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Article

China’s Carbon Emissions Trading Scheme Improved the Land Surface Ecological Quality

by
Diwei Zheng
1 and
Daxin Dong
2,*
1
School of Management, Xiamen University, Xiamen 361005, China
2
Institute of Western China Economic Research, Southwestern University of Finance and Economics, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(2), 616; https://doi.org/10.3390/su18020616
Submission received: 9 November 2025 / Revised: 20 December 2025 / Accepted: 4 January 2026 / Published: 7 January 2026
(This article belongs to the Special Issue Sustainability in Carbon Emissions, Carbon Pricing and Trading, and Carbon Neutrality)
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Abstract

The previous studies have suggested that the cap-and-trade carbon emissions trading scheme (ETS) was effective in reducing greenhouse gas emissions and atmospheric pollution. Are there other environmental benefits of this policy? This research question remains unanswered in the literature. Our study reports that China’s carbon ETS significantly improved the land surface ecological quality (LSEQ). The study analyzes the data of 328 Chinese cities during 2005–2020. A difference-in-differences (DID) regression model is used for quantitative policy evaluation. The land surface ecological quality is measured by a synthetic indicator of the remote sensing ecological index (RSEI). There are three main findings. (1) On average, the carbon ETS improved the land surface ecological quality index by 0.0113, which contributed 51% of the ecological quality improvement in ETS-implementing regions in the post-policy period. The positive effect of the policy increased over time. (2) The implementation of the carbon ETS reduced pollution emissions, promoted green innovation, and expanded the share of land with natural vegetation coverage. These phenomena provide explanations for why the policy improved the land surface ecological quality. (3) The policy effect exhibited some heterogeneities contingent on local climatic conditions. The effect was stronger in regions with more precipitation, shorter sunlight duration, and higher temperature.

1. Introduction

1.1. Research Background

In the first two decades of the 21st century, China’s overall ecological quality has shown an upward trend [1,2,3]. What factors have contributed to the improvement of China’s ecological quality? In the literature, the influencing factors are generally categorized as natural and anthropogenic factors. Natural factors include climate, geographic conditions, etc., while anthropogenic factors include urbanization, economic growth, demographic changes, etc. In addition, it is particularly noteworthy that China has implemented many environmental protection policies in an attempt to improve the eco-environment. For example, China has exerted several emissions trading policies, adopted a number of action programs to reduce air pollution, and provided compensation for ecosystem services.
Since 2013, China has conducted its cap-and-trade carbon emissions trading scheme (ETS). The policy was initially experimented in only a few provinces and was eventually extended to all parts of mainland China in 2021. This policy has received much attention, given that China is currently the largest carbon dioxide (CO2) emitter in the world. Some previous studies have suggested that the policy was effective in reducing CO2 emissions and atmospheric pollution. Are there other environmental benefits of this policy in addition to the abatements of greenhouse gas and air pollutant emissions? This research question remains unanswered.
Humans live on the land, and the ecological quality of the land surface provides essential services that underpin human well-being and long-run economic development, such as climate regulation, water retention, soil conservation, food and biomass production, and biodiversity habitat. Deterioration in the ecological quality of the land surface can amplify environmental risks (e.g., heat stress, drought vulnerability, and land degradation) and impose persistent welfare losses, while improvements can generate broad co-benefits that extend beyond reductions in carbon emissions and conventional air pollution indicators. Therefore, assessing the land surface ecological quality (LSEQ) offers a comprehensive perspective for evaluating the environmental consequences of climate and environmental policies, especially for a large emitter like China. Against this background, this study intends to investigate whether and to what extent the carbon ETS affected the LSEQ in China.
Before proceeding to the formal empirical analysis, we present the preliminary visual evidence in Figure 1. The figure shows the dynamics of the LSEQ index in Chinese districts from 2001 to 2020. The LSEQ is a broad concept that refers to the overall health and functioning of the ecosystem on the land surface [4,5,6,7]. LSEQ involves a set of surface biological, chemical, and physical properties and processes, as well as their interplays. In this study, the LSEQ is measured by a synthetic index based on remote sensing ecological data, comprising five components: greenness, dryness, heat, wetness, and the abundance of land cover types. (This indicator is provided by Xu et al. [8] and will be explained in detail in Section 3.2.1). We categorize the regions in mainland China into two groups: one group consists of the areas that have implemented the cap-and-trade carbon ETS before 2020; the other group contains the areas that have never implemented the policy during the sample period.
In Figure 1a, the average LSEQ values in the ETS-implementing regions and non-ETS-implementing regions are indicated by the blue solid line and orange dashed line, respectively. As can be observed, before 2013, the LSEQ values were roughly stable in both groups. However, after 2013, the LSEQ in ETS-implementing regions experienced an evident increase, while the variations in non-ETS-implementing regions were small. To inspect the difference between the two groups more clearly, we present Figure 1b, in which the two curves are displayed with two different vertical axes. Before 2013, the LSEQ trends of the two groups were analogous. However, the trends diverged after 2013. During the eight years between 2013 and 2020, the LSEQ in the ETS-implementing group rose by 0.016 from 0.538 in 2013 to 0.554 in 2020, with a peak of 0.569 in 2017; the LSEQ in the non-ETS-implementing group changed by only 0.004 from 0.468 in 2013 to 0.472 in 2020. Indeed, on average, the LSEQ in ETS-implementing regions increased more largely after 2013. Based on the graph, it is reasonable to conjecture that the implementation of the ETS had a positive effect on the LSEQ.

1.2. Research Objective and Contributions

In the empirical sections of this study, we will utilize econometric regressions to carefully quantify the impact of China’s carbon ETS on land surface ecological quality. This is the research purpose of this study.
This study makes several contributions to the literature and policy evaluation:
(1)
This study reveals that the carbon ETS had a significant impact on the land surface ecological environment. The previous literature assessing the environmental effects of the ETS has focused on the policy influences on carbon emissions and air quality but has rarely analyzed the impacts on other aspects of ecological quality. We extend the evaluation of carbon ETS from conventional outcomes to a broader ecosystem-based outcome, provide new knowledge for a comprehensive understanding of the environmental effects of the carbon ETS, and provide causal evidence on whether a market-based carbon regulation policy can generate comprehensive ecological co-benefits rather than only improving single environmental indicators. This extension matters because LSEQ integrates multiple dimensions of environmental conditions and is closely linked to long-run welfare and ecosystem resilience.
(2)
This study proposes that the implementation of the carbon ETS has been a key contributor to the improvement of China’s land surface ecological quality in recent years. This provides novel insights for a deeper understanding of the dynamics of land eco-environmental status in China. A large amount of the environmental science literature has previously analyzed the determinants of land surface ecological quality. While most of the studies have intensively inspected the impacts of natural factors such as geographic features and climatic conditions or macroeconomic variables such as GDP per capita and population density, fewer have specifically quantified the effect of a particular public policy. This study offers empirical evidence that well-designed public policies can generate beneficial effects.
(3)
Our findings provide evidence that a carbon market policy can deliver co-benefits for ecological quality, not only for emissions-related outcomes. This supports the policy rationale of using market-based instruments to advance China’s broader objectives of ecological civilization and high-quality development, and it highlights that the benefits of ETS may be underestimated if evaluation frameworks focus exclusively on emissions or conventional air pollution metrics.
(4)
Our analyses yield actionable insights for differentiated policy design by clarifying how the effectiveness of ETS in improving ecological quality may vary with local climatic conditions. This implies that ETS implementation can be complemented by regionally tailored supporting measures—such as ecological restoration, green infrastructure, and coordinated land use management—to maximize ecological co-benefits.

2. Literature Review and Research Hypotheses

2.1. Literature Review

This paper is closely related to the literature on two topics: appraising the environmental effects of carbon ETS and analyzing the factors influencing land surface ecological quality in China.
The carbon emissions trading scheme is an important public environmental protection policy implemented in China in recent years. There has been a lot of literature analyzing the environmental impacts of China’s carbon ETS, but these studies have concentrated on carbon emissions (e.g., [9,10,11]) and air quality (e.g., [12,13,14,15]). Few studies have examined the impact of this policy on other environmental aspects. The eco-environment of the land surface has a critical impact on human survival and development. As an environmental policy, does the carbon ETS affect land surface ecological quality? This research question has practical significance. If the carbon ETS can improve the LSEQ, in practice, this aspect should be taken into account when assessing the effects of the policy. In contrast, if the carbon ETS reduces the LSEQ, it is necessary to make a trade-off between the different environmental effects of the policy when considering whether to implement the policy in a certain region.
A large body of literature has analyzed various factors influencing the ecological quality of the land surface. However, their focus has been mainly on natural factors such as geographic and meteorological conditions (e.g., [16,17,18]) or general macro-level socioeconomic factors such as urbanization, population expansion, and economic growth (e.g., [5,19,20]). In the literature, there is less specific focus on the impact of a particular public policy. In practice, however, it is necessary to quantify the impact of public policies. In the short term, human activities cannot systematically reshape natural conditions such as geography and meteorology. Macroeconomic and social development is also a slow process. However, effective public policies can alter the land eco-environment in a planned and purposeful way. For instance, Sun et al. [21] analyzed the impact of a land consolidation policy in Yan’an district, China. They found that the policy had a negative influence on the land ecological quality. There have also been several previous studies on the effects of public policies affecting land ecological quality in China, concentrating on the impacts of land use policies, such as the Grain for Green Program [22,23], the Grassland Ecological Compensation Policy [24,25], the Rural Land Marketization Policy, and a series of land ecological restoration projects [26,27]. The impact of the carbon ETS on China’s land surface ecological quality has not been explored in the literature.
To fill the research gap in the literature, this study attempts to take a rigorous empirical approach to assess the influence of the carbon ETS on land surface ecological quality. Two methods were popular in previous empirical studies analyzing the determinants of the LSEQ: regression equation estimation and the GeoDetector. The literature using regression equation modeling has primarily adopted the ordinary least squares (OLS) model or geographically weighted regression (GWR) model. For example, Zhang et al. [28] studied the effects of geographic and meteorological conditions, and Tang et al. [29] analyzed the impacts of GDP and population density. There was also a stream of literature using the GeoDetector, such as Aizizi et al. [30], Lv et al. [31], and Zhang et al. [32]. Regression estimation and GeoDetector are able to detect whether two variables are statistically correlated, but it is difficult to precisely quantify the magnitude of the influence of a specific factor on the dependent variable. In this study, we will utilize the difference-in-differences (DID) methodology. Based on a combination of before–after period and treatment–control group comparisons, DID is a reliable method for identifying the causality for the purpose of policy evaluation [33,34,35,36,37]. The DID method has valuable advantages over methods such as the OLS, GWR, and GeoDetector: it can be used to accurately estimate the average treatment effect of a policy; it can also be easily extended for further analysis, for example, to analyze the impacts resulting from the interaction of a policy and other factors.

2.2. Research Hypotheses

2.2.1. Impact of Carbon ETS on Land Surface Ecological Quality

Based on the existing studies, we speculate that the carbon ETS is likely to have a positive impact on land surface ecological quality. Almond and Zhang [12], Li et al. [13], Tan-Soo et al. [14], Yu et al. [15], and many others have shown that carbon ETS was effective at abating pollution emissions in China. Liu and Sun [38], Teixido et al. [39], and Zhao et al. [40], among others, have suggested that the carbon ETS could promote green innovation. Both the reduction in pollution emissions and the expansion of green innovation may positively affect land surface ecological quality. In addition, the carbon ETS probably caused changes in socioeconomic activities, leading to changes in land use. For example, people might pay more attention to the maintenance of green space, the avoidance of potential damage to land, and more efficient and cleaner land use. Therefore, the core research hypothesis in this study can be stated as follows:
Hypothesis 1 (H1). 
China’s carbon emissions trading scheme improved the land surface ecological quality.

2.2.2. Possible Mechanisms

We also intend to investigate the mechanisms through which the carbon ETS influences land surface ecological quality. We consider three potential channels: reduction in pollution emissions, increase in green innovation, and change in land use.
Pollution damages land surface ecological quality. Toxins and dusts in pollutants directly harm the biological processes involved in the growth and development of vegetation and plants [41,42]. Pollutants also indirectly impair land surface ecological quality by lowering the quality of soil and water [43,44,45,46]. If a policy can reduce pollutant emissions effectively, the land surface ecological quality would be ameliorated.
Many economic production processes emit both greenhouse gases and environmental pollutants. The carbon ETS may have an effect of reducing greenhouse gas emissions and pollutant emissions simultaneously as some high-emission production processes are depressed after policy implementation. Several previous studies have analyzed the data of some Chinese firms and districts and reported that the carbon ETS reduced the emissions of crucial air pollutants such as PM2.5 [13,47] and SO2 [14,15]. Therefore, we propose the following research hypothesis regarding the pollution abatement effect of the ETS:
Hypothesis 2 (H2). 
China’s carbon emissions trading scheme caused the reduction in pollution emissions.
Green innovation is a key factor in promoting environmental sustainability. Green innovation broadly refers to innovation activities that can facilitate the reduction in pollution, environmental risks, and resource wastes. The enhancement of local green innovation would increase the efficiency of resource utilization and reduce pollution and, thus, lower the depletion of and damage to land. As a result, the land surface ecological quality would be improved. Several previous studies, such as Calel and Dechezleprêtre [48], Liu and Sun [38], Teixido et al. [39], and Zhao et al. [40], have analyzed Chinese and European cases and reported that the carbon ETS could promote green innovation through carbon pricing and allowance constraints, which increase the expected returns to low-carbon R&D and steer firms’ innovation efforts toward cleaner technologies, while also facilitating industrial upgrading and structural transformation that encourage firms to shift toward cleaner production and more innovation-intensive activities. Thus, we propose the following research hypothesis regarding the impact of the ETS on green innovation:
Hypothesis 3 (H3). 
China’s carbon emissions trading scheme caused the increase in green innovation.
The carbon ETS can cause changes in socioeconomic activities, which lead to changes in land use. For example, the implementation of the policy probably strengthens people’s awareness of environmental protection, which can increase societal demand for green-space maintenance and reduce tolerance for land-degrading behaviors. Meanwhile, by introducing a carbon price and compliance requirements, the ETS also reshapes firms’ production and investment decisions (e.g., discouraging the expansion of carbon-intensive activities, accelerating industrial upgrading, and reallocating capital toward cleaner sectors) and can influence local governments’ land use governance (e.g., stricter environmental oversight and greater investment in green infrastructure and ecological restoration), thereby changing the intensity and composition of land use and improving land cover conditions. These phenomena will make people pay more attention to the maintenance of green space and the avoidance of potential damage to land. In addition, the policy promotes innovation and pollution abatement, allowing for more efficient and cleaner land use. The existing literature, such as Firozjaei et al. [5], Gutiérrez Rodríguez et al. [23], Sun et al. [21], and Zhang et al. [32], among others, has suggested that land use type is an important factor influencing ecological quality. Hence, we propose the following hypothesis regarding the impact of the ETS on land use:
Hypothesis 4 (H4). 
China’s carbon emissions trading scheme caused the change in land use.

3. Materials and Methods

3.1. Empirical Method

In order to identify the causal effect of the CO2 ETS on land surface ecological quality in China, we build a difference-in-differences model. The DID is a linear regression approach widely used for policy impact evaluation [33,36,37]. It is applicable if the sample periods contain one before-policy period and one after-policy period and the sample units can be explicitly classified into one treatment group and one control group. In the treatment group, the policy of interest has been implemented since a specific point in time and has remained thereafter; in the control group, the policy has never been implemented.
Since 2013, the cap-and-trade carbon ETS has been implemented in Beijing City, Shanghai City, Tianjin City, and Guangdong Province. In 2014, Chongqing City and Hubei Province started this policy. In 2016, Fujian Province started this policy. These regions formed the treatment group in the DID analysis. Other areas where the cap-and-trade carbon ETS was not implemented until 2021 formed the control group in the analysis. It is notable that in 2016, Sichuan Province started its pilot project of the Certified Emission Reduction (CER) scheme, which was a voluntary carbon market. In this study, we still consider Sichuan as a part of the control group, given that the CER scheme is essentially different from the cap-and-trade ETS, which was a mandatory carbon market [49,50].
The DID model employed in this study is expressed by the following two-way fixed-effects regression equation:
EcologicalQualityit = αCO2ETSit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
In Equation (1), the dependent variable EcologicalQualityit is an indicator of land surface ecological quality in city i in year t. The subscripts i and t denote the region and period, respectively. The core explanatory variable CO2ETSit indicates whether the CO2 ETS was implemented. The model also contains a set of covariates that might influence ecological quality. These covariates are grouped in the two vectors ControlVariablesit and OtherPoliciesit. The definitions of the dependent and independent variables will be explained in detail in Section 3.2. The term ui in the regression equation controls the city-fixed effects. All possible time-invariant city-specific idiosyncratic characteristics, such as elevation and geographic location, are absorbed by ui. The term vt in the regression equation controls the year-fixed effects. Some nationwide shocks that affected all regions in the same periods, such as the global financial crisis in 2008 and the COVID-19 pandemic, are absorbed by vt. εit is the residual term. The parameters α, β, and γ are coefficients that would be estimated via standard regression methods. We are particularly interested in the value of α. This coefficient captures the average treatment effect of the CO2 ETS on land surface ecological quality.

3.2. Variables

3.2.1. Dependent Variable

The dependent variable is EcologicalQualityit. This variable reflects the land surface ecological quality. We use a synthetical remote sensing ecological index (RSEI) provided by Xu et al. [8] to measure the level of EcologicalQualityit. The approach of RSEI is widely used in the environmental literature, such as Aizizi et al. [30], Boori et al. [51], Firozjaei et al. [52], Karbalaei Saleh et al. [53], Qureshi et al. [54], and Sun et al. [21], among others.
EcologicalQualityit is constructed based on five component indicators: NDVI, NDBSI, LST, WET, and AI. NDVI is the normalized difference vegetation index, representing greenness; NDBSI is the normalized difference built-up and bare soil index, representing dryness; LST is the land surface temperature, reflecting heat; WET is the wetness index, reflecting wetness; and AI is the abundance index, reflecting the abundance of land cover types. The principal component analysis (PCA) method was used to extract the first principal component (PC1) from these five indicators. Then, PC1 was normalized to the range [0, 1] to generate the index of EcologicalQualityit. A higher value means better land surface ecological quality. More detailed explanations on this index are provided in Supplementary Material S1 and S2. The convenience and advantage of this index have also been verified by subsequent studies, such as Chen et al. [26], Ji et al. [55], Jiang et al. [56], and Li et al. [57].

3.2.2. Core Explanatory Variable

The core explanatory variable of interest is CO2ETSit. This is a binary dummy variable denoting the implementation of China’s cap-and-trade CO2 emissions trading scheme. This variable is defined as follows: CO2ETSit = 1 if the ETS was implemented in city i in year t; otherwise, CO2ETSit = 0.

3.2.3. Covariates

A set of covariates are included in the regression equations. The covariates are grouped into two vectors: ControlVariablesit and OtherPoliciesit. ControlVariablesit contains thirteen control variables that describe the meteorological and socioeconomic characteristics of different regions: Precipitationit, Sunlightit, Temperatureit, GDPPerCapitait, PopulationDensityit, IndustrialStructureit, FinancialDevelopmentit, TradeOpennessit, GovernmentSizeit, HighSpeedRailit, RoadDensityit, PublicHeatlhit, and Afforestationit. The definitions of these variables are as follows. (1) Precipitationit is the annual precipitation level (mm). (2) Sunlightit is the annual sunshine duration (h). (3) Temperatureit is the annual average temperature (°C). (4) GDPPerCapitait refers to the gross domestic product (GDP) per capita, measured in the constant price level in 2000 (CNY). (5) PopulationDensityit denotes the population density, i.e., the number of residents living in each unit of area (person/km2). (6) IndustrialStructureit is the industrial structure, measured by the proportion of agricultural value added in local GDP. (7) FinancialDevelopmentit indicates the level of financial development, proxied by the ratio of bank credits to GDP. (8) TradeOpennessit indicates the level of trade openness, calculated by the ratio of international trade volume to GDP. (9) GovernmentSizeit is government size, measured by the ratio of government spending to GDP. (10) HighSpeedRailit is a binary variable indicating whether there was at least one high-speed railway station in the corresponding city. (11) RoadDensityit is road density, i.e., road length (km) divided by land area (km2). (12) PublicHeatlhit denotes the abundance of public health infrastructure, measured by the number of hospital beds per thousand residents. (13) Afforestationit measures the accumulative afforested area as a proportion of the local land area. The original data of the variables Precipitationit, Sunlightit, GDPPerCapitait, PopulationDensityit, RoadDensityit, and PublicHeatlhit are expressed in levels. We take logarithms on them and use the log-transformed variables in regression estimation to mitigate the heteroscedasticity issue.
OtherPoliciesit contains thirty variables indicating the implementation of other public policies that may affect ecological quality. These policies are listed as follows: (1) pollution emissions trading system pilot zones, (2) energy use rights trading system pilot zones, (3) low-carbon city pilot project, (4) new energy demonstration cities, (5) clean winter-heating plan in northern China, (6) clean energy demonstration provinces, (7) Action Plan for Air Pollution Prevention and Control, (8) Three-Year Action Plan to Fight Air Pollution, (9) national pilot zones for ecological conservation, (10) grassland ecological compensation policy, (11) ecological environment monitoring pilot zones, (12) south-to-north water diversion project, (13) smart-city pilot project, (14) smart-tourism city pilot project, (15) internet demonstration cities, (16) Broadband China pilot project, (17) national big data comprehensive pilot zones, (18) information benefiting-the-people pilot cities, (19) e-commerce demonstration city project, (20) cross-border e-commerce comprehensive pilot zones, (21) national independent innovation demonstration zones, (22) pilot project to promote the integration of technology and finance, (23) circular-economy city pilot project, (24) plan on the rise of central China, (25) national new-type urbanization comprehensive pilot zones, (26) resource-exhausted city support policy, (27) national sustainable development plan for resource-based cities, (28) demonstration zones for industrial transformation and upgrading in old industrial cities and resource-based cities, (29) household registration system reform, and (30) environmental regulation.
For the first twenty-nine place-based policies, we use twenty-nine binary dummy variables to denote whether the corresponding policies were implemented in a city. The last policy, i.e., environmental regulation, was executed in all cities but with different degrees of stringency. Following Chen et al. [58], Deng and Yang [59], and Zhang and Chen [60], we use the proportion of environment-related text in local government annual work reports to measure the stringency of environmental regulation.

3.3. Data Sources

The data were collected from several sources. (1) The original ecological quality index data were downloaded from the website of China’s National Earth System Science Data Center (https://doi.org/10.12041/geodata.190747515712302.ver1.db, accessed on 1 March 2024), which provides the grid data at 500 m resolution based on remote sensing. Then, the grid data were processed to generate the annual average value for every Chinese city. (2) Meteorological data were obtained from the ERA5-Land dataset of the European Union’s Copernicus Climate Change Service. (3) Information on high-speed railway stations was provided by the Chinese Research Data Services Platform. (4) The afforestation data were extracted from the China Forestry Statistical Yearbooks. (5) The data about various public policies were manually collected from relevant governmental announcements, news, and reports. (6) The other variables such as GDP and population were available from the EPS China Data platform.

3.4. Sample

The research sample has a panel data structure, including a time dimension and a spatial dimension. In the time dimension, the sample spans a 16-year period between 2005 and 2020. In other words, the data of 8 years (2005–2012) before policy implementation and 8 years (2013–2020) after policy implementation are utilized. The study sample does not include the period after 2020. The validity of the DID method requires a persistent, untreated control group in the sample to construct a counterfactual trend for the treatment group. In 2021, the regional Chinese carbon ETS was expanded into a nationwide carbon market, meaning that all cities in mainland China became subject to the policy treatment. As a result, after 2021, we no longer have a control group. At this point, any macro-level shocks affecting all sample cities (such as economic cycles or industry trends) become indistinguishable from the policy effect, leading to estimation bias.
In the spatial dimension, the sample spans 324 prefecture-level administrative regions and 4 province-level municipalities. For convenience, we simply refer to them as “328 cities” in this article. This set of 328 cities covers most areas in China. Figure 2 demonstrates the geographical distribution of these sample cities in China. In the sample, the ETS-implementing regions consist of 47 cities, and the non-ETS-implementing regions contain 281 cities. Some areas (including Danzhou City and Sansha City in Hainan Province, the Hong Kong Special Administrative Region, the Macao Special Administrative Region, and the cities in Taiwan Province and in the Tibet Autonomous Region) are not analyzed because of data unavailability.
Some data for a small proportion of cities in a few years were missing. The final sample includes 5222 observations. Table 1 reports the descriptive statistics of the dependent variable, the ETS policy variable, and the control variables regarding the regional meteorological and socioeconomic characteristics.

4. Main Empirical Results

This section reports the main empirical results of this study. In Section 4.1, we show that the carbon ETS policy had a significantly positive effect on land surface ecological quality. In Section 4.2, we verify the robustness of the estimated policy effect. In Section 4.3, we test the parallel trends assumption, which is a crucial premise of using the DID model for policy evaluation. The empirical results reported in this section together provide solid support for our research hypothesis that China’s carbon ETS improved the land surface ecological quality.

4.1. Positive Effect of the Policy

Column (i) of Table 2 reports the regression estimation result for Equation (1). The estimated coefficient of CO2ETSit is 0.0113 (95% confidence interval: [0.0044, 0.0182]), which is positive and statistically significant at the 1% level. Therefore, the core research hypothesis H1 in this study is verified. On average, the CO2 ETS caused the land surface ecological quality index of ETS-implementing regions to increase by 0.0113. This effect is substantial. In the ETS-implementing regions, the average LSEQ (=0.5583) in the post-policy period was 0.0222 higher than the average LSEQ (=0.5361) in the pre-policy period. The positive effect (=0.0113) of the ETS policy contributed 51% (=0.0113/0.0222) of the LSEQ improvement in the post-policy period.
Some of the covariates included in the regression equation also influenced the LSEQ. Precipitation, sunlight, GDP per capita, industrial structure, trade openness, and public health infrastructure had significantly positive effects on the LSEQ; temperature and financial development had significantly negative effects. Several other public policies also had impacts on the LSEQ. Since these variables are not the focus of this study, we do not discuss them in detail.

4.2. Robustness Checks

A series of robustness checks are conducted to examine whether the baseline estimation result in Column (i) of Table 2 is sensitive to the selections of the research samples, ecological quality indicators, and regression methods.
In the first robustness check, to exclude the bias caused by potential outliers, all continuous economic variables in the original sample are winsorized at their corresponding top and bottom 1% quantiles. Then, we estimate Equation (1) based on the winsorized sample and report the regression result in Column (ii) of Table 2.
In the second robustness check, we examine whether our main research finding is still valid if we choose the annual average NDVI, an indicator of vegetation coverage and greenness exposure, to measure land surface ecological quality. We use the NDVI as the dependent variable in Equation (1) and report the regression result in Column (iii) of Table 2.
In addition, we re-estimate the policy effect of the carbon ETS based on several alternative estimation methods. We adopt the propensity-score-matching difference-in-differences (PSM-DID) method and the imputation DID method and report the estimated coefficients in Columns (iv) and (v) of Table 2, respectively. Furthermore, we also try eight other kinds of alternative DID estimation methods. We also extend the data to 2021 or 2022. Moreover, we conduct a placebo test as an additional robustness test.
The details of all these robustness checks are described in Supplementary Material S3. In all robustness checks, we obtain a significantly positive coefficient of CO2ETSit, explicitly supporting our core research finding that the carbon ETS improved the land surface ecological quality in Chinese regions.

4.3. Test of the Parallel Trends Assumption

In this study, we utilize a difference-in-differences model to assess the policy effect of the carbon ETS. A key premise of using the DID model is the “parallel trends assumption”. This assumption means that if the treatment (i.e., the carbon ETS) was not executed, the outcome variables in the treatment group (i.e., the ETS-implementing regions) would have followed the trend of the outcome variables in the control group (i.e., the non-ETS-implementing regions). In case the assumption of parallel trends does not hold, the DID approach is not suitable for policy evaluation. To test the parallel trends assumption, we adopt a regression-based event-study method. The details of the regression equation used for the parallel trends test are presented in Supplementary Material S4.
The result of the test is graphically shown in Figure 3. Before the policy implementation (k < 0), the difference between the treatment and control groups was statistically nonsignificant. In other words, the parallel trends assumption does hold. Therefore, the DID model can be reliably used to quantify the policy’s causal effect.
Figure 3 also demonstrates the dynamic effects of the policy over time. A positive policy effect on ecological quality appeared after one year of policy implementation and got larger as time went by. After seven years of policy implementation, the estimated policy effect reached the level of 0.0306 (95% confidence interval: [0.0186, 0.0426]).

5. Extended Analyses

In this section, we conduct some extended analyses. In Section 5.1, we explore the possible mechanisms through which the ETS policy affected the land surface ecological quality. Three channels are inspected: reduction in pollution emissions, increase in green innovation, and change in land use. In Section 5.2, we examine the heterogeneity of the policy effect conditional on different climatic conditions: precipitation, sunshine duration, and temperature. In Section 5.3, the possible spatial spillover effect of policy is analyzed.

5.1. Analysis on Mechanisms

We analyze three potential mechanisms through which the carbon ETS influences land surface ecological quality. These three mechanisms are reduction in pollution emissions, increase in green innovation, and change in land use.

5.1.1. Reduction in Pollutant Emissions

We utilize our research sample to provide evidence on the pollution abatement effect of the ETS. We estimate the following regression equation:
PollutantEmissionit = αCO2ETSit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
The dependent variable in Equation (2) is PollutantEmissionit. We use the logarithmic value of the PM2.5 concentration in ambient air to proxy the degree of pollutant emissions, given that PM2.5 is one of the most important pollutants that have caused severe environmental risks in China. The right-hand-side variables in Equation (2) are the same as in Equation (1). Column (i) in Table 3 reports the estimated coefficient of CO2ETSit in Equation (2), which is −0.0746. It is negative and significant at the 0.1% level. We have also tried to take the PM10 and SO2 emissions as the dependent variable in Equation (2) and found significantly negative coefficients of CO2ETSit. The corresponding estimation results are not reported here to save space. It is confirmed that the carbon ETS induced a reduction in pollutant emissions. The research hypothesis H2 is supported.

5.1.2. Increase in Green Innovation

We use our research sample to examine the impact of the carbon ETS on green innovation. The analysis is based on the following regression equation:
GreenInnovationit = αCO2ETSit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
In Equation (3), the dependent variable is GreenInnovationit. The other variables in Equation (3) are the same as those in Equation (1). GreenInnovationit is the logarithmic value of the number of green patent applications plus one in city i in year t. We add one to the number of patents to avoid the computational problem of taking logarithms to zero, if one city had zero patents. Column (ii) in Table 3 reports the estimated coefficient of CO2ETSit in Equation (3), which is 0.218. It is positive and significant at the 1% level. This result indicates that the carbon ETS indeed promoted green innovation. The research hypothesis H3 is verified. This is in line with the previous studies.

5.1.3. Change in Land Use

To inspect the impact of the ETS policy on land use in China, we estimate the following regression equation:
LandwithVegetationit = αCO2ETSit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
where the dependent variable is LandwithVegetationit. This variable is the area of lands with natural vegetation coverage (including grassland, shrub, and forest) as a proportion of all lands. The independent variables in Equation (4) are identical to those in Equation (1).
Keeping other factors constant, if the ETS policy increased the proportion of land with vegetation coverage, it is not surprising to observe an increase in land surface ecological quality. We estimate the coefficients in Equation (4) and report the result in Column (iii) of Table 3. The coefficient of CO2ETSit is 0.00555. It is positive and significant at the 5% level. Indeed, the carbon ETS affected land use and contributed to the rise in the share of land with vegetation. The research hypothesis H4 is confirmed.

5.1.4. Quantify the Mediation Mechanisms

To formally quantify the mediation mechanisms, we adopt the causal steps approach. If the dependent variable is Y, the independent variable is X, and the mediator variable is M, we estimate the following system of three equations:
Y = cX + e1
M = aX + e2
Y = c′X + bM + e3
A mediation effect is verified if (i) coefficient c in the first equation is significant (total effect), (ii) coefficient a in the second equation is significant (effect of independent variable on the mediator), and (iii) coefficient b in the third equation is significant (effect of the mediator on the dependent variable). The direct effect of the independent on the dependent variable is captured by coefficient c′. The indirect effect of the independent variable through the mediator is calculated as a × b. The total effect is equal to direct effect plus indirect effect, i.e., c = c′ + a × b. We apply this three-step approach to the variables analyzed in this study: the dependent variable is EcologicalQualityit; the independent variable is CO2ETSit; and the three mediators are PollutantEmissionit, GreenInnovationit, and LandwithVegetationit. All analyses control for the covariates as well as city- and year-fixed effects.
We report the results of the three-step mediation analysis in Table 4. The three mediator variables each play a partial mediating role, indicating that a portion of the policy’s total effect on LSEQ operates through these three channels. Based on our calculations, the proportions of the total effect of the carbon ETS mediated by the pollution reduction mechanism, the green innovation mechanism, and the land use mechanism are 9.1% (=0.00103/0.01129), 1.6% (=0.00018/0.01129), and 6.5% (=0.00073/0.01129), respectively.

5.2. Analysis on Heterogeneities Contingent on Climatic Conditions

Now we explore whether the positive effects of the carbon ETS were heterogeneous across different districts. In the literature, it was confirmed that climatic conditions substantially influence the ecological quality because climatic factors play crucial roles in the growth and development of creatures (e.g., [18,61,62]). Therefore, we conjecture that the effect of the carbon ETS might be contingent on the local climatic conditions. To verify this conjecture, we classify different regions into several subgroups, depending on their average precipitation, sunshine duration, and temperature in the sample period. Then, we estimate the policy effects on regions with less precipitation, more precipitation, shorter sunlight duration, longer sunlight duration, lower temperature, and higher temperature. The associated regression equations and coefficient estimates are presented in Supplementary Material S5. To visually observe the heterogeneities, we draw Figure 4.
The figure demonstrates the estimated policy impact on the land surface ecological quality with corresponding 95% confidence intervals in various subgroups. It is observed that substantial heterogeneities indeed existed. (1) The effect of the carbon ETS was nonsignificant in districts with low precipitation levels. However, the policy effect was positive and statistically significant in districts with high precipitation levels. (2) The policy effects were both significantly positive in regions with shorter and longer sunlight durations. However, the effect in shorter-sunlight-duration regions was stronger than that in longer-sunlight-duration regions. (3) The policy impact was nonsignificant in lower-temperature regions. However, the effect was positive and significant in higher-temperature regions.
In brief, the heterogeneity analyses above show that the impacts of the carbon ETS were actually contingent on local climatic conditions. The effects were stronger in regions with more precipitation, shorter sunlight duration, and higher temperature.

5.3. Analysis on the Possible Spatial Spillover Effect of Policy

A prerequisite for using the difference-in-differences method to evaluate policy effects is the absence of interference between treatment and control units, also known as the stable unit treatment value assumption (SUTVA). If this assumption is violated, the DID method cannot accurately estimate the policy effect. In our study, we are particularly concerned with whether the carbon ETS generated spatial spillover effects that may influence the land surface ecological quality in non-ETS regions. For example, economic activities might relocate from ETS-implementing cities to geographically adjacent non-ETS cities, thereby affecting the ecological environment in those areas and introducing bias into the policy evaluation results.
To analyze potential spatial spillover effects of the policy, we employ Equations (5) and (6), both of which are extensions of Equation (1). Specifically, we augment Equation (1) by introducing a new binary dummy variable, AdjacentCitiesit, resulting in Equation (5). This variable indicates whether a city is geographically adjacent to any ETS-implementing city. AdjacentCitiesit = 1 if city i did not implement the ETS itself in year t but shared a land border with at least one ETS-implementing city; otherwise, AdjacentCitiesit = 0. If the coefficient of AdjacentCitiesit is statistically significant, it implies that the ETS leads to changes in the LSEQ of geographically adjacent cities.
EcologicalQualityit = αCO2ETSit + θAdjacentCitiesit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
Based on Equation (1), we introduce a new binary dummy variable, AdjacentProvincesit, to form Equation (6). This variable indicates whether a city is located within a province that is geographically adjacent to any ETS-implementing city. AdjacentProvincesit = 1 if, in year t, city i itself did not implement the ETS but the province in which it is located shared a land border with at least one ETS-implementing city; otherwise, AdjacentProvincesit = 0. If the coefficient of AdjacentProvincesit is statistically significant, it implies that the ETS leads to changes in the LSEQ of cities located in geographically adjacent provinces.
EcologicalQualityit = αCO2ETSit + λAdjacentProvincesit + βControlVariablesit + γOtherPoliciesit + ui + vt + εit
The regression results of Equations (5) and (6) are reported in Columns (i) and (ii) of Table 5, respectively. The coefficients of AdjacentCitiesit and AdjacentProvincesit are both statistically nonsignificant. We find no significant spatial spillover effects of the carbon ETS on the land surface ecological quality in neighboring areas. Therefore, the use of the DID method to evaluate the policy effect is reasonable.

6. Conclusions and Discussion

6.1. Conclusions

This study reveals that China’s cap-and-trade carbon emissions trading policy significantly improved the land surface ecological quality. The study is based on data from 328 Chinese cities between 2005 and 2020, using a difference-in-differences regression model. The land surface ecological quality is measured by a remote sensing ecological index. The results of the empirical analysis can be summarized as follows. (1) In ETS-implementing regions, there was a significant improvement in land surface ecological quality after policy implementation. The policy contributed 51% of the ecological quality improvement in the post-policy period. (2) Further analysis shows that the carbon ETS reduced pollution emissions, promoted green innovation, and increased the coverage of vegetation and green space on land. These phenomena can explain why the policy improved the ecological quality of the land surface. (3) There were heterogeneities in the policy effects, which were related to local climatic conditions of precipitation, sunshine duration, and temperature.

6.2. Discussion

This study argues that the carbon emissions trading policy improved the land surface ecological quality in China. This policy had beneficial environmental effects. Prior studies document that the ETS pilots reduce greenhouse gas emissions (e.g., [9,10,63]) and can also improve local air quality by lowering air pollution (e.g., [12,13,64]). These studies collectively indicate that the ETS contributes to environmental improvement, which is consistent with our conclusion that the ETS enhances environmental quality. However, our study differs from the existing evidence in the dimension of environmental quality examined: prior work typically focuses on single indicators such as CO2 emissions or specific air pollutants, whereas we assess a more comprehensive ecological outcome. Extending this literature, we focus on land surface ecological quality, a remote-sensing-based, biophysical indicator that captures broader ecosystem conditions rather than a single indicator. This aspect should be taken into account when assessing the overall environmental benefits of the policy. Improvements in land surface ecological quality not only promote human health but also have potential economic benefits. For instance, denser forests can provide more timber resources, greener grasslands can provide more pasture to increase livestock production, and improved land surface eco-environment can promote tourism. Therefore, our results suggest that carbon ETS policy may generate broader ecological co-benefits and provide a complementary perspective for evaluating the overall environmental effectiveness of the ETS.
This study also finds that the carbon ETS reduced pollution emissions, promoted green innovation, and changed land use types. These aspects are potential channels through which the policy can improve land surface ecological quality. Both the public and private sectors should take steps to encourage these mechanisms to play greater roles. For example, the public sector should vigorously publicize the carbon ETS policy to raise the environmental awareness of entrepreneurs and citizens. The government should adopt fiscal and financial means to support the green innovation of enterprises, which will enable them to improve their resource utilization efficiency and reduce their consumption of and damage to land resources. Local governments should have open long-term land planning and establish nature reserves in ecologically fragile areas, among other things, to prevent the destruction of vegetation in key areas.
The effects of policies are not homogenous across various regions. The heterogeneity needs to be considered when evaluating policy impacts. The heterogeneity analysis in this study reveals that the positive effect of the policy was considerable in regions with more precipitation and high temperature. However, in areas with less precipitation and low temperature, the ETS policy did not have a significant effect on land surface ecological quality. In these areas, to improve the eco-environment of the land surface, it is necessary to focus on other factors or the role of other policies.

6.3. Limitations and Future Research Directions

This study has the following limitations. These aspects can be addressed in future research. (1) Due to the implementation of China’s national carbon market in 2021, our sample no longer includes a control group unaffected by the policy after that year. Consequently, the DID-based analysis in this study does not incorporate data beyond 2021. In the future, researchers could collect more granular data and employ more sophisticated methods to identify the effects of the carbon ETS after 2021. This would contribute to gaining insights into the most recent policy impacts. (2) This study is based only on Chinese data and does not analyze the circumstances in other countries. We have not verified whether the findings in this study can be generalized to other countries. Future researchers can collect international data and use our method to analyze other regions in the world. (3) This research only analyzes land surface ecological quality. Other categories of ecological quality, such as water quality, were not investigated. In the future, researchers can inspect the variations in other forms of ecological quality to gain a more comprehensive understanding of the policy’s environmental influences.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su18020616/s1: Table S1: Further robustness checks: Eight other kinds of alternative DID estimation methods; Table S2: Further robustness checks: Extending the data to 2021 or 2022; Table S3: Estimated coefficients in heterogeneity analysis; Figure S1: Result of the placebo test; Supplementary Material S1. A detailed description of the land surface ecological quality index; Supplementary Material S2. Formulas used to calculate the five components of the RESI-2; Supplementary Material S3. Details of robustness checks; Supplementary Material S4. Regression equation for the parallel trends test; Supplementary Material S5. Regression equations and coefficient estimates in heterogeneity analysis. References [65,66,67,68,69,70,71,72,73,74,75,76,77,78,79] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, D.D.; methodology, D.D.; data curation, D.Z. and D.D.; formal analysis, D.Z. and D.D.; software, D.Z. and D.D.; validation, D.Z. and D.D.; visualization, D.Z. and D.D.; writing—original draft, D.Z. and D.D.; writing—review and editing, D.Z. and D.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study were obtained from publicly available sources cited throughout the article. All data sources are explicitly referenced in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Comparison of the average land surface ecological quality indices in ETS-implementing regions and non-ETS-implementing regions during 2001–2020. Data source: Xu et al. [8] and authors’ own calculations.
Figure 1. Comparison of the average land surface ecological quality indices in ETS-implementing regions and non-ETS-implementing regions during 2001–2020. Data source: Xu et al. [8] and authors’ own calculations.
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Figure 2. Geographical distribution of sample regions in China. Note: The regions that have implemented the cap-and-trade carbon ETS since 2013, 2014, and 2016 are shown in green, blue, and red, respectively. The regions that have never implemented the policy until 2021 are shown in white. The regions not included in the research sample because of data unavailability are shown in gray.
Figure 2. Geographical distribution of sample regions in China. Note: The regions that have implemented the cap-and-trade carbon ETS since 2013, 2014, and 2016 are shown in green, blue, and red, respectively. The regions that have never implemented the policy until 2021 are shown in white. The regions not included in the research sample because of data unavailability are shown in gray.
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Figure 3. Test of parallel trends assumption. Note: The circles on the solid red curve reflect the point estimates of the coefficients αk (k = −7, −6, …, 7) in the regression equation in Supplementary Material S4. The black dashed lines demonstrate the corresponding 95% confidence intervals. The period of k = −8 is taken as the base period for comparison.
Figure 3. Test of parallel trends assumption. Note: The circles on the solid red curve reflect the point estimates of the coefficients αk (k = −7, −6, …, 7) in the regression equation in Supplementary Material S4. The black dashed lines demonstrate the corresponding 95% confidence intervals. The period of k = −8 is taken as the base period for comparison.
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Figure 4. Estimated heterogeneous effects in different subgroups contingent on climatic conditions. Note: In the graph, the solid dots correspond to the point estimations of policy effects in various subgroups, i.e., the coefficients α1 and α2 in Equations (S3)–(S5) in Supplementary Material S5. The dashed lines correspond to the 95% confidence intervals of the estimated coefficients.
Figure 4. Estimated heterogeneous effects in different subgroups contingent on climatic conditions. Note: In the graph, the solid dots correspond to the point estimations of policy effects in various subgroups, i.e., the coefficients α1 and α2 in Equations (S3)–(S5) in Supplementary Material S5. The dashed lines correspond to the 95% confidence intervals of the estimated coefficients.
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Table 1. Descriptive statistics of variables.
Table 1. Descriptive statistics of variables.
VariablesNumber of ObservationsMeanStandard DeviationMinimumMaximum
EcologicalQuality52220.4820.1390.0350.812
CO2ETS52220.0630.2430.0001.000
Precipitation52226.6840.7103.2907.923
Sunlight52227.5820.2726.6238.129
Temperature522213.1546.006−7.82225.726
GDPPerCapita52229.8580.6867.62211.723
PopulationDensity52225.4101.344−0.3798.275
IndustrialStructure52220.1450.0930.0000.670
FinancialDevelopment52220.8760.5200.0754.487
TradeOpenness52220.2140.5830.00017.176
GovernmentSize52220.2200.2000.0433.581
HighSpeedRail52220.3530.4780.0001.000
RoadDensity5222−0.3550.857−5.8181.650
PublicHealth52221.3850.446−0.0882.704
Afforestation52220.1190.1120.0001.241
Note: The variables Precipitationit, Sunlightit, GDPPerCapitait, PopulationDensityit, RoadDensityit, and PublicHeatlhit are log-transformed. The minimum value of IndustrialStructureit is 0.000 because of rounding errors, even though the original data is not exactly equal to 0.
Table 2. Estimated impact of the CO2 emissions trading scheme on land surface ecological quality.
Table 2. Estimated impact of the CO2 emissions trading scheme on land surface ecological quality.
VariablesBaseline
Estimation
Result		Robustness Checks
Use
Winsorized Sample		Use NDVI
to Measure
Ecological Quality		Use
PSM-DID
Estimation		Use
Imputation
DID
Estimation
(i)(ii)(iii)(iv)(v)
CO2ETS0.0113 **0.0119 ***0.00449 *0.00854 *0.0177 ***
[0.004][0.004][0.002][0.004][0.003]
Precipitation0.00620 **0.00691 **0.0166 ***0.001790.00933 ***
[0.002][0.002][0.001][0.002][0.002]
Sunlight0.0218 ***0.0196 ***−0.0217 ***0.0263 ***0.0189 ***
[0.005][0.005][0.003][0.005][0.005]
Temperature−0.0216 ***−0.0212 ***0.00453 ***−0.0242 ***−0.0222 ***
[0.002][0.002][0.001][0.002][0.002]
GDPPerCapita0.0115 **0.00827 *0.004550.0108 **0.0146 ***
[0.004][0.004][0.003][0.004][0.004]
PopulationDensity0.009130.006040.001910.0114 *0.00596
[0.005][0.004][0.003][0.006][0.003]
IndustrialStructure0.0714 **0.0529 *0.01730.04060.0688 **
[0.022][0.022][0.012][0.026][0.022]
FinancialDevelopment−0.00799 ***−0.00961 ***−0.00024−0.00884 ***−0.00831 ***
[0.002][0.002][0.002][0.002][0.002]
TradeOpenness0.00409 **0.0142 **0.00141 **0.00394 *0.00412 **
[0.001][0.005][0.000][0.002][0.001]
GovernmentSize−0.00515−0.0106−0.0006520.0114−0.00566
[0.004][0.007][0.003][0.010][0.004]
HighSpeedRail0.002150.001910.001650.00180.00239
[0.001][0.001][0.001][0.001][0.001]
Road−0.003310.000225−0.00189−0.00408−0.00147
[0.002][0.003][0.002][0.002][0.002]
PublicHealth0.0121 ***0.0110 **0.0116 ***0.00942 *0.00613
[0.003][0.003][0.002][0.004][0.003]
Afforestation−0.0254−0.02640.0252 *−0.0440 **−0.0111
[0.013][0.014][0.010][0.015][0.012]
Control other policiesYesYesYesYesYes
City-fixed effectsYesYesYesYesYes
Year-fixed effectsYesYesYesYesYes
Number of cities328328328309328
Number of observations52225222522147015222
Within R20.3200.3140.6770.359-
Note: *, **, and *** represent the 5%, 1%, and 0.1% significance levels, respectively. The robust standard errors clustered at the city level are reported in brackets below the estimated coefficients. The coefficients of thirty other policies are not reported to save space.
Table 3. Analysis on the influencing mechanisms of the CO2 emissions trading scheme.
Table 3. Analysis on the influencing mechanisms of the CO2 emissions trading scheme.
VariablesPollutant
Emissions		Green
Innovation		Proportion of Lands with
Vegetation
Coverage
(i)(ii)(iii)
CO2ETS−0.0746 ***0.218 **0.00555 *
[0.010][0.079][0.003]
CovariatesYesYesYes
City-fixed effectsYesYesYes
Year-fixed effectsYesYesYes
Number of cities328328328
Number of observations522252225222
Within R20.7990.8630.137
Note: *, **, and *** represent the 5%, 1%, and 0.1% significance levels, respectively. The robust standard errors clustered at the city level are reported in brackets below the estimated coefficients. The coefficients of covariates are not reported to save space.
Table 4. Estimated indirect, direct, and total effects of the CO2 emissions trading scheme.
Table 4. Estimated indirect, direct, and total effects of the CO2 emissions trading scheme.
EffectsPollutant
Emissions		Green
Innovation		Proportion of Lands with
Vegetation
Coverage
(i)(ii)(iii)
Indirect effect0.00103 **0.00018 *0.00073 ***
[0.00025][0.00009][0.00010]
Direct effect0.01025 ***0.01110 ***0.01055 ***
[0.00199][0.00189][0.00185]
Total effect0.01129 ***0.01129 ***0.01129 ***
[0.00194][0.00194][0.00194]
Note: *, **, and *** represent the 5%, 1%, and 0.1% significance levels, respectively. The bootstrapped standard errors are reported in brackets below the estimated values.
Table 5. Analysis on the possible spatial spillover effect of policy.
Table 5. Analysis on the possible spatial spillover effect of policy.
VariablesPolicy Spillover Effect on Adjacent CitiesPolicy Spillover Effect on Cities in Adjacent Provinces
(i)(ii)
CO2ETS0.0121 ***0.0131 ***
[0.004][0.004]
AdjacentCities0.0043-
[0.003]-
AdjacentProvinces-0.0029
-[0.002]
CovariatesYesYes
City-fixed effectsYesYes
Year-fixed effectsYesYes
Number of cities328328
Number of observations52225222
Within R20.3210.321
Note: *** represents the 0.1% significance level. The robust standard errors clustered at the city level are reported in brackets below the estimated coefficients. The coefficients of covariates are not reported to save space.
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Zheng, D.; Dong, D. China’s Carbon Emissions Trading Scheme Improved the Land Surface Ecological Quality. Sustainability 2026, 18, 616. https://doi.org/10.3390/su18020616

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Zheng D, Dong D. China’s Carbon Emissions Trading Scheme Improved the Land Surface Ecological Quality. Sustainability. 2026; 18(2):616. https://doi.org/10.3390/su18020616

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Zheng, Diwei, and Daxin Dong. 2026. "China’s Carbon Emissions Trading Scheme Improved the Land Surface Ecological Quality" Sustainability 18, no. 2: 616. https://doi.org/10.3390/su18020616

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Zheng, D., & Dong, D. (2026). China’s Carbon Emissions Trading Scheme Improved the Land Surface Ecological Quality. Sustainability, 18(2), 616. https://doi.org/10.3390/su18020616

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