1. Introduction
The pursuit of ecological civilization is an integral component of China’s contemporary modernization agenda, particularly within the industrial sector, which is centered around enhancing sustainable development in industrial enterprises. Commencing in the early 21st century, amidst China’s rapid economic expansion and accelerated industrialization, environmental concerns have become significant impediments to socio-economic progression. The industrial sector, being a primary resource consumer and a central source of pollution, is pivotal to ecological transformation initiatives. In addressing the balance between industrial growth and environmental protection, the Chinese government has enacted a comprehensive array of environmental regulations, positioning industrial greening as a critical national development goal. The Eleventh Five-Year Plan (2006–2010) of China marked the initial establishment of quantifiable energy efficiency and emissions reduction goals, which were subsequently expanded and strengthened in the Twelfth (2011–2015) and Thirteenth (2016–2020) Five-Year Plans, clarifying green development as a primary pathway for economic advancement. The government has also issued numerous directives to promote industrial greening, including but not limited to the Action Plan for Air Pollution Prevention and Control, the Action Plan for Water Pollution Prevention and Control, revisions to the Law on the Prevention and Control of Environmental Pollution by Solid Waste and the Implementation Guide for the Green Manufacturing Project (2016–2020). These regulations specify pollution reduction requirements and champion the advancement of green manufacturing practices, the erection of green factories and the development of green supply chains. Simultaneously, the state actively supports the adoption and diffusion of energy-conserving and ecofriendly technologies and products, supporting industrial enterprises by encouraging technological innovation with financial incentives, tax relief and green financing options.
Enshrined within the Fourteenth Five-Year Plan (2021–2025) and the vision delineated for 2035, the People’s Republic of China has enunciated strategic milestones of attaining “carbon peak” and advancing toward “carbon neutrality”. These goals are anticipated to bolster policy support and proffer guidance essential for fostering sustainability in the nation’s industrial landscape. This strategic pivot is designed to synchronize the trajectory of economic expansion with the imperatives of environmental stewardship, catalyzing the progressive evolution and augmentation of the industrial and energy matrices. On an international scale, China pledges to fortify its congruence with global environmental governance frameworks, engage proactively in collaborative efforts addressing climate change and adhere unwaveringly to the stipulations of international environmental treaties, inclusive of the Paris Agreement. Such commitments underscore China’s resolve to contribute actively to the stewardship of the global environment and the pursuit of sustainable development. Through the implementation of these strategic policies and diligent efforts, China is incrementally transitioning its industrial growth model. This transition is characterized by a steadfast commitment to enhancing the efficiency of resource utilization and the environmental benchmarks in industrial production, thus advancing toward an ecologically resilient economic structure and high-quality developmental outcomes.
To safeguard the equilibrium of the coexistence between humans and the natural environment, governmental entities across various tiers have intensified efforts in environmental governance through myriad approaches, encompassing the enactment of environmental regulations and levying of ecological taxes. The intent underpinning these initiatives is the regulation of industrial production activities and the safeguarding of regional ecosystems. Nevertheless, the incremental intensification of environmental policy frameworks has precipitously increased the financial burdens associated with industrial effluent management, concomitantly exerting an impact on the nation’s industrial landscape and its prospective developmental trajectory. Concurrently, with the burgeoning endorsement of green production modalities and sustainable production philosophies, the “ecological efficiency” paradigm is progressively being integrated within the rubric of sustainable development proficiency assessments. Pertaining to industrial entities, “ecological efficiency” underscores the imperative to optimize resource utilization efficacy in congruence with extant technological capacities. This optimization strives to concurrently amplify industrial value generation and truncate resource depletion and waste generation, thereby mitigating deleterious ecological ramifications. Post-economic reform and liberalization, exogenous capital infusion, the in-sourcing of expertise and the currents of the global marketplace have indubitably catalyzed both technological refinement and industrial economic flourishing within the Chinese milieu. Nonetheless, given the extrinsic nature of ecological variables, such advancements do not unequivocally translate to substantive elevations in industrial ecological efficiency.
With the escalation of industrialization, the societal pursuit of a higher standard of living is increasingly met through the strategic provision of fundamental material resources. As enterprises persist in generating industrial value, a pronounced increase in resource depletion and an attendant intensification of industrial pollution emerge. To comply with environmental mandates, a tranche of corporate investment is directed toward environmental remediation and the innovation of sustainable technologies. The impact of such regulatory frameworks on the ecological efficiency of industrial firms hinges on a cost-benefit analysis, balancing expenditures against the gains from regulatory adherence. The 20th National Congress of the Communist Party of China underscored the imperative of accelerating the transition to green development practices, underscoring sustainability as a core tenet of China’s unique industrial modernization narrative. Consequently, a rigorous evaluation of the ecological efficiency within the industrial domain is vital, not merely for improving industrial zone environments but also for fortifying the foundation for sustained and stable industrial progression. This evaluation further provides an essential schema for strategic policy development, catalyzing a green transformation and expediting the evolution of industrial modernization.
Within the milieu wherein globalization and the pursuit of sustainable development have emerged as a global consensus, the crafting and enforcement of environmental regulatory frameworks exert profound effects on both the environmental quality of regions and the economic prowess along with the competitive edge of industrial firms. The promulgation of the Porter Hypothesis has shed light on the complex interplay between environmental stewardship and industrial ecological efficiency. Contemporary scholarly endeavors in the domain of “environmental regulation and industrial enterprise ecological efficiency” are predominantly concentrated in three critical spheres: Firstly, rigorous investigations pertaining to the precise quantification, assessment and spatial variance in the ecological efficiency of industrial entities. In detail, a constellation of academics has embarked on this path by delving into the essence of ecological efficiency, formulating comprehensive “input-output” indicator matrices and harnessing analytical tools such as data envelopment analysis [
1,
2,
3], stochastic frontier models [
2,
4] and ecological footprint methodologies [
5,
6] to ascertain relative efficiencies. The dimensions of industrial ecological efficiency are explored through diverse lenses by researchers, ranging from macroeconomic assessments across nations like China [
1], South Korea [
2] and other burgeoning economies including the Philippines [
7] to granular scrutiny of individual industrial precincts [
8], corporations [
9] or specific ventures [
6]. The corpus of research unveils that despite a trajectory of ascension in ecological efficiency among Chinese industrial enterprises [
10], stark inefficiencies prevail across provinces [
11] with more ecologically efficient hotspots clustering in the eastern territories [
12]. This intimates that the greening of China’s industrial framework is yet in a formative phase, beset by developmental disparities and inadequacies that necessitate further amelioration. It is postulated that the spatial concentration of industrial ecological efficiency within certain locales could be ascribed to a convergence of analogous production conditions alongside the aggregation of human and technological capitals [
13,
14]. In aggregate, while the metrication of industrial ecological efficiency has been a focal point of academic discourse, the absence of a standardized methodology and a consensus on indicators constrain the cross-comparison of disparate findings. Henceforth, it is imperative that future inquiries refine, harmonize and standardize the metrics for appraising the ecological efficiency of industrial enterprises and that any development of such evaluative frameworks intricately accounts for the distinct characteristics intrinsic to diverse regional and industrial production ecosystems.
The second strand of investigation addresses the intricate interplay between industrial ecological efficiency and the combined forces of green production and environmental safeguarding. The enhancement of industrial ecological efficiency not only necessitates the adept management of production workflows and the refinement of resource allocation strategies [
15,
16] but also mandates the stewardship and elevation of services provided by industrial ecosystems [
17,
18,
19], a critical facet for the continuous provision of industrial goods and the sustainable progression of urban milieus. Empirical research delineates the intricate and multifaceted nexus between improved industrial ecological efficiency and environmental fortification. Efficiently orchestrated energy stewardship and the cyclic utilization of waste serve to amplify output per product unit, hence diminishing the prospects of resource depletion and environmental perils [
20,
21]. Conversely, industries adhering to ecofriendly practices play a pivotal role in pollution mitigation, ecological diversity preservation and natural resource stewardship, all of which are foundational to the durability of industrial progression and environmental solidity [
22,
23]. Furthermore, the advancement in industrial ecological efficiency acts as a bulwark for industrial sectors against the vicissitudes of economic perturbations and climatic shifts [
12,
24], thus enhancing their adaptive capacity to impending uncertainties. Nevertheless, it has been observed that an increase in ecological efficiency does not consistently act as a catalyst for production enhancement [
25]. High ecological efficiency in industries might entail considerable investments in capital and technology, which could initially pose challenges for smaller-scale enterprises [
26,
27]. Thus, in the quest to boost industrial ecological efficiency across diverse production dimensions and economic climates, the imperative for equitable and inclusive strategies within the industrial framework cannot be overstated. In summation, the intensification of industrial ecological efficiency is vital for steering the course toward sustainable development. The reformation of industrial production modalities, through the adoption of clean production methodologies and the utilization of high-efficiency energy technologies, not only uplifts industrial ecological efficiency and curtails environmental degradation but also stabilizes industrial throughput in the long run, betters the quality of life for the workforce and contributes to the aggregate welfare of society.
The tertiary focus encompasses investigations into the “mechanisms dictating how environmental regulations modulate the ecological efficiency within the industrial sector”. As a strategic lever, environmental regulations exert a direct influence on industrial firms’ production choices and operational tactics, channeling enhancement in ecological efficiency. Scholars have argued that inflexible environmental regulations serve as a catalyst, compelling firms to diminish emissions and optimize energy use. Their methodologies often entail the development of an integrated indicator framework to quantify the stringency of environmental regulations prior to probing their effects on ecological efficiency [
28]. In theory, such proximate policies advocate for reduced resource utilization and lessened environmental impact during manufacturing, thereby substantively elevating industrial ecological efficiency. Yet, it is recognized that the influence of disparate regulatory frameworks on ecological efficiency may be divergent [
29,
30]. The consolidated assessment of these indicators might lead to an attenuation of observable effects due to partial neutralizations. Therefore, measures like environmental taxes [
31,
32], emission intensity [
33] and the volume of environmental complaints [
31] are sequentially utilized as representative proxies for market-oriented, prescriptive and participatory environmental regulations to investigate their differential impacts on ecological efficiency. From the standpoint of “compliance costs,” the extant literature infers that environmental regulations can escalate production expenditures and erode profit margins, with the encumbrance of environmental management displacing investments in environmental innovation [
34], thereby impeding improvements in ecological efficiency. Conversely, through the lens of “innovation compensation,” prudent environmental regulations may engender technological inventiveness in corporations. Once the innovation effect is fully operational, it can frequently counterbalance the financial burden introduced by environmental regulations [
35], thus serving the dual purpose of environmental amelioration and productivity enhancement. In practice, the repercussions of varying environmental regulations on the economic framework may be starkly contrasting [
36,
37] and even within the same typology of regulation, its influence on ecological efficiency may exhibit regional- and level-specific variances. These variations may be interlinked with the extant production paradigm of enterprises and the regional distribution of natural resources [
38]. The indirect conduits through which environmental regulations affect industrial ecological efficiency are multifarious and intricate. These pathways potentially involve technological ingenuity [
39], enterprise behavioral and strategic realignments [
40], market forces [
41] and competitive edge [
42] as well as corporate social responsibility and public engagement. The body of existing research furnishes pivotal insights into comprehending how environmental regulations exert a nuanced and profound influence on corporate ecological efficiency, necessitating continued dissection and scholarly pursuit.
In summary, the extant body of literature serves as a valuable reference for investigating the effects of environmental regulation on the ecoefficiency of industrial enterprises, yet it exhibits potential areas for broader inquiry. Firstly, extant scholarly works have chiefly employed indicators including patents related to clean production processes [
43], the environmental consciousness of top management [
44] and metrics of carbon emissions [
45,
46] to explore the capacity for sustainable advancement in corporations. Nevertheless, scholarly endeavors that provide a direct evaluation of ecological efficiency in the industrial corporate sphere are noticeably scant, accompanied by a significant lack of consistent evaluative criteria and methodological frameworks. Secondly, while current research identifies environmental regulation [
3,
47], industrial structure [
48] and R&D investment [
49] as key factors influencing ecoefficiency, it seldom incorporates macrolevel variables like economic development, global integration or the science and technology landscape as moderators in examining the interplay between environmental regulation and industrial ecoefficiency. Thirdly, prior analyses have largely centered on the correlation between ecoefficiency and the synergistic progression of environmental systems [
50], spatiotemporal dynamics [
51,
52] or industry-specific heterogeneity [
53]. Although research has acknowledged spatial spillover phenomena for environmental regulation [
54] and industrial ecoefficiency [
55], studies directly probing the spatial interdependencies between environmental regulation and industrial ecoefficiency remain scarce. This research evaluates the ecological efficiency of industrial enterprises across various regions from 2003 to 2021, employing the global Super-SBM model. Drawing upon theoretical frameworks such as Porter’s hypothesis, this study examines the impact of command-based as well as market-based environmental regulations on industrial ecological efficiency. The censored least absolute deviation spatial Durbin model (CLAD-SDM), alongside the analysis of multiple moderating effects, is utilized to substantiate these impact mechanisms, focusing on the magnitude and spatial distribution of diverse environmental regulations’ effects on ecological efficiency. The objective is to provide an empirical foundation for advancing sustainable industrial practices, shaping green development trajectories and fostering the progress of China’s industrial modernization.
This research has multiple contributions. The study develops a comprehensive ecoefficiency measurement system tailored for industrial enterprises and applies it to assess the ecoefficiency of Chinese industrial firms from 2003 to 2021. This approach contributes to the growing body of literature on industrial green growth. The research employs a detailed multiregulatory effect framework to analyze the influences of economic progress, international commerce and the technological environment on the interplay between environmental regulation and the ecoefficiency of industrial enterprises. This analysis clarifies the complex role of moderating variables within this context. Finally, the paper provides an in-depth analysis of both the direct impacts and spatial spillovers of market-based versus command-based environmental regulations on industrial ecoefficiency, employing the Clad-SDM model. This method addresses statistical distortions common in conventional econometric models and confirms the robustness of the findings, which has global implications for the development of environmental policies and sustainable industry evolution in other emerging economies.
The structure of the remainder of the document is as follows:
Section 2 presents the theoretical framework and formulates research hypotheses concerning the varied effects of environmental regulations on the ecoefficiency of industrial enterprises.
Section 3 outlines the index system designed for measuring industrial enterprise ecoefficiency, explains the econometric models used to assess the impact of various environmental regulations on ecoefficiency and describes variable definitions and data sources.
Section 4 presents the findings of the empirical analysis and expands the discussion with insights into regional diversity and robustness assessments.
Section 5 discusses the systematic design and refinement of ecoefficiency assessments for industrial firms, explains how various environmental regulatory frameworks influence ecoefficiency and identifies the associated spatial regularities. Furthermore, this section identifies current gaps in the research and suggests potential improvements.
Section 6, summarizing the manuscript’s content, offers final insights and recommends policy actions.
4. Results
4.1. Correlation Statistical Tests
- (1)
Unit root test
To safeguard against the risk of spurious regression arising from the extensive chronological range of the data, it is customary to administer unit root tests to evaluate the constancy of variable trends before proceeding with the empirical analysis. In this context, the current investigation undertook four widely recognized tests—Levin, Lin & Chu (LLC), Harris & Tzavalis (HT), Im, Pesaran & Shin (IPS) and Fisher-Augmented Dickey–Fuller (Fisher-ADF)—to examine the stationarity of the variables implicated in the regression. The results, delineated in
Table 3, reveal that all variables conform to the LLC test criteria; the majority satisfy at least three tests, while some fulfill the requirements of only one or two. In alignment with a conservative testing approach, variables that fail to meet the threshold of at least one test from this battery of four are preliminarily classified as nonstationary.
Table 3 reveals that, using the rigorous criteria of the quadruple approach, variables mer, cer, itc and trn are classified as stationary, obviating the need for differencing. Therefore, the present study implements differencing on variables eff, eco, tec, tax, fdi, inno and rd, resulting in first-order differenced series. The results of the unit root tests for these series are exhibited in
Table 4.
Table 4 reveals that, with a 1% significance threshold, the first-order differenced series for eff, eco, tec, tax, fdi, inno and rd successfully undergo the unit root test, corroborating that these variables are integrated of order one.
- (2)
Panel Cointegration Test
In light of the unit root test outcomes, a thorough investigation into the long-term equilibrium dynamics among the variables within the model was conducted using a multifaceted approach that encompasses the Kao, Pedroni and Westerlund cointegration tests. The findings of this examination are detailed in
Table 5.
The evidence presented in
Table 5 unequivocally suggests that all employed test methodologies corroborate the formation of a cointegration relationship among the variables under consideration. Consequently, the model is deemed appropriate for subsequent investigations into the nexus between environmental regulation and the ecological efficiency of industrial enterprises.
- (3)
Multicollinearity Test
Given the complexity of the research model with its multitude of variables, assessing multicollinearity is critical to ensure the accuracy of the model’s estimations, which can be compromised by high intercorrelations among the independent variables. The variance inflation factor (VIF) serves as the standard diagnostic tool to detect the presence of collinearity, where a VIF near zero denotes minimal multicollinearity. It is widely accepted that a VIF value below 10 indicates a tolerable level of collinearity, obviating the need for remedial actions.
Table 6 presents the results of multicollinearity diagnostics conducted on the study variables.
As delineated in
Table 6, the variance inflation factor (VIF) values for all assessed variables fall below the threshold of 10 [
78,
79]. This is in accordance with established statistical norms, which implies a negligible presence of multicollinearity within the variable set, thereby justifying the advancement to regression analysis.
4.2. Analysis of Direct and Moderating Effects
Utilizing Equation (1), we established an ordinary least squares (OLS) model that employs clustered robust standard errors to evaluate the effect of environmental regulation on the ecological efficiency of industrial enterprises, as detailed in
Table 3. Model 1 delineates the regression outcomes using only two principal explanatory variables. In contrast, Model 2 extends the analysis by incorporating additional control variables. Subsequent models (3–7) integrate various interaction terms to explore more complex relationships: Model 3 examines the interplay between economic development and market-based environmental regulation (interaction
); Model 4 investigates the interaction of the degree of openness with market-based environmental regulation (interaction
); Model 5 assesses the influence of economic development level when combined with command-based environmental regulation (interaction
); Model 6 analyzes the effects of technology market development level in tandem with command-based environmental regulation (interaction
); and Model 7 considers how the innovation level interacts with command-based environmental regulation (interaction
). The results are presented in
Table 7.
Model 1 and Model 2 show that market-based environmental regulation (mer) exerts a significant negative effect on the ecoefficiency of industrial enterprises at the 1% significance level, whereas command-based environmental regulation (cer) displays a significant positive effect at the same level of significance. Moreover, the introduction of control variables in Model 2 not only reduces the magnitude of the regression coefficients but also improves the model’s goodness-of-fit relative to Model 1, thereby supporting Hypotheses H1a and H1b. These findings suggest that market-based environmental regulation has a more pronounced crowding-out effect on pollution-intensive industries than the compensatory benefits it offers to cleaner enterprises, to the extent that emissions trading schemes may not immediately fulfill their intended role of stimulating ecoefficiency improvements in regional industries. However, command-based environmental regulation remains effective within the paradigm of industrial modernization, indicating that, at this juncture, administrative intervention is essential to ameliorate market failures. These regulatory tools can effectively encourage enterprises to adopt green innovation.
In Models 3 to 7, the regression coefficients for both command-based and market-based environmental regulations are significant, and with the incorporation of interaction terms between moderating variables and core explanatory variables, the goodness-of-fit of each model improves to varying degrees. Each interaction term is statistically significant, which supports the potential for a moderating effect. Specifically, Model 3 reveals that the regression coefficient for the economic development level is significantly positive, suggesting that economic advancement positively influences industrial ecoefficiency. However, the interaction term between market-based environmental regulation and economic development level is significantly negative, which implies that as economic development progresses, the negative impact of market-based environmental regulation on industrial ecoefficiency intensifies, thereby supporting Hypothesis H2a. Conversely, in Model 5, the interaction term between economic development level and command-based environmental regulation is significantly positive, indicating that the facilitative influence of command-based environmental regulation on industrial ecoefficiency is amplified with higher levels of economic development, supporting Hypothesis H2b.
Model 4 examines the moderating role of international trade in the relationship between market-based environmental regulations and the ecoefficiency of industrial enterprises. The regression analysis reveals that the coefficient for the interaction term between market-based environmental regulations and import–export trade is significantly negative. This finding suggests that as import–export trade flourishes, it amplifies the detrimental effects of market-based environmental regulations on industrial enterprise ecoefficiency, thereby supporting Hypothesis H3.
The interaction between the technological environment and command-based environmental regulation on the ecoefficiency of industrial firms was explored using Models 6 and 7. Model 6 shows a significant positive coefficient for the interaction term between technology market development and command-based environmental regulation, suggesting that the facilitative effect of command-based environmental regulation on industrial ecoefficiency is magnified with enhanced technology market development, thereby supporting Hypothesis H4a. In Model 7, the regression coefficient for innovation level was significantly negative, illustrating its inhibitory influence on industrial ecoefficiency. However, the interaction term between innovation level and command-based environmental regulation yielded a significant positive coefficient, indicating that higher levels of innovation bolster the positive impact of command-based environmental regulation on the ecoefficiency of industrial firms, thus supporting Hypothesis H4b.
4.3. Analysis of Spatial Spillover Effects
Utilizing the nearest neighbor weight matrix as defined in Equation (5), a spatial correlation analysis was conducted to examine the relationship between environmental regulation and the ecoefficiency of industrial firms, as delineated in Equation (4). The results, encompassing Moran’s I and its associated
p-value significance, are presented in
Table 8.
Table 8 indicates that Moran’s I, associated with environmental regulation, remains consistently positive over an extended period, with a majority of the years showing statistically significant values, which denotes a spatial spillover presence from environmental regulations. Between 2003 and 2011, the Moran’s I for the ecoefficiency of industrial enterprises were negative, hovering near zero, and not statistically significant, indicating a negligible spatial spillover effect for those years. In contrast, from 2012 to 2021, Moran’s I for industrial ecoefficiency became positive and statistically significant, supporting the findings from the binary neighborhood matrix and confirming the existence of a spatial spillover effect in industrial ecoefficiency that has become increasingly prominent with a strong positive spatial correlation in recent years. Considering the movement of industrial pollutants and the establishment of industrial parks through corporate clustering, along with other pertinent factors, the study hypothesizes, from both a theoretical and an economic geography perspective, that encapsulating the impact of environmental regulation on industrial ecoefficiency within a spatial analytical framework is essential for deeper comprehension. To enhance comparative analysis of findings from conventional spatial econometric methods to those derived from Tobit-SDM and Clad-SDM frameworks, this investigation presents the regression coefficients of variables and their corresponding significance levels employing stratified heatmaps for visualization, as exemplified in
Figure 7.
Examination of
Figure 7 reveals that the regression coefficients associated with market-based environmental regulation across all considered models are significantly negative, whereas those pertaining to command-based environmental regulation emerge as positive, corroborating established econometric axioms. This pattern indicates a salient influence of environmental regulation on the ecological efficiency of industrial entities, underscored by the dimension of spatial spillover effects. In these models, the majority of the local coefficients for variables are positive, resonating with the economic theory of self-interest. In contrast, the spatial interaction term coefficients for the bulk of the variables manifest as negative, reflecting the absence of a cooperative paradigm conducive to reciprocal development among provinces in the current evolutionary stage. When regional economies and societal frameworks meet the prerequisites for enhancing the ecological efficiency of local industries, progress may be stymied by a deficiency in public services and the ramifications of environmental externalities, thereby constraining the advancement of industrial greening in adjacent regions. Subsequent analyses, utilizing maximum likelihood estimation, probed the likelihood of the spatial Durbin model (SDM) being reducible to a spatial autoregressive model (SAR) or spatial error model (SEM). The outcomes, statistically significant at the 1% threshold, substantiate the integrity of the SDM, implying that its application is statistically more judicious compared to the SAR or SEM.
Consequently, a comparative analysis of traditional spatial econometric SDM models with the Tobit-SDM and CLAD-SDM censored regression models, as presented in
Figure 7, reveals that market-based environmental regulations have a significant inhibitory effect on the ecological efficiency enhancement of industrial firms, in contrast to command-based regulations, which appear to facilitate it. The investigation into the spatial lag of market-based environmental regulations revealed that their effects on the ecoefficiency of industrial enterprises were not statistically significant in both the standard spatial Durbin model (SDM) and the Tobit-SDM model. Conversely, the results from the Clad-SDM model indicated a significant negative impact of the spatial lag of market-based environmental regulations on industrial enterprise ecoefficiency. Across all examined spatial Durbin models, the spatial lags of command-based regulations were uniformly associated with positive coefficients. Taking into account the control variables and their spatial lags, the CLAD-SDM model outperformed in terms of variable regression significance, thereby highlighting potential truncation within the dataset and non-normality in the residuals. This finding underpins the decision to utilize the CLAD-SDM model in future research endeavors to further elucidate the dynamics between environmental regulations and the ecological efficiency of industrial firms. To facilitate subsequent comparative analyses, the regression results for the Clad-SDM method depicted in
Figure 7 are designated as Model 8.
The analysis of the censored least absolute deviations spatial Durbin model (Clad-SDM), as presented in
Figure 7, reveals that market-based environmental regulation exhibits a significantly negative effect on industrial firms’ ecoefficiency at the 1% level when applied under the spatial K-nearest neighbors (KNN7) weight matrix. This finding corroborates the inhibitory impact of market-based environmental regulation on ecoefficiency improvement, aligning with the inferences drawn in the previous section. Within the Clad-SDM framework, both the regression coefficient of market-based environmental regulation and its corresponding spatial interaction term are markedly negative. Notably, the spatial interaction term’s coefficient is slightly larger, underscoring that market-based environmental regulation not only hampers ecoefficiency enhancement locally but also has a more pronounced inhibitory effect on firms in adjacent areas, thereby confirming Hypothesis H5a. Conversely, the command-based environmental regulation’s coefficients and those of its spatial interaction term are positive and significant at the 1% level, with a larger magnitude for the spatial interaction term. This indicates that command-based environmental regulation is conducive to improved ecoefficiency among local industrial firms and even more so among those in neighboring regions, substantiating Hypothesis H5b.
4.4. Subregional Regressions
Given the pronounced disparities in resource endowments, natural conditions and societal development among China’s three major regions, distinctive regional traits have emerged within the spheres of industrial progression and ecological protection. Consequently, a comparative analysis is warranted to elucidate the disparate impacts of nationwide environmental regulations on the ecological efficiency of industrial firms throughout the eastern, central and western regions. To visualize these differences, we have emulated the approaches of preceding researchers [
80], creating distribution diagrams that reflect the regression coefficients of the variables and their associated degrees of significance, as depicted in
Figure 8.
As shown in
Figure 8, in the eastern region of China, the impact of market-based environmental regulation on industrial firms’ ecoefficiency is more pronounced than on the national level—the negative coefficients of the regulation and its spatial interaction are significant, with the absolute value of the former more than double that observed nationally. This amplification may be attributed to the region’s active ecoquotas trading, spurred by the high concentration of industrial enterprises. Moreover, the command-based environmental regulation appears to exert a substantially stronger positive effect on the ecoefficiency of both local and neighboring industrial enterprises within the eastern region compared to other areas. This suggests that in areas with a robust industrial economy, the presence of efficacious environmental protection policies, legislation and management standards are crucial and that well-calibrated command-based environmental regulations can significantly enhance the ecoefficiency of local industrial enterprises.
For the central region, the suppressive impact of market-based environmental regulations on the ecoefficiency of industrial firms is modest. However, the spatial interaction term associated with these regulations indicates a significantly positive effect, potentially reflecting the region’s relatively uniform industrial infrastructure distribution. Such homogeneity may facilitate interprovincial industrial activity when ecoefficiency quotas in one area are limited, incentivizing firms to expand production into neighboring provinces. Additionally, this dynamic might contribute to the exportation of industrial products from these provinces due to their lower marginal costs and pricing advantages. Consequently, this leads to a scenario where market-based environmental regulations can paradoxically diminish the ecoefficiency of local enterprises while enhancing that of firms in adjacent regions. The limited influence of command-based environmental regulation and its spatial interaction term on the central region’s industrial ecoefficiency can be ascribed to the generally medium-low ecoefficiency levels. Firms in this area are not yet positioned to capitalize on the dual benefits of exporting environmental protection technology or the increased costs imposed on less efficient competitors by the regulatory enforcement.
In the western region, market-based environmental regulations and their spatial interactions exert a more pronounced inhibitory effect on the ecoefficiency of industrial enterprises compared to other regions, attributable largely to the region’s heavy industrial structure. The western region, rich in energy yet technologically underdeveloped, hosts a higher proportion of pollutant-generating enterprises than those engaged in clean production. Consequently, these enterprises often act as buyers in emissions trading, leading to a significant “crowding out effect”. Additionally, the region’s hilly terrain impedes air circulation, hindering pollutant dispersal and reducing environmental capacity, thereby increasing the cost of pollutant management per unit. This study uses the pollution control cost per unit of industrial value added as a proxy for the intensity of market-based environmental regulation, revealing a substantial suppressive impact on the western region’s industrial ecoefficiency. In contrast to other regions, the western region’s ecoefficiency is also significantly constrained by command-based environmental regulations. Many local industrial enterprises, characterized by outdated production processes and resistance to ecofriendly transformation, find that the ecological compensation provided by such regulations fails to offset economic losses, resulting in an overall negative effect. However, the spatial interaction term associated with command-based environmental regulation is significantly positive, with its coefficient’s absolute value markedly exceeding that of the local term. This indicates that although local industrial ecoefficiency suffers under current command-based regulations, there is a notably positive spillover effect on the ecoefficiency of enterprises in neighboring areas. Therefore, despite the present challenges, the western region must persist with the integrated strategy of “joint prevention and control” to navigate these challenges and expedite the sustainable development of its industrial enterprises.
4.5. Robustness Check
- (1)
A Comparative Assessment of Ecological Efficiency in Industrial Enterprises Utilizing Diverse Methodological Approaches
Given the relative nature of “ecological efficiency” metrics, a juxtaposition of the ecological efficiencies of industrial enterprises as evaluated by both the global Super-SBM and EBM models, alongside the examination of consistent temporal and spatial efficiency trends, serves to substantiate the reliability of the global Super-SBM model in assessing ecological efficiency of industrial enterprises to a certain degree. Consequently, this study initially computes the annual geometric mean of the ecological efficiencies of industrial enterprises, followed by a provincial calculation, with the respective outcomes depicted in
Figure 9 and
Figure 10.
Figure 9 reveals that although the ecological efficiency of industrial enterprises appraised by the global super-efficiency EBM model consistently registers as higher, the evolutionary trends in efficiency, as indicated by both models, align closely. These trends include an ascendant phase from 2003 to 2008, a “V”-shaped fluctuation between 2008 and 2010, a decline from 2010 to 2015 and a subsequent rise post-2015. While the temporal and spatial patterns of ecological efficiency in industrial enterprises and their driving factors fall outside the scope of this paper and are therefore not examined, the figure does suggest that post-2013, industrial enterprises tend to enter a phase of stable growth in ecological efficiency. This trend hints at the increasing effectiveness of China’s environmental policies and the enhanced commitment of industrial enterprises to harmonize production with environmental conservation. According to
Figure 10, the ecological efficiency of industrial enterprises exhibits considerable heterogeneity across provincial administrative regions, yet the areas of high and low efficiency identified by both methodologies largely overlap. Notably, regions such as Beijing, Guangdong, Zhejiang, Tianjin, Shanghai, Shandong and Jiangsu—all in China’s eastern corridor—stand out for their superior ecological efficiency. In sum, cross-referencing the annual and regional mean efficiencies as ascertained by both models suggests a fundamental agreement in the ecological assessment of industrial enterprises, lending further credence to the global Super-SBM model’s accuracy.
- (2)
Examination of Spatial Spillover Effects Using Diverse Spatial Matrices
The rationale for employing spatial econometric models in addressing empirical phenomena is embedded in the spatial interdependence among variables. The First Law of Geography suggests a universal interrelation, positing that all entities are interconnected; however, the explanatory capacity of a model is markedly enhanced when such interconnections are proximate and pronounced. As previously delineated, spatial spillover effects associated with command-based environmental regulation, market-based environmental regulation and the ecological efficiency of industrial enterprises have been identified through the utilization of a k-nearest neighbors (knn7) matrix. In this stage of the analysis, we modify the k-order neighbor matrix parameter to 6 to reassess the existence of spatial spillover effects, with findings presented in
Table 9.
The findings presented in
Table 9 reveal that with the application of a k-nearest neighbors (knn6) matrix, the Moran’s I index for the variables consistently displays positive and significant values across most years. This consistency suggests that the spatial spillover effects previously detected with the neighbor matrix parameter at k = 7 are not merely circumstantial. Additionally, we have recalibrated the k-order neighbor matrix to an contiguity matrix and have continued to scrutinize the Moran’s I index for environmental regulation and industrial enterprise ecological efficiency, the results of which are delineated in
Table 10.
The findings presented in
Table 10, following the adjustment of the spatial weight matrix to a contiguity configuration, reveal that the Moran’s I index for market-based environmental regulation exhibits a predominantly positive significance across the majority of the observed years. In contrast, the Moran’s I index for command-based environmental regulation displays unwavering positivity throughout the entire study period. Additionally, the indices correlating to industrial enterprise ecological efficiency, when scrutinized under a contiguity weight matrix, demonstrate a robust congruence with those ascertained via the knn6 and knn7 neighbor matrices, with a marked positive significance persisting from 2012 through 2021. Given these results, this paper advocates for the validity of utilizing spatial econometric models for the exploration of how environmental regulation influences the ecological efficiency of industrial enterprises.
- (3)
Assessing the Effects of Heterogeneous Environmental Regulations on the Ecological Efficiency of Industrial Enterprises Using Various Approaches
To rigorously validate the conclusions articulated in this manuscript, three methodological approaches were systematically implemented: the re-evaluation of the dependent variable, the reconfiguration of the spatial weights matrix and the modification of the temporal scope of the sample. This tripartite analysis was designed to scrutinize the potential interaction between the diversity of environmental regulations and the ecological efficiency of industrial firms, hereby referred to as Model 9, Model 10 and Model 11. In conjunction with these analyses, a figure delineating the coefficients of the variables and their robustness distribution has been generated, informed by the regression results, as depicted in
Figure 11.
Figure 11 presents the robustness checks of the study’s findings. Model 9 illustrates the results after substituting the explanatory variables from
Table 1 with the ecoefficiency of industrial firms, as measured by the EBM method, while controlling for all other variables and employing the Clad-SDM model for regression analysis. In Model 10, the previously utilized KNN7 nearest-neighbor matrix is replaced with a contiguity spatial weights matrix to test the stability of the results. Model 11 addresses potential biases from the period 2003–2011, where the Moran’s I for industrial ecoefficiency did not meet significance, by narrowing the research period to 2012–2021 for a re-evaluation. Across Models 8 to 11, consistent patterns emerge: market-based environmental regulations and their spatial interaction terms exert a negative impact on the ecoefficiency of industrial enterprises, whereas the local and spillover effects of command-based environmental regulations are significantly positive. These findings corroborate earlier analyses, confirming the robustness of the research outcomes.