Balancing Environmental Regulation and Marketization: A Quantile Analysis of Energy Efficiency in China’s Provinces

Abstract

This study investigates the impact of environmental regulations and marketization on energy efficiency in China using panel data from 30 provinces covering the period from 2008 to 2016. The analysis employs fixed effects regression, quantile regression, and heterogeneity analysis methods to provide comprehensive insights. The fixed effects regression results reveal that both command-and-control (CAC) regulations and market-based (MBR) regulations exert a negative impact on energy efficiency. Notably, marketization significantly mitigated the adverse effects of CAC regulations. Quantile regression analysis indicates that both negative impacts are more pronounced at lower energy efficiency levels, whereas marketization (MR) significantly mitigates these effects. Heterogeneity analysis further identified regional disparities, with CAC regulations yielding more significant negative impacts in the Western and Central regions and MBR showing stronger effects in the Western region. The results advocate for regionally differentiated policies that account for local economic, infrastructural, and institutional contexts to enhance energy efficiency outcomes rather than relying on one-size-fits-all approaches.

1. Introduction

In an era of escalating concerns over climate change, greenhouse gas emission reduction and enhanced energy efficiency have become crucial for transitional and developing economies [1,2,3,4,5], which requires a systematic project with support from policy and technology [6]. This effort aims to minimize fossil fuel dependency and reduce greenhouse gas emissions, thereby driving sustainable development [7]. Against this backdrop, emerging nations have actively implemented a range of regulations to improve environmental quality [8,9]. Substantial scholarly attention has been devoted to the intricate relationship between environmental regulations and energy efficiency; however, this topic remains a hotly debated frontier, highlighting critical gaps in existing knowledge [10].

Scholarly discourse on environmental regulation underscores the critical role of market-based instruments in shaping effective governance structures, particularly in addressing complex ecological challenges [8,11]. The adoption of market-oriented energy allocation and facilitation of access to private finance has historically been associated with incremental advancements in energy efficiency [4,12], concurrently catalyzing waves of energy conservation innovations [13,14]. However, while these studies provide valuable insights, they often treat market mechanisms and environmental regulations in isolation. The complex interplay between these factors and their combined impact on energy efficiency across China’s diverse provinces remains underexplored. This study aims to fill this gap by examining how environmental regulations and marketization jointly influence energy efficiency, offering a novel perspective on their interdependent effects.

The urgency to decode the ramifications of environmental regulations within the ambit of economic marketization gains pronounced significance in the milieu of developing economies. These economies are at a crossroads, navigating the balance between economic expansion and adherence to environmental regulations. Scholarly endeavors in this domain have predominantly engaged with energy efficiency, marketization, and environmental regulation as discrete entities [15,16,17,18]. Our research contributes to the literature by integrating these themes into a comprehensive analytical framework that provides a holistic understanding of how market-oriented policies can enhance the effectiveness of environmental regulation. By doing so, we address nuanced interdependencies and offer empirical evidence of the synergistic effects of these factors, thereby advancing the discourse on sustainable development in emerging economies.

This study ventures into empirical terrain by examining the impact of environmental regulation and marketization on energy efficiency, focusing on China’s diverse landscape. This country is characterized by significant regional disparities in the implementation of market-oriented reforms and the stringency of environmental regulations. Drawing on a rich dataset spanning Chinese provincial panels [10,11,19], this study employs a moderation model to analyze how marketization influences the relationship between environmental regulations and regional energy efficiency. To uncover heterogeneous regulatory effects across efficiency distributions, the analysis employs quantile regression to examine how marketization moderates the relationship between environmental regulation and energy efficiency at different performance levels.

This study distinguishes itself from prior research by explicitly modeling the moderating role of marketization, which is often overlooked in studies [20]. By integrating moderation models and quantile regression, we capture the heterogeneous effects of environmental regulations on China’s regional efficiency distributions. These methods reveal direct regulatory impacts and quantify how market mechanisms ameliorate inefficiencies through technological diffusion and resource reallocation [11]. Our empirical findings illuminate a complex portrait in which energy efficiency is adversely affected by both market and command policies. However, the integration of market mechanisms acts as a moderating force against the negative impact of CAC policies on economic growth. Notably, in regions with lower energy efficiency, market-based (MBR) environmental policies hinder efficiency gains, whereas this negative trend diminishes in areas with higher efficiency. By elucidating these dynamics, this study contributes significantly to bridging the identified gap in the literature and offers novel insights into the interplay between environmental regulation, marketization, and energy efficiency. Such understanding is crucial for stakeholders, including policymakers and industry leaders, to devise strategies that promote energy efficiency and align with sustainable development goals. The contributions of this study are numerous. It offers a critical evaluation of how shifts toward market systems at the regional economic level moderate environmental policy effectiveness. Unlike previous studies that treat these factors in isolation, our research integrates them into a comprehensive analytical framework that captures their interdependence. Moreover, our findings highlight the importance of considering regional disparities in marketization and the stringency of regulations. By employing a moderating model and quantile regression, we reveal that the impact of environmental regulations on energy efficiency varies significantly across different levels of marketization and efficiency. This nuanced perspective underscores the need for tailored policy interventions that account for regional specificities, which is a departure from the one-size-fits-all approach often observed in the literature.

In addition, this study proposes the “Marketization Buffering Hypothesis”, which posits that marketization mitigates the negative impact of command-and-control (CAC) regulations on energy efficiency through competitive innovation and resource reallocation; however, this effect is contingent on regional institutional capacity. Drawing on compliance cost and institutional theories, our findings reveal that CAC regulations impose heavier efficiency penalties in low-marketization regions (e.g., Western China) because of underdeveloped infrastructure and resource dependence, whereas marketized regions (e.g., Eastern China) offset these costs via technological diffusion and private-sector participation. This hypothesis challenges the assumption of uniform regulatory efficacy by demonstrating that market institutions act as critical moderators, which is consistent with recent studies on regulatory heterogeneity [12]. Empirically, we quantify this buffering effect as a reduction in CAC’s negative impact for every one-unit increase in the marketization index, highlighting the importance of institutional development for regulatory success in emerging economies. Furthermore, our research provides actionable policy recommendations, underscoring the nuanced exigencies of regional specificity in formulating environmental strategies. For instance, in regions with suboptimal energy efficiency, market-based policies should be designed to complement, rather than substitute, CAC regulations. Conversely, in areas with higher energy efficiency, the focus should be on strengthening market mechanisms to sustain and enhance their efficiency.

The remainder of this paper is organized as follows. Section 2 reviews the literature on marketization, environmental regulation, and energy efficiency. Section 3 outlines the methodology and examines the data and variables in detail. Section 4 presents the research findings, Section 5 discusses these findings, and Section 6 concludes the study.

2. Literature Review

2.1. Environmental Regulation and Energy Efficiency

The debate on the impact of environmental regulations on energy efficiency is ongoing, with traditional compliance cost theory suggesting that such regulations may hinder energy efficiency by increasing production costs [21]. This view is supported by evidence from Danish and Mexican enterprises [21,22]. However, studies have shown that environmental regulations positively influence energy efficiency through mechanisms such as green technology innovation, industrial structure upgrading and energy structure transformation [23]. The introduction of market-based environmental regulations, such as China’s carbon emissions trading scheme, has been found to enhance green total factor energy efficiency by promoting technological innovation and upgrading industrial structures, particularly in Eastern China [8,24]. Moreover, the pollution halo hypothesis in China suggests that strict environmental regulations can amplify the positive effects of foreign direct investment on urban green development efficiency, especially in the eastern and central regions, while inhibiting such effects in Western China [25].

Notably, the Porter Hypothesis argues that well-designed environmental regulations can stimulate technological innovation, leading to improved energy efficiency and potentially offsetting the increased production costs of such regulations [26,27]. This hypothesis is empirically supported by previous studies that have demonstrated the potential of both market-based policies and command-and-control regulations to enhance energy efficiency [28,29]. An examination of plant-level data also concluded that environmental regulations yield greater energy efficiency and reduced contamination [30].

In addition, recent studies have highlighted a dual mechanism through which environmental regulations influence energy efficiency: direct technological upgrading and indirect market restructuring. Stricter regulations not only enhance industrial green efficiency [14] but also drive renewable innovation via market-oriented tools, such as China’s SO2 emissions trading and low-carbon city pilots, which show long-term structural benefits despite industry-specific variations [20,24]. Conversely, command-and-control instruments, such as daily penalties, yield immediate yet uneven outcomes, particularly burdening small enterprises with compliance costs. Firm-level energy–environment efficiency outcomes are shaped by the heterogeneity of environmental policy tools, whereas technology import modes and financial innovation thresholds modulate regulatory impacts. Nevertheless, debates persist over whether efficiency gains justify regulatory costs, particularly in regions with underdeveloped market infrastructure. China has implemented environmental regulations, including the Energy Conservation Law, Air Pollution Prevention and Control Law, Environmental Protection Law, and market-based instruments, to enhance industrial energy efficiency through pollution reduction mandates, sustainability incentives, and green technology innovation.

In summary, while earlier studies laid the groundwork for understanding the relationship between environmental regulations and energy efficiency, the results remain mixed and context dependent, suggesting a complex interplay between regulatory frameworks, market forces, and technological innovations in shaping energy efficiency outcomes, which necessitates further investigation.

This complexity is further underscored by the varying impacts observed across different regions and sectors, highlighting the need for a more nuanced approach to policy design and implementation. Moreover, the existing literature often fails to fully explore the synergistic effects of combining market-based mechanisms with command-and-control regulations, leaving a significant gap in our understanding of how these approaches can be effectively integrated to enhance energy efficiency in China’s multi-scalar regulatory regime. Therefore, this study aims to bridge these gaps by providing a detailed examination of the interplay between environmental regulations, marketization, and energy efficiency, offering fresh insights and practical guidance for policymakers striving to achieve sustainable development.

2.2. Marketization and Energy Efficiency

Marketization in China has significantly impacted energy efficiency. Studies have revealed that market-oriented reforms positively influence energy efficiency by attracting industries with high efficiencies and low carbon emissions. Additionally, the marketization of industrial land transfer has been linked to a reduction in regional carbon emission intensity, especially in the mid-western regions through industry selection mechanisms. Furthermore, financial marketization suppresses regional energy intensity, whereas fiscal decentralization promotes energy intensity, with financial marketization moderating the negative effects of fiscal decentralization [31]. These findings highlight the complex interplay between marketization policies, industrial structures, and energy efficiency outcomes in China, emphasizing the importance of targeted strategies to enhance energy efficiency through market-oriented approaches in the manufacturing sector.

Previous studies have established a foundational understanding that economic principles, such as pricing efficiency and private investment, significantly contribute to improvements in energy efficiency. This assertion is further substantiated by the pivotal shifts observed in China, where the transition from state-run to more market-oriented enterprise models has led to notable gains in energy efficiency [32]. Fisher-Vanden [33] highlights the positive repercussions of marketization reforms on reducing China’s energy intensity, affirming the critical influence of market liberalization on energy performance.

Similarly, studies have delved into macroeconomic reforms and market liberalization efforts in developing countries, identifying them as catalysts for significant improvements in energy efficiency [11,17,18]

Building on this established narrative, recent research has revealed how the marketization policy of commercial land transfer enhances local governments’ competitiveness [15]. This insight underscores the nuanced mechanisms through which marketization influences energy efficiency, extending beyond mere deregulation and competition. In conclusion, the implementation of market mechanisms plays an instrumental role in significantly enhancing energy performance across various sectors. The synergistic effect of market forces fosters a conducive environment for energy conservation and propels industries toward sustainable growth and innovation.

In light of the existing literature, this study offers a novel contribution by introducing the moderating role of marketization in the relationship between environmental regulations and energy efficiency. Unlike previous studies, which often treat these factors in isolation, our approach integrates them into a comprehensive analytical framework to explore their interdependencies. By employing advanced econometric techniques, such as quantile regression, we provide a nuanced understanding of how marketization reforms can modulate the effectiveness of environmental regulations across different levels of energy efficiency.

3. Model, Variables, and Data

3.1. Model Specification

3.1.1. Basic Regression Model

In this section, we establish a foundational framework by presenting a basic regression model that examines the impact of environmental regulations and marketization on energy efficiency across China’s provinces. The basic regression model, as specified in Equation (1), was designed to assess the direct effects of two distinct categories of environmental policy—CAC regulations and MBRs—on provincial energy efficiency (EE).

$${EE}_{it}={\beta }_0+{\beta }_1{CAC}_{it}+{\beta }_2{MBR}_{it}+{\beta }_3{\mathbf{X}}_{\mathbf{i}\mathbf{t}}+{\epsilon }_{it}$$

(1)

where ${EE}_{it}$ is the energy efficiency of province $i$ in year $t$ and ${CAC}_{it}$ denotes command-and-control environmental regulations. ${MBR}_{it}$ denotes environmental regulations based on market mechanisms. To account for the wide variety of factors that could affect energy efficiency, control variables (${\mathbf{X}}_{\mathbf{i}\mathbf{t}}$) is a vector of variables including energy endowment, trade openness, urbanization rate, and industry structure. ${\epsilon }_{it}$ is the error term.

In subsequent analyses, we extended this basic model to include interaction terms that capture the moderating effects of marketization [34]. The model setup was as follows.

$${EE}_{it}={\beta }_0+{\beta }_1{CAC}_{it}+{\beta }_2{MBR}_{it}+{\beta }_3{MR}_{it}+{\beta }_4{CAC}_{it}\times {MR}_{it}+{\beta }_5{MBR}_{it}\times {MR}_{it}+{\beta }_6{\mathbf{X}}_{\mathbf{i}\mathbf{t}}+{\epsilon }_{it}$$

(2)

The model incorporates the interaction terms between regional marketization and two kinds of environmental regulation, ${CAC}_{it}\times {MR}_{it}$ and ${MBR}_{it}\times {MR}_{it}$. The regression coefficients ${\beta }_4$ and ${\beta }_5$ provide moderating effect estimations of the interaction variables.

Conducting the Hausman test was an essential preliminary step to ensure the robustness and validity of our regression model for panel data. The Hausman test results confirmed that the fixed-effects model was the most suitable for our dataset. The choice of a fixed-effects model allows for a nuanced analysis that accounts for unobservable, time-invariant differences across provinces that could bias the results. This model specification is particularly advantageous in our context, given the significant heterogeneity among Chinese provinces in terms of industrial structure, energy consumption patterns, and the intensity and type of environmental regulations applied. Moreover, the fixed-effects approach is well-suited for analyzing the dynamic impact of policy changes on energy efficiency, as it effectively isolates the influence of time-varying explanatory variables (such as environmental regulation and marketization) from those that remain constant over time but vary across provinces. This methodological choice is supported by extensive research indicating its appropriateness for panel data analyses involving policy impact evaluation, which highlights the fixed-effects model’s ability to provide unbiased and consistent estimators in the presence of unobserved heterogeneity.

Recognizing the potential endogeneity bias stemming from the reciprocal relationship between industrial structure, energy endowment, and energy efficiency, we introduce instrumental variables to mitigate this concern. Specifically, we employ a one-year lag in both industrial structure and energy endowment as instruments in a fixed-effects two-stage least squares (2SLS) regression framework to address this issue. This approach allows us to isolate the exogenous variation in these variables and obtain consistent estimates of the causal effects of environmental regulations and marketization on energy efficiency.

3.1.2. Quantile Regression Analysis

To address the heterogeneous impact of environmental regulations on energy efficiency across provinces, we employed quantile regression. Unlike conventional panel fixed-effects models, which estimate only the average effect of explanatory variables, quantile regression allows us to capture the entire conditional distribution of energy efficiency. This is particularly important in our context, as provinces differ widely in their baseline efficiency levels, and the effect of regulatory interventions is likely to vary across the distribution of provinces.

The rationale for using quantile regression lies in its ability to provide a more granular view of the conditional distribution of the dependent variable—energy efficiency—unlike mean-based models, which offer a single average effect. Quantile regression overcomes the limitations inherent in mean-based methodologies by allowing the estimation of the effects of independent variables across different dependent variable quantiles. Quantile regression is less sensitive to outliers and heteroskedasticity than mean-based methods, making it an ideal tool for exploring non-uniform effects. In our study, the adoption of quantile regression enables us to examine whether the impacts of market-based and command-and-control environmental regulations differ in low- versus high-efficiency provinces. This approach is particularly suited to our study’s focus on the heterogeneity of policy impacts across provinces with varied energy efficiency baselines. By utilizing quantile regression, we explore how environmental regulations might have disproportionate effects across the spectrum of energy efficiency from the least to the most efficient provinces. The quantile regression model can be mathematically expressed as follows:

$$y_i={\beta }_q{x}_i^{\mathrm{\text{'}}}+e_{qi},0<q<1$$

(3)

$${Quant}_q\left(y_i|x_i\right)={\beta }_q{x}_i$$

(4)

where $\beta$ denotes the parameter requiring estimation. The dependent variable is denoted as $y$, whereas $x$ signifies the independent variables, $e$ represents the term for random error, and q represents the percentile point. The quantile regression model was estimated using Heteroskedasticity-Consistent Standard Errors.

Furthermore, incorporating interaction terms (moderation) within the quantile framework allows us to assess how marketization moderates these regulatory effects across the efficiency spectrum.

This study presents a model derived from the theoretical framework of the quantile model to examine how environmental regulation influences energy efficiency at different levels of energy efficiency.

$$Q_q{(EE}_{it})=C_q+{\beta }_{1q}{CAC}_{it}+{\beta }_{2q}{MBR}_{it}+{\beta }_{3q}{MR}_{it}+{\beta }_{4q}{CAC}_{it}\times {MR}_{it}+{\beta }_{5q}{MBR}_{it}\times {MR}_{it}+{\beta }_{6q}{\mathbf{X}}_{\mathbf{i}\mathbf{t}}+{\epsilon }_{it}$$

(5)

where $Q$ represents quantile regression, and $q$ represents the percentile point, with a value range between 0 and 1. Typically, empirical analyses in the extant literature include percentile points deemed representative, such as the 10th, 50th, 75th, and 90th percentiles. Similarly, this study applies this approach to empirical analysis.

3.1.3. Heteroskedasticity Test

The modified Wald test for groupwise heteroskedasticity in our fixed-effects regression model yielded an χ² statistic of 3081.36 (df = 30, p < 0.0000), strongly rejecting the null hypothesis of homoskedasticity (σ(i)² = σ² for all i) and confirming significant cross-sectional variation in error variances. To address this issue, we employ heteroskedasticity-robust standard errors in our fixed-effects estimations.

3.2. Data and Variables

3.2.1. Dependent Variable

Energy efficiency (EE): measured as the total-factor energy efficiency. Total-factor energy efficiency is a valid measure because it accounts for multiple inputs and undesirable output. This study employs the Shephard energy distance function and stochastic frontier analysis methodology to assess and quantify energy efficiency. Therefore, the energy efficiency of province $i$ in time $t$ can be stated as ${EE}_{it}=\exp \left(-{\widehat{u}}_{it}\right),{\widehat{u}}_{it}=ln{D}_E\left(K_{it},L_{it},E_{it},Y_{it},B_{it}\right)$.

The input components considered in this equation include capital, energy, and labor (L). Capital is characterized by capital stock, which is calculated using the perpetual inventory method. Energy is typically expressed in terms of standard coal equivalents, which offers valuable insights into the overall energy consumption. Labor is quantified based on the total number of employed workers. Gross domestic product (GDP) at the regional level was used as a desirable output and all GDP figures were normalized to 2000. SO₂ emissions are undesirable outputs. The primary rationale for focusing on SO₂ is that it is a significant pollutant in China and a prime target for pollution reduction in the industrial sector [8].

3.2.2. Independent Variables

This study examined the impacts of two distinct regulatory paradigms: CAC and MBR. CAC regulations represent a direct regulatory approach in which governments set specific environmental standards and impose penalties for non-compliance. To quantify the stringency of CAC regulations, we adopt the widely used indicator of environmental protection expenditure as a percentage of industrial added value [35,36]. This measure reflects the financial commitment of the government to pollution control and environmental enforcement in the industrial sector. A higher ratio signifies stricter CAC regulations, indicating greater government intervention in environmental management.

MBR instruments, on the other hand, leverage market mechanisms to incentivize pollution reduction. These mechanisms often involve pricing environmental externalities, such as pollution, to encourage firms to internalize the environmental costs of their production processes. We focus on pollutant discharge fees as a key market-based instrument in China’s environmental governance. Specifically, we used the ratio of sewage charges to industrial added value as a proxy for MBRs’ stringency. This ratio reflects the financial burden imposed on industries for pollution discharge, encouraging them to adopt cleaner production technologies and practices to minimize their environmental impact and reduce their pollution liability. A higher ratio indicates greater financial incentives for pollution abatement and, consequently, stricter MBRs.

3.2.3. Moderating Variable

Over the past four decades, China has undergone a remarkable economic transformation, transitioning from a centrally planned system dominated by state-owned enterprises (SOEs) to a more market-oriented economy characterized by burgeoning growth in private and foreign ownership [37]. This marketization process, which was initiated in designated test zones and subsequently expanded nationwide, has significantly reshaped the economic landscape of China. However, this transition has not been geographically uniform. Coastal regions, particularly in East China, have experienced more rapid marketization and economic growth, driven by factors such as industrial diversification, foreign direct investment, and export-oriented industries [8,38]. Consequently, China exhibits significant regional disparities in marketization levels.

Despite substantial progress, challenges remain, particularly in reforming critical input factor markets, including the energy sector [11]. This lag in energy market liberalization underscores the need for further reforms to optimize energy allocation, promote energy efficiency, and foster a more sustainable future in China. A more fully marketized energy sector can unlock substantial efficiency gains by enhancing price signals, stimulating competition, and attracting investments. In essence, deepening market reforms in China’s energy sector is economically sound and environmentally imperative. China can accelerate its transition toward a more sustainable and energy-secure future by aligning energy pricing with market forces, fostering competition, and attracting investment in energy efficiency [18,37].

In this study, we assessed the degree of marketization across Chinese provinces using the Marketization Index, a standardized and multidimensional measure [39]. This index provides a systematic evaluation of market-oriented reforms across 31 provinces, capturing the regional disparities in market development. The Marketization Index is constructed from five key dimensions: (1) the relationship between the government and the market, (2) the development of the non-state sector, (3) the maturity of the product market, (4) the development of factor markets, and (5) the evolution of market intermediaries and the legal environment. Each dimension is decomposed into hierarchical subindices, where the foundational indices at the most granular level originate directly from primary data sources. These layered components work together to systematically evaluate the extent to which market mechanisms regulate regional economic activity. Our study used the latest dataset from the China Market Index Database (http://cmi.ssap.com.cn).

3.2.4. Control Variables

Our econometric model includes the selected control variables. Industrial structure (IS), measured as the tertiary sector’s value-added share of GDP, is included because economies transitioning toward service industries tend to exhibit higher energy efficiency owing to reduced reliance on energy-intensive manufacturing. Urbanization rate (UR), defined as the proportion of the population residing in urban areas, captures the dual effect of urbanization: while it may increase energy demand, it also fosters efficiency improvements through economies of scale, advanced infrastructure, and technological adoption. Energy endowment (EM), represented by the coal production-to-consumption ratio, was incorporated to reflect regional disparities in energy resource availability. A higher ratio suggests energy abundance and potential inefficiencies due to reliance on cheap domestic coal, whereas a lower ratio indicates external dependence, which may incentivize efficiency improvements due to cost considerations. Trade openness (OP), calculated as the export-to-GDP ratio, is included because greater economic integration facilitates technology transfer, enhances industrial competitiveness and promotes energy-efficient production processes. By accounting for these variables, our model ensures a more precise estimation of the relationship between environmental regulations, marketization, and energy efficiency.

To enhance the precision of our analysis, we incorporated provincial and annual dummies to adjust for unobserved heterogeneity across regions and temporal spans. This methodology is designed to neutralize distortions stemming from unaccounted variable influences, thereby safeguarding the veracity of our estimation.

Although additional factors, such as energy efficiency investments and technological development, could further influence energy efficiency, we opted to omit these variables from our model to avoid potential multicollinearity. While previous research does not explicitly report high correlations between these measures and our current indicators—industrial structure, urbanization, and trade openness—the underlying economic dynamics imply strong interdependencies between them. In particular, technological development is often accompanied by shifts in industrial composition, urban agglomeration, and greater integration into the global market. These shared drivers suggest that including additional proxies for technological progress or energy efficiency investments could confound the distinct effects of existing control variables. We acknowledge this as a limitation and suggest that future research employ methodologies that are specifically designed to disentangle these closely related factors.

To provide readers with a better understanding of the abbreviations in this paper, we have used

Table 1. Summary of the abbreviations.

Abbreviation	Definition
EE	Energy Efficiency
CAC	Command-and-Control Regulations
MBR	Market-Based Regulations
MR	Marketization
IS	Industrial Structure
UR	Urbanization Rate
EM	Energy Endowment
OP	Trade Openness

to summarize the abbreviations for our variables.

3.2.5. Data Source and Summary Statistics

The dataset analyzed in this study spans 30 Chinese provinces from 2008 to 2016, a period selected to capture China’s critical transition toward market-oriented environmental governance, as reflected in policies such as the 11th and 12th Five-Year Plans. While acknowledging the dataset’s temporal scope, this timeframe offers unique advantages: it documents China’s institutional evolution during a phase of rapid industrialization and regulatory experimentation, providing a benchmark for emerging economies currently navigating similar transitions. Specifically, the findings remain highly relevant for understanding how marketization moderates the effects of command-and-control and market-based regulations in China, as the regulatory frameworks established during this era—including emission caps and carbon trading pilots—continue to underpin China’s current dual-carbon strategy. Moreover, the study’s insights extend beyond China, offering actionable implications for developing countries undergoing similar green development and market liberalization trajectories. This relevance stems from the shared challenges these nations face: balancing state intervention with market-driven growth, addressing regional disparities in policy implementation, and adopting hybrid regulatory toolkits to achieve sustainable goals. By examining China’s experiences, this study provides an analytical framework for policymakers in emerging economies to design interventions that align environmental objectives with local institutional contexts and foster inclusive and sustainable growth.

Tibet, Hong Kong, Taiwan, and Macao were excluded due to insufficient data. The data utilized in this study were collected from reliable sources, such as the China Statistical Yearbook, the China Energy Statistics Yearbook, the China Environmental Yearbook, and the official website of the National Bureau of Statistics of China.

Table 2. Descriptive statistics.

Var.	Mean	SD	Min	Max	P25	P75
EE	0.5143	0.1931	0.2141	0.9612	0.3800	0.6226
CAC	0.0044	0.0039	0.0004	0.0310	0.0018	0.0054
MBR	0.0012	0.0008	0.0001	0.0061	0.0007	0.0015
MR	1.7715	0.3106	0.8459	2.3026	1.5769	1.9769
UR	0.5439	0.1340	0.2912	0.8958	0.4498	0.6047
IS	0.4523	0.0888	0.2979	0.8227	0.3978	0.4783
EM	0.7069	0.7588	0.0001	2.9831	0.1055	1.0093
OP	0.2419	0.2532	0.0225	1.1636	0.0744	0.3218

presents the descriptive statistics of the variables. The average EE during the observation period was 0.514. In addition, the value corresponding to the 75th percentile was 0.623. These findings suggest that Chinese provinces exhibit relatively low average energy efficiency. The 30 provinces also showed substantial variation in their implementation of market- and command-based environmental rules, with a generally low degree of environmental control observed in most provinces. The overall marketization level across all areas is relatively high, yet several provinces exhibit much lower marketization levels than the average.

Table 3. Correlation analysis.

	EE	CAC	MBR	MR	UR	IS	EM	OP
EE	1
CAC	−0.102	1
MBR	−0.330 *	0.545 *	1
MR	−0.044	−0.444 *	−0.554 *	1
UR	0.107	−0.172	−0.358 *	0.666 *	1
IS	0.448 *	−0.022	−0.251 *	0.319 *	0.688 *	1
EM	−0.360 *	0.364 *	0.567 *	−0.535 *	−0.357 *	−0.277 *	1
OP	−0.052	−0.297 *	−0.422 *	0.617 *	0.671 *	0.290 *	−0.487 *	1

Notes: After Bonferroni adjustment a star (*) is added to correlations significant at a 1% level.

presents the results of the correlation analysis. The maximum coefficient is 0.688 for urbanization and industry structure. Additionally, a diagnostic study was performed to assess the variation in inflation factor (VIF). The VIF, which measures the extent of multicollinearity among variables, did not exceed 5. Therefore, it can be deduced that multicollinearity does not present significant obstacles in the dataset.

4. Empirical Results

4.1. Basic Analysis Results

Table 4. Regression results for the basic models.

	Model 1	Model 2	Model 3
UR	0.020	0.020	0.014
	(0.014)	(0.014)	(0.016)
IS	0.038 ***	0.038 ***	0.038 ***
	(0.014)	(0.014)	(0.014)
EM	0.003	0.003	0.002
	(0.003)	(0.003)	(0.003)
OP	−0.003 ***	−0.003 ***	−0.003 ***
	(0.001)	(0.001)	(0.001)
CAC	0.001	0.001	−0.006 **
	(0.001)	(0.001)	(0.004)
MBR	−0.003 ***	−0.003 ***	−0.009
	(0.001)	(0.001)	(0.008)
MR		−0.002	0.039 *
		(0.006)	(0.026)
CAC × MR			0.004 **
			(0.002)
MBR × MR			0.003
			(0.004)
_cons	−0.715 ***	−0.712 ***	−0.796 ***
	(0.020)	(0.020)	(0.056)
R2	0.845	0.845	0.854
Wald chi2	45,282.357 ***	53,760.986 ***	94,105.288 ***
F	67,398.746 ***	65,180.345 ***	60,512.820 ***
N	240	240	240

Fixed-effects regression with instrument variables. Year dummy is fixed. Standard errors are in parentheses. * p < 0.15, ** p < 0.1, *** p < 0.05.

presents the results of this study. Model 1 demonstrates the effects of the main variables in Equation (1). Model 2 incorporates the MR and Model 3 incorporates interaction terms that account for the interplay between environmental regulation variables and the level of marketization. This allows for an examination of how the level of marketization affects the linkage between the ER and energy efficiency.

The results of Model 3 indicate that CAC adversely affects energy efficiency. However, the coefficient of CAC in Models 1 and 2 is not statistically significant in the absence of interaction terms. This finding underscores the critical role of regional marketization in moderating the relationship between CAC and energy efficiency. Neglecting to account for the level of marketization could introduce bias into the analysis.

A statistically significant negative coefficient for the MBR indicates that energy efficiency is adversely affected when the MBR is implemented.

Additionally, the interaction term coefficient between CAC and marketization (MR) is positive and significant at the 10% level. This suggests that marketization reduces the negative impact of CAC on energy efficiency. However, the coefficient of the interaction term between MBR and MR is insignificant.

Figure 1 further visualizes the moderating effect of MR. When the MR value increases from 0.8 to 2.4, the marginal effect of CAC gradually approaches and exceeds 0, while the marginal effect of the MBR also shifts from negative to positive; both values surpass 0 at MR = 2.4. This visual trend aligns with the significant positive coefficient of the interaction term CAC × MR in Model 3, demonstrating that marketization reduces the negative impact of CAC on energy efficiency.

4.2. Quantile Regression Analysis Results

Quantile regression analysis provides insights into how environmental regulation and marketization influence energy efficiency across different points in the energy efficiency distribution. We used quantile regression analysis to evaluate the effects of environmental regulations and marketization levels at various energy efficiency levels, setting quantile points at 0.1, 0.5, 0.75, and 0.9.

Table 5. Quantile regression analysis.

	0.1	0.5	0.75	0.9
CAC	−0.0041 ***	−0.0065 *	−0.0035	−0.0027
	(0.0015)	(0.0042)	(0.0027)	(0.0036)
MBR	−0.0092 ***	−0.0135 **	−0.0029	0.0012
	(0.0026)	(0.0072)	(0.0038)	(0.0044)
MR	0.0461 ***	0.0590 ***	0.0074	0.0029
	(0.0103)	(0.0240)	(0.0144)	(0.0183)
CAC × MR	0.0025 ***	0.0035 *	0.0020	0.0015
	(0.0010)	(0.0023)	(0.0015)	(0.0018)
MBR × MR	0.0040 ***	0.0056 *	0.0003	−0.0013
	(0.0015)	(0.0034)	(0.0019)	(0.0026)
UR	0.0144 ***	0.0176 *	0.0330 ***	0.0307 ***
	(0.0059)	(0.0113)	(0.0058)	(0.0060)
IS	0.0272 ***	0.0437 ***	0.0405 ***	0.0313 ***
	(0.0020)	(0.0078)	(0.0054)	(0.0039)
EM	0.0007 *	−0.0005	−0.0006	−0.0004
	(0.0005)	(0.0014)	(0.0007)	(0.0005)
OP	−0.0040 ***	−0.0036 ***	−0.0032 ***	−0.0021 ***
	(0.0007)	(0.0014)	(0.0010)	(0.0011)
_cons	−0.2652 ***	−0.2909 ***	−0.1829 ***	−0.1663 ***
	(0.0180)	(0.0519)	(0.0282)	(0.0297)
Year FE	Yes	Yes	Yes	Yes
ind FE	Yes	Yes	Yes	Yes

Standard errors are in parentheses * p < 0.15, ** p < 0.1, *** p < 0.05.

presents the results of the quantile regression analysis. The findings suggest that CAC has varying impacts on energy efficiency across different CAC quantiles. A similar trend was observed for the MBR.

For the interaction terms, at the 0.1 quantile, the coefficient of CAC × MR is 0.0025 (standard error = 0.0010), statistically significant at the p < 0.05 level. Economically, this indicates that a one-unit increase in MR level reduces the negative impact of CAC on energy efficiency in low-energy-efficiency provinces by 0.0025 units. The underlying mechanism lies in the buffering role of marketization, which mitigates CAC compliance costs through channels such as technological diffusion and resource reallocation. In contrast, at the 0.9 quantile, the CAC × MR coefficient is 0.0015 (standard error = 0.0018), lacking statistical significance. In provinces with high energy efficiency, the buffering effect of marketization on CAC weakens, as these regions already leverage advanced technologies and efficient resource allocation, diminishing the marginal benefit of further marketization.

Concurrently, the coefficient of MBR × MR at the 0.1 quantile is 0.0040 (standard error = 0.0015), which is also statistically significant (p < 0.05). Economically, this signifies that a one-unit increase in marketization strengthens the positive effect of MBR on EE in low-energy-efficiency provinces by 0.0040 units, reflecting how marketization enhances MBRs’ efficacy, primarily by improving market signal transmission (e.g., carbon pricing) to incentivize enterprises to proactively optimize energy. However, at the 0.9 quantile, its coefficient becomes −0.0013 (standard error = 0.0026), losing significance. This suggests that in provinces with high energy efficiency, the synergy between market-based regulation and marketization fades, likely because these regions have already optimized energy use under mature market mechanisms, leaving minimal room for improvement.

CAC adversely affected energy efficiency across the 0.1 and 0.5 groups, although the extent of this effect may vary. The development of regional marketization holds promise for mitigating the detrimental effects of CAC on energy efficiency. Furthermore, MBRs reduce energy efficiency in regions with low energy efficiency. However, in places with high energy efficiency, the adverse impact of MBRs is less substantial.

4.3. Heterogeneity Analysis

Variations in the economic, political, and cultural environments among provinces can lead to disparate effects of environmental regulations on energy efficiency in the provinces. To explore these differences, we divided China into three regions: East, Central, and West. The Eastern region includes provinces and municipalities such as Beijing, Tianjin, Jiangsu, Zhejiang, Fujian, Hebei, Guangdong, Liaoning, Shanghai, Shandong, and Hainan, which have historically been at the forefront of market-oriented reforms. These provinces benefited from early openness, advanced institutional frameworks, and test programs that promoted market liberalization. The Central region covers provinces including Shanxi, Jilin, Heilongjiang, Henan, Hubei, Hunan, Anhui, and Jiangxi, capitalizing on manufacturing clusters to attract industrial migration from the coastal regions. However, institutional modernization has lagged behind. The Western region encompasses provinces such as Chongqing, Inner Mongolia, Guangxi, Sichuan, Yunnan, Guizhou, Gansu, Shaanxi, Ningxia, Qinghai, and Xinjiang. This region has faced prolonged challenges in market-oriented reforms, primarily due to geographical barriers and delayed inclusion in national policy.

Table 6. Heterogeneity analysis.

	East	Central	West
CAC	−0.009	−0.030 **	−0.008 *
	(0.010)	(0.015)	(0.005)
MBR	0.024	−0.012	−0.016 **
	(0.018)	(0.013)	(0.008)
MR	−0.083	0.156 ***	0.096 ***
	(0.083)	(0.031)	(0.032)
CAC × MR	0.004	0.017 **	0.005
	(0.005)	(0.009)	(0.003)
MBR × MR	−0.012	0.006	0.010 *
	(0.009)	(0.009)	(0.006)
UR	0.061 ***	0.019	−0.046
	(0.017)	(0.021)	(0.047)
IS	0.049 ***	0.033 ***	0.012
	(0.018)	(0.010)	(0.019)
EM	0.001	0.003 *	0.010 ***
	(0.001)	(0.002)	(0.004)
OP	0.014 ***	−0.002 ***	−0.005 ***
	(0.004)	(0.001)	(0.001)
_cons	−0.503 ***	−1.046 ***	−0.873 ***
	(0.184)	(0.038)	(0.092)
Adjustment R2	0.885	0.957	0.848
RMSE	0.003	0.002	0.003
N	99	72	90

Standard errors are in parentheses. * p < 0.15, ** p < 0.1, *** p < 0.05.

presents the regression results. The findings indicate that CAC regulations significantly reduce EE in the Central and Western regions, whereas no such adverse effect is observed in the Eastern region. One plausible explanation for these differences is that the Western and, to a lesser extent, Central provinces often face higher compliance burdens because of their relatively underdeveloped market mechanisms and weaker institutional support. These historical and institutional constraints may lead to inefficient policy implementation and misaligned regulatory frameworks that amplify CAC’s negative effects. Conversely, the Eastern provinces, with their more established market-oriented environments and robust institutional capacities, can better absorb and offset the costs associated with stringent environmental regulations, resulting in a less detrimental or even neutral effect on energy efficiency.

For East China, the MBR coefficient of 0.024 (SE = 0.018) is insignificant. Conversely, the West exhibited a coefficient of −0.016 (SE = 0.008, p < 0.1), which was significant. This stems from the West’s fragile market foundation; MBR policies (e.g., environmental taxes) impose short-term operational costs on enterprises with limited adaptability, thereby inhibiting energy efficiency

When examining the interaction terms for CAC × MR, the East’s coefficient of 0.004 (SE = 0.005) is insignificant. In the Central region, the significantly positive coefficient of the CAC × MR interaction term is 0.017 (SE = 0.009, p < 0.1), illustrating that marketization enhances CAC’s energy efficiency improvements by channeling resources to energy-saving sectors. In the Western region, the significantly positive MBR × MR coefficient is 0.010 (SE = 0.006, p < 0.15), demonstrating that marketization strengthens MBRs’ energy efficiency promotion by optimizing resource allocation, thereby aiding enterprises in adapting to market-based regulations.

In summary, our heterogeneity analysis underscores that the effectiveness of environmental regulations is not uniform across regions. Instead, historical factors, such as the timing and intensity of market reforms, and institutional differences play critical roles in shaping how these regulations influence energy efficiency. This nuanced understanding supports the need for tailored policy approaches that consider regional disparities in market development and institutional capacity.

4.4. Robustness Checks

Table 7. Robustness checks for the quantile regression.

	0.15	0.3	0.7	0.85
CAC	−0.0045 ***	−0.0038 ***	−0.0030	−0.0039
	(0.0014)	(0.0015)	(0.0039)	(0.0041)
MBR	−0.0076 ***	−0.0087 ***	−0.0024	0.0016
	(0.0032)	(0.0039)	(0.0056)	(0.0048)
MR	0.0418 ***	0.0400 ***	0.0070	−0.0046
	(0.0119)	(0.0129)	(0.0225)	(0.0143)
CAC × MR	0.0028 ***	0.0024 ***	0.0018	0.0022
	(0.0008)	(0.0008)	(0.0021)	(0.0021)
MBR × MR	0.0032 ***	0.0037 ***	0.0001	−0.0019
	(0.0015)	(0.0018)	(0.0027)	(0.0023)
UR	0.0149 ***	0.0268 ***	0.0328 ***	0.0310 ***
	(0.0066)	(0.0077)	(0.0074)	(0.0058)
IS	0.0268 ***	0.0282 ***	0.0404 ***	0.0302 ***
	(0.0021)	(0.0060)	(0.0050)	(0.0051)
EM	0.0011	0.0008	−0.0004	−0.0004
	(0.0008)	(0.0008)	(0.0011)	(0.0005)
OP	−0.0039 ***	−0.0044 ***	−0.0036 ***	−0.0024 ***
	(0.0005)	(0.0009)	(0.0007)	(0.0010)
_cons	−0.2539 ***	−0.2474 ***	−0.1816 ***	−0.1584 ***
	(0.0269)	(0.0286)	(0.0462)	(0.0262)
Year FE	Yes	Yes	Yes	Yes
ind FE	Yes	Yes	Yes	Yes

Standard errors are in parentheses * p < 0.15, ** p < 0.1, *** p < 0.05.

applies quantile regression to newly specified efficiency quantiles (15th, 30th, 70th, and 85th percentiles) to systematically capture the variations in policy impact across the energy efficiency spectrum. The results consistently demonstrate that CAC regulations exert statistically significant negative effects at lower mid-range quantiles (15th and 30th percentiles: coefficients of −0.0045 and −0.0038, respectively, p < 0.05), aligning with our primary findings for low-efficiency regions. MBRs also exhibit persistent adverse impacts in these segments (−0.0076 and −0.0087, p < 0.05), reinforcing the vulnerability of low-efficiency provinces to regulatory compliance costs. The interaction terms exhibit directional consistency, with CAC × MR showing positive and significant coefficients at lower quantiles (0.0028 at 15th, 0.0024 at 30th, p < 0.05). Similarly, MBR × MR displays significant positive effects at lower quantiles (0.0040 at the 10th and 0.0037 at the 30th, p < 0.05). This pattern reinforces the asymmetric moderating role of marketization, mitigating regulatory inefficiencies in low-efficiency areas while showing limited effects in high-efficiency areas. By addressing heteroskedasticity through robust standard errors and expanding beyond conventional median comparisons, this analysis reinforces the stability of our core conclusions while demonstrating their critical policy implications.

5. Discussion of Findings

Building on the empirical results presented, this section delves into a comprehensive discussion of our findings, situating them within the broader context of the existing literature on environmental regulation, marketization, and energy efficiency.

First, we revisited the hotly debated topic of the impact of environmental regulations on energy efficiency in China. Unlike previous studies that focused on a single type of regulation [8,24], this study simultaneously examines market-based and command-and-control regulatory approaches. This dual examination aligns with recent studies that have explored various types of environmental regulations [35,36]. Our findings reveal that both CAC and market-based regulations negatively affect energy efficiency. This is consistent with the traditional compliance cost theory, which suggests that environmental regulations may hinder productivity and energy efficiency by increasing production costs [21].

Second, to delve deeper into the effects of different environmental regulations on energy efficiency, this study incorporates the moderating role of marketization. Recent studies have highlighted that marketization is a crucial factor influencing energy efficiency [12,16,17,40]. Our research finds that marketization reduces CAC’s negative impact on energy efficiency. This suggests that market reforms can incentivize energy-saving technologies and practices by facilitating competitive markets, reducing barriers to entry, and encouraging private sector participation. Moreover, market-based pricing mechanisms that reflect the true cost of energy consumption can encourage conservation and improve energy efficiency.

In addition, our findings suggest that the negative impact of both market-based and CAC regulations on energy efficiency, particularly in low-performing regions, can be attributed to a sequential mechanism involving policy misalignment, inefficient policy implementation, and high compliance costs. This integrated mechanism provides a more comprehensive explanation of our empirical results and highlights the complex interplay between regulatory frameworks, market forces, and regional characteristics.

However, the interaction term between market-based regulations and marketization is not statistically significant, underscoring the importance of examining the different types of environmental regulations. This finding suggests that while marketization can mitigate the adverse effects of CAC regulations, its impact on market-based regulations is less clear. Therefore, policymakers should consider implementing or enhancing market-based pricing mechanisms and promoting market reforms to improve energy efficiency in this sector.

Finally, we examine the heterogeneous impacts of environmental regulations by integrating quantile regression analysis with a regional breakdown of China into Eastern, Central, and Western areas. Our results reveal that command-and-control (CAC) regulations tend to reduce energy efficiency more substantially in the western region than in the eastern region. This pronounced effect in the West can be attributed to several historical and institutional factors. For example, the Eastern provinces were early adopters of market reforms and generally benefited from stronger governance, better developed infrastructure, and a more diversified industrial structure. These factors facilitate the effective implementation of environmental regulations and enable market mechanisms to mitigate the adverse effects of such policies on the economy. In contrast, the western provinces often lag in market-oriented reforms, possess less advanced infrastructure, and rely more heavily on resource-intensive industries. Such conditions not only increase compliance costs but also diminish the capacity of marketization to counterbalance regulatory burdens. Moreover, differences in institutional frameworks and governance quality across regions further contribute to the observed disparities, suggesting that regulatory impacts are intricately linked to specific regional contexts. The interplay between historical market reform trajectories, institutional quality, and industrial composition explains why environmental regulations have a more significant negative influence on energy efficiency in the western region than in the eastern region. These findings underscore the need for tailored, region-specific policies that account for local economic, infrastructural, and institutional realities to enhance energy efficiency.

The quantile regression robustness checks confirmed the stability and heterogeneity of our core findings. Both CAC and MBR regulations consistently exhibit stronger negative impacts in low-efficiency regions (15th–30th percentiles), aligning with their vulnerability to compliance expenses. MR significantly mitigates these adverse effects at lower quantiles, but this moderating role diminishes in high-efficiency regions, consistent with the advanced adaptability of developed areas. These results, which are robust to heteroskedasticity and distributional variations, underscore the necessity of efficiency-dependent policy designs that prioritize market reforms for lagging regions while leveraging institutional strengths in advanced areas.

In summary, our study not only validates the existing literature but also provides novel insights into the intricate dynamics between environmental regulation, marketization, and energy efficiency. By presenting a comprehensive framework for policymakers, our findings significantly contribute to the broader discourse on sustainable development, especially in developing economies that navigate the dual challenges of economic growth and environmental sustainability.

6. Conclusions and Implications

This study elucidates the complex relationship between China’s environmental regulations, marketization, and energy efficiency. Contrary to the prevailing assumption that stricter environmental regulations inherently drive energy efficiency improvements, our findings indicate that both CAC and MBR negatively affect energy efficiency. These results suggest that the compliance costs associated with these regulatory frameworks may, at least in the short term, surpass the benefits derived from technological innovations and process improvements. However, our study highlights the crucial role of marketization in mitigating these adverse effects. Specifically, we found that higher levels of marketization partially offset the negative impact of CAC regulations on energy efficiency. This suggests that a more competitive market environment, characterized by fewer distortions and greater private sector participation, can help unlock efficiency gains even in the presence of stringent environmental regulations. The study concludes that advancing marketization policies represent a strategic opportunity to mitigate the adverse impacts of stringent environmental regulations on energy efficiency, thereby fostering sustainable economic development in China. Notably, MBR predominantly affect regions with low energy efficiency, whereas their influence diminishes in areas with higher energy efficiency. Furthermore, our exploration of heterogeneity demonstrates that the effects of both types of environmental regulation are statistically significant in China’s Western and Central regions, but not in the Eastern region.

This study makes a significant contribution to the existing literature by exploring the complex interplay between environmental regulations, marketization, and energy efficiency. Our research offers novel insights that advance the understanding of how market-oriented reforms moderate the impact of environmental policies on energy efficiency, particularly in the context of China’s economic development. Our study bridges a notable gap in the literature by integrating environmental regulations and marketization into a comprehensive framework. Unlike previous research that often examined these factors in isolation, we capture their interdependence and provide a holistic understanding of their triadic relationship with energy efficiency. This approach allows us to demonstrate how shifts toward market systems at the regional economic level influence environmental policy effectiveness.

We propose the “Marketization Buffering Hypothesis”, which posits that marketization can mitigate the negative impact of CAC regulations on energy efficiency through mechanisms such as competitive innovation and resource reallocation in the market. This hypothesis is grounded in compliance costs and institutional theories. Our findings reveal that in low-marketization regions, such as Western China, CAC regulations impose heavier efficiency penalties because of underdeveloped infrastructure and resource dependence. In contrast, marketized regions, such as Eastern China, can offset these costs through technological diffusion and private-sector participation. This hypothesis challenges the uniform regulatory efficacy assumption prevalent in prior research and aligns with recent studies on regulatory heterogeneity [12,40].

Based on our findings, policymakers should adopt a differentiated approach that reflects the distinct historical, institutional, and infrastructural contexts of China’s regions. In regions where market development lags, particularly in Central and Western China, efforts should be made to reduce entry barriers, enhance competition, and strengthen property rights. These steps can help correct misaligned market signals and lower the compliance burden, which diverts resources from long-term efficiency improvements. Accelerating market-oriented reforms in these areas will not only stimulate innovation but also improve resource allocation efficiency, thereby mitigating the negative impacts of the current regulatory frameworks. Conversely, in regions with more advanced market mechanisms, policies can focus on fine-tuning regulatory measures to encourage sustainable practices while maintaining a competitive environment.

Given the short-term negative effects of environmental regulations on energy efficiency, designing smarter, more flexible, and performance-based regulatory frameworks is imperative. Such measures should aim to minimize compliance costs while ensuring that environmental goals are met. For instance, dynamic regulatory systems that reward technological adoption in energy-intensive sectors can help offset the increased production costs that are typically associated with strict regulations.

A tailored, region-specific approach is also necessary because of the heterogeneous effects of environmental regulations across different areas. In Eastern regions, where early market liberalization, robust institutional frameworks, and advanced infrastructure have already been established, policymakers should focus on fine-tuning market-based instruments, such as optimizing the carbon trading system to better internalize environmental costs, while continuing to foster innovation through selective deregulation in well-developed sectors. In the Central regions, which are in a transitional phase with moderate market development and institutional capacity, targeted interventions such as subsidies for energy-efficient technologies and comprehensive capacity-building initiatives (e.g., training programs for effective energy management) are essential. Gradual adjustments to regulatory frameworks that align with local market dynamics can alleviate compliance burdens. In the Western regions, where lower levels of marketization and underdeveloped infrastructure contribute to more pronounced negative impacts of environmental regulations, direct financial support, technical assistance, and investments in upgrading local infrastructure are critical. Additionally, strengthening local governance and institutional frameworks will empower these regions to implement more effective environmental policies and leverage market-based instruments for sustainable energy.

Ultimately, by carefully balancing these strategies, policymakers can foster a competitive marketplace that simultaneously supports economic growth and sustainable development. This differentiated policy framework not only enhances energy efficiency across regions but also offers practical, context-sensitive recommendations to address the complex interplay between market forces, regulatory frameworks and regional characteristics.

Investing in the research and development (R&D) of sustainable energy technologies is crucial. Public funding should be targeted at projects with significant potential to enhance energy efficiency and reduce emissions. Public–private partnerships are vital for accelerating the development and application of innovative energy solutions.

By systematically implementing these recommendations, policymakers can effectively address the multifaceted challenges of improving energy efficiency, fostering an environment conducive to sustainable energy practices, and making significant strides toward sustainability.

While our study pioneers the exploration of the intricate relationship between environmental regulation, marketization, and energy efficiency in China, it acknowledges certain limitations that pave the way for future research. The temporal scope, constrained by data availability, restricts our ability to capture long-term effects and emerging trends beyond 2016, highlighting the need for ongoing studies as China advances its environmental policies and market reforms. Moreover, our quantitative approach, while robust, could be complemented by qualitative research to uncover the micro-level mechanisms and institutional contexts influencing these dynamics. Future studies incorporating case studies or interviews with policymakers and industry stakeholders would provide deeper insights into the practical challenges and opportunities for enhancing energy efficiency through regulatory and market-driven interventions. Furthermore, while our findings offer valuable insights into the Chinese context, their applicability to other economies—particularly emerging markets with similar developmental trajectories—should be approached cautiously. Given the variations in energy systems, regulatory structures, and market conditions, the effectiveness of environmental regulations may differ across economies. Instead of serving as a direct policy blueprint, our findings should be viewed as a conceptual framework adaptable to local contexts, considering factors such as economic development stage, energy infrastructure, and institutional environment. Comparative research applying and testing these models in diverse economic settings is essential to refine our understanding of how environmental regulations and marketization interact in different regulatory and economic landscapes. By expanding this line of inquiry, future research can contribute to the broader discourse on sustainable energy governance and inform policymakers in economies undergoing similar market transitions. Future research should also consider the dynamic effects of environmental regulations and marketization over time, including potential lagged effects and the evolution of their interactions as economies develop and policies mature. Incorporating more detailed sectoral analyses could reveal how different industries respond to regulatory and market forces, providing valuable insights for targeted policy interventions.