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Article

The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations

by
Wei Zhang
,
Zetian Wang
and
Shaohua Wang
*
School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(22), 9842; https://doi.org/10.3390/su16229842
Submission received: 28 September 2024 / Revised: 6 November 2024 / Accepted: 9 November 2024 / Published: 12 November 2024
Browse Figures
Figure 1
<p>Mechanisms of land-use carbon efficiency on ecological resilience map.</p> "> Figure 2
<p>Kernel density estimates of ecological resilience development levels in China and four regions.</p> ">
Versions Notes

Abstract

:
China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

1. Introduction

The unrelenting advancement of industrialization and urbanization since the start of China’s Reform and Opening Up program has drawn a significant influx of individuals and enterprises to cities and towns. Consequently, the urban and town population has surged, and various production activities have become highly concentrated in these areas. This concentration has led to frequent occurrences of ecological risk issues, such as environmental pollution and climate change, resulting in increased vulnerability of ecosystems and a subsequent decline in ecological resilience [1]. This has led to the introduction of the idea of resilient cities in the “14th Five-Year Plan and 2035 Vision Outline”, with the creation of resilient cities heavily counting on ecological resilience. The plan emphasizes the proactive promotion of resilient city construction as a pre-emptive measure to enhance ecosystem security and strengthen the capacity to withstand risks [2].
The core issue underlying the various ecological issues associated with urbanization is the unbalanced connection that exists between humans and the environment. The key solution to addressing these challenges lies in harmonizing the human–land relationship to achieve sustainable development across society, economy, and ecology [3]. Land, as a basic production factor, has carried all human production and living activities since ancient times [4]. In September 2020, at the 75th session of the UN General Assembly, President Xi announced China’s objective to reach peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060 [5]. With land-use change ranking as the next biggest contributor to greenhouse gas emissions after chemical energy combustion, global warming has become a serious ecological concern in the twenty-first century [6]. Greenhouse gases resulting from land-use alterations impact the carbon cycle within ecosystems and significantly influence ecological resilience. Balancing economic development with green, low-carbon initiatives is crucial under the dual objectives of high-quality economic growth and reduced carbon emissions [7,8]. Therefore, conducting an assessment of land-use carbon efficiency and analyzing its effects on urban ecological resilience hold substantial theoretical and practical importance.
The nation’s participation in ecological and environmental governance is greatly aided by environmental regulation. The creation of an ecological governance structure incorporating social groups and the general public, which is headed by the government with businesses as the main players, has been suggested by the 19th Party Congress. Market-incentive and spontaneous-participation environmental regulations, on the other hand, represent public and market involvement in environmental governance and facilitate sustainable land-use practices and the advancement of ecological civilization [9]. Command-and-control environmental regulation is a vital instrument over the government’s oversight in environmental governance. Given this context, the question arises: Does land-use carbon efficiency enhance ecological resilience? Furthermore, what mechanisms underlie the influence of land-use carbon efficiency on ecological resilience? Is there heterogeneity in the relationship between these two variables based on geographic location and resource endowment? Additionally, does land-use carbon efficiency exert heterogeneous impacts on different dimensions of ecological resilience? What are the moderating effects of diverse environmental regulations? Examining these issues will help to clarify how land-use carbon efficiency affects ecological resilience and will offer insightful information that will help direct national initiatives for ecological development.

2. Literature Review

Holling introduced the notion of ecological resilience to ecosystems, which originated from the more general notion of resilience [10]. Urban research has made substantial use of this idea over the years, especially in the areas of infrastructure resilience, ecological resilience, economic resilience, and other related fields [11,12,13]. Ecological resilience—the capacity of ecosystems to withstand shocks, manage them, and subsequently recover and adapt to external disruptions—is a crucial component of resilience growth [14]. Current scholarly investigations on ecological resilience primarily revolve around three key areas: the quantification of ecological resilience, the examination of factors influencing ecological resilience, and the exploration of the interplay between ecological resilience and other systems. Academics initially elucidate the notion of ecological resilience, with various scholars offering their perspectives and interpretations. For example, ecological resilience is defined by Brand as an ecosystem’s capacity to withstand shocks and maintain a particular state [15]. Mumby et al. differentiate between ecological resilience, robustness, and vulnerability, while also outlining the contexts in which each concept is applicable [16]. Dakos and Sonia discuss engineered resilience and ecological resilience from both localized and non-localized standpoints, defining ecological resilience as a state in which disturbances are significant enough to potentially prevent the system from reverting to its original equilibrium state, possibly leading to a transition to a different state [17]. About assessing ecological resilience levels, three methodologies are proposed: (1) the development of an indicator framework to evaluate recovery capacity, adaptability, and resistance [18,19,20]; (2) the establishment of an indicator framework to assess the pressure, state, response, and innovation dimensions [2,21]; (3) the creation of an indicator framework to evaluate scale resilience, density resilience, and morphology resilience [22]. Current research on the factors that impact ecological resilience examines demographic, economic, social, scientific, and technological aspects [14]. An extensive analysis of the variables affecting ecological resilience from the viewpoints of geographical, environmental, and socioeconomic aspects was given by Shi et al. [12]. While there is some existing research on ecological resilience, it is not as extensive as studies on other forms of resilience, such as economic resilience. Furthermore, a subset of scholars has dedicated their research to exploring the intricate relationship between ecological resilience and other systems. To examine the effect of environmental restrictions on ecological resilience, for example, some scholars have used spatial econometric modeling [19]. Furthermore, a few researchers have concentrated on the combined and linked growth of new urbanization, ecological resilience, city cluster urbanization intensity, and regional urbanization, respectively [22].
Presently, the advancement of “concept definition—measurement methods—influencing factors” is the main focus of scholarly study on land-use carbon efficiency. Determining land-use carbon efficiency, or the assessment of the balance between carbon intake and production, was the primary objective of the first investigation. The term “carbon efficiency” describes initiatives aimed at maximizing benefits to economy, society, and the environment while reducing carbon emissions in accordance with carbon regulations [23]. When considering land use, land-use carbon efficiency refers to how well land-use methods sustain steady, high levels of productivity while reducing carbon emissions [7]. Regarding measurement techniques, scholars have employed both single-factor and total-factor approaches. Early researchers predominantly relied on single indicators for assessment, typically substituting land-use carbon efficiency with variables like the ratio of GDP to carbon emissions. For instance, Ang utilized the multiplication of GDP and carbon factor along with energy intensity to represent carbon emission efficiency [24]. Sun et al. and Gao and Tian characterized carbon emission efficiency by the gross regional product generated per unit of carbon emissions [25,26]. While assessing a singular indicator is straightforward, it only captures a fraction of the carbon efficiency related to land use, thus failing to offer a holistic evaluation of the multiple factors at play. Consequently, certain scholars have developed an indicator framework to facilitate a more comprehensive evaluation. Yu et al. identified 11 indicators to establish an indicator system focusing on low-carbon perspectives [27]. They subsequently assessed carbon efficiency utilizing a Topsis gray correlation projection dynamic evaluation model that is grounded in level-difference maximization combination assignment. Tang developed an indicator system centered on three primary indicators of “three living” space and gauged the carbon efficiency of “three living” space through hierarchical analysis [28]. Nevertheless, certain scholars argue that this evaluation approach is excessively subjective. Consequently, the data envelopment analysis method has gained prominence as the prevailing methodology following extensive research. To address the issue of “unexpected” output components within utilization efficiency, some researchers have introduced the directional distance function [29]. In order to include slack variables, Tone later suggested the Slack-Based Measure (SBM) model [30]. However, a problem arises when there are numerous decision units since it is difficult to compare multiple decision units that have an efficiency score of 1. The super-efficient SBM model was developed to get over this restriction and effectively solved the issues with earlier research [31]. Kuang et al., Gai et al., and Feng et al. considered carbon emissions resulting from land use unanticipated outcomes and analyzed variations in land-use carbon efficiency across different spatial scales in China using the SBM model of unexpected outputs [32,33,34]. Furthermore, the factors impacting land-use carbon efficiency have been studied in the past. These variables include, but are not limited to, rates of urbanization, governmental focus, scientific and technological developments, natural resource conditions, and economic development [7,35,36]. Wang et al., Liu et al., and Fan et al. use various methodological approaches, including multi-period double-difference, triple-difference, and asymptotic double-difference methods, to examine the impact of relevant national policies upon land-use efficiency while accounting for carbon emissions [37,38,39].
Current research on the relationship between land-use carbon efficiency and ecological resilience is scarce. The relationship and cooperation between land-use efficiency and urban resilience have not received much attention from academics, and the mechanism by which land-use carbon efficiency affects ecological resilience is still not well understood. The subject matter of this study will be restricted to 30 Chinese provinces (cities) between 2009 and 2022 in an effort to close this gap. In this work, the entropy approach is used to assess the composite ecological resilience index, while the super-efficient SBM model with unwanted outputs is used to measure the land-use carbon efficiency. To find out how land-use carbon efficiency affects ecological resilience and how different environmental restrictions affect the way these two factors interact, a two-way fixed-effects model is used. Possible innovations encompass the following: Firstly, despite the ample research conducted on land-use carbon efficiency and ecological resilience, there remains a paucity of studies delving into the intricate mechanisms through which land-use carbon efficiency impacts ecological resilience. Secondly, leveraging Kaya’s constant equation as our starting point, we have established a research framework that explores the mediating effects, thereby elucidating the various influence pathways of land-use carbon efficiency on ecological resilience. Furthermore, we offer tailored recommendations to diverse stakeholders, aiming to furnish both research underpinnings and decision-making guidelines for promoting the nation’s high-quality and environmentally sustainable ecological development.

3. Theoretical Analysis and Hypothesis Formulation

3.1. Direct Effects of Land-Use Carbon Efficiency on Ecological Resilience

Countries are separated into three distinct zones in the field of territorial spatial planning: production space, living space, and ecological space [40]. The “three living” zones encompass a variety of human activities associated with daily existence and the production of materials. Using this framework as a foundation, land activities are further divided into three categories: ecological, living, and production activity zones. The production space is the most important of the three functional spaces of land since land use is mostly determined by economic factors [41]. Productive space is essentially used for business operations, which include the on-land production of industrial goods and the provision of service products. Due to China’s rapid industrialization and urbanization, there is a growing need for building space for both residential and industrial purposes, which is encroaching on an increasing quantity of agricultural and ecological land. Inefficient land use has resulted from this invasion, which has given birth to problems including overexploitation and harsh utilization. Consequently, this has triggered problems including climate change, soil erosion, and soil pollution. Ecological resilience has finally declined as a result of the environment’s decreased load capacity and increased ecosystem fragility. The goal of both production and ecological space is to support living spaces, thus the ecological space provides the framework necessary to ensure that both spaces function properly. As urban development progresses, cities are enhancing their infrastructure, including public transportation and landscaping, to improve land-use carbon efficiency. By implementing integrated planning for mountain, water, forest, field, lake, and grass systems, and establishing an interconnected ecological security network, the adverse effects on the ecosystem resulting from inefficient land use and high carbon emissions can be effectively mitigated. This approach will significantly enhance ecosystem resilience, bolster ecological sustainability, and facilitate the harmonious cohabitation of humans and nature [42].
Hypothesis 1 (H1). 
Land-use carbon efficiency positively contributes to ecological resilience.

3.2. Indirect Effects of Land-Use Carbon Efficiency on Ecological Resilience

Land elements provide both unwanted environmental outputs, such as carbon emissions, and desired outputs, including social and economic advantages, when they are used. China, which is a major carbon emitter, has clearly set goals to reach a “carbon peak” by 2030 and “carbon neutrality” by 2060. Nowadays, most researchers use the decomposition of Kaya’s constant equation to examine carbon emissions from a variety of angles, including scale, structure, and technology [43,44]. The intention of this research is to determine whether reducing carbon emissions through increased land-use efficiency might enhance ecological resilience. By examining the scale, structure, and technology, this study looks into how land-use carbon efficiency affects ecological resilience. During the enhancement of land-use carbon efficiency, considerations are made for the advancement of economies of scale and sustainable practices, facilitating the ongoing adjustment of industrial and energy structures. This process encourages green technological advancements and contributes to the strengthening of ecosystem resilience.

3.2.1. Scale Promotion Effect

Improving the utilization of land by optimizing urban land spatial arrangements and lowering transportation and information costs, carbon efficiency can promote economic development. Provinces with a higher economic standing are more capable of orchestrating rational allocation of resources, devoting greater attention to ecosystem governance. Well-conceived land planning and utilization policies can alleviate ecological stress, subsequently bolstering ecological resilience. Furthermore, a province with a high degree of economic development can foster technical innovation, which strengthens the ecosystem’s resistance to change and adaptability.

3.2.2. Structural Upgrading Effect

The enhancement of land-use carbon efficiency will foster the revitalization and advancement of enterprises situated on the land, concurrently driving technological innovation and green development within these enterprises. The creation of an ecological governance structure incorporating social groups and the general public, which is headed by the government with businesses as the main players, has been suggested by the 19th Party Congress. Notably, enterprise modernization in conjunction with the transformation of high requiring energy and environmentally damaging sectors offers a workable plan to improve resource efficiency, reduce greenhouse gas emissions, and eventually strengthen ecological resilience.

3.2.3. Technological Progress Effect

An increase in land-use carbon efficiency attracts a concentration of highly skilled and qualified personnel. This, in turn, prompts the labor force to upgrade their skills to maintain competitiveness and avoid displacement. Consequently, this drives the continuous development and upgrading of production methods, further promoting technological progress. Simultaneously, individuals with advanced education levels typically exhibit heightened environmental consciousness and a greater inclination toward utilizing public green amenities. The rising interest in eco-friendly consumption, particularly within the contemporary service sector, is expected to drive an upsurge in green investments. Furthermore, the establishment of a more robust infrastructure can effectively cater to the needs of urban ecological development, enabling it to effectively respond to crises. Consequently, this contributes to reducing the ecosystem’s vulnerability and enhancing its resilience [45].
Hypothesis 2 (H2). 
Land-use carbon efficiency will foster ecological resilience via the synergistic effects of scale promotion, structural upgrading, and technological progress.

3.3. Analysis of the Moderating Role of Heterogeneous Environmental Regulation

Environmental regulation is an important part of environmental governance. Currently, academics categorize them into three main types according to the different participating subjects: command-and-control environmental regulation, market-incentive environmental regulation, as well as spontaneous-participation environmental regulation [9]. These several categories of environmental regulatory functions determine how land-use carbon efficiency affects ecological resilience. The diverse strengths and routes exhibited by each type of regulatory function result in variety in the consequences that they produce.
Command-and-control environmental regulation describes how the government directly addresses environmental issues by passing laws, establishing regulations, and imposing mandatory practices that are intended to lower pollution and safeguard the environment. The government can effectively control the emission of pollutants by enforcing these restrictions and levying administrative penalties on companies that are discovered to be in violation. However, the maximum fines that can be imposed on businesses using this regulatory approach might not be enough to encourage them to invest in modernizing and changing their operations, so they choose to pay fines rather than making structural changes to maximize land resources [20]. Simultaneously, the law enforcement process involving “rent-seeking” behavior can significantly diminish the effectiveness of environmental regulations. In market-incentive environmental regulation, the government refrains from direct intervention and instead utilizes market mechanisms, primarily through the imposition of sewage charges on businesses [9]. The competitive character of the market “pushes” businesses to optimize the distribution of land resources and seek technical innovation, which is how these sewage charges work on them. Consequently, businesses choose to upgrade and change, which improves land-use carbon efficiency, encourages resource conservation and industrial greening, and eventually builds ecological resilience [19]. In the context of contemporary governance in China, spontaneous-participation environmental regulation refers to the active engagement of the public in national decision-making processes through various avenues such as public opinion, correspondence, visits, recommendations from the National People’s Congress (NPC), proposals from the Chinese People’s Political Consultative Conference (CPPCC), and other similar mechanisms [9]. The emergence of a new Chinese-style socialist age has brought about a fundamental contradiction in society, which has increased public attention to issues of quality of life such as public transit and landscaping. Additionally, the public now has more means to exercise their right to oversee through platforms like online public opinion, letters, and visits thanks to the widespread availability of feedback channels in the big data era. This increased scrutiny inevitably exerts pressure on both enterprises and governmental bodies, thereby influencing their conduct [20]. This can serve as a catalyst for enterprises to intensively and efficiently utilize land in an environmentally sustainable manner, promoting clean production and mitigating the impact on the ecosystem. Additionally, it can oversee the government to undertake effective governance of land ecological space, thereby establishing a more comprehensive ecological network with strengthened ecological resilience.
Hypothesis 3 (H3). 
Command-and-control environmental regulation will inhibit land use carbon efficiency to enhance ecological resilience, while on the other hand, land-use carbon efficiency will be promoted to improve ecological resilience through market-incentive environmental regulation with spontaneous-participation environmental regulation.
For ease of comprehension and reference, Hypotheses 1 to 3 are summarized graphically in Figure 1 below.

4. Research Design

4.1. Baseline Model

In order to look into how land-use carbon efficiency affects ecological resilience, the following steps were taken to create a baseline econometric model (1):
C E R i t = α 0 + α 1 L U C E i t + α 2 C O N T R O L S i t + μ i + δ t + ε i t ,
In this context,  C E R i t  serves as the primary explanatory variable within the model, signifying the inclusive measure of ecological resilience across provinces or cities;  α 0  denotes the constant term;  L U C E i t  represents the pivotal explanatory variable of the model, indicating the land-use carbon efficiency within provinces or cities;  α 1  stands for the coefficient associated with the core explanatory variable; and  C O N T R O L S i t  encompasses the control variables of the model.

4.2. Variable Selection

4.2.1. Explained Variable: Ecological Resilience (CER)

Ecological resilience in urban ecosystems encompasses the capacity to withstand various disruptions, uphold system equilibrium, facilitate ecological recovery, and expedite cyclic rejuvenation in response to external impacts. In order to improve the thorough and accurate evaluation of ecological resilience, knowledge from researchers including Shi et al. and Lv et al. as well as Shi and He is incorporated. Based on the characteristics of resistance, adaptation, and recovery, ten indicators are selected to create an assessment framework [12,14,19]. To assign weights to each indicator, the entropy technique is used, as shown in Table 1.
Resistance is one of the metrics that measures an ecosystem’s ability to resist the effects of outside factors. It is measured by using markers including the amount of domestic waste removed, the intensity of fertilizer application, and industrial sulfur dioxide emissions [14]. The intensity of fertilizer application indicates the pollution levels related to agricultural operations, whereas industrial sulfur dioxide emissions represent the discharge of industrial pollutants. Additionally, the volume of domestic waste removed sheds light on the patterns of production pollution and day-to-day activity pollution. Pollutant emissions at lower levels indicate a greater resistance, meaning that the ecosystem is better able to endure and adjust to external shocks. An ecosystem’s ability to maintain its equilibrium in the face of disruptions is referred to as its adaptability. Variables such as the rate of innocuous treatment of residential waste, the percentage of central treatment in sewage treatment facilities, and the volume of overall use of universal industrial solid waste serve as examples of this [1,19]. These indicators are utilized to gauge the pollution treatment capabilities in industrial, production, and residential settings. A higher treatment capacity signifies a greater ability to uphold the status quo and a heightened resilience to disturbances. When we talk about ecosystem recovery, we mean the ability of an ecosystem to go back to how it was before the disturbance occurred. Key indicators utilized to measure this concept include the ratio of green space to developed areas, green space availability per individual, water resources per capita, and the percentage of financial expenditure allocated to environmental protection [12,14]. These measures, which include the percentage of developed land that is green, the quantity of green space per person within parks, along with the availability of water resources per person, are used as indicators of resource allocation and urban growth. A city that has effectively integrated green infrastructure and manages its resources is more likely to be resilient to disruptions and recover from them more quickly. Furthermore, the level of official support is reflected in the financial resources devoted to environmental protection initiatives, with stronger governmental support being associated with more successful outcomes for ecosystem recovery.

4.2.2. Explanatory Variable: Land-Use Carbon Efficiency (LUCE)

Land-use carbon efficiency is the holistic assessment of the outputs and services generated by a specific land area within defined environmental limitations [7]. It is employed to accomplish a number of goals, including maximizing resource utilization, lessening an environmental impact, and fostering coordinated ecological and economic development [46]. This metric, as a comparative indicator of the inputs and outputs of land resources, significantly influences the advancement and future potential of urban development, contributing significantly to the attainment of high-quality growth and having long-lasting, far-reaching consequences on the development of ecological resilience.
To give a more complete and accurate assessment of land-use carbon efficiency, an indicator system has been constructed based on research conducted by Zhou, Lu et al., and Zeng [23,35,47]. This system incorporates inputs, outputs, and undesired outputs, as outlined in Table 2. Based on prior research [23,39], the input indicators that were chosen are the built-up area of land factor input, total fixed asset investment of capital factor input, labor in both secondary and tertiary industries of labor factor input, and electricity consumption of energy factor input. The desired outputs encompass not merely the economic and social benefits achieved but also the environmental protection benefit arising from the sustainable utilization of land. The indicators used to identify these outputs include the rate of green coverage in metropolitan areas and public finance income, as well as the value added of the secondary and tertiary sectors [23,39]. Conversely, the undesired output primarily pertains to the environmental pollution generated by inputs and is quantified through the total carbon dioxide emissions [7,35].

4.2.3. Control Variables

We must make sure that our estimation results are robust because there are many variables that affect ecological resilience. In order to do this, we have chosen the following control variables, taking cues from the research methods of scholars like Tao et al. as well as Zhu and Sun [1,48].
Infrastructure level (ROA), quantified by the per capita road area within each region, serves as an indicator of urban development and efficiency [49]. A higher level of urban development not only minimizes intra-regional factor flow costs but also facilitates inter-regional factor flow, thus having a favorable impact on improving urban ecological resilience.
Openness to the outside world (OPE), the amount of interaction with the global economy, is measured by the sum of imports and exports [48]. An increase in foreign investment and production scale expansion can intensify resource pressures, thereby exacerbating the strain on ecological resilience.
Technology and innovation capacity (TEC) is determined by how many patent applications each region receives annually [48]. Enhancing TEC can encourage businesses to modernize their production methods and abandon outdated practices, leading to a gradual improvement in ecological resilience.
Urbanization level (URB) is established by the ratio of each province’s total population to its urban population [19]. Population concentration in urban areas, known as agglomeration, can bring about scale effects that help alleviate pressure on the ecosystem.
Urban scale (UPD), the ecological resilience of a city, is largely determined by its urban population density [1]. The government’s prudent distribution of land resources is becoming more and more important in reducing the ecological stresses that the urban ecology must withstand as the city grows.
Government support (GOV) is determined by calculating government spending as a share of the GDP [49]. By formulating policies and allocating funds, the government can bolster urban ecological governance and systematically enhance ecological resilience.

4.3. Data Description

4.3.1. Data Sources

We chose a research sample that included thirty provinces (cities) in mainland China for the years 2009–2022. Tibet was left out of this study since the necessary data were not available. The data used in the sample came from a number of official sources, including the China Statistical Yearbook, China Environmental Statistical Yearbook, and China Industrial Statistical Yearbook. Other official sources included the Ministry of Ecology and Environment of the People’s Republic of China, the National Bureau of Statistics of China, and regional environmental status bulletins. An interpolation technique was used to fill in the missing data to finish the dataset.

4.3.2. Descriptive Statistics of Variables

This paper contains a total of 420 sets of data samples, and Table 3 presents the descriptive statistics of relevant variables. Ecological resilience is moderate overall, with a mean value of 0.299, a minimum of 0.137, additionally a high of 0.708. With a low of 0.194 and a high of 1.844, the standard land-use carbon efficiency in China is 0.465, indicating a modest level of efficiency. However, there are significant disparities in land-use carbon efficiency among provinces and cities.

5. Empirical Analysis

5.1. Characterizing the Temporal Evolution of Ecological Resilience

The variation in ecological resilience levels across different regions in China exhibits regional disparities. The complete distribution and evolution patterns of ecological resilience in various eras and locations were examined using kernel density estimation. Figure 2 illustrates the plotted evolution of ecological resilience for the whole country and its four main regions between 2009 and 2022.
Figure 2a presents the kernel density estimates of the national-level ecological resilience development. A little rightward shift in the distribution trend at the wave’s peak points to a moderately higher degree of ecological resilience development at the national level, but at a slower rate. As for the distribution pattern, the peak exhibits a distinct “rising-declining-rising” trend, paralleled by a “narrowing-broadening-narrowing” pattern in the wave width. This indicates that the absolute differences in ecological resilience among various provinces undergo a corresponding evolution, characterized by “narrowing-widening-narrowing” trends. In terms of the polarization pattern, the presence of distinct side peaks is evident, yet a notable transition has occurred from the initial emergence of multiple side peaks to the subsequent emergence of a singular side peak. This shift suggests a gradual weakening of the polarization phenomenon. Additionally, a clear right trailing feature is observed, indicative of a nationwide phenomenon where the development of ecological resilience in a particular region significantly surpasses that of other regions.
Figure 2b–e illustrates the kernel density estimation maps representing the ecological resilience development level in different regions of China, specifically the eastern, middle, western, and northeastern regions. The wave peak in the eastern and western regions has not changed significantly, according to an analysis of the distribution trend, but it has migrated to the right in the middle region. This finding implies that while ecological resilience is clearly developing at a significantly higher rate in the middle region, it is growing at a very slow rate in the east and west. The distribution pattern analysis shows that there are not many noticeable variances in the absolute differences in the eastern area, as seen by the wave peak and width in this area being rather constant. Conversely, in the middle region, there is a trend of the wave crest extreme values becoming narrower while the wave width is expanding, indicating an increase in the absolute differences within the middle regions. The cyclical pattern of “rising-declining-rising” for the wave peaks and “narrowing-broadening-narrowing” for the wave widths in the western region show how unstable and constantly fluctuating the absolute differences are within the west. From the standpoint of the polarization pattern, the eastern region underwent a transformation characterized by a sequence of “single-multiple-single” peaks. This suggests that between 2009 and 2022, the eastern region’s ecological resilience development level exhibited polarization followed by mitigation. Conversely, the middle region displayed double peaks in 2009 and 2010, which subsequently disappeared in later years, indicating the absence of a clear polarization trend in this region. In the west, apart from a triple-peak occurrence in 2021, there was a consistent presence of double peaks, signifying that the polarization in the western region persisted without alleviation, notably, the pronounced right-skewed distribution suggests the presence of disparities in the ecological resilience development levels among different regions. Comparing to the eastern and western regions, the northeastern region appears to have more noticeable and intense polarization phenomena due to the existence of distinct double peaks of greater amplitude.

5.2. Analysis of Baseline Regression Results

We analyze the relationship of land-use carbon efficiency on ecological resilience using Stata. Initially, a baseline regression analysis was conducted to establish a foundation for our inquiries. After that, control variables were systematically included using a stepwise regression technique; Table 4 presents the summary of the outcomes. The data in column (1) show that, in absence of control variables, land-use carbon efficiency has a statistically significant positive relationship on ecological resilience. Columns (2) through (7) further elaborate on this relationship by incrementally introducing control variables, building upon the baseline regression findings. This result validates Hypothesis 1 by confirming the idea that land-use carbon efficiency contributes to increased ecological resilience. The results imply that improving land-use carbon efficiency can serve as an effective measure in mitigating environmental pollution and climate change resulting from land-use activities, which exacerbate ecosystem vulnerability. Consequently, this enhancement can lead to increased ecological resilience within ecosystems.

5.3. Robustness Tests

The relationship land-use carbon efficiency ecological resilience was initially tested in the baseline regression results. To further test the robustness of the relationship, five methods were used to validate the relationship: shrinking the tails, excluding special cities, shortening the time horizon, changing the ecological resilience measure, and replacing the measurement model. The detailed treatments and corresponding results are shown in Table 5, while the detailed regression results are presented in Table 6.

5.4. Endogenous Discussion

Endogenous factors are a significant consideration in research, stemming primarily from issues such as reverse causality between explanatory and dependent variables, the presence of omitted variables, and measurement inaccuracies. (1) There is evidence of reverse causality in the connection of ecological resilience and land-use carbon efficiency. Land-use carbon efficiency can bolster ecological resilience by stimulating urban economic growth, facilitating upgrades in the industrial structure, and enhancing technological advancements within enterprises. Conversely, ecological resilience also plays a role in enhancing land-use carbon efficiency. Improving ecological resilience helps to reduce problems such as mismatch of production factors, while encouraging greater carbon efficiency in land use, leading to more stable economic performance. (2) The issue of missing data could persist even if a two-way fixed effects model is employed in the research and additional control variables that could influence ecological resilience are taken into account. This could result in potential endogenous issues stemming from unobservable omitted variables. (3) Endogenous problems may arise from measurement errors in land-use carbon efficiency.
Identifying appropriate external instrumental variables is crucial for mitigating endogeneity concerns. The two-stage least squares (2SLS) test was conducted with the following two instrumental variables chosen. Specifically, Saiz and Chen and Kung have demonstrated that land slope influences property development, consequently affecting land values whereby steeper terrains command higher prices [50,51]. This pricing dynamic influences land-resource allocation and subsequently impacts land-use carbon efficiency. The average slope of each region was calculated using the Asrtm 30 m elevation data model, which respects the concepts of exogeneity and pertinence as a natural geographical datum. This computation was based on research undertaken by Li et al. and Xi and Mei [52,53]. Nevertheless, as slope is a non-temporal element, an instrumental variable called the interaction term (IV1) of the average geographical slope of the regions multiplied by the year was incorporated into the model. Furthermore, the current efficiency of carbon utilization in land use impacts the current ecological resilience, while the current ecological resilience does not influence the historical efficiency of carbon utilization in land use. As a result, the explanatory variable lagging by a single period (IV2) is presented as an instrumental variable in accordance with the methods described by Zhu et al. [54].
The regression results for the instrumental factors are presented in Table 7. The endogenous examination’s p-value of 0.0503 shows that the instrument variables’ applicability and the underlying factors’ exogenous nature have been refuted. The results of the first-stage regression show a substantial adverse relationship between each province’s geographical slope and land-use carbon efficiency, indicating that less favorable conditions for increasing land-use carbon efficiency are associated with greater geographical slopes. Furthermore, it is demonstrated that a one-period lag in land-use carbon efficiency helps to promote land-use carbon efficiency advancements in the current era. The Kleibergen–Paap rk Wald F statistic exceeds the threshold of 10, leading to the rejection of the weak instrumental variables hypothesis. The correlation p-value of the Kleibergen–Paap rk LM statistic in the unidentified test is 0.0001. As a result, it is determined that the instrumental variables are not well-identified. On the other hand, the over-identification test’s p-value for the Hansen J statistic is 0.4035, confirming the validity of the hypothesis that all instrumental factors are exogenous. Regression analysis in the second stage, in particular, shows a noteworthy positive association of land-use carbon efficiency the promotion of ecological resilience, suggesting that Hypothesis 1 remains valid even after taking endogenous concerns into consideration.

5.5. Heterogeneity Analysis

5.5.1. Analysis of Regional Heterogeneity

The east, middle, west, and northeast were the four regions into which the research sample was split to perform a regional heterogeneity study and look into any possible regional variations in the relationship of land-use carbon efficiency ecological resilience. (East: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan; Middle: Shanxi, Henan, Hubei, Hunan, Anhui, Jiangxi; West: Inner Mongolia Autonomous Region, Chongqing, Sichuan, Guangxi Zhuang Autonomous Region, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region; Northeast: Heilongjiang, Jilin, and Liaoning Provinces.) According to Table 8’s findings, ecological resilience in the eastern, middle, and western regions is positively impacted by land-use carbon efficiency at a 5% level. Conversely, in the northeastern region, the results do not demonstrate statistical significance, while revealing a negative association between land-use carbon efficiency and ecological resilience. This discrepancy could be attributed to the northeast’s status as a longstanding industrial hub, characterized by a predominant presence of heavy industrial enterprises on its land. Following the implementation of the National Plan for the Adjustment and Reform of Old Industrial Bases, the northeastern region has experienced an acceleration in its development trajectory, characterized by the optimization of spatial structure and the utilization of land resources. Nevertheless, the region has predominantly pursued an outward-flat expansion strategy, resulting in heightened ecosystem vulnerability and a detrimental interplay between economic growth and environmental sustainability. Due to its geographic location, ecological environment, and other issues, the western area of the country does not have the same level of economic development as the central and eastern regions. Nonetheless, the State establishes stringent guiding laws for the development of ecological land in addition to tilting the construction land index to the west under the auspices of great conservation, great development, and high-quality development. The western area has seized development possibilities under the direction of government programs, and the improvement of ecological resilience has been significantly impacted by the carbon efficiency of land use.

5.5.2. Analysis of Resource Endowment Heterogeneity

The sample is further split into resource-based and non-resource-based provinces for heterogeneity analysis with the aim to investigate the variability of provincial resource endowment gaps. Adhering to the procedures described in Ru et al.’s and Zhang et al.’s investigations, a selection of nine provinces, including Shanxi, Inner Mongolia, Heilongjiang, Guizhou, Yunnan, Shaanxi, Qinghai, Ningxia Hui, and Xinjiang Uygur Autonomous Region, were categorized as resource-based provinces, while the remaining 21 provinces were classified as non-resource-based provinces [55,56]. Table 9’s (1) and (2) columns display the regression’s findings. Ecological resilience is significantly improved by land-use carbon efficiency in the two types of provinces. Regression coefficients for resource-based provinces are much higher than those for non-resource-based provinces, indicating that provinces with substantial endowments in natural resources are more likely to see a greater impact from land-use carbon efficiency in boosting ecological resilience. Resource-rich provinces are blessed with ample natural resources, serving as pivotal support for their development. However, their simplistic approach to development has led to wasteful use of land, which has resulted in problems like excessive emissions of carbon and degradation of the environment. These challenges, in turn, have a detrimental impact on the ecosystem, creating a significant scope for enhancing efficiency. Moreover, provinces that depend on natural resources have historically struggled with the “resource curse.” However, in an effort to effectively regulate these provinces, the country has recently implemented policies like the “14th Five-Year Plan for Promoting High-Quality Development of Resource-Based Regions” in 2021 and the National Sustainable Development Plan for Resource-Based Cities (2013–2020). The symbiotic relationship between “soft” institutional arrangements and the “hard” resource base has synergistically enhanced the land-use carbon efficiency, thereby making its positive impact on ecological resilience more pronounced in resource-based provinces.

5.5.3. Analysis of Heterogeneity in Ecological Resilience Dimensions

The Ecological Resilience Indicator System evaluates ecosystem resistance, adaptation, and recovery as distinct dimensions to investigate potential variation in the effects of each component. Table 9 displays this study’s findings in columns (3) through (5). They look at how land-use carbon efficiency relates to ecological resilience’s three components: resistance, adaptability, and recovery. The regression results are as follows: land-use carbon efficiency positively affects ecological resistance and ecological recovery, with regression coefficients of 0.0095 and 0.0461, respectively, which are significant at the levels of 1% and 5%, indicating that the land-use carbon efficiency can enhance ecosystem resistance and recovery, thus contributing to the enhancement of ecological resilience. However, the results show that land-use carbon efficiency has a non-significant negative effect on ecosystem adaptability. Generally speaking, land-use carbon efficiency positively affects ecosystem adaptability by optimizing land use and promoting carbon cycle and carbon sequestration. However, this relationship is not absolutely significant in the process of improving land-use carbon efficiency, where different functional types of land conversion exist, which may lead to the reduction or loss of ecological functions of the land. In addition, it may also lead to the reduction of biodiversity on the land, all of which will negatively affect the ecosystem adaptability, hence the negative insignificant result.

6. Further Discussions

6.1. Intermediation Effect

The preceding section demonstrated that improving land-use carbon efficiency can bolster ecological resilience. This section will delve deeper into the underlying mechanism of this phenomenon. To this end, the present section will utilize the study conducted by Wen and Ye to establish a mediation model that aims to validate the aforementioned three pathways [57].
C E R i t = α 0 + α 1 L U C E i t + α 2 C O N T R O L S i t + μ i + δ t + ε i t ,
M E D i t = α 0 + α 1 L U C E i t + α 2 C O N T R O L S i t + μ i + δ t + ε i t ,
C E R i t = α 0 + α 1 L U C E i t + α 2 C O N T R O L S i t + α 3 M E D i t + μ i + δ t + ε i t ,
M E D i t  represents the pertinent mediating factors, which include the impact of scale promotion (ECO), the enhancement of structure (IDU), and the advancement in technology (CAP).
Scale promotion effect (ECO): To learn more about how resources and the environment affect economic development levels, an assessment method was developed to measure the degree of green economic development in each province. This system was created using prior research conducted by academics like Wang et al. and Jia and Shi [58,59]. In addition to nine secondary indicators, namely GDP per capita, urban disposable income per capita, industrial value added, tertiary industry value added, expenditures on energy conservation and environmental protection, industrial pollution control investment, water consumption per 10,000 yuan of GDP, energy consumption per 10,000 yuan of GDP, and electricity consumption per 10,000 yuan of GDP, the evaluation of green economic development takes into account three main indicators pertaining to economic growth, government support, and resource environment. The China Statistical Yearbook and the China Environmental Statistics Yearbook provided the data used in this analysis.
Structural upgrading effect (IDU): China’s carbon trading pilot program now focuses mostly on industrial sectors that have substantial energy consumption and emissions. The creation of the carbon market has made it easier for these businesses to upgrade, adapt, alongside optimize their structural design. Consequently, this study employs the inverse of the annual total asset value of five high-energy-consuming and high-polluting industries, as published by China’s National Development and Reform Commission and other relevant departments, as a metric for assessing industrial structure upgrading [60]. The treatment of petroleum, coal, and other fuels; the manufacturing of chemical raw materials and products; the non-metallic mineral products industry; the ferrous metal smelting-and-rolling processing industry; and the non-metallic melting-and-rolling processing industry are all included in these five industries. The China Statistical Yearbook provided the data used in this investigation.
Technological progress effect (CAP): This study chooses to measure technological advancement using the level of human capital as a proxy variable, inspired by the work of researchers like Zhao et al. [61]. The data about human capital levels were sourced from the CHLR database.
The data required for this investigation are not accessible since the China Environmental Statistics Yearbook 2022 has not yet been published. As a result, the period of study for this section is restricted to 2009–2021.

6.1.1. Scale Promotion Effect

Table 10 displays the results of the scale promotion effect in columns (1), (2), and (3). The coefficient of land-use carbon efficiency on economies of scale in column (2) is 0.0246, indicating statistical significance. The third column suggests that ecological resilience can be improved by the degree of economic growth. Regression coefficient is smaller than in column (1), but land-use carbon efficiency is still considerably positive, indicating that land-use carbon efficiency can improve ecological resilience through the scale promotion effect.

6.1.2. Structural Upgrading Effect

Table 10 displays the results pertaining to the structural upgrading effect in columns (1), (4), and (5). As evident from column (4), the coefficient of land-use carbon efficiency on industrial structure upgrading is 5.9534. This coefficient successfully meets the criteria for a 5% significance test, thereby indicating a statistically significant positive impact of land-use carbon efficiency on industrial structure upgrading. Furthermore, column (5) demonstrates that industrial structure upgrading can enhance ecological resilience, and land-use carbon efficiency still contributes to this enhancement. Interestingly, column (5)’s regression coefficient is lower than column (1)’s, indicating that land-use carbon efficiency’s structural upgrading effect helps to improve ecological resilience.

6.1.3. Technological Progress Effect

Table 10 shows the outcomes of the technological advancement effect in columns (1), (6), and (7). Column (6) reveals that land-use carbon efficiency positively contributes to technological progress. Additionally, column (7) shows how ecological resilience is strengthened by technological advancement, while column (1) reports that the land-use carbon efficiency coefficient is still positive but has decreased from its previous value. This evidence supports the assertion that land-use carbon efficiency can indeed enhance ecological resilience through the mediation of technological progress.
In conclusion, Hypothesis 2 has been successfully corroborated by the present analysis.

6.2. Moderating Effects of Heterogeneous Environmental Regulation

An important factor in a country’s participation in environmental governance is environmental regulation. To enhance the understanding of how environmental regulation moderates the relationship of land-use carbon efficiency on ecological resilience, a moderating effect model (5) is developed as outlined below.
C E R i t = α 0 + α 1 L U C E i t + α 2 C O N T R O L S i t + α 3 E R i t + α 4 L U C E i t × E R i t + μ i + δ t + ε i t ,
E R i t  is the relevant regulation variable. Environmental regulation has been divided into three categories by scholars, including Wu et al. and Yao et al. [9,62]. These categories are command-and-control environmental regulation (ER1), market-incentivized environmental regulation (ER2), and spontaneous-participation environmental regulation (ER3). Command-and-control environmental management is measured using the natural logarithm of the total number of environmental administrative penalty cases received in a given year. The ratio of sewage fees paid to the reservoir in a given year to the GDP (gross domestic product) indicates the market-incentive environmental regulation. The ratio of all NPC recommendations and CPPCC environmental suggestions to each region’s population is used as a proxy variable in the spontaneous participation type of environmental management. As data on the command-and-control and spontaneous-participation environmental regulations will no longer be available post-2020, the period from 2009 to 2020 has been chosen as the timeframe for examining the moderating impact.
After examining the moderating impact of several environmental regulatory types on the effect of land-use carbon efficiency on ecological resilience, the empirical results are displayed in Table 11.
When command-and-control environmental regulation is utilized as the moderating variable, the results are displayed in column (1). The interaction coefficient between command-and-control environmental regulation and land-use carbon efficiency is 0.016285, which is significantly negative at the 1% level, implying that command-and-control environmental regulation will reduce the positive effect of land-use carbon efficiency on ecological resilience. This might occur because the government’s strong administrative means and rent-seeking behavior reduce enterprises’ motivation to reduce pollution and carbon emissions while also exacerbating the difficulties of enterprise transformation and upgrading, thereby weakening the positive influence of the two relationships. Furthermore, due to the existence of the “green paradox”, enterprises boost their development and use in the short term to maximize short-term gains, which is obviously detrimental to land-use carbon efficiency in order to promote ecological resilience.
The findings of using market-incentivized environmental regulation as a moderating variable are given in column (2). The interaction coefficient between market-incentivized environmental regulation and land-use carbon efficiency is 67.216943, which is statistically significant at the 5% level, implying that market-incentivized environmental regulation promotes the positive impact of land-use carbon efficiency on ecological resilience. The market regulation system “pushes” governments and corporations at all levels to hasten the transformation process by levying environmental taxes and sewage charges, among other measures. Encourage firms to create clean technologies and technical innovation in order to save energy and reduce carbon emissions. Due to the high cost of environmental laws, several businesses have chosen to migrate or convert, resulting in changes in local land use. Improving the quality of land use will further support ecological resilient growth.
The findings of using spontaneous-participation environmental regulation as a moderating variable are given in column (3). The coefficient of the interaction term between spontaneous-participation environmental regulation and land-use carbon efficiency is 0.079682, indicating that spontaneous-participation environmental regulation has a non-significant positive moderating effect on land-use carbon efficiency to improve ecological resilience. This could be due to the fact that the current degree of disclosure of environmental penalty events is low, and the Chinese people’s environmental knowledge is insufficient, which does not impose enough restraints on businesses to have a substantial promotional effect. In addition to this, China’s large population base requires more systematic and comprehensive feedback channels in order for the public to fully participate in national governance and further contribute to the development of the country’s territory and ecosystem.
In conclusion, it can be inferred that Hypothesis 3 is only partially supported by the findings.

7. Conclusions and Recommendations

This paper empirically analyzed the relationship between land-use carbon efficiency and ecological resilience in 30 provinces (municipalities) in China from 2009 to 2022 and analyzes the heterogeneity in terms of three dimensions: geographic region, resource endowment, and ecological resilience. Robustness and endogeneity tests confirmed the validity of the regression results. In addition to this, the moderating role played by heterogeneous environmental regulations in the relationship is further examined. The conclusions are as follows:
(1) Land-use carbon efficiency positively affects ecological resilience and has obvious regional heterogeneity, with the east, middle and west consistent with the baseline results, while the northeast shows a non-significant negative effect.
(2) The effects of land-use carbon efficiency on the three dimensions of ecological resilience also show heterogeneity. Land-use carbon efficiency can enhance the resistance and resilience of ecosystems, thus boosting ecological resilience. However, land-use carbon efficiency has a non-significant negative effect on ecological resilience.
(3) The regulatory effect of environmental regulation is also heterogeneous. Command-and-control environmental regulation can weaken the positive effect of land-use carbon efficiency on ecological resilience, market-incentive environmental regulation promotes the positive effect of land-use carbon efficiency on ecological resilience, and spontaneous-participation type of environmental regulation has a non-significant positive moderating effect on the land-use carbon efficiency to enhance ecological resilience.
Drawing upon the empirical findings presented in the preceding article and taking into account China’s current development landscape and pertinent policies, the following recommendations are proffered:
As far as the government is concerned, it should formulate good land planning and utilization policies to reduce ecological pressure, as well as formulate development strategies for different types of regions according to local conditions. For example, in the northeast, it is necessary to prevent the development mode of flat extension, strictly delineate the three zones and three lines, strengthen the control of industrial land and construction land, and treat and rehabilitate inefficient and polluted land. At the same time, the government should provide conditions for technological innovation, to provide policy support for the transformation and upgrading of enterprises, such as providing technology research and development loans for enterprises to alleviate the pressure of financing through risk sharing.
For enterprises, they should comply with the requirements of the times, implement cleaner production through technological upgrading and transformation. For the public, the public should actively improve their own skills, enhance their environmental awareness, develop themselves into highly skilled people oriented to green and high-quality development. In addition, they should make suggestions to the relevant organizations on China’s feedback channels and China’s infrastructural construction, so as to participate in the country’s environmental governance and improve the quality of their own living environment.
To give full play to the role of heterogeneous environmental regulation, we cannot rely solely on governmental coercion but must give full play to the role of market regulation, further release the enthusiasm of spontaneous public participation, so that the three can form a good interactive pattern. For example, the government could develop unified production standards, emission licenses, the market for enterprises to collect environmental taxes, sewage charges, increase the degree of information disclosure to the public, and other measures to expand the development of environmental governance space. In addition, according to the level of development of different regions, focusing on the adoption of different environmental regulatory means. For example, in the eastern part of the country, where the level of development is high, market incentives and public participation should be relied upon as much as possible in order to promote the green development of enterprises, while in the middle and western parts of the country, where the level of development is weaker, the government should increase the incentives of the policies of the region and guide enterprises in their transformation and upgrading.

Author Contributions

Conceptualization, W.Z. and S.W.; Methodology, W.Z.; Software, Z.W.; Data curation, Z.W.; Writing—original draft, Z.W.; Writing—review & editing, W.Z.; Project administration, W.Z.; Funding acquisition, W.Z. and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Natural Science Foundation of Hebei Province] grant number [G2024203029], [Natural Science Foundation of Hebei Province] grant number [G2024203004] and [Social Science Development Research Projects in Hebei Province] grant number [20230302027].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tao, J.Y.; Dong, P.; Lu, Y.Q. Spatial-temporal analysis and influencing factors of ecological resilience in Yangtze River Delta. Resour. Environ. Yangtze Basin 2022, 31, 1975–1987. [Google Scholar]
  2. Wang, S.M.; Niu, J.L. Dynamic evolution and obstacle factors of urban ecological resilience in Shandong Peninsula urban agglomeration. Econ. Geogr. 2022, 42, 51–61. [Google Scholar] [CrossRef]
  3. Li, B.C. Problems of municipal ecological environment and the sustainable development in China. J. Arid Land Resour. Environ. 2006, 2, 1–6. [Google Scholar] [CrossRef]
  4. Wang, D.Q.; Pang, X.Q. Research on green land-use efficiency of Beijing-Tianjin-Hebei urban agglomeration. China Popul. Resour. Environ. 2019, 29, 68–76. [Google Scholar]
  5. Xi, J.P. Building on the past, starting a new journey in the global response to climate change. People’s Daily, 13 December 2020. [Google Scholar] [CrossRef]
  6. Qu, F.T.; Lu, N.; Feng, S.Y. Effects of land use change on carbon emissions. China Popul. Resour. Environ. 2011, 21, 76–83. [Google Scholar] [CrossRef]
  7. Zhang, Y.; Dai, Y.Q.; Chen, Y.Y.; Ke, X.L. Spatial-temporal evolution and driving factors of low-carbon use efficiency of cultivated land in China. Trans. Chin. Soc. Agric. Eng. 2022, 38, 234–243. [Google Scholar] [CrossRef]
  8. Sepehriar, A.; Eslamipoor, R. An economical single-vendor single-buyer framework for carbon emission policies. J. Bus. Econ. 2024, 94, 927–945. [Google Scholar] [CrossRef]
  9. Wu, L.; Jia, X.Y.; Wu, C.; Peng, J.C. Impact of heterogeneous environmental regulation on green total factors productivity. China Popul. Resour. Environ. 2020, 30, 82–92. [Google Scholar]
  10. Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Eco. Evo. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
  11. Martin, R. Regional economic resilience, hysteresis and recessionary shocks. J. Econ. Geogr. 2012, 12, 1–32. [Google Scholar] [CrossRef]
  12. Shi, C.C.; Zhu, X.; Wu, H.W.; Li, Z.H. Assessment of urban ecological resilience and its influencing factors: A case study of the Beijing-Tianjin-Hebei urban agglomeration of China. Land 2022, 11, 921. [Google Scholar] [CrossRef]
  13. Yacamán, O.C.; Ferrer, J.D.; Rafael, M.O. Green infrastructure planning in metropolitan regions to improve the connectivity of agricultural landscapes and food security. Land 2020, 9, 414. [Google Scholar] [CrossRef]
  14. Lv, T.G.; Hu, H.; Fu, S.F.; Kong, A.N. Spatio-temporal differentiation and influencing factors of urban ecological resilience in the Yangtze River Delta. Areal Res. Dev. 2023, 42, 54–60. [Google Scholar] [CrossRef]
  15. Brand, F. Critical natural capital revisited: Ecological resilience and sustainable development. Ecol. Econ. 2009, 68, 605–612. [Google Scholar] [CrossRef]
  16. Mumby, P.J.; Chollett, I.; Bozec, Y.M.; Wolff, N.H. Ecological resilience, robustness and vulnerability: How do these concepts benefit ecosystem management? Curr. Opin. Environ. Sustain. 2014, 7, 22–27. [Google Scholar] [CrossRef]
  17. Dakos, V.; Sonia, S. Ecological resilience: What to measure and how. Environ. Res. Lett. 2022, 17, 043003. [Google Scholar] [CrossRef]
  18. Zhao, L.D.; Sun, Z.X. Evolution of coordinated development between economic development quality and ecological resilience in coastal cities and its spatial convergence features. Econ. Geogr. 2023, 43, 119–129+140. [Google Scholar] [CrossRef]
  19. Shi, C.X.; He, X.R. Influence of environmental regulation on urban ecological resilience: A case study of urban agglomeration in the middle reaches of the Yangtze River. J. Shaanxi Norm. Univ. (Nat. Sci. Edit.) 2023, 51, 56–71. [Google Scholar] [CrossRef]
  20. Zhang, M.D.; Ren, Y.T. Impact of environmental regulation on ecological resilience a perspective of “Local-neighborhood” effect. J. Beijing Inst. Technol. (Soc. Sci. Edit.) 2022, 24, 16–29. [Google Scholar] [CrossRef]
  21. Guo, H.H. The coupling and coordinated development of new-type urbanization and ecological resilience in Qingdao-based on the comparison of the central cities along the Yellow River. J. China Univ. Pet. (Edit. Soc. Sci.) 2023, 39, 22–31. [Google Scholar] [CrossRef]
  22. Wang, S.J.; Cui, Z.T.; Lin, J.J.; Xie, J.Y.; Su, K. Coupling relationship between urbanization and ecological resilience in the Pearl River Delta. Acta Geogr. Sin. 2021, 76, 973–991. [Google Scholar] [CrossRef]
  23. Zhou, P. The spatial differentiation of regional low carbon efficiency based on super efficiency DEA model. Econo. Geogr. 2017, 37, 188–192. [Google Scholar] [CrossRef]
  24. Ang, B.W. Is the energy intensity a less useful indicator than the carbon factor in the study of climate change? Energy Policy 1999, 27, 943–946. [Google Scholar] [CrossRef]
  25. Sun, Z.R.; Liu, Y.; Tian, P.P. Industrial agglomeration, degree of marketization and urban carbon efficiency. Ind. Technol. Econ. 2021, 40, 46–57. [Google Scholar] [CrossRef]
  26. Gao, Y.J.; Tian, T. Research on the Impact Mechanism of Carbon Trading on the Carbon Efficiency of Pilot Provinces. Inq. Econ. Issues 2022, 3, 106–119. [Google Scholar]
  27. Yu, P.; Ma, H.; Zhou, F.L. Dynamic evaluation of regional carbon efficiency based on combined weighting of level difference maximization and TOPSIS Grey incidence projection method. Oper. Res. Manag. Sci. 2019, 28, 170. [Google Scholar] [CrossRef]
  28. Tang, W. Research on the evaluation of Ecological-Production-Living space development of towns and villages in severe cold region based on carbon efficiency. Master’s Thesis, Harbin Institute of Technology, Harbin, China, 2020. [Google Scholar] [CrossRef]
  29. Liang, L.T.; Yong, Y.J.; Yuan, C.G. Measurement of urban land green use efficiency and its spatial differentiation characteristics: An empirical study based on 284 cities. China Land Sci. 2019, 33, 80–87. [Google Scholar] [CrossRef]
  30. Tone, K. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 2001, 130, 498–509. [Google Scholar] [CrossRef]
  31. Lu, X.H.; Ren, W.Q.; Yang, H.; Ke, S.G. Impact of compact development of urban transportation on green land use efficiency: An empirical analysis based on spatial measuremen. China Popul. Resour. Environ. 2023, 33, 113–124. [Google Scholar] [CrossRef]
  32. Kuang, B.; Lu, X.H.; Zhou, M.; Chen, D.L. Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered. Technol. Forecast. Soc. Chang. 2020, 151, 119874. [Google Scholar] [CrossRef]
  33. Gai, Z.X.; Sun, P.; Zhang, J.Q. Cultivated land utilization efficiency and its difference with consideration of environmental constraints in major grain producing area. Econ. Geogr. 2017, 37, 163–171. [Google Scholar] [CrossRef]
  34. Feng, Y.G.; Peng, J.; Deng, Z.B.; Wang, J. Spatial-temporal variation of cultivated land’s utilization efficiency in China based on the dual perspective of non-point source pollution and carbon emission. China Popul. Resour. Environ. 2015, 25, 18–25. [Google Scholar] [CrossRef]
  35. Lu, X.H.; Kuang, B.; Li, J. Regional differences and its influencing factors of cultivated land use efficiency under carbon emission constraint. Nat. Resour. 2018, 33, 657–668. [Google Scholar]
  36. Cheng, L.L.; Zhang, J.B.; He, K. Different spatial impacts of agricultural industrial agglomerations on carbon efficiency: Mechanism, spatial effects and groups differences. J. Chin. Agricult. Univ. 2018, 23, 218–230. [Google Scholar] [CrossRef]
  37. Wang, A.; Lin, W.F.; Liu, B.; Wang, H.; Hong, X. Does smart city construction improve the green utilization efficiency of urban land? Land 2021, 10, 657. [Google Scholar] [CrossRef]
  38. Liu, J.B.; Feng, H.Y.; Wang, K. The low-carbon city pilot policy and urban land use efficiency: A policy assessment from China. Land 2022, 11, 604. [Google Scholar] [CrossRef]
  39. Fan, X.Y.; Lu, X.H.; Liu, J.J. Effects of digital economy development on the urban land green use efficiency: Based on the moderating effect of infrastructure construction. China Land Sci. 2023, 37, 79–89. [Google Scholar] [CrossRef]
  40. Huang, A.; Xu, Y.Q.; Lu, L.H. Research progress of the identification and optimization of production-living-ecological spaces. Prog. Geogr. 2020, 39, 503–518. [Google Scholar] [CrossRef]
  41. Huang, J.C.; Lin, H.X.; Qi, X.X. A literature review on optimization of spatial development pattern based on ecological-production-living space. Prog. Geogr. 2017, 36, 378–391. [Google Scholar] [CrossRef]
  42. Yu, J.; Chen, Y.H.; Tang, Y.X.; Chen, Y.Q.; Lou, Z.J.; Nie, Y. Study on the pattern optimization of “production-living-ecological spaces” in the Yangtze River Economic Belt based on the suitability of land space. J. Cent. China Norm. Univ. (Nat. Sci.) 2020, 54, 632–639. [Google Scholar] [CrossRef]
  43. Hu, C.Z.; Huang, X.J.; Zhong, T.Y.; Tan, D. Character of carbon emission in China and its dynamic development analysis. China Popul. Resour. Environ. 2008, 3, 38–42. [Google Scholar]
  44. Huang, C.; Lou, H. Selection of Pathway to Peak Carbon Emission from Perspective of Scale, Structure and Technology in China. Environ. Sci. Technol. 2022, 45, 10–20. [Google Scholar] [CrossRef]
  45. Shen, S.Y. The Impact of Regional Integration on Urban Land Green Use Efficiency. J. Tech. Econ. Manag. 2022, 8, 110–116. [Google Scholar]
  46. Eslamipoor, R. Direct and indirect emissions: A bi-objective model for hybrid vehicle routing problem. J. Bus. Econ. 2024, 94, 413–436. [Google Scholar] [CrossRef]
  47. Zeng, L. The driving mechanism of urban land green use efficiency in China based on the EBM model with undesirable outputs and the spatial Dubin model. Int. J. Environ. Res. Public Health 2022, 19, 10748. [Google Scholar] [CrossRef]
  48. Zhu, J.H.; Sun, H.X. Research on spatial-temporal evolution and influencing factors of urban resilience of China’s three metropolitan agglomerations. Soft Sci. 2020, 34, 72–79. [Google Scholar] [CrossRef]
  49. Zhang, M.D.; Ren, Y.T.; Zhou, L. Spatio temporal evolution characteristics and influencing factors of urban ecological resilience in the Yellow River Basin. Arid Land Geogr. 2024, 47, 445–454. [Google Scholar] [CrossRef]
  50. Saiz, A. The geographic determinants of housing supply. Q. J. Econ. 2010, 125, 1253–1296. [Google Scholar] [CrossRef]
  51. Chen, T.; Kung, J.K.S. Do land revenue windfalls create a political resource curse? Evidence from China. J. Dev. Econ. 2016, 123, 86–106. [Google Scholar] [CrossRef]
  52. Li, L.X.; Huang, P.Y.; Ma, G.R. Land resource misallocation and productivity differences of Chinese industrial firms. J. Manag. World 2016, 8, 86–96. [Google Scholar] [CrossRef]
  53. Xi, Q.M.; Mei, L. Industrial land price, selection effect and industrial efficiency. Econ. Res. 2019, 54, 102–118. [Google Scholar]
  54. Zhu, J.G.; Xu, J.W.; Li, X.J.; Lou, F.; Chen, Y.R. Impact of land urbanization and population urbanization on economic growth in China. Sci. Geogr. Sin. 2020, 40, 1654–1662. [Google Scholar] [CrossRef]
  55. Ru, G.; Liu, H.; Ren, Z.A. The Impact of Resource Endowment on Rural-Urban Income Gap-Panel Data and Experiences from Major Energy Producing Provinces in China. Financ. Econ. 2016, 6, 4–10+82. [Google Scholar] [CrossRef]
  56. Zhang, L.M.; Wang, H.R.; Pan, C.Z.; He, J.H. Empirical analysis on the influence of industrial structure adjustment on water resource utilization efficiency in resource-based regions: Empirical evidence from 10 resource-based provinces in China. J. Beijing Norm. Univ. (Nat. Sci.) 2021, 57, 353–362. [Google Scholar] [CrossRef]
  57. Wen, Z.L.; Ye, B.J. Analyses of mediating effects: The development of methods and models. Adv. Psychol. Sci. 2014, 22, 731–745. [Google Scholar] [CrossRef]
  58. Wang, L.; Zou, J.H.; Liu, S.M. Measurement, dynamic evolution and spatial effect of green economy development level in China. Statist. Decis. 2023, 39, 97–102. [Google Scholar] [CrossRef]
  59. Jia, H.W.; Shi, X.Y. Measurement of green economy development level from the provincial perspective based on the empirical analysis of fourteen prefecture-level cities in Gansu Province. J. Lanzhou Univ. (Soc. Sci.) 2020, 48, 113–121. [Google Scholar] [CrossRef]
  60. Xue, F.; Zhou, M.L. Research on the emission reduction effect of the scale of China’s carbon trading market. East China Econ. Manag. 2021, 35, 11–21. [Google Scholar] [CrossRef]
  61. Zhao, H.H.; Wang, Y.; Wang, Y.C.; Gu, X.M.; Wang, F. Evaluation of public environmental awareness of citizens in Nanjing and influence factor analysis. Resour. Environ. Yangtze Basin 2012, 21, 406–411. [Google Scholar]
  62. Yao, L.; Wang, S.H. Spatial spillover effects of financial agglomeration on green economy efficiency in the Yellow River Basin: Also on the regulating effect of environmental regulation. Sci. Geogr. Sin. 2023, 43, 1783–1792. [Google Scholar] [CrossRef]
Sustainability 16 09842 g001
Figure 1. Mechanisms of land-use carbon efficiency on ecological resilience map.
Figure 1. Mechanisms of land-use carbon efficiency on ecological resilience map.
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Figure 2. Kernel density estimates of ecological resilience development levels in China and four regions.
Figure 2. Kernel density estimates of ecological resilience development levels in China and four regions.
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Table 1. Ecological resilience indicator system.
Table 1. Ecological resilience indicator system.
Standardized LevelPrimary IndicatorsSecondary IndicatorsIndicator AttributesIndicator Weights
Ecological ResilienceResistanceIndustrial sulfur dioxide emissionsNegative0.039
Domestic waste removalNegative0.020
Fertilizer application intensityNegative0.012
AdaptabilityComprehensive utilization of general industrial solid wastePositive0.233
Centralized treatment rate of sewage treatment plantsPositive0.028
Non-hazardous treatment rate of domestic wastePositive0.027
RecoveryGreen space ratio in built-up areasPositive0.029
Green space per capita in parksPositive0.078
Water resources per capitaPositive0.428
Share of environmental protection in fiscal expenditurePositive0.108
Table 2. Land-use carbon efficiency indicator system.
Table 2. Land-use carbon efficiency indicator system.
Standardized LevelPrimary IndicatorsSecondary IndicatorsDescription of Indicators
LUCEInputsLand elementSize of built-up area
Capital elementTotal investment in fixed assets
Labor elementEmployment in the secondary and tertiary
Energy elementElectricity consumption
Desired outputsEconomic benefitValue added of secondary and tertiary
Social benefitPublic finance revenue
Environmental benefitGreening coverage in built-up areas
Undesired outputsEnvironmental pollutionTotal carbon dioxide emissions
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
VariablesNumberMeanStdMinMax
CER4200.2990.0820.1370.708
LUCE4200.4650.2530.1941.844
ROA42015.9605.1514.04034.430
OPE420921814,8922383,002
TEC42025.66046.0600.079354.500
URB42058.82012.73029.89089.600
UPD420290411557645821
GOV4200.2430.1010.0960.643
CER4200.2990.0820.1370.708
Table 4. Baseline regression results.
Table 4. Baseline regression results.
Variables(1)(2)(3)(4)(5)(6)(7)
CERCERCERCERCERCERCER
LUCE0.028872 ***0.028882 ***0.027565 **0.044307 ***0.053630 ***0.053739 ***0.054531 ***
(3.45)(3.68)(2.75)(3.18)(4.60)(4.66)(4.47)
ROA 0.001897 ***0.001793 **0.001879 **0.0011310.0011130.001175
(3.12)(2.39)(2.31)(1.54)(1.39)(1.48)
OPE −0.000000−0.000002 ***−0.000001 **−0.000001 **−0.000001 **
(−0.44)(−3.54)(−2.69)(−2.67)(−2.29)
TEC 0.000260 ***0.000257 **0.000259 ***0.000253 **
(3.08)(2.93)(3.05)(2.73)
URB 0.002192 ***0.002179 ***0.002539 ***
(3.26)(3.38)(3.32)
UPD −0.000001−0.000000
(−0.20)(−0.20)
GOV 0.120120 *
(2.12)
Constant0.231851 ***0.208438 ***0.211392 ***0.206879 ***0.099838 ***0.102087 ***0.056052
(54.27)(23.22)(14.60)(12.94)(3.92)(4.19)(1.55)
Observations420420420420420420420
Id FEYESYESYESYESYESYESYES
Year FEYESYESYESYESYESYESYES
R20.57640.58720.58740.59730.60780.60780.6143
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 5. Robustness tests.
Table 5. Robustness tests.
MethodologiesTreatmentsResults
1. TailoringTo reduce the influence of outliers in the dataset, a two-sided 1% tailing strategy was applied.The regression coefficient is 0.068296, which is significantly positive at the 1% level, maintaining the conclusion in line with the results of the benchmark regression.
2. Excluding special citiesFour particular municipalities-Beijing, Tianjin, Shanghai, and Chongqing-were left out of the investigation in order to eliminate their distinctive features.The regression coefficient is 0.074574, which is significantly positive at the 1% level and the conclusion remains consistent.
3. Shortening the time horizonWith regard to the significant disruptions that the COVID-19 pandemic would cause after 2020, time span was shortened to 2009–2019.The regression coefficient is 0.081383, which is significantly positive at the 1% level and the conclusion remains consistent.
4. Replacement of the explanatory variables measurement methodChanging the entropy method to the entropy weight-topsis method for measuring ecological resilience.The regression coefficient is 0.064043, which is significantly positive at the 1% level, maintaining the conclusion in line with the results of the benchmark regression.
5. Changing the measurement methodChanging the two-way fixed effects model to an OLS model for regression.The regression coefficient is 0.054531, which is significantly positive at the 1% level and the conclusion remains consistent.
Table 6. Robustness test empirical results.
Table 6. Robustness test empirical results.
Variables(1)(2)(3)(4)(5)
TailoringExcluding Special CitiesShortening Time HorizonWeight-TopsisOLS
LUCE0.068296 ***0.074574 ***0.081383 ***0.064043 ***0.054531 ***
(11.83)(8.47)(4.77)(4.06)(3.09)
ROA0.001489 *0.0005210.0021390.0003940.001175
(1.87)(0.69)(1.49)(0.69)(1.27)
OPE−0.000001 ***−0.000001 **−0.000001 *−0.000001 **−0.000001 *
(−3.14)(−3.01)(−1.98)(−2.24)(−1.70)
TEC0.000390 ***0.000344 ***0.000485 ***0.000309 ***0.000253 ***
(5.06)(5.49)(3.93)(3.60)(2.92)
URB0.002124 **0.004880 **0.001920 *0.001947 ***0.002539 ***
(2.84)(2.79)(2.05)(4.10)(3.11)
UPD−0.0000010.0000020.000001−0.000001−0.000000
(−0.60)(0.68)(0.42)(−0.28)(−0.18)
GOV0.094429 **0.1006800.0391450.0764590.120120 **
(2.25)(1.35)(0.74)(1.20)(2.11)
Constant0.074723 **−0.0344120.084665 *0.043903−0.075249
(2.25)(−0.43)(1.96)(1.34)(−1.00)
Observations420364330420420
Id FEYESYESYESYESYES
Year FEYESYESYESYESYES
R20.62780.64990.65810.32220.9356
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 7. Endogenous analysis.
Table 7. Endogenous analysis.
VariablesFirst StageSecond Stage
LUCECER
IV1−0.000710 *0.074574 ***
(−1.95)(8.47)
IV20.494061 ***0.000521
(3.09)(0.69)
LUCE 0.103204 ***
(2.80)
CONTROLSYESYES
Observations390390
Id FEYESYES
Year FEYESYES
Cragg–Donald Wald F statistic53.98
Kleibergen–Paap rk Wald F statistic11.57
Kleibergen–Paap rk LM statistic19.43 ***
P-Hansen J0.40
Notes: t-statistics in parentheses; *** p < 0.01, * p < 0.1.
Table 8. Analysis of regional heterogeneity.
Table 8. Analysis of regional heterogeneity.
Variables(1)(2)(3)(4)
EastMiddleWestNortheast
LUCE0.043119 **0.159653 **0.077805 **−0.077894
(2.31)(2.97)(2.35)(−0.45)
ROA0.0003570.0038630.0015450.012129 ***
(0.25)(1.59)(1.25)(5.71)
OPE0.000001−0.000000−0.000004−0.000004
(1.28)(−0.00)(−1.39)(−0.65)
TEC0.0001570.0004620.000296−0.001319
(1.31)(1.20)(0.32)(−1.03)
URB0.000742−0.0025510.0075420.013603 **
(1.32)(−0.67)(1.68)(2.58)
UPD−0.0000010.000000−0.0000000.000007
(−0.22)(0.06)(−0.01)(1.50)
GOV0.490329 ***−0.1357790.0220940.253830 **
(6.76)(−0.61)(0.18)(2.35)
Constant0.074623 **0.263726−0.102552−0.718589 *
(2.20)(1.68)(−0.50)(−2.02)
Observations1408415442
Number of groups106113
Id FEYESYESYESYES
Year FEYESYESYESYES
R20.59000.81970.65030.9344
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 9. Analysis of heterogeneity in resource endowment and heterogeneity in ecological resilience dimensions.
Table 9. Analysis of heterogeneity in resource endowment and heterogeneity in ecological resilience dimensions.
Variables(1)(2)(3)(4)(5)
Resource-Based ProvincesNon-Resource-Based ProvincesResistanceAdaptabilityRecovery
LUCE0.084440 **0.042708 **0.009518 ***−0.0010510.046064 **
(2.37)(2.80)(4.57)(−0.11)(2.25)
ROA0.0003560.000426−0.0000520.000575 *0.000653
(0.31)(0.53)(−0.87)(2.11)(0.72)
OPE−0.000012 **−0.0000000.000000 ***−0.000001 ***−0.000000
(−2.70)(−1.46)(6.33)(−3.72)(−0.20)
TEC0.0035390.000224 **0.000021 *0.000142 ***0.000089
(1.38)(2.57)(2.04)(3.92)(0.83)
URB0.0044380.002022 ***0.000952 ***0.001420 ***0.000168
(1.03)(3.22)(10.15)(5.73)(0.20)
UPD−0.000001−0.0000020.0000000.000000−0.000001
(−0.28)(−0.37)(1.00)(0.01)(−0.43)
GOV0.1057730.195801 ***0.030600 ***0.0603910.029128
(0.54)(6.02)(5.18)(1.72)(0.37)
Constant0.0120880.067808 **−0.010336 **−0.0154760.081864 *
(0.06)(2.37)(−2.40)(−0.79)(1.82)
Observations126294420420420
Id FEYESYESYESYESYES
Year FEYESYESYESYESYES
R20.70040.61210.73830.67970.2347
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 10. Intermediary effect results.
Table 10. Intermediary effect results.
Variables(1)(2)(3)(4)(5)(6)(7)
CERECOCERINDCERCAPCER
LUCE0.063231 ***0.024627 *0.058349 ***5.953402 **0.029353 *103.121679 ***0.056410 ***
(4.81)(1.71)(4.52)(2.65)(1.88)(4.02)(6.40)
ROA0.002173 **0.0002950.002114 **−0.0293250.002340 *−2.861493 *0.002362 **
(2.48)(0.31)(2.47)(−0.58)(2.13)(−1.89)(2.65)
OPE−0.000001 **0.000003 ***−0.000002 ***0.000036−0.000002 ***−0.002283 *−0.000001 **
(−1.97)(3.52)(−2.75)(1.15)(−3.81)(−2.00)(−2.71)
TEC0.000336 ***0.000989 ***0.0001400.023449 ***0.000203 **0.951562 ***0.000273 ***
(3.61)(9.69)(1.36)(3.41)(2.42)(5.49)(4.51)
URB0.002788 ***−0.002683 ***0.003320 ***0.0815390.002324 **0.9991280.002722 ***
(3.62)(−3.17)(4.34)(1.48)(2.37)(0.59)(3.55)
UPD0.0000010.0000000.0000010.0000620.0000010.006044 ***0.000001
(0.45)(0.02)(0.46)(0.43)(0.43)(3.76)(0.33)
GOV0.106384 **−0.327133 ***0.171237 ***1.2398510.099328 *33.9426190.104139 *
(2.09)(−5.87)(3.29)(1.15)(1.99)(0.68)(2.06)
ECO 0.198246 ***
(4.11)
IND 0.005691 ***
(6.19)
CAP 0.000066 *
(2.18)
Constant0.0367820.290016 ***−0.020712−2.7806480.052606142.191704 *0.027377
(0.83)(5.98)(−0.46)(−0.82)(1.15)(1.79)(0.93)
Observations390390390390390390390
Id FEYESYESYESYESYESYESYES
Year FEYESYESYESYESYESYESYES
R20.66410.88130.68000.49710.69350.88170.6669
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
Table 11. Intermediary effect results.
Table 11. Intermediary effect results.
Variables(1)(2)(3)
CERCERCER
LUCE0.059993 ***0.074453 ***0.074637 ***
(3.73)(5.64)(8.48)
c.c_LUCE#c.c_ER−0.016285 ***67.216943 **0.079682
(−3.28)(2.28)(0.89)
ROA0.002661 **0.002655 **0.003009 **
(2.67)(2.56)(2.73)
OPE−0.000001−0.000001−0.000001
(−1.53)(−1.29)(−1.72)
TEC0.000235 *0.000327 ***0.000366 ***
(2.04)(3.40)(4.30)
URB0.0017250.0020570.002265 **
(1.70)(1.79)(2.79)
UPD0.0000010.0000010.000001
(0.21)(0.41)(0.28)
GOV0.121767 *0.127033 **0.103333
(2.07)(2.27)(1.76)
LnER10.002867 *
(2.13)
ER2 −19.841615 *
(−1.89)
ER3 0.022350
(1.31)
Constant0.0601560.0666520.043988
(1.17)(1.09)(1.07)
Observations360360360
Id FEYESYESYES
Year FEYESYESYES
R20.67260.67050.6624
Notes: t-statistics in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.
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Zhang, W.; Wang, Z.; Wang, S. The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations. Sustainability 2024, 16, 9842. https://doi.org/10.3390/su16229842

AMA Style

Zhang W, Wang Z, Wang S. The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations. Sustainability. 2024; 16(22):9842. https://doi.org/10.3390/su16229842

Chicago/Turabian Style

Zhang, Wei, Zetian Wang, and Shaohua Wang. 2024. "The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations" Sustainability 16, no. 22: 9842. https://doi.org/10.3390/su16229842

APA Style

Zhang, W., Wang, Z., & Wang, S. (2024). The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations. Sustainability, 16(22), 9842. https://doi.org/10.3390/su16229842

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