value of 1 if the province has carbon emission trading rights and 0 otherwise.\u003c/p\u003e\n\u003cp\u003eVoluntary Environmental Regulation (VER): The core idea of this type is that enterprises or other relevant institutions voluntarily provide public goods or suggestions to improve the environment based on their actual situations. Referring to the study by Luo and Wang (2022), this paper measures the intensity of voluntary environmental regulation using the number of proposals related to environmental protection in the People\u0026apos;s Congress and Political Consultative Conference of each province from 2017 to 2021.\u003c/p\u003e\n\u003cp\u003eRecessive Environmental Regulation (RER): This category encompasses the collective environmental awareness and concepts of the general public. The stronger the public\u0026rsquo;s educational attainment and income level, the higher their demand for environmental quality and the more pronounced their environmental protection intentions. Additionally, a higher proportion of young people correlates with a greater emphasis on environmental quality. Drawing insights from studies by Xia et al. (2017), Ma et al. (2022), this paper adopts four indicators\u0026mdash;education level, age structure, income level, and population density\u0026mdash;to comprehensively measure the intensity of recessive environmental regulation. Education Level: Measured by the proportion of the population with a college education or above in each province. Age Structure: Measured by the proportion of the population under 15 years old in each province. Income Level: Measured by the average wage of urban employees in each province. Population Density: Measured by the ratio of the total population to the total area of each province. To standardize these indicators for a comprehensive assessment of recessive environmental regulation intensity, this paper utilizes the ratio of provincial GDP to national GDP in 2021 as a weight coefficient. Each indicator is multiplied by its corresponding weight, summed, and then averaged to derive the final composite measure of recessive environmental regulation intensity for each province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Variable calibration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo adhere to the logical requirements of Boolean algebra, it is essential to calibrate each variable. Only after calibration, where raw data is transformed into fuzzy membership values, can empirical analysis be conducted. It\u0026apos;s crucial to note that before calibrating the raw data, calibration thresholds for each variable must be established. This involves using quartiles as anchor points for complete non-membership, crossover, and complete membership. Considering the characteristics of the data, this paper adopts the 75%, 50%, and 25% percentiles as the anchor points for complete membership, crossover, and complete non-membership, respectively, for the variables CCER, VER, RER, and CP (Zhang et al 2019). As MIER has already undergone binary assignment, it does not require further calibration and does not appear in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1 Each variable aligns the anchor point\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.545126353790614%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eResearch Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.84115523465704%\" valign=\"top\"\u003e\n \u003cp\u003ecomplete membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07942238267148%\" valign=\"top\"\u003e\n \u003cp\u003ecrossover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.534296028880867%\" valign=\"top\"\u003e\n \u003cp\u003ecomplete non-membership\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.216606498194945%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.32851985559567%\" valign=\"top\"\u003e\n \u003cp\u003eCCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.84115523465704%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07942238267148%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.534296028880867%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.216606498194945%\" valign=\"top\"\u003e\n \u003cp\u003eCondition Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.32851985559567%\" valign=\"top\"\u003e\n \u003cp\u003eVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.84115523465704%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07942238267148%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.534296028880867%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.216606498194945%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.32851985559567%\" valign=\"top\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.84115523465704%\" valign=\"top\"\u003e\n \u003cp\u003e788.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07942238267148%\" valign=\"top\"\u003e\n \u003cp\u003e396.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.534296028880867%\" valign=\"top\"\u003e\n \u003cp\u003e208.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.216606498194945%\" valign=\"top\"\u003e\n \u003cp\u003eOutcome Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.32851985559567%\" valign=\"top\"\u003e\n \u003cp\u003eCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.84115523465704%\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07942238267148%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.534296028880867%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5 Empirical Analysis Results","content":"\u003cp\u003e\u003cstrong\u003e5.1 Necessity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore the configurational analysis, it\u0026apos;s crucial to assess the necessity of individual conditions, and the result is used as the necessary condition to judge whether a certain condition is a result. A consistency threshold of 0.9 is commonly used, conditions with consistency exceeding this value are considered necessary (Zhang et al. 2019). Examining Tables 2 and 3 reveals that the consistency for individual variables (CCER, MIER, VER, and RER) is consistently below 0.9. This suggests that none of these environmental regulations is individually necessary for the enhancement of carbon productivity. It means that there are \u0026ldquo;multiple concurrent causes and effects\u0026rdquo; in the improvement of carbon productivity, which is the result of the \u0026ldquo;combined punch\u0026rdquo; of various environmental regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Necessity analysis results of each variable of high carbon productivity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003econsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003ecover degree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003eCCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003e～CCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003eMIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003e～MIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003eVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003e～VER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"35.684647302904565%\"\u003e\n \u003cp\u003e～RER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.892116182572614%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.42323651452282%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026quot;~\u0026quot; represents the \u0026quot;not\u0026quot; of the logical operation, that is, the condition does not exist. The following table is the same.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eTable3 Necessity analysis results of each variable of no-high carbon productivity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003econsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003ecover degree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003eCCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003e～CCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003eMIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003e～MIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003eVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003e～VER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25.8772%;\" valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003e～RER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.8421%;\" valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.096774193548384%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Conditional Configuration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the necessity analysis, a further examination of conditional configuration sufficiency will be conducted using truth tables. Parameters for the conditional configuration analysis should be set based on research requirements. Referring to studies by Du and Jia (2017), the original consistency threshold is set at 0.8. Considering the limited number of cases (30), an instance frequency threshold is set at 1 to avoid excluding too many practical situations. Additionally, to minimize the likelihood of contradictory configurations, the PRI consistency threshold is set at 0.7 (Greckhamer T et al. 2018). Since the complex solution fails to consider the cases that have not been included in the study, it has a certain one-sidedness, the analysis will focus on contrasting simple and intermediate solutions to identify conditional properties. Specifically, variables present in both simple and intermediate solutions will be identified as core variables, while those appearing solely in intermediate solutions will be deemed marginal conditions (Greckhamer 2016). The results are presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable4 Environmental regulation configurations that produce high and non-high carbon productivity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" rowspan=\"2\" valign=\"top\" width=\"20.818505338078293%\"\u003e\n \u003cp\u003eCondition Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.4211%;\" colspan=\"3\" valign=\"top\" width=\"48.04270462633452%\"\u003e\n \u003cp\u003eHigh carbon productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.13879003558719%\"\u003e\n \u003cp\u003eNo-high carbon productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"21.252796420581657%\"\u003e\n \u003cp\u003eConfiguration1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.686800894854585%\"\u003e\n \u003cp\u003eConfiguration2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.686800894854585%\"\u003e\n \u003cp\u003eConfiguration3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.686800894854585%\"\u003e\n \u003cp\u003eConfiguration4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.686800894854585%\"\u003e\n \u003cp\u003eConfiguration5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eCCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026otimes;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eMIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026otimes;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.74468085106383%\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.9474%;\" valign=\"top\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.602836879432624%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.818505338078293%\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.4211%;\" colspan=\"3\" valign=\"top\" width=\"48.04270462633452%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.13879003558719%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.2105%;\" valign=\"top\" width=\"20.818505338078293%\"\u003e\n \u003cp\u003eOverall coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54.4211%;\" colspan=\"3\" valign=\"top\" width=\"48.04270462633452%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.13879003558719%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ● is the core condition exists; \u0026otimes; is the absence of core condition; \u0026bull; is edge conditions exist; \u003csup\u003e\u0026otimes;\u003c/sup\u003e is the absence of edge condition;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Table 4, there are three environmental regulatory configurations (Configuration 1, Configuration 2, Configuration 3) associated with high carbon productivity, and two configurations (Configuration 4, Configuration 5) associated with non-high carbon productivity. The consistency of each condition configuration and overall are higher than 0.75, demonstrating the sufficiency of every configuration in influencing the outcomes. Coverage indicates the explanatory power of configuration solutions for the results. The coverage rates for high carbon productivity and non-high carbon productivity in the table are 49% and 34%, respectively, both surpassing 30%, signifying the robust explanatory power of each configuration on the results.\u003c/p\u003e\n\u003cp\u003e(1) Environmental Regulation Configuration Paths for Achieving High Carbon Productivity\u003c/p\u003e\n\u003cp\u003eConfiguration 1: ~CCER\u0026times;MIER\u0026times;VER\u0026times;RER, that is, active environmental protection under the dominance of market incentive. This path indicates that\u0026nbsp;robust\u0026nbsp;market incentive environmental regulation, robust voluntary environmental regulation and non-robust command-and-control environmental regulation are taken as the core conditions, and complementary robust recessive environmental regulation is taken as the edge condition. Configuration 1 exhibits a consistency of 0.97 and an original coverage of 0.07, explaining 7% of the cases. The study reveals that a combination of market incentive, voluntary, and recessive environmental regulations, where the market signals environmental pressure, companies express environmental commitments, and the public effectively supervises environmental protection, can effectively enhance carbon productivity. A typical case of this configuration is observed in Guangdong Province. With robust economic development, Guangdong, amidst transitioning to new energy sources and the \u0026ldquo;strengthening foundation and consolidating basics\u0026rdquo; transformation in manufacturing, has witnessed a thriving carbon emission trading market. Accounting for over 35% of the national trading volume, it maintains a leading position among the seven pilot markets. Leveraging market forces to enhance carbon productivity becomes an inevitable choice. Moreover, as an economically influential province, Guangdong increasingly emphasizes the importance of sustainable development, wielding stronger discourse power in relevant proposals. Consequently, the following propositions can be derived:\u003c/p\u003e\n\u003cp\u003eProposition 1: The combination of robust market incentive and robust voluntary environmental regulations, supplemented by robust recessive environmental regulations, contributes to improving carbon productivity.\u003c/p\u003e\n\u003cp\u003eConfiguration 2: CCER\u0026times;~MIER\u0026times;RER, i.e., Type I Driven by Government Constraints and Public Supervision. In Configuration 2, robust command-and-control environmental regulation and robust recessive environmental regulation are taken as the core conditions, non-robust market incentive environmental regulation is taken as the edge condition. Configuration 2 exhibits a consistency of 0.93 and an original coverage of 0.36, explaining 36% of the cases. The study reveals that under a non-robust market incentive environmental regulation, when robust command-and-control environmental regulation and robust recessive environmental regulation simultaneously exert force, increasing pollution control investments and compensating for the shortcomings of voluntary environmental regulation through recessive environmental regulation can still yield high carbon productivity. Typical cases of this configuration are observed in Henan and Sichuan provinces. Both provinces, economically developed, have sufficient finances to ensure that they are not stingy in their investment in pollution control. With large populations, higher education levels, and per capita income surpassing the national average, these provinces exhibit a relatively strong awareness of green development.\u003c/p\u003e\n\u003cp\u003eConfiguration 3: CCER\u0026times;~VER\u0026times;RER, i.e., Type II Driven by Government Constraints and Public Supervision. In Configuration 3, robust command-and-control environmental regulation and robust recessive environmental regulation are taken as the core conditions, supplemented by non-robust voluntary environmental regulation. Configuration 3 exhibits a consistency of 0.89 and an original coverage of 0.29, explaining 29% of the cases. The study reveals that this configuration, similar to Configuration 2, though having non-robust voluntary environmental regulation, does not affect the efficacy of robust command-and-control and robust recessive environmental regulation as core conditions, yielding high carbon productivity. This emphasizes the greater effectiveness of current robust command-and-control environmental regulation. Typical cases of this configuration are observed in Hunan and Jiangsu provinces. Both provinces rank among the top ten economically in the country, especially Jiangsu consistently in the top three. The government holds stronger dominance in pollution control investments. From Configurations 2 and 3, the following proposition can be derived:\u003c/p\u003e\n\u003cp\u003eProposition 2: The combination of robust command-and-control and robust recessive environmental regulation contributes to improving carbon productivity.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 3, it presents typical explanatory cases of the above three configurations corresponding to high carbon productivity solutions.\u003c/p\u003e\n\u003cp\u003eThere are two configurations corresponding to non-high carbon productivity, each with core condition distributed in market incentive environmental regulation and voluntary environmental regulation respectively. Overall, these configurations lack a complete chain for achieving high carbon productivity, making it challenging to form configurations with significantly positive outcomes.\u003c/p\u003e\n\u003cp\u003e(2) Environmental Regulation Configuration Pathways to Non-High Carbon Productivity\u003c/p\u003e\n\u003cp\u003eConfiguration 4: ~CCER\u0026times;MIER\u0026times;~VER\u0026times;~RER, i.e., Market Incentive Type. In Configuration 4, robust market incentive environmental regulation and non-robust recessive environmental regulation are taken as the core conditions, complemented by non-robust command-and-control environmental regulation and non-robust voluntary environmental regulation as marginal conditions. Configuration 4 exhibits a consistency of 0.86 and an original coverage of 0.05, explaining 5% of the cases. The study reveals that in situations where pollution control investments are insufficient, public supervision is lacking, companies lacking environmental willingness struggle to achieve high carbon productivity solely relying on market incentives. Tianjin is a typical representative of this configuration. Despite being a direct-administered municipality and benefiting from its proximity to Beijing, Tianjin\u0026apos;s favorable market environment has not translated into significant achievements in low-carbon transformation in recent years.\u003c/p\u003e\n\u003cp\u003eConfiguration 5: ~CCER\u0026times;~MIER\u0026times;VER\u0026times;~RER, i.e., Voluntary Regulation Type. In Configuration 5, robust voluntary environmental regulation, non-robust command-and-control environmental regulation, and non-robust recessive environmental regulation act as core conditions, complemented by non-robust market incentive environmental regulation as a marginal condition. Configuration 5 exhibits a consistency of 0.95 and an original coverage of 0.29, explaining 29% of the cases. The study reveals that this configuration, in contrast to the market incentive environmental regulation and voluntary environmental regulation in Configuration 4, demonstrates that while strong voluntary environmental regulation can compensate for the shortcomings of non-strong recessive environmental regulation, it struggles to bridge the significant gap left by non-robust command-and-control environmental regulation and non-robust market incentive environmental regulation. This indirectly indicates that a singular environmental regulation is not a necessary condition for achieving high carbon productivity. Jilin and Inner Mongolia are typical representatives of this configuration. Despite introducing favorable policies, the absence of an open carbon emission trading market hinders strong market incentives. Additionally, financial constraints on the government make it challenging to undertake large-scale pollution control investments and high carbon productivity, emphasizing the importance of the combined effect of environmental regulations. Based on the above, the following proposition can be derived:\u003c/p\u003e\n\u003cp\u003eProposition 3: Solely focusing on market development or policy proposals is challenging to effectively improve carbon productivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs illustrated in Figure 4, it presents typical explanatory cases for the two configurations corresponding to non-high carbon productivity solutions mentioned above.\u003c/p\u003e\n\u003cp\u003eLooking at the overall situation of configurations corresponding to high and non-high carbon productivity, it\u0026apos;s evident that the number of conditions in configurations associated with high carbon productivity is significantly higher than those for non-high carbon productivity. In configurations corresponding to high carbon productivity, there are 2-3 core or marginal conditions, whereas configurations for non-high carbon productivity have only one core condition. This suggests that a diversified combination of environmental regulations plays a positive \u0026ldquo;combination punch\u0026rdquo; role in enhancing carbon productivity. This aligns with recent policy discussions focusing on policy combinations (Yang et al. 2022), and the idea that policy coordination promotes high-quality development (Lu et al. 2022). Therefore, we can conclude that:\u003c/p\u003e\n\u003cp\u003eProposition 4: Diverse combinations of environmental regulations contribute to improving carbon productivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThrough the analysis of the five environmental regulation configurations mentioned above, it is evident that there is no symmetry between conditions and outcomes. In other words, the configurations leading to high carbon productivity are not the opposite direction of those leading to non-high carbon productivity. This indicates that merely adjusting the intensity of environmental regulations in the opposite direction cannot achieve a practical transformation from low carbon productivity to high carbon productivity. It also suggests that the \u0026ldquo;combination punch\u0026rdquo; of environmental regulations is not a simple accumulation of regulatory tools but should be based on the actual development of the economy and society, adjusting the combination of regulatory tools to effectively achieve the configurational effects of environmental regulation linkage for enhancing carbon productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Potential relationships between conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough the analysis of configuration sets, configuration solutions generating high carbon productivity and non-high carbon productivity have been identified. While individual condition variables cannot independently produce outcomes, a horizontal comparison between configurations can better reveal the potential relationships between conditions.\u003c/p\u003e\n\u003cp\u003e(1)There is a potential substitutive relationship between the combination of robust market incentive environmental regulation and robust voluntary environmental regulation and a single strong command-and-control environmental regulation. Comparing Configuration 1 with Configurations 2 and 3 reveals that in the presence of recessive environmental regulation, command-and-control environmental regulation does not coexist with market incentive environmental regulation and voluntary environmental regulation within a single configuration. However, configurations with either command-and-control environmental regulation or a combination of market incentive and voluntary environmental regulations can both lead to high carbon productivity under specific conditions, indicating a substitutive relationship. This is illustrated in Figure 5.\u003c/p\u003e\n\u003cp\u003eBy applying the \u0026ldquo;fuzzy or\u0026rdquo; operation in the fsQCA 3.0 software, it is found that the consistency and coverage results for the new combination of factors are both higher than the original results, confirming the existence of this substitutive relationship (refer to Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable5 Potential relationship analysis results\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u0026nbsp;\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.598513011152416%\"\u003e\n \u003cp\u003ePotential substitution relationships analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.53531598513011%\"\u003e\n \u003cp\u003eAnalysis result\u003c/p\u003e\n \u003cp\u003e(Consistency, coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.53531598513011%\"\u003e\n \u003cp\u003eOriginal result\u003c/p\u003e\n \u003cp\u003e(Consistency, coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.33085501858736%\"\u003e\n \u003cp\u003eConclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.598513011152416%\"\u003e\n \u003cp\u003ecommand-and-control and combination of market incentive and voluntary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.53531598513011%\"\u003e\n \u003cp\u003e（0.75，0.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.53531598513011%\"\u003e\n \u003cp\u003e（0.79，0.77）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.33085501858736%\"\u003e\n \u003cp\u003ePotential substitution relationships exist\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(2) Robust command-and-control and robust recessive environmental regulations play a dominant role in the generation of high carbon productivity. The results of the condition configuration analysis indicate that both Configuration 2 and Configuration 3, leading to high carbon productivity, have core conditions of robust command-and-control and robust recessive environmental regulations. In contrast, Configuration 4, leading to non-high carbon productivity, has a core condition of non-robust recessive environmental regulation, and command-and-control environmental regulation is absent. Configuration 5, also leading to non-high carbon productivity, has core conditions of non-robust command-and-control and non-robust recessive environmental regulations. Additionally, Configurations 2 and 3 each have non-robust market incentive and non-robust voluntary environmental regulations as core conditions. However, in configurations corresponding to non-high carbon productivity, Configurations 4 and 5 have robust market incentive and robust voluntary environmental regulations as core conditions. Therefore, the inference drawn is that robust command-and-control and robust recessive environmental regulations play a dominant role in enhancing carbon productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Robust test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the approach employed by Zhang et al. (2019) in prior studies utilizing the fsQCA method, a robust test was conducted. Robust tests typically involve raising the original consistency threshold, increasing PRI consistency, or modifying the number of cases\u0026mdash;commonly selecting one of these methods for examination. This study, referencing the work of Du et al. (2019), increased the original consistency threshold from 0.8 to 0.85. The test results indicate complete consistency between the new configuration and the original 0.8 consistency threshold. Consequently, this suggests that the findings related to the environmental regulation configuration exhibit strong robustness, as detailed in Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable6 Robustness analysis of environmental regulation configurations that produce high and non-high carbon productivity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"20.795660036166364%\"\u003e\n \u003cp\u003eConditional Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"47.55877034358047%\"\u003e\n \u003cp\u003eHigh carbon productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.645569620253166%\"\u003e\n \u003cp\u003eNo-high carbon productivity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eConfiguration1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eConfiguration2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eConfiguration3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eConfiguration4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eConfiguration5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eCCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eMIER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eRER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.72072072072072%\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.855855855855856%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.795660036166364%\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"47.55877034358047%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.645569620253166%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.795660036166364%\"\u003e\n \u003cp\u003eOverall coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"47.55877034358047%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"31.645569620253166%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003e\u003cstrong\u003e6.1 Research findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper, adopting a configurational perspective on the \u0026ldquo;completeness\u0026rdquo; of the factor set, establishes a research and analysis framework for the impact of environmental regulation configurations on carbon productivity. Utilizing data from 30 provinces (municipalities, autonomous regions) in China, the fsQCA method is employed to explore, from a configurational perspective, the combined and concurrent effects of command-and-control, market incentive, voluntary, and recessive environmental regulations on carbon productivity. The study reveals a synergistic \u0026ldquo;combination punch\u0026rdquo; effect. The conclusions drawn from this analysis are as follows:\u003c/p\u003e\n\u003cp\u003eFirstly, a single factor alone is not a necessary condition for improving carbon productivity, instead, the effective \u0026ldquo;combination punch\u0026rdquo; of environmental regulations proves to be the key measure driving enhancements. The inability of command-and-control, market incentive, voluntary, and recessive environmental regulations to individually increase carbon productivity supports this conclusion. It underscores that the pathway to achieve high carbon productivity lies in the complex combination of multiple conditions. This further emphasizes that regions should move away from the current emphasis on traditional singular policies. Optimal improvements in carbon productivity can only be achieved by judiciously selecting and combining types and intensities of environmental regulations based on local conditions.\u003c/p\u003e\n\u003cp\u003eSecondly, there are three environmental regulation configuration pathways to achieve high carbon productivity: namely, the active environmental protection under the dominance of market incentive (Configuration 1), Type I driven by government constraints and public supervision (Configuration 2) and Type II (Configuration 3). According to the configuration analysis results, Configuration 2 is the most prevalent. This suggests that the robust combination of command-and-control and recessive environmental regulations is highly significant. By leveraging government leadership, increasing environmental governance investment, actively promoting green development concepts to the public, and practicing low-carbon lifestyles, carbon productivity can be effectively improved. On the other hand, two pathways leading to non-high carbon productivity are identified: the market incentive type (Configuration 4) and the voluntary regulation type (Configuration 5). This indicates that a singular reliance on market incentive and voluntary corporate environmental initiative cannot achieve high carbon productivity, emphasizing the necessity and imperative of combining various environmental regulatory tools.\u003c/p\u003e\n\u003cp\u003eThirdly, in the presence of recessive environmental regulation, robust command-and-control environmental regulation can potentially form a substitution relationship with robust market incentive and voluntary environmental regulations. As companies trend towards environmentally friendly transformation and the public exhibits a strong awareness of environmental protection, reducing the compulsory policy intensity by the government can also promote an increase in carbon productivity. Further analysis indicates that robust command-and-control and robust recessive environmental regulations play a central role in the driving pathways of high carbon productivity, serving as the primary conditions for achieving it. Regions can uniformly strengthen both command-and-control environmental regulation and robust rececssive environmental regulation, recognizing the government as both the leader in pollution control and the promoter of green development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Research contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study makes three primary contributions:\u003c/p\u003e\n\u003cp\u003eFirstly, from a methodological perspective, the introduction of the fsQCA method into the research on environmental regulation and carbon productivity enriches the study of carbon productivity driving mechanisms. The strength of fsQCA lies in its ability to integrate qualitative and quantitative methods, allowing for a thorough exploration of the complexity of antecedent conditions and causal asymmetry, aligning well with the practical complexities of improving carbon productivity\u003c/p\u003e\n\u003cp\u003eSecondly, in terms of research content, this paper shifts the focus from a single environmental regulation driving carbon productivity to the synergistic effects of heterogeneous environmental regulation \u0026ldquo;combination punch\u0026rdquo;, expanding theoretical research on the \u0026ldquo;Porter Hypothesis\u0026rdquo;. It provides valuable insights for China in constructing a diversified combination of environmental regulation tools.\u003c/p\u003e\n\u003cp\u003eThirdly, in terms of research conclusions, this study contends that the interaction among heterogeneous environmental regulations, rather than independent effects, leads to different configurations of environmental regulations affecting high and non-high carbon productivity. Further analysis reveals the heterogeneous roles of different types of environmental regulations in various configurations, significantly enhancing the applicability and reliability of strategies for improving carbon productivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Policy recommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo enhance carbon productivity across provinces (municipalities, autonomous regions) and achieve green, high-quality development, this study proposes the following recommendations:\u003c/p\u003e\n\u003cp\u003eFirstly, Strengthen the use of environmental regulation tool \u0026ldquo;combination punch\u0026rdquo;: Local governments should design appropriate environmental regulation intensities and policies based on their actual conditions, considering the advantages and cross-effects of various environmental regulations. They should comprehensively leverage the strengths of different types of environmental regulations, avoiding overreliance on a single regulation. In the implementation of market incentive environmental regulation, consider adding suitable categories and implement precise strategies for different regions. For example, establish marine carbon sink trading markets for coastal provinces like Shandong, Fujian, Guangdong and implement environmental tax policies for regions with excessive carbon emissions, such as Shanxi, actively harnessing market incentive.\u003c/p\u003e\n\u003cp\u003eSecondly, Establish a carbon reduction and emission reduction environment led by the government, responded by the market, voluntarily undertaken by enterprises, and participated by the public: In less economically developed central and western regions, where the willingness and intensity of recessive environmental regulation are weak, the government can build information platforms to guide public, non-profit organizations, and other entities to actively participate in ecological construction. This multi-dimensional collective effort can enhance the level of recessive environmental regulation. During the government-led process, attention should be given to strengthening the \u0026ldquo;innovation compensation effect\u0026rdquo; for enterprises, offsetting or reducing the costs incurred by enterprises in adapting to new environmental regulation policies.\u003c/p\u003e\n\u003cp\u003eThirdly, Adjust the environmental regulation combination in response to global energy shortages, economic downturn, and emission constraints: Enhancing carbon productivity is a crucial measure for achieving a green \u0026ldquo;Butterfly transformation\u0026rdquo; under multiple pressures. Governments should actively adjust the combination of environmental regulations, promote low-carbon transformation and green development, and establish a positive long-term mechanism to effectively boost carbon productivity. Particularly in economically underdeveloped areas, there should be a proactive response to the call for \u0026ldquo;green\u0026rdquo; actions. Governments can use measures like green finance, investments in key areas, and institutional innovation to drive post-pandemic economic green recovery, continuously addressing the weaknesses in China\u0026apos;s green and high-quality development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declared that we have no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: [Dongri Han, Xiaoli Lv]; Methodology: [Dongri Han, Su Yan]; Software: [Dongri Han]; Writing-original draft: [Xiaoli Lv, Su Yan]; Writing\u0026mdash;review and editing: [Dongri Han, Xiaoli Lv, Su Yan]; Funding acquisition: Dongri Han; Resources: Dongri Han; Supervision: [Dongri Han].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to editors and anonymous reviews for reviewing this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy on driving mechanism and path selection of synergistic effect of pollution reduction and carbon reduction in energy-rich areas under whole-process governance (23CGL041).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data can be downloaded from China\u0026apos;s National Bureau of Statistics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed Z, Ahmad M, Rjoub H et al (2022) Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. 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Nankai J (Philosophy Literature Social Sci Ed) (05):97\u0026ndash;109\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Environmental Regulation Configuration, Carbon Productivity, fsQCA, “Combination Punch” Effect","lastPublishedDoi":"10.21203/rs.3.rs-3838878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3838878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBuilding an effective environmental regulation \u0026ldquo;combination punch\u0026rdquo; to improve carbon productivity is of great significance for achieving the \u0026ldquo;dual carbon\u0026rdquo; goal. Taking 30 provinces (municipalities and autonomous regions) in my country as research cases, the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method is used to explore the impact of command-and-control, market incentive, voluntary and recessive environmental regulation linkages on carbon productivity. The research found that: ① A single type of environmental regulation antecedent condition does not constitute a necessary condition for the improvement of carbon productivity, and the linkage of heterogeneous environmental regulation has a significant \u0026ldquo;combination punch\u0026rdquo; effect on the improvement of carbon productivity. ②There are three configuration paths to achieve high carbon productivity, namely active environmental protection under the leadership of market incentive, government restraint and public supervision and promotion type I, and government restraint and public supervision and promotion type II. ③ There is a potential substitution relationship between the combination of robust market incentive and robust voluntary environmental regulation and a single robust command-and-control environmental regulation. The conclusions can provide policy suggestions and useful references for formulating an effective and diversified environmental regulation tool portfolio.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Research on the “combination punch” effect of heterogeneous environmental regulation linkage on carbon productivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-09 19:21:38","doi":"10.21203/rs.3.rs-3838878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-11T17:36:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-01T18:16:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"dcfe8c4c-4903-4b73-867d-c5ebc5e5ba1a","date":"2024-01-22T05:50:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-22T02:55:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-16T16:15:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-06T10:41:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2024-01-06T05:15:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"02e42119-a73b-480f-afe5-b011a185e93b","owner":[],"postedDate":"January 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T20:04:17+00:00","versionOfRecord":{"articleIdentity":"rs-3838878","link":"https://doi.org/10.1007/s10098-024-02834-x","journal":{"identity":"clean-technologies-and-environmental-policy","isVorOnly":false,"title":"Clean Technologies and Environmental Policy"},"publishedOn":"2024-05-03 19:58:21","publishedOnDateReadable":"May 3rd, 2024"},"versionCreatedAt":"2024-01-09 19:21:38","video":"","vorDoi":"10.1007/s10098-024-02834-x","vorDoiUrl":"https://doi.org/10.1007/s10098-024-02834-x","workflowStages":[]},"version":"v1","identity":"rs-3838878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3838878","identity":"rs-3838878","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}