Impact of China’s Energy-Consuming Right Trading on Urban Land Green Utilization Efficiency

Abstract

China is facing development challenges, such as the red line of arable land, resource shortage, and tightening ecological and environmental constraints. In this context, improving land green utilization efficiency (LGUE) is not only an important undertaking to optimize the spatial layout of the country and improve resource carrying capacity but also an inevitable choice for the comprehensive green transformation of economic and social development. China’s energy-consuming right trading (ECRT) is an important energy transition demonstration policy; however, its effect on LGUE has yet to be scientifically evaluated in academic research. Using panel data of 260 prefecture-level cities in China from 2009 to 2021, this study first uses a difference-in-difference model to test the effect of ECRT on LGUE, analyze its transmission mechanism, and further examine the impact of urban characteristic heterogeneity on policy effects from multiple perspectives. Results show the following: (1) The pilot policy of ECRT significantly improves urban LGUE, as confirmed by robustness tests. (2) The ECRT pilot policy enhances urban technological innovation, promotes the upgrading of industrial structure, and thereby improves LGUE. (3) The ECRT has a more significant enhancement effect on the central and western cities, large-scale cities, and resource-based cities. (4) Government environmental protection assessment can have a positive moderating effect, that is, further amplifying the effect of ECRT on improving urban LGUE. In conclusion, we should solidly promote the construction of a unified national ECRT market, formulate policy implementation plans tailored to local conditions, and steadily improve LGUE. To a certain extent, this paper reveals the inherent logic of how ECRT affects LGUE, which provides opportunities for cities to improve LGUE through ECRT, and provides reference for promoting the comprehensive green transformation of economic and social development.

1. Introduction

As the basic material carrier of urban production, life, and ecological space, urban land green utilization efficiency (LGUE) reflects the spatial transformation of input and output factors in social and economic activities. While considering economic benefits, it emphasizes the environmental effects generated by land use, with significant features of efficiency and greening. High pollution in urban land use has had an undeniable negative impact on the health of the people [1]. Moreover, promoting urban LGUE has become a focus in coordinating economic development and environmental protection. In addition, the pace of urbanization construction in China is accelerating to meet the housing needs of the people. As of the end of 2023, the urbanization rate has reached 66.16%. With the excessive expansion of urban space, land use efficiency in most cities is low, which is not matched with the speed of urbanization development, and is leading to a series of problems, such as economic stagnation, shortage of arable land, food security, and environmental pollution. The focus of achieving green utilization of urban land lies in transforming the mode of economic development, optimizing the structure of urban land use, improving the ecological service capacity of land resources, and seeking to maximize land economic output and social welfare, while reducing ecological environmental risks. In this context, improving urban LGUE is not only a key path to efficient utilization of land resources but also an inevitable choice to promote green development.

Market-oriented emission trading policies, such as sulfur dioxide and carbon emissions trading, have progressively emerged to achieve green development of the economy and society. Market-based environmental regulation (ER) leverages market-driven mechanisms to address inherent inefficiencies in resource allocation profoundly. However, these market-based license trading policies primarily concentrate on the end-stage management of pollution emissions from enterprises [2]. In a bid to expedite energy conservation efforts, the Chinese government has shifted its focus toward upstream control. In 2016, an innovative policy known as energy-consuming right trading (ECRT) was introduced in Zhejiang, Fujian, Henan, and Sichuan Provinces. Under the ECRT policy, companies can effectively manage their total energy consumption, acquire energy rights in compliance with regulations, and remit a one-time payment for these rights [3]. Enterprises can also trade these legally obtained total energy consumption rights. In contrast to carbon and sulfur dioxide emission transactions that address end-of-life environmental governance, ECRT primarily centers on upfront environmental management. Its goal is to adjust energy consumption indicators among enterprises using market-oriented strategies while maintaining control over total energy utilization. This approach fosters energy conservation, spurs enterprise transformation, and promotes modernization. ECRT mitigates overinvestment issues stemming from the expansion of energy systems, ultimately enhancing production efficiency and incentivizing companies to engage in green innovation [4].

Scholars have empirically examined the implementation effect of ECRT, pointing out that ECRT can promote economic growth, enhance green benefits, improve the urban environment, and achieve synergistic effects with other policies [5,6]. Some scholars have focused on the core goals of ECRT and reached a consensus on the energy-saving role of ECRT in improving regional energy consumption and improving energy efficiency [7,8]. As the basic material carrier of social production and life, the reconstruction and adjustment of economic activities triggered by the pilot policy of ECRT undoubtedly rely on land as an important resource, and its series of economic and environmental effects has a significant impact on land use. Scholars have defined the urban LGUE as the comprehensive mapping of the input system of urban production factors and the output system of urban land use in urban space [9]. Scholars have used an improved unexpected output super efficiency SBM model to measure LGUE comprehensively, analyzed the current situation and characteristics of urban LGUE in China [10], and pointed out that economic development [11] and the degree of openness to the outside world [12] can affect urban LGUE. The important role of local government behavior in improving the LGUE has also been studied [13,14]. Hence, can the ECRT pilot promote the improvement of urban LGUE? What is the mechanism of action? What are the heterogeneous manifestations? The answers to the above questions are not only related to the test effectiveness of the ECRT pilot and the optimization and improvement of the national unified ECRT market but also closely related to urban land use model transformation. Substantial research has been conducted on the economic effects of ECRT and the influencing factors of LGUE, but research testing the effect of ECRT on LGUE is lacking. The present study regards ECRT as a quasi-natural experiment, using 260 cities in China from the year 2009 to 2021 as research samples. On the basis of the measurement of LGUE using the super efficiency SBM model, a difference-in-difference (DID) model is used to test the effect of ECRT on LGUE systematically. This study also uncovers the transmission mechanisms of ECRT on LGUE from technology innovation (TI) and industrial structure (IS), and examines the moderating influence of environmental protection assessment (EPA) on the relationship between ECRT on LGUE. In addition, we also conduct an in-depth analysis of the heterogeneous impact of ECRT on LGUE from three dimensions: urban geographical location, urban resource type, and urban scale.

The marginal contribution of this study mainly lies in the exploration and verification of the mechanism by which the pilot policy of ECRT promotes TI and IS upgrading to improve the LGUE of cities. It enriches and deepens the research on the economic effects of ECRT construction and helps deepen the understanding of the internal driving force and effective path to achieve the improvement of LGUE of cities. The second is to explore further and verify the regulatory effect of government EPA on the impact of ECRT on urban LGUE and the heterogeneous effects of location, scale, resources, and other dimensions, thus providing experience and policy inspiration for accelerating the construction of the national ECRT market and effectively improving urban LGUE in various regions.

The rest of this study is organized as follows: Section 2 presents the literature review, policy background, and theoretical analysis. Section 3 describes the research design. Section 4 provides the empirical results about the impact of ECRT on LGUE. Section 5 concludes the study.

2. Literature Review, Policy Background, and Theoretical Analysis

2.1. Literature Review

Research related to this study mainly focuses on three aspects: measuring LGUE, analyzing influencing factors, and conducting research on the ECRT pilot policy. First, the topic of LGUE has received widespread attention. The ratio of the output value of secondary and tertiary industries to the construction land area [15] and the ratio of input to output measured by the SBM model have been measured as LGUE [10]. However, in the context of continuous promotion of ecological civilization construction, environmental benefits as a companion output of land use are increasingly valued. Therefore, existing research mostly incorporates environmental pollution levels characterized by industrial wastewater, sulfur dioxide, and smoke into the SBM model [11,16]. In addition, scholars have combined carbon emission constraints under the dual carbon background to incorporate carbon emissions into the model simultaneously [17]. After considering the abovementioned undesirable outputs, the connotation of land use efficiency is further extended to LGUE [18]. At the same time, the exploration of relevant influencing factors of LGUE has become a focus of attention for scholars. The improvement of factors, such as manufacturing agglomeration, TI, regional collaborative innovation, compact transportation development, IS upgrading, and new urbanization, have a positive impact on LGUE [19,20]. External policy shocks, such as environmental assessment policy, low-carbon pilot policy, regional integration, and national e-commerce demonstration city construction, have also had the aforementioned effects [21,22,23].

Research related to ECRT mainly focuses on two aspects. The first is the analysis of the top-level design of ECRT, involving qualitative and quantitative analysis methods. Among them, qualitative analysis mainly focuses on the legal attributes of ECRT and the conflicts and connections between different policies [24]. Quantitative analysis is mainly based on methods, such as the DEA model [25,26], the CGE model, and counterfactual simulation [27] to simulate and predict the quota allocation scheme, trading mechanism, and effectiveness of ECRT [28,29,30]. The second is the empirical analysis of policy effectiveness, which can be divided into two stages. Before the pilot of ECRT, scholars mainly used pre-policy data to predict the effectiveness of ECRT and compared the emission reduction potential of ECRT with other policies [31]. After the pilot implementation of ECRT, scholars began to turn to evaluating the actual effectiveness through the implemented data. Specifically, studies have found that ECRT can significantly improve energy efficiency and have a positive impact on energy consumption and energy intensity [32,33]. In addition, studies have found that ECRT has spatial spillover effects, which not only reduce energy consumption and intensity in the local area but also have an impact on neighboring areas [34]. As research deepens, scholars are no longer limited to analyzing the dual control of energy consumption in ECRT but are beginning to focus on its environmental and economic effects. Research has shown that ECRT reduces the total amount and intensity of CO₂ emissions while promoting economic development and generating a dual dividend of environment and economy. In addition, ECRT has been found to promote low-carbon transformation in the industrial sector [35,36], carbon neutrality [37], TFP [38], and green innovation of enterprises [4].

In summary, ECRT, as an important emission reduction policy for China to promote an energy consumption revolution and achieve the “dual carbon” goals, is a market-oriented ER tool. The market mechanism theory of environmental economics believes that market mechanisms can promote the effective allocation of resources and incentivize emission reduction behavior through price signals [39]. ECRT requires units with excess energy usage to purchase a sufficient amount of energy rights, thus encouraging energy users to improve energy efficiency, adjust energy usage structure, reduce energy consumption and corresponding pollutant emissions, and help improve LGUE. Therefore, studying the impact of ECRT on LGUE not only helps verify the effectiveness of market mechanism theory in environmental economics but also provides improved guidance for environmental policy formulation and improvement. Moreover, it provides important information on pollution reduction and carbon reduction for society, enterprises, and governments. Therefore, this study focuses on the perspective of LGUE and examines the impact of ECRT pilot policy.

2.2. Policy Background

In order to explore green development methods for resource conservation and efficient utilization, the National Development and Reform Commission of China issued the ECRT policy in 2016, proposing to carry out paid use and trading pilot projects for energy rights in four provinces: Zhejiang, Fujian, Henan, and Sichuan. Up to now, various pilot provinces have gradually established a system of rules and regulations for ECRT by issuing a series of normative documents. As a market-oriented carbon reduction mechanism, the basic logic of ECRT is that the government formulates a list of emission control enterprises and allocates initial energy use rights quotas based on national carbon reduction targets and the emission situation of various industries while constraining the total amount of energy-consuming rights in the market. During this process, enterprises can earn profits by selling unused energy usage quotas or compensate for their excess emissions by purchasing energy usage quotas [40]. Similar to the principle of emissions trading, the ECRT is actually a market-oriented ER that uses energy-consuming indicators as property rights and the market price of energy-consuming indicators as signals. Through the market trading mechanism, ECRT achieves a balance of total energy consumption, thereby achieving the goal of energy conservation, emission reduction, and promoting the green transformation of enterprises [6,8].

Although China’s ECRT policy has been in operation for many years, there are still some shortcomings. China’s ECRT market is still in the early stages of development, with issues such as incomplete legal and regulatory construction, low market activity, and slow progress in pilot projects and demonstration promotion. For example, the legislative level of the legal basis for ECRT is significantly lower compared to the white certificate trading system in Europe. Due to the lack of legal enforcement in the rules of the ECRT market, it lacks binding force on both trading entities and regulatory authorities, which can easily lead to inefficient government regulation and insufficient incentives for trading entities to engage in transactions. In addition, the trading entities of white certificates in Europe are not only large energy suppliers with mandatory energy-saving obligations, but also small and medium-sized energy suppliers without mandatory energy-saving obligations. Moreover, the trading entities have almost no industry restrictions and cover various sectors based on the industrial sector. However, the trading entities of ECRT in China are relatively single, and the trading entities in various pilot provinces are mainly large enterprises that have reached a certain energy consumption level, resulting in certain restrictions on the trading volume in the ECRT market. In terms of trading methods, ECRT is mainly conducted through government led public resource or environmental energy-saving trading institutions, with spot trading as the main trading method. Compared with the European white trading system, it appears to be less flexible [8]. Therefore, the policy design of ECRT still needs further improvement and optimization. Can ECRT affect the LGUE through resource allocation? We will conduct research around this topic next.

2.3. Theoretical Analysis

2.3.1. Direct Impact of ECRT on the LGUE

The impact of ECRT on LGUE in urban areas is mainly reflected in three aspects: cost push effect, economic incentive effect, and policy support effect. The first is the cost push effect of ECRT. Given the internalization of external emission reduction costs, energy use rights are no longer free public resources. Enterprises that exceed emissions must purchase corresponding quotas to avoid punishment. This strict regulatory mechanism forces enterprises to incorporate energy use rights into production cost considerations and adopt measures, such as improving production processes, optimizing supply chains, and improving production efficiency, to achieve profit maximization and emission reduction goals [39], thereby improving land output quality and reducing pollution emissions. The second is the economic incentive effect of ECRT. The existence of potential benefits in the ECRT market helps reduce the positive externalities of emission reduction for enterprises and also becomes an effective means for enterprises to use market mechanisms to compensate for innovation and marginal emission reduction costs, thereby forming positive incentives for enterprises to invest in energy-saving technologies and improve production efficiency [38]. The third is the policy support effect of ECRT. During the implementation of ECRT pilot areas, the government often introduces supporting measures to promote the green transformation of enterprises, maximizing the emission reduction effect of ECRT, to achieve coordinated development of regional economic growth and emission reduction [40]. The development of various economic activities and the efficient allocation of production factors ultimately release vitality through land as a carrier, which helps increase the output of established land areas and reduce emissions.

On this basis, this study proposes theoretical hypothesis H1: ECRT can help improve the LGUE of cities.

2.3.2. Indirect Impact of ECRT on LGUE

As for the internal mechanism of how ECRT affects LGUE, through what channels does the pilot policy of ECRT affect LGUE? On the basis of ECRT’s operational mechanism and existing research, this study believes that ECRT mainly enhances the urban LGUE through TI and IS.

ECRT can promote TI in relevant enterprises to improve production processes through economic incentives and cost push effects. At the same time, pilot areas often introduce policies, such as tax exemptions and financial subsidies, to reduce the risks and costs of enterprises implementing innovation activities and promote the application of low-carbon technologies. Furthermore, TI can effectively reduce the dependence on energy, labor, and other factors in land use activities, improve the conversion rate of production factors, and reduce pollution emissions and resource waste [12]. At the same time, breakthroughs in low-carbon technologies contribute to the reduction of clean energy usage costs and the increase in demand [19], which in turn has a positive impact on LGUE.

The cost pressure under the total control of energy rights forces high-energy consuming enterprises to transform, upgrade, or exit the market. The low-carbon tertiary industry, represented by the modern service industry, has received further attention and development, thereby promoting the upgrading of regional IS. Furthermore, research has shown that upgrading the IS can promote the flow of funds, technology, and talent to areas with high investment returns, reduce the dependence of economic development on land factors, increase economic output per unit land area, and exert the effect of pollution reduction, thus empowering LGUE [41].

On this basis, this study proposes theoretical hypothesis H2: ECRT can improve LGUE by improving the level of urban TI and promoting IS upgrading.

2.3.3. Moderating Effect of Government EPA

The implementation of EPA policies guides local governments in their development strategy choices and behavioral goals and have an impact on regional environmental governance and development models. After the one-vote veto system for EPA was incorporated into the performance evaluation, local governments tend to allocate more attention to the operation of ECRT to avoid being punished by the central government for failing to meet EPA standards. The increased attention of the government to ECRT encourages local governments to take effective measures to play the environmental role of local ECRT. First, local governments can promote local environmental protection and green development by strengthening the implementation of ECRT and investing in environmental protection funds. Second, local governments can guide enterprises in green production and operation through measures, such as industry access control, enterprise investment approval, and tax incentives, thereby reducing the use of high energy-consuming materials and the emission of pollutants. Third, local governments can control industrial land scale by adjusting the proportion of land use types, optimize the resource allocation of industrial land, and reduce unexpected outputs. Fourth, local governments can improve marketization of land resources by regulating land transfer systems to eliminate the phenomenon of attracting enterprises to settle in industrial land at low prices or even in violation of regulations and improve LGUE.

On this basis, this study proposes theoretical hypothesis H3: EPA has a positive moderating effect that is further amplifying the effect of ECRT on improving urban LGUE.

The logical framework of this study is shown below (Figure 1).

3. Research Design

3.1. DID Model

Zhejiang, Fujian, Henan, and Sichuan launched the ECRT pilot in 2016, providing reasonable conditions for the use of the DID method in this study. In this study sample, cities from four pilot provinces formed the treatment group, while cities that do not implement the ECRT pilot formed the control group. The urban-level data from the year 2009 to 2021 come from the CNRDS database. The specific DID model is as follows:

$${LGUE}_{it}={\alpha }_0+{\alpha }_1{D}_i\times T_t+{\alpha }_2{Conl}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it}$$

(1)

Here, LGUE denotes the LGUE of cities; i and t represent city and year, respectively; D is a dummy variable of the pilot cities of ECRT. If the city is subject to policy intervention from the ECRT, D = 1; otherwise, D = 0. T is a time dummy variable. If the year is after 2016, then T = 1; otherwise, T = 0; D × T can represent the ECRT policy, and its coefficient α₁ reflects the impact of ECRT on LGUE. If α₁ is positive and significant, it indicates that the ECRT has improved the LGUE of pilot cities; μ represents the fixed effect of the city and γ represents the time fixed effect. Conl denotes a series of control variables.

3.2. Variable Definitions

LGUE: This study selects a super efficiency SBM model to measure LGUE based on Bian and Zhong (2023) [41]. The specific indicator system design for urban LGUE is shown in

Table 1. Measuring indicators of LGUE.

Indicator Type	Indicator Name	Indicator Connotation	Reference Literature
Input	Land	Urban built-up area	Lu et al., 2020; Lu et al., 2022 [9,16]
	Capital	Urban fixed assets investment
	Labor	Urban employment
Desirable output	Economic output	Value added of the second and third industries	Lu et al., 2022; Bian and Zhong, 2023 [16,41]
	Social output	Average salary of urban employees
	Ecological output	Green coverage rate in built-up areas	Bian and Zhong, 2023; Fan and Lu, 2023; Ma et al., 2024 [18,21,41]
Undesirable output	Pollutant emission	Industrial wastewater discharge
		Industrial sulfur dioxide emissions
		Industrial dust emissions

.

Control variables: referring to Lu et al., 2022 [16], we chose the following urban characteristics as control variables. Economic development (ED) is measured by the logarithm of per capita GDP. Generally, cities with higher levels of ED can invest more funds and policies in areas, such as innovation, land planning, and environmental governance, thereby having a positive impact on LGUE. However, a higher level of development may mean more industrial activities, transportation demand, and energy consumption, thereby hindering the improvement of urban LGUE. The extent of opening up (OU) is measured by the logarithm of the proportion of FDI to GDP. The improvement of OU level may be accompanied by technology transfer and industrial upgrading, which can help improve LGUE. However, it may also lead to problems, such as excessive land development and intensified pollution, due to the pursuit of short-term economic benefits, which have a negative impact on LGUE. The level of financial development (FD) is expressed as the logarithm of the balance of deposits in financial institutions. The higher the level of FD in a region, the more government can use financial means to increase financial support for green production technologies of enterprises, promote the application of green technologies, and thereby improve LGUE. The city size (CS) is measured by the logarithm of the total population. A higher population density means a higher demand for land use efficiency. To utilize land effectively, reasonable planning must be conducted, and measures, such as high-rise buildings and intensive utilization, must be adopted to save land use area, which is conducive to improving LGUE. The infrastructure construction (IC) level is measured by the logarithm of per capita road area. The construction of regional infrastructure can bring spillover effects of TI and IS upgrading, thereby improving LGUE. Descriptive statistics are shown in Appendix A 

Table A1. Descriptive statistics.

Variable	Obs.	Mean	SD	Min.	Max.
LGUE	3380	0.298	0.147	0.043	1.011
ED	3380	10.675	0.707	4.799	15.832
OU	3380	−6.411	1.226	−13.11	−2.159
FD	3380	14.423	2.807	4.554	21.423
CS	3380	8.112	1.587	3.211	16.52
IC	3380	1.737	4.431	−0.566	6.094

.

4. Research Results

4.1. DID Results

Table 2. Effect of ECRT on LGUE.

	(1)	(2)
	LGUE	LGUE
D × T	0.063 ***	0.055 ***
	(0.021)	(0.0157)
ED		−0.0596
		(0.0497)
OU		0.018
		(0.0525)
CS		−0.061 ***
		(0.0191)
FD		0.0261
		(0.0614)
IC		0.0515
		(0.0617)
City Effect	NO	YES
Year Effect	NO	YES
Observations	3380	3380
R-squared	0.188	0.435

Note: *** represents significance level at 1%.

presents the benchmark regression results of the effect of ECRT on urban LGUE. Among them, Column (1) does not add control variables, while Column (2) considers a two-way fixed effect based on the inclusion of control variables. In either case, the core explanatory variable (D × T) coefficient is positive, indicating that ECRT can significantly improve the urban LGUE. Theoretical hypothesis H1 has been preliminarily verified. This study conducted analysis based on the results of Column (2), with a coefficient of 0.055 for the core explanatory variable, and passed the 1% significance test, indicating that under other unchanged conditions, the LGUE of the treatment group cities improved by approximately 0.055 units compared with the control group. We compared the main results with previous studies and find them similar. Although the ECRT is not combined with LGUE, the literature presents a near consensus that market-oriented ER improves LGUE. Xu et al. (2021) [14] found that a certain degree of ER can increase the environmental pressure on enterprises, thereby improving the LGUE of cities. Bian and Zhong (2023) [41] found that market-oriented ER can optimize multi-dimensional output per unit of land through market-oriented means and reduce pollution emissions, thereby improving the LGUE.

4.2. Robustness Test

4.2.1. Parallel Trend Test

A DID model is used to ensure that the experimental and control groups have parallel trends before the exogenous shock event, i.e., the introduction of the ECRT policy. On the basis of the event study method of Deschênes et al. (2017) [42], annual dummy variables are set up separately by sample year to interact with group dummy variables.

Table 3. Parallel trend test.

	(1)	(2)
	LGUE	LGUE
D × T2013	0.018	0.012
	(0.097)	(0.089)
D × T2014	0.025	0.019
	(0.105)	(0.094)
D × T2015	0.021	0.014
	(0.115)	(0.096)
D × T2016	0.051 ***	0.048 ***
	(0.013)	(0.014)
D × T2017	0.059 ***	0.057 ***
	(0.016)	(0.015)
D × T2018	0.062 ***	0.059 ***
	(0.015)	(0.018)
Control	NO	YES
City FE	YES	YES
Year FE	YES	YES
Observations	3380	3380
R-squared	0.223	0.454

Note: *** represents significance level at 1%.

displays the parallel trend results. They show that the estimated coefficients (D × T₂₀₁₃, D × T₂₀₁₄, D × T₂₀₁₅) of group dummy variables and annual interaction terms are insignificant before the introduction of the ECRT, and the coefficients (D × T₂₀₁₆, D × T₂₀₁₇, D × T₂₀₁₈) are significant, providing supporting evidence for the parallel trend test.

4.2.2. Counterfactual Test

To avoid interference from other random factors, this study adopts a counterfactual test based on Zhou et al. (2023) [43]. This study assumes that the implementation time of ECRT is in the years 2013, 2014, and 2015 while deleting the samples from the years 2016–2021. The counterfactual test results are shown in

Table 4. Counterfactual test.

	(1)	(2)	(3)
	LGUE	LGUE	LGUE
D × T	0.032	0.024	0.035
	(0.188)	(0.142)	(0.135)
Control	YES	YES	YES
City FE	YES	YES	YES
Year FE	YES	YES	YES
Observations	1820	1820	1820
R-squared	0.312	0.319	0.317

, and the interaction coefficients from the first to third columns are not significant. Thus, the changes in pilot cities’ LGUE are not influenced by other factors but rather by the construction of ECRT.

4.2.3. Propensity Score Matching–DID

Although ECRT pilot cities have standardized approval procedures and standards, their final selection is inevitably due to factors that have a certain degree of nonrandomness, such as administrative level and economic scale. To reduce the nonrandom selection bias of the DID method further, this study uses a propensity score matching (PSM)–DID model for verification. On the basis of the dummy variable of whether it is an ECRT pilot city, we use control variables as covariates for logit regression to calculate the propensity scores of each city and then use kernel matching to find the control group sample with the most suitable characteristics for the treatment group sample. Finally, we re-regress Model (1).

Table 5. PSM–DID estimation results.

	(1)	(2)
	LGUE	LGUE
D × T	0.038 ***	0.029 ***
	(0.012)	(0.008)
Control	NO	YES
City FE	YES	YES
Year FE	YES	YES
Observations	2860	2860
R-squared	0.167	0.417

Note: *** represents significance level at 1%.

shows that after using the PSM-DID method, the estimated coefficients of D × T are positive.

4.2.4. Other Robustness Tests

The first test excludes the central city. In consideration of the significant advantages of central cities in terms of geographical location, economic development foundation, and resource agglomeration capacity, which make it easier to unleash policy dividends, the samples of municipalities directly under the central government, provincial capital cities, and sub-provincial cities are excluded, and Model (1) is retested. The second is the lag treatment of the core explanatory variable. Given that policy effects may have a certain lag, the model is retested after the core explanatory variable is lagged for one period to weaken the impact of reverse causality. The third is to control the impact of other policies. Previous studies have found that low-carbon and carbon trading policies also have an impact on the LGUE of cities [11,41]. Referring to the mainstream practices in academia, we add the dummy variables of these two policies in Model (1) for retesting to alleviate the cross effects of relevant policies. The above test results in

Table 6. Other robustness tests.

	(1)	(2)	(3)	(4)
	Excluding Central Cities	The Core Explanatory Variable Lags by One Period	Excluding Low-Carbon Pilot Policy	Excluding Carbon Trading Pilot Policy
	LGUE	LGUE	LGUE	LGUE
D × T	0.046 ***	0.062 ***	0.051 ***	0.049 ***
	(0.014)	(0.017)	(0.013)	(0.016)
Control	YES	YES	YES	YES
City FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	2938	3380	3380	3380
R-squared	0.435	0.432	0.436	0.435

Note: *** represents significance level at 1%.

show that the regression coefficients of D × T are positive.

4.3. Mechanism Verification

After fully confirming the positive impact of ECRT on LGUE, this study further uses the benchmark regression model to reveal and verify the internal transmission mechanism of ECRT on LGUE. The total number of invention patent authorizations obtained by a city is the measured TI of the city, and the ratio of the output value of tertiary industry to secondary industry is the measured IS of the city. We use IS and TI as the dependent variables in the benchmark model for regression. From the results in Columns (1) and (2) of

Table 7. Mechanism research results.

	(1)	(3)
	TI	IS
D × T	0.137 ***	0.049 **
	(0.033)	(0.021)
Control	YES	YES
City FE	YES	YES
Year FE	YES	YES
Observations	3380	3380
R-squared	0.566	0.719

Note: **, *** represents significance level at 5%, 1% respectively.

, the estimated coefficients of the core explanatory variable are 0.163 and 0.008, respectively, indicating that the ECRT policy can improve TI and promote IS upgrading. Given that the positive impact of TI and IS on LGUE has been supported by existing literature [44], ECRT can enhance LGUE through TI and IS. As theoretical analysis suggests the ECRT policy can not only stimulate the drive for TI among enterprises, but also contribute to the transformation of high energy-consuming enterprises and the development of modern service industries. Based on the above analysis, improving the level of TI and promoting IS upgrading are two important mechanisms for ECRT policy to enhance the urban LGUE, which validates hypothesis H2.

4.4. Moderating Effect of Government EPA

The product term (EPA × D × T) between the EPA of the region and ECRT is added to the benchmark regression model to verify the moderating effect of environmental assessment. On 23 May 2007, the State Council issued a document officially incorporating the one-vote veto system for EPA into the performance evaluation system, becoming an important part of official promotion evaluation. The assessment indicators of EPA mainly include air quality improvement goals. Existing research has confirmed the important role of EPA in promoting regional environmental improvement. We set a dummy variable to represent EPA, with a value of 0 before the year 2008 and a value of 1 after the year 2008. The coefficients of EPA × D × T in

Table 8. Moderating effect of EPA.

	(1)	(2)
	LGUE	LGUE
EPA × D × T	0.007 **	0.006 **
	(0.003)	(0.003)
Control	NO	YES
City FE	YES	YES
Year FE	YES	YES
Observations	3380	3380
R-squared	0.584	0.533

Note: ** represents significance level at 5%.

are significantly positive, which means that EPA can generate a positive moderating effect on ECRT and LGUE. After the increase in EPA intensity of the region, the environmental policies to support ECRT in the region have been strengthened, promoting IS adjustment. Polluting enterprises must bear great pressure to reduce emissions, as rising costs force them to choose to shut down production. Environmentally friendly enterprises can receive certain policies and financial support, have more motivation and advantages in updating environmental protection equipment and innovation, and adjust and optimize regional IS. As a spatial carrier for human survival and development, urban EPA intensity amplifies the environmental protection effect of ECRT on land, guides the optimization of land use patterns, reduces nonenvironmentally friendly energy consumption, and achieves effective control of unexpected output through strict control of green production and pollution control. Therefore, EPA can amplify the improvement effect of ECRT on LGUE.

4.5. Heterogeneity Analysis

Considering the varying levels of economic development, industrial structure, and resource types in different types of cities in China, the impact of ECRT on LGUE may also vary. We analyze the heterogeneity of LGUE from three dimensions: geographical location, urban size, and resource type. Due to the implementation of ECRT in 2016, we compare the average LGUE of different types of cities in China in the year before the policy was implemented, as shown in Figure 2. The LGUE of eastern cities is higher than that of central and western cities, while that of large-scale cities is higher than that of small and medium-sized cities. The LGUE of non resource-based cities is higher than that of resource-based cities. This indicates that there are differences in the LGUE of cities in terms of geographical location, urban scale, and resource types. So let’s proceed with heterogeneity testing to analyze the heterogeneous impact of energy rights trading on the green utilization efficiency of land in different cities. Therefore, the next step of this study is to conduct heterogeneity testing to analyze the heterogeneous impact of ECRT on the LGUE from geographical location, urban size, and resource type.

4.5.1. Regional Heterogeneity

The spatial differences in regional development are important issues facing the realization of green land development. The eastern and central western regions are completely different in terms of market environment, economic foundation, and innovation level; thus, policy implementation conditions and effects may also differ. Therefore, this study divides the sample cities into two categories based on their location: eastern and central western for testing. The results of Columns (1) and (2) in

Table 9. Heterogeneity test.

	(1)	(2)	(3)	(4)	(5)	(6)
	Eastern	Central Western	Large Scale	Small and Medium	Resource-Based	Non-Resource-Based
	LGUE	LGUE	LGUE	LGUE	LGUE	LGUE
D × T	0.044 ***	0.061 ***	0.065 ***	0.041	0.068 ***	0.039 ***
	(0.015)	(0.017)	(0.021)	(0.068)	(0.019)	(0.012)
Control	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	1300	2080	1144	2236	1482	1898
R-squared	0.501	0.484	0.5	0.496	0.432	0.447

Note: *** represents significance level at 1%.

show that for eastern and central western cities, the pilot policy of ECRT can significantly improve LGUE at a 1% level. However, the difference is that the intensity of this effect in central western regions is higher than that in eastern regions. The reason for this is that the ecological environment of central and western regions is relatively fragile, and the LGUE can be improved further with evident policy effects. Under the sound market mechanism and economic foundation, the pilot policy of ECRT in the eastern region also contributes to efficient urban LGUE. However, given its early emphasis on low-carbon development and the relatively tight supply of land resources, the policy effect is slightly lower than central western regions.

4.5.2. Scale Heterogeneity

Different CSs imply vastly different resource agglomeration capabilities, economic and social vitality, and land allocation patterns, thereby affecting the effectiveness of policies and land use conditions. Therefore, this study divides the sample into two categories: small- and medium-sized cities (with a permanent population of less than 1 million in urban areas) and large-scale cities (with a permanent population of more than or equal to 1 million in urban areas) and further reveals whether differences exist. Table 9 shows that the estimated coefficients of D × T are positive only in large-scale cities while not significant in small- and medium-sized cities, indicating that the ECRT can only significantly improve the LGUE in large cities. Large-scale cities have a dense population distribution and economic activity; thus, the demand for energy rights must be managed urgently. In addition, their strong ability to gather talent, technology, and resources makes the policy effect evident. However, small- and medium-sized cities limit their motivation to implement innovation activities and industrial upgrading due to economic, technological, and financial constraints.

4.5.3. Resource Heterogeneity

China has a large number and wide distribution of resource-based cities. Compared with non-resource-based cities, their ecological environment and land use are deeply influenced by natural resource endowments; thus, policy effects of ECRT may also differ. This study divides the sample into two categories for testing based on the research of Zhou and Qi (2024) [45]. Table 9 shows that the estimated coefficients of the core explanatory variables are positive for resource-based and non-resource-based cities, indicating that ECRT can simultaneously improve the LGUE of these two types of cities. However, in terms of coefficient size, the effect of this policy on resource-based cities is significantly higher than non-resource-based cities. The IS of resource-based cities is dominated by the heavy chemical industry, resulting in increased carbon emissions. Therefore, the ECRT is targeted and has great room for development. The IS of non-resource-based cities is mainly composed of service and manufacturing industries. Driven by a high economic foundation and the demand for low-carbon development, it can still improve the LGUE by strengthening TI and other means, although the space for the implementation of the ECRT is relatively limited.

5. Research Conclusions, Policy Recommendations, and Limitations

The construction of ECRT is an important strategic measure to implement dual carbon goals. Currently, China is facing development challenges, such as red lines on arable land, resource shortages, and tightening ecological and environmental constraints. Effectively improving the LGUE is of great strategic significance. This study empirically tests the effect and mechanism of ECRT on urban LGUE by using the DID method. We mainly draw the following conclusions: (1) The ECRT can improve the LGUE level of pilot cities. (2) The ECRT mainly affects urban LGUE through TI and IS, and EPA can amplify the improvement effect of ECRT on LGUE. (3) ECRT has a more significant enhancement effect on central western cities, large-scale cities, and resource-based cities.

ECRT is an important lever for China to deepen its green and low-carbon development strategy. The reconstruction and adjustment of economic activities triggered by it cannot be separated from land, which is the basic material carrier. The improvement of LGUE is an inevitable choice to achieve optimal allocation of land resources. Against the backdrop of accelerating the development of the national ECRT market, this study integrates the ECRT and LGUE into a unified framework for research. The relevant conclusions are enlightening for the improvement of the ECRT market and the improvement of urban LGUE. To leverage the role of ECRT in improving LGUE, we propose the following policy recommendations:

First, given that ECRT can significantly improve LGUE, promoting ECRT nationwide will become an important policy tool for effectively addressing climate change, achieving dual carbon goals, and promoting low-carbon transformation in various regions. China should continue to strengthen the institutional construction of ECRT, improve the linkage mechanism between energy rights and carbon emission rights, achieve collaborative linkage between front-end and back-end management, strengthen the capacity building of enterprises and third-party services and certification institutions, build diversified trading entities, gradually incorporate the construction of ECRT system into the legal track, and continuously enhance the applicability, effectiveness, and authority of ECRT.

Second, the government must continue to support TI development and IS upgrading, providing guarantees for the continuous improvement of LGUE. In the implementation of ECRT, not only should the emission reduction incentives and constraints of the policy be used to promote TI and IS upgrading, but regions should also actively strengthen support for the above two mechanisms by increasing research funding and strengthening policy support, thereby empowering the continuous improvement of LGUE. The government should pay attention to differences in urban location, scale, and type, and formulate policies tailored to local conditions. Each city should consider its own location conditions, scale, and natural resource endowment characteristics, fully consider the focus of ECRT, formulate ECRT quota standards and trading rules that are suitable for the actual development of the region and supplement them with corresponding policy support and incentive mechanisms to stimulate the enthusiasm of key industries for green development, and promote the comprehensive green transformation of land use models.

Third, the central government should continuously optimize EPA policies based on summarizing existing experiences. The research conclusion of this study affirms the positive role of EPA in improving urban LGUE in the region, indicating that the policy effect of environmental assessment has become apparent. The central government should further strengthen the assessment method that combines the quality and quantity of EPA, gradually improve and optimize environmental assessment policies, and improve LGUE. The government should fully consider the incentives and constraints for local government officials in the design of EPA to promote local governments to make more efforts in the field of environmental protection. For example, the central government can start with specific indicator design and target assessment to incentivize local government officials who have completed the assessment effectively, thus providing promotion advantages. For local government officials who fail to meet environmental assessment standards, criticism, education, and promotion restrictions can be imposed.

Due to the limited availability of data, the number of indicators used in constructing LGUE index is limited and may not fully reflect urban LGUE level. We plan to use more indicators in the future to construct a comprehensive LGUE index. The impact of ECRT on the urban LGUE may have spatial spillover effects, that is, ECRT can improve the LGUE in other adjacent cities by influencing the TI and IS of pilot cities. In the future, we will use a spatial DID model to study the spatial spillover effects of ECRT on LGUE. In addition, we will visualize the empirical research results clearly through maps to better present the conclusions.