Synergistic Effects of Dual Low-Carbon Pilot Policies on Urban Green Land Use Efficiency: Mechanisms and Spatial Spillovers Through Difference-in-Differences and Spatial Econometric Analysis

Abstract

China’s rapid urbanization has driven significant economic growth, but has also resulted in resource depletion, ecological degradation, and inefficient land use, collectively hindering sustainable development. In response, pilot policies for “low-carbon cities” and “carbon emissions trading” have been implemented to enhance urban land use efficiency. This study evaluates the green land use efficiency of 282 prefectural-level cities in China from 2006 to 2023, using the non-expected output super-efficiency SBM model. Some cities serve as pilot sites for both “low-carbon cities” and “carbon emissions trading.” A multi-period Difference-in-Differences model is employed to empirically assess the impact and mechanisms of this “dual-pilot” policy on green land use efficiency. The findings indicate the following: (1) The dual-pilot policy significantly improves green land use efficiency, with coordinated implementation yielding greater efficiency gains than single-policy approaches. (2) Mechanism analysis suggests that these policies enhance efficiency by promoting technological innovation and industrial agglomeration. (3) Heterogeneity analysis reveals that the policy’s impact is stronger in western regions, cities with high human capital, large urban centers, areas with stringent environmental regulations, and non-resource-dependent cities. (4) Spatial econometric analysis shows that while low-carbon policies improve local land use efficiency, they also create a siphoning effect on neighboring areas, with positive impacts observed within a 100–600 km range, diminishing and turning negative beyond 600 km. These insights provide a valuable framework for sustainable urban planning, emphasizing the importance of adaptive, context-sensitive policy design in addressing complex ecological and economic challenges.

1. Introduction

The world is undergoing rapid urbanization, which is driving extensive land development. However, this process presents significant challenges, including low land use efficiency, uneven development, and environmental degradation [1]. These issues are particularly evident in China, which has maintained one of the highest urbanization rates globally in recent decades [2]. Land plays a crucial role in China’s economic and social development, with a significant portion of its GDP derived from land finance [3]. As the fundamental material basis for the “production, life, and ecology” three-dimensional space, land’s green utilization efficiency reflects the balance between the input of production factors and the output of land use within a given area.

However, China faces a severe imbalance between land resources and development needs. The low efficiency of land use has led to excessive resource consumption, increased pollutant emissions, and a rising carbon footprint, all of which hinder the country’s green development goals [4]. In the context of growing resource scarcity and tightening ecological constraints, enhancing land use efficiency has become essential. It is pivotal for optimizing the national spatial layout, expanding resource-carrying capacity, and driving the comprehensive green transformation of economic and social development. In response, China has established Urban Land Green Use Efficiency (ULGUE) as an important policy objective [5]. ULGUE can be precisely defined as the harmonization of economic output and ecological sustainability in land resource utilization, measured by the ratio of desirable outputs (economic and social benefits) to both inputs (land, labor, capital) and undesirable outputs (environmental pollution and carbon emissions) under existing technological conditions. During urban planning, integrating the concept of ecological protection is necessary, which involves scientifically coordinating ecological and urban construction spaces, strictly defining ecological protection red lines to safeguard the integrity and stability of urban ecosystems, which lays an ecological groundwork for urban development. To achieve a dynamic equilibrium between economic growth and environmental sustainability, the efficient utilization of land resources is critical for fostering green economic development. This requires strategically optimizing resource allocation systems that are tailored to the distinctive positioning and specific requirements of diverse urban functional zones. Effective policy implementation is also key to sustainably improving land use efficiency and achieving green development goals, which can be realized through precise policy execution.

In the context of ongoing global economic growth, the demand for sustainable urban land use has become increasingly urgent, highlighting the importance of ULGUE, which extends beyond China. The ULGUE challenge is inherently global, as emphasized by the United Nations Sustainable Development Goal 11, which specifically targets sustainable cities and communities. Recent international studies have shown significant differences in land use efficiency across countries with different economic structures, for example, in how metropolitan areas in the United States struggle with sprawl-induced inefficiencies, despite pioneering smart growth policies in North America [6]. Meanwhile, rapidly urbanizing regions in South Asia and Africa face even greater challenges, with land use efficiency in cities such as Mumbai and Lagos severely constrained by informal settlements and inadequate infrastructure [7].

As a key indicator of urban sustainable development, ULGUE enables the scientific evaluation of land input–output systems under specific technological conditions, emphasizing both efficiency and sustainability, thereby profoundly influencing resource conservation, environmental protection, and economic development. The global significance of ULGUE is underscored by comparative studies such as that by Ustaoglu and Williams [8], which demonstrated how variations in urban form across 32 global cities correlated strongly with differences in carbon emissions, energy consumption, and economic productivity. Similarly, international policy frameworks for enhancing ULGUE have evolved significantly, with the OECD’s recent analysis revealing that integrated spatial–environmental planning approaches have yielded superior outcomes compared to siloed governance structures [9]. Over 75% of Chinese cities exhibit low-to-moderate land use efficiency, while cities worldwide grapple with issues such as land resource scarcity, extensive development models, and rising ecological risks, exacerbating resource constraints and environmental pressures. To effectively address these challenges, a strategic shift is necessary, moving away from traditional land expansion models towards more sustainable and efficient land use practices. This not only helps to alleviate resource bottlenecks and balance supply–demand contradictions, but also provides strong support for coordinated ecological conservation efforts [10]. By enhancing ULGUE, cities can reduce environmental impacts while strengthening economic resilience, injecting strong momentum into global urbanization’s sustainable development.

This study is motivated by the urgent need to enhance land sustainability amidst rapid urbanization and continuous urban expansion. Specifically, it examines the role of low-carbon construction policy coordination in improving ULGUE. Given the importance of ULGUE for effective green development and sustainable policies, this research focuses on its relevance in China and other developing countries. First, the study investigates how low-carbon construction policy coordination impacts ULGUE, using a Difference-in-Differences model to empirically validate this relationship. Second, it explores the mechanisms through which policy coordination improves land efficiency, employing a mediation effect model to test these pathways. Third, the study examines the spatial impact of low-carbon policy coordination on ULGUE, utilizing a spatial econometric model to assess spatial dependencies and regional variations.

Despite the implementation of low-carbon policies, there remains a significant knowledge gap regarding their efficacy in enhancing ULGUE. Previous research has primarily focused on the individual impacts of either low-carbon city initiatives or carbon emissions trading schemes on environmental outcomes, energy consumption, or economic growth. However, the synergistic effects of these dual policies on urban land use efficiency remain underexplored, particularly in the context of China’s complex and heterogeneous urban landscape. This research gap is especially critical given China’s ambitious targets to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, as the land use sector represents a major opportunity for emission reduction and sustainable development [11].

Previous studies have shown that low-carbon policies can lead to reductions in carbon intensity [12] and promote technological innovation [13]. These studies have used a variety of methodological approaches to assess policy effectiveness. For example, some scholars have utilized a quasi-experimental double-differencing approach to isolate the causal impact of low-carbon city pilots on carbon emissions [14], while controlling for confounding variables such as economic development and industrial structure. Similarly, other scholars have combined propensity score matching with double-differencing to address selection bias in assessing the impact of carbon emissions trading on industrial transformation [15]. Other researchers have used spatial econometric modeling to capture cross-regional spillovers and found significant spatial dependence in diffusion, with low-carbon initiatives promoting green technology development [16]. However, the interaction between these policies and their cumulative impact on ULGUE remains underexplored. Low-carbon cities refer to urban areas that implement strategies aimed at reducing greenhouse gas emissions through sustainable practices, while carbon emissions trading involves market-based mechanisms that allow for the trading of emission allowances to incentivize reductions.

This study aims to quantify the impact of dual low-carbon policies on ULGUE, and hypothesizes that (1) the coordinated implementation of low-carbon city initiatives and carbon emissions trading will significantly enhance ULGUE, and (2) this impact will vary across regions with differing levels of human capital and environmental regulations. The research makes several distinctive contributions to the existing literature. First, unlike previous studies that have examined low-carbon policies in isolation, we investigate the interactive effects of dual policy implementation, providing novel insights into policy synergy in environmental governance. Second, we employ an innovative methodological framework that integrates advanced econometric techniques with spatial analysis to capture both the temporal dynamics and spatial heterogeneity of policy impacts. Third, by identifying specific mechanisms through which low-carbon policies enhance land use efficiency, our study bridges the gap between policy implementation and outcome evaluation, offering valuable guidance for evidence-based policymaking in sustainable urban development.

The remainder of this paper is as follows: Section 2 reviews the relevant literature, while Section 3 introduces the research methodology. Section 4, Section 5, Section 6 and Section 7 present the empirical results. Section 8 discusses the conclusions, policy recommendations, limitations, and future research directions.

2. Literature Review

2.1. Low-Carbon Policy Synergy and Urban Sustainability

Urban development in its early phases frequently emphasizes short-term economic benefits, often leading to excessive resource consumption, ecological strain, and increased emissions that threaten the global climate [17]. Recognizing these challenges, governments have taken action. Numerous proactive measures have been implemented globally. Emission reduction has become a key policy focus. Sustainable development initiatives are being actively promoted. These efforts aim to balance growth and environmental protection [18]. This shift reflects evolving societal values, redefining the balance between economy and ecology, and prioritizing sustainable urban planning.

China is undergoing a government-led low-carbon urbanization process, emphasizing top-down coordinated planning and pilot projects at the regional level [19]. This approach is in sharp contrast to those of the European Union and North America, where market-driven mechanisms play a central role [20,21]. In contrast, China mainly relies on national intervention, regional pilots, and phased expansion to implement low-carbon policies.

To tackle environmental issues and move towards a low-carbon, green, and sustainable future, the National Development and Reform Commission (NDRC) started a series of policy actions in 2010 [22]. In July 2010, the first batch of low-carbon city pilot (LCCP) projects was launched, covering five provinces and eight cities, aiming to explore a low-carbon path fitting China’s situation. In November 2012, the pilot scope expanded, with 27 more regions joining, covering diverse areas in terms of geography, economic levels, and resource endowments. By January 2017, a comprehensive low-carbon development network was formed, transitioning from local pilots to full-scale implementation [23]. However, during implementation, differences in economic structure, industrial foundation, energy supply, technological level, and public awareness among regions have led to various challenges and inconsistent policy effectiveness, affecting the overall policy implementation.

Internationally, the carbon emissions trading system is a key tool for reducing greenhouse gases. China has made significant strides in implementing this system, with Beijing, Tianjin, and five other regions piloting carbon trading markets starting in 2013. By December 2016, Fujian Province launched its emissions trading market, completing the eight provincial-level pilot regions. These initiatives use market mechanisms to encourage companies to cut emissions and optimize resource allocation, contributing to global climate governance [24].

The dual-pilot approach, involving both low-carbon cities and emissions trading pilots, is expected to enhance the green utilization efficiency of urban land. The cities selected for this dual-pilot initiative (

Table 1. Dual-pilot cities for low-carbon construction.

Year	Dual-Pilot Cities
2013	Beijing, Tianjin, Shanghai, Wuhan, Huangshi, Shiyan, Yichang, Ezhou, Jingmen, Xiaogan, Huanggang, Xianning, Suizhou, Guangzhou, Shaoguan, Shenzhen, Zhuhai, Shantou, Foshan, Jiangmen, Zhanjiang, Maoming, Zhaoqing, Huizhou, Meizhou, Shanwei, Heyuan, Yangjiang, Qingyuan, Dongguan, Zhongshan, Chaozhou, Jieyang, Yunfu, Chongqing
2016	Xiamen, Nanping, Guangyuan
2017	Sanming, Chengdu

, Figure 1) are poised to create significant synergistic effects, empowering land resource sustainability. Therefore, the dual-pilot initiative offers a practical basis for the research presented in this paper.

2.2. Low-Carbon Policies Promote Sustainable Urban Transformation: Environmental and Economic Effects

Extensive research has explored the diverse impacts of low-carbon policies on urban development. These impacts cover areas such as economic growth [25], industrial restructuring [26], technological innovation [27], and carbon emission reduction [28]. Liu et al. [29] demonstrated that LCCP programs significantly reduced carbon intensity in participating cities, highlighting their environmental benefits. However, the long-term sustainability of these reductions hinges on consistent policy enforcement and public engagement. Zhang et al. [30] found that the CETP not only curbed emission growth, but also encouraged low-carbon innovations within regulated industries. These environmental and economic benefits directly contribute to enhancing ULGUE, as they promote more efficient land use practices. However, the literature also identifies significant challenges and limitations associated with these policies. Compared to the central and western regions, the eastern region has achieved more favorable results [31], due to its more developed economic base and stronger policy implementation capacities. Additionally, non-resource-intensive cities, characterized by economies that do not heavily depend on natural resource extraction or processing industries, face higher financial burdens [32]. These findings stress the importance of region-specific and strategically tailored policy interventions. Such interventions should account for the local industrial and economic contexts.

Low-carbon policies can significantly influence land use efficiency by driving industrial upgrading, fostering green technology development, and encouraging compact urban planning, all of which contribute to optimizing land utilization while reducing environmental degradation [33]. Green technology development, supported by low-carbon incentives, further boosts productivity and reduces the spatial footprint of industrial operations, directly enhancing ULGUE. As a result, industrial parks and economic zones can accommodate more high-value industries without expanding their land use footprint, improving green total factor productivity [34].

In summary, low-carbon policies have proven effective in reducing carbon emissions and promoting innovation. However, their long-term success depends on consistent enforcement, public participation, and context-specific implementation.

2.3. Optimizing Urban Land Green Utilization Efficiency: A Multi-Dimensional Approach to Sustainable Land Use and Policy Integration

Green land use efficiency has become a key metric for assessing urban land sustainability. It integrates economic, social, and ecological dimensions. This concept aims to maximize economic and social benefits while minimizing ecological degradation [35]. It promotes a balanced approach to urban growth. To measure ULGUE, scholars have employed various methodologies. These include Data Envelopment Analysis (DEA) [36], Stochastic Frontier Analysis (SFA) [37], and the Entropy Method [38]. While DEA provides a comprehensive view of efficiency, its limitations in accounting for undesirable outputs necessitate the use of more robust models, like the super-efficiency SBM model. Combining these methodologies could yield a more nuanced assessment of ULGUE, capturing both efficiency and sustainability dimensions. These methods allow researchers to systematically assess urban land use performance, identify inefficiencies, and propose targeted improvements.

Existing research has explored ULGUE from various perspectives, emphasizing the importance of aligning human development with natural ecosystems and highlighting the need for integrated approaches involving regional economic integration [39], urbanization management [40], technological innovation [41], and effective government policies [42,43]. For example, Wang et al. [44] found that economic growth and industrial upgrading significantly improve ULGUE in Chinese cities, underscoring the role of land use planning in balancing urban expansion and sustainability. Similarly, Xie et al. [45] underscored the critical role of technological innovation, particularly in green technology, in improving ULGUE. The adoption of environmentally friendly technologies through targeted policies can alleviate the environmental pressures associated with urban land use. However, the relationship between land use efficiency and environmental sustainability remains complex, with some studies pointing to potential trade-offs between economic growth and ecological protection [46]. For instance, Peng et al. [47] revealed that China’s rapid urbanization has led to significant agricultural land loss, resulting in increased environmental degradation and posing challenges to sustainable land management. These findings highlight the need for a nuanced understanding of the balance between economic development and environmental preservation in urban contexts, which can be achieved through integrated policy frameworks.

2.4. Synergistic Effects of Dual-Pilot Low-Carbon Policies on Urban Land Green Utilization Efficiency: An Integrated Analysis of Policy Interaction and Spatial Impact

Low-carbon policies and urban land use efficiency are central topics in the global pursuit of sustainable development, attracting significant scholarly attention. As urbanization accelerates, efficient land use and achieving low-carbon development goals are vital for long-term urban growth and environmental protection, highlighting the practical importance of this research. Looking at the research landscape in this field, while numerous studies have examined low-carbon policies and land use efficiency, significant gaps remain in our understanding of their comprehensive impacts. For example, research indicates that the land use efficiency of low-carbon cities demonstrates a fluctuating development pattern and tends to converge [48]. Evidence suggests that at certain levels, low-carbon city initiatives may exert a negative impact on land use efficiency [49], with positive effects limited primarily to the environmental dimensions of land use efficiency [50]. However, these isolated findings have yet to coalesce into a comprehensive understanding of the overall impact of such policies on the green use efficiency of urban land. The lack of comprehensive studies on the interactions between low-carbon policies limits policymakers’ ability to design synergistic frameworks that maximize ULGUE. ULGUE involves economic, ecological, and social benefits, with complex influencing factors such as urban planning, industrial layout, and energy use. There is a lack of in-depth, systematic research into the comprehensive impact of low-carbon policies in this context.

Most existing research focuses on individual low-carbon measures. Xu et al. [51] found a positive link between the CETP and land use efficiency in Chinese cities, showing that the policy encouraged enterprises to innovate and optimize land use. However, Duan et al. [50] argued that while the policy improved environmental outcomes, its economic impact was less clear due to external factors. These conflicting findings highlight the need for further investigation. Many scholars have also studied the green effects of dual-pilot policies in China’s LCCP or CETP in combination with other policies [52,53,54]. These studies have demonstrated that the dual-pilot approach significantly enhances urban carbon emission efficiency and facilitates residents’ transition toward greener lifestyles. Research consistently shows that cities implementing both policies simultaneously achieve more substantial environmental improvements compared to those with single or no policy implementations. However, these studies have not fully addressed the interactions between different policies. The overlap and divergence of their goals, targets, and implementation methods remain underexplored. Given this gap, the present study aims to examine the combined impact of the LCCP and CETP. This research is crucial for improving the policy framework and enhancing ULGUE, ensuring more effective low-carbon development in China.

Despite the increasing volume of research on low-carbon policies, significant gaps remain in our understanding of the cumulative impacts of various policies. Cheng et al. [55] examined the impact of the LCCP and CETP on green total factor productivity, but found no significant positive effect. The overlapping objectives of these policies can lead to conflicts, undermining their overall effectiveness. Future research should focus on two key areas: first, conducting longitudinal analyses of dual-pilot policies to assess their long-term impacts on ULGUE across varying urban contexts [56]; second, exploring integrated policy frameworks that unify objectives to ensure complementarity rather than conflict. Developing a comprehensive evaluation framework encompassing economic, social, and environmental indicators is crucial for precise assessment, enhancing land use efficiency, and balancing urban development with ecological conservation. Additionally, ULGUE is influenced by geographic and developmental contexts, resulting in spatiotemporal variations. Wu et al. [57] found that coastal cities in China exhibit higher land green utilization efficiency compared to inland areas. Therefore, it is essential to formulate context-specific policies to enhance ULGUE in different urban settings.

This study makes several key contributions to the literature. First, it examines the impact of low-carbon construction policies in China’s dual-pilot framework, emphasizing the link between the LCCP and carbon trading pilots. Second, it identifies two main mechanisms, green technology innovation and industrial agglomeration, that influence urban land green use efficiency. Third, it highlights regional differences in policy effectiveness, particularly in western regions, cities with high human capital, large cities, cities with strict environmental regulations, and non-resource-based cities. Finally, the study reveals a negative spatial spillover effect on ULGUE in neighboring cities, underscoring the need for coordinated low-carbon strategies across regions.

3. Theoretical Analysis and Hypothesis

3.1. The Direct Impact of the Low-Carbon Construction Dual-Pilot Policy on Urban Land Green Utilization Efficiency

This study defines ULGUE as a measure of the balance between economic output and environmental sustainability in urban land use. It is quantified using a non-expected output super-efficiency SBM model, which incorporates both desirable outputs (e.g., economic production) and undesirable outputs (e.g., carbon emissions) to comprehensively evaluate land use performance.

China’s low-carbon city pilot (LCCP) and carbon emissions trading pilot (CETP) are central to its environmental and economic policies. The LCCP provides a framework for policy guidance and technological innovation, aiming to promote low-carbon urban development. It encourages cities to adopt practices such as developing renewable energy infrastructure and implementing energy-efficient building standards [58]. On the other hand, the CETP introduces a market-based mechanism to incentivize emission reduction by allowing the trading of carbon allowances. Companies that can reduce emissions at a lower cost can sell excess allowances, while those facing higher abatement costs can purchase allowances, leading to efficient allocation of reduction efforts [59].

According to the Environmental Kuznets Curve (EKC) theory, the relationship between economic development and environmental quality follows an inverted U-shape. The LCCP and CETP policies aim to accelerate the transition towards the downward slope of the EKC, where economic growth is decoupled from environmental degradation. By promoting cleaner technologies and more efficient resource use, these policies can help cities to achieve higher land use efficiency at a given level of economic development.

Moreover, agglomeration theory suggests that the spatial concentration of economic activities can generate positive externalities, such as knowledge spillovers and shared infrastructure, leading to increased productivity. The LCCP encourages the clustering of low-carbon industries, research institutions, and supporting services, fostering an innovation ecosystem. This agglomeration effect can enhance the efficiency of land use by enabling the sharing of green technologies and best practices among firms [60].

When implemented together, the LCCP and CETP policies complement each other. The LCCP creates a supportive environment for low-carbon development, while the CETP provides the market with incentives for emission reduction. This synergy can lead to a virtuous cycle, where the adoption of low-carbon practices promoted by the LCCP is reinforced by the financial benefits of participating in the carbon trading market. Firms are motivated to continuously improve their carbon efficiency to gain a competitive advantage in the market [61].

Specifically, cities transitioning from LCCP to CETP status gain significant advantages in their green, low-carbon development. By engaging in carbon trading markets, these cities can attract green investments and accelerate the deployment of clean technologies. The additional financial resources from selling carbon allowances can be reinvested into low-carbon infrastructure and research, further improving land use efficiency. LCCP cities can also leverage the comprehensive low-carbon development support from national and local governments, which strengthens their transition [62]. These cities can learn from successful experiences, avoid common challenges, and accelerate progress. Cooperation among LCCP cities fosters regional and national synergies, promoting broader green development. Based on this, the following hypothesis is proposed:

H1. 

The implementation of the low-carbon construction dual-pilot policy can significantly improve ULGUE.

3.2. Indirect Effects of the Dual-Pilot Low-Carbon Construction Policy on the Efficiency of Green Land Use

3.2.1. Mediating Effect of Green Technology Innovation

Green technological innovation plays a crucial mediating role in the relationship between the low-carbon construction dual-pilot policy and ULGUE. The LCCP provides comprehensive policy guidance and establishes an institutional environment conducive to urban green technology innovation [63]. By articulating clear low-carbon development goals and implementation pathways, it stimulates demand for low-carbon transformation across multiple sectors. This policy framework incentivizes and accelerates the research, development, and application of green technologies, including, but not limited to, renewable energy systems, energy-efficient buildings, and clean transportation solutions [64]. Furthermore, the LCCP generates market demand for green innovation by implementing financial subsidies, tax incentives, and innovation funds that effectively lower costs and risks, thereby attracting enterprises, universities, and research institutions to engage in collaborative innovation networks [65].

Simultaneously, the CETP introduces a market mechanism to support green innovation. By pricing carbon emission rights, it ensures the efficient use and allocation of resources and incentivizes firms to invest in cleaner production methods [66]. The carbon market also directs investments towards green technology development. These technologies enhance land use efficiency by enabling precise management and optimizing resource allocation. As a result, land degradation and pollution are reduced, leading to better improved ULGUE and ecological outcomes [59]. To quantitatively assess the mediating effect of green technology innovation, this study will employ a mediation effect model to examine how it facilitates the improvement of ULGUE under the low-carbon construction dual-pilot policy [67]. Based on this, hypothesis 2 is proposed:

H2. 

The low-carbon construction dual-pilot policy can promote ULGUE through the mediating effect of green technology innovation.

3.2.2. Mediating Effect of Industrial Agglomeration

Industrial agglomeration is another key mechanism through which the low-carbon construction dual-pilot policy can enhance ULGUE. According to the agglomeration theory, synergistic agglomeration enhances resource acquisition, accelerates technology diffusion, and fosters cross-industry interactions, generating positive externalities [68]. These interactions improve productivity, increasing the output per unit of land and improving ULGUE.

Dual-pilot cities serve as testing grounds for policy and technology innovation, encouraging enterprises to align with low-carbon standards. This drives improvements in environmental performance and competitiveness by promoting the adoption of cleaner production processes and more efficient resource utilization [69]. The agglomeration of low-carbon firms and supporting industries in pilot cities creates a green innovation ecosystem, facilitating knowledge spillovers and collaborative R&D [70].

To optimize land use efficiency, governments have established low-carbon industrial clusters in pilot cities. Industrial agglomeration reduces environmental externalities and enhances eco-innovation diffusion, improving ULGUE by enabling the sharing of green infrastructure and waste exchange networks [71]. These cities have achieved significant progress in greening existing industries, curbing the expansion of carbon-intensive industries while supporting the growth of clean-tech sectors. This promotes a sustainable industrial system based on low-carbon, circular economy principles.

Resources are redirected toward low-carbon and knowledge-based industries, while outdated production facilities are phased out. This leads to a more efficient industrial system oriented towards green, low-carbon development, ensuring high-quality economic growth with a reduced environmental footprint [72]. To measure the mediating effect of industrial agglomeration, this study will calculate agglomeration indices for the manufacturing and productive service sectors, and assess their impact on ULGUE using mediation analysis. Based on this, the following hypothesis is proposed:

H3. 

The low-carbon construction dual-pilot policy can promote ULGUE through the mediating effect of industrial agglomeration.

In summary, this study proposes a theoretical framework that links the low-carbon construction dual-pilot policy to improved ULGUE through the mediating effects of green technology innovation and industrial agglomeration (Figure 2). The LCCP and CETP are hypothesized to work synergistically to create a policy environment and market incentives conducive to low-carbon development, leading to more efficient and sustainable land use in Chinese cities. The effectiveness of this dual-pilot approach is expected to vary across regions with different economic and environmental conditions, as well as between resource-based and non-resource-based cities. Empirically testing these hypotheses using the DID method and spatial econometric models will provide insights into the complex mechanisms and spatial dynamics of low-carbon policy impacts on ULGUE. These findings can inform the design and implementation of more targeted and effective low-carbon policies to promote green urban development in China and other developing countries facing similar sustainability challenges.

4. Research Methodology and Model Specification

4.1. Model Selection and Justification

Among the widely used methods for evaluating ULGUE, three primary approaches are recognized: the DEA model, the SFA model, and the SBM model [73,74,75]. However, traditional DEA models face significant limitations when handling multi-input and multi-output systems that include undesirable outputs such as environmental pollution and carbon emissions. These limitations include “crowding” and “slack” issues with input factors, which can produce results that deviate substantially from true efficiency levels. In land use evaluation contexts, where economic benefits and ecological costs inherently coexist, these methodological shortcomings become particularly problematic. As a result, their applicability in evaluating green land use efficiency is limited. In land use practices, economic benefits and ecological costs often coexist. The pursuit of maximizing economic benefits from land development is frequently accompanied by undesirable outputs. These outputs, such as environmental pollution and ecological damage, negatively impact the overall eco-efficiency of land use. To address this, Tone introduced the ultra-efficient SBM model, which is specifically designed to handle unwanted outputs. This model overcomes the limitations of traditional DEA methods by fully incorporating unexpected outputs into the efficiency evaluation, ensuring a more accurate measurement of green space utilization efficiency. Given the model’s superior ability to distinguish efficient units and adapt to undesirable outputs, this study uses the ultra-efficient SBM model to assess ULGUE.

4.2. Variable Definitions

4.2.1. Dependent Variable

The dependent variable, green land utilization efficiency, is measured using the super-efficient SBM model, which incorporates both desirable and undesirable outputs. This advanced non-radial and non-angular efficiency measurement approach offers critical advantages over conventional models by simultaneously accounting for input excesses and output shortfalls, while incorporating undesirable outputs (environmental pollution and carbon emissions) directly into the efficiency evaluation framework. By allowing efficiency values to exceed 1, the model provides greater discriminatory power among efficient decision-making units, offering a more nuanced and precise evaluation of the differences in green land use efficiency across diverse urban contexts. Therefore, the model is constructed as follows:

$$Min \gamma ={\displaystyle \frac{\frac{1}{m}{\sum }_{i=1}^m\left(\overline{x}/x_{ik}\right)}{\frac{1}{r_1+r_2}({\sum }_{s=1}^{r_1}{\overline{y}}^d/y_{sk}^d+{\sum }_{q=1}^{r_2}{\overline{y}}^u/y_{uk}^u)}}$$

(1)

$${\{}_{{\lambda }_j\ge 0,i=\mathrm{0,1},\cdots ,m;j=\mathrm{0,1},2\cdots ,n,j\ne 0;s=\mathrm{1,2},\cdots ,r_1;q=\mathrm{1,2},\cdots ,r_2}^{\overline{x}\ge {\sum }_{j=1,\ne k}^n{x}_{ij}{\lambda }_j;{\overline{y}}^d\le {\sum }_{j=1,\ne k}^n{y}_{sj}^d{\lambda }_j;{\overline{y}}^d\ge {\sum }_{j=1,\ne k}^n{y}_{qj}^d{\lambda }_j;\overline{x}\ge x_k;{\overline{y}}^d\le y_k^d;{\overline{y}}^u\ge y_k^u}$$

(2)

where $\gamma$ is the efficiency value; there are $n$ DMUs (i.e., decision-making units), each of which consists of inputs m, desired outputs $r_1$, and undesired outputs $r_2$; and, $x$, $y^d$, and $y^u$ are elements in the input matrix, the desired output matrix, and the undesired output matrix, respectively.

The variables required for measurement are the following [41,76,77].

Desired inputs: (1) Land inputs: urban built-up area is used to measure land inputs. (2) Capital input: fixed asset investment is used to measure urban capital input. (3) Labor input: the number of urban employees in secondary and tertiary industries is selected to represent labor input.

Desired outputs: (1) Economic efficiency output: the sum of the value added by urban secondary and tertiary industries is used to represent the urban economic efficiency output. (2) Social benefit output: the average salary of urban workers is chosen to reflect the social utility of land construction.

Non-expected outputs: (1) Environmental pollution: using the entropy value method, the comprehensive pollution indicators of industrial wastewater emission, industrial sulfur dioxide emission, and industrial smoke and dust emission are measured as the environmental pollution indicators. (2) Carbon emission pollution: carbon emissions are selected to represent carbon emission pollution. The description of land green utilization efficiency indicators is shown in

Table 2. Land green utilization efficiency indicator system.

Input and Output	Indicators	Variables	Units
Input	Land	Actual development and construction area within urban administrative region	km2
	Capital	Total investment in fixed assets	100 million CNY
	Labor	Employees in secondary and tertiary industries	10,000 people
Desired output	Economic benefits	Value added by secondary and tertiary industries	100 million CNY
	Social benefit	Average salary of urban unit employees	10 thousand CNY
	Ecological benefit	Per capita green space area	m2/per
Undesired output	Pollutant emissions	Comprehensive index of environmental pollution	-
	Carbon emissions	Total CO2 emissions	10,000 tons

.

Figure 3 shows the evolution of land green utilization efficiency in sample cities from 2006 to 2023. At a broad level, both pilot and non-pilot cities for low-carbon construction show a similar wave-like development trend. This trend reflects common challenges and stage-specific characteristics of land use in China. A closer comparison reveals significant differences in green land use efficiency between pilot and non-pilot cities. Between 2006 and 2011, non-pilot cities generally had higher green land use efficiency. This may be due to the pilot cities still adjusting policies at the early stage, with policy effects not fully realized. Since 2011, the green land use efficiency of pilot cities has significantly surpassed that of non-pilot cities. This reflects the effectiveness of the dual-pilot policy for low-carbon construction. It highlights the role of combining policy guidance and technological innovation in driving the improvement of green land use efficiency. At this stage, pilot cities have achieved a green transformation in land use patterns. They actively practice low-carbon development concepts, optimize land use structures, and improve land use efficiency. These cities have also leveraged technological innovation to promote intelligent and refined land resource management, further enhancing green land use efficiency.

4.2.2. Independent Variables

The independent variables include the implementation of low-carbon policies, specifically the dual-pilot framework of the LCCP and CETP. The impact of these policies is assessed through a multi-period DID model, which accounts for both temporal and spatial variations in policy effects. The criteria for dividing the experimental and control groups are based on the implementation of two strategies: the LCCP and the CETP. Cities selected as pilots for both programs are considered part of the experimental group. These cities receive policy intervention. The remaining cities, not selected as pilots, are categorized into the control group. Initially, dummy variables for all cities are set to zero. This indicates that these cities are unaffected by the policy before its implementation. When a city is officially included in the LCCP and CETP list, its dummy variable is set to 1. This occurs in the same year the city is included, and continues for all subsequent years. The dummy variable represents the low-carbon construction policy.

4.2.3. Control Variables

With reference to the relevant literature, the control variables used in the model are as follows: (1) the urbanization rate (Urban), measured by the ratio of non-farm employment to total population in each city; (2) the foreign investment level (FDI), measured by the ratio of actual utilized foreign investment to GDP; (3) population density (POP), expressed by the natural logarithm of the resident population of the city; (4) the government’s financial self-sufficiency (Gov), expressed by the natural logarithm of the resident population of the city, and pressure, expressed as the ratio of local general fiscal revenues to local general fiscal expenditures; (5) transportation facilities (Road), measured by the road area per capita (taking into account the natural logarithm); (6) and the ecological level (UE), which was chosen to be expressed as forest cover.

4.3. Data Sources

This study employs a carefully designed sampling strategy to ensure robust causal inference while maximizing data availability. The sample selection process began with all prefecture-level cities in China, but excluded regions with substantial missing data (such as Lhasa and Shigatse) to maintain analytical integrity. The final balanced panel dataset comprises 282 prefecture-level cities observed from 2006 to 2023, a timeframe specifically chosen to encompass both pre-policy periods and sufficient post-implementation years for capturing policy effects. This extended period allows us to track both immediate responses and long-term adaptations to the dual-pilot low-carbon policies, enabling a comprehensive assessment of their sustained impact on urban land green use efficiency. The primary data sources for this paper include the China Urban Statistical Yearbook, the China Energy Statistical Yearbook, the China Research Data Service Platform, and local government work reports. Descriptive statistics are shown in

Table 3. Descriptive statistics.

Variable	Obs.	Mean	SD.	Min.	Max.
ULGUE	5076	0.505	0.240	0.000	1.417
DID	5076	0.083	0.276	0	1
Urban	5076	0.393	0.213	0.075	1.000
Fdi	5076	0.017	0.019	0	0.199
Pop	5076	8.014	0.716	5.513	9.920
Gov	5076	0.458	0.224	0.054	1.541
Road	5076	2.746	0.469	0.315	4.096
UE	5076	0.395	0.066	0.010	0.746

. All econometric analyses in this study were performed using STATA/SE 16.0

4.4. Model Setting

4.4.1. Benchmark Model

The multi-period DID model was selected to evaluate the effects of the dual-pilot low-carbon construction policy on green land use efficiency. This methodological approach offers distinct advantages in addressing the endogeneity concerns inherent in policy evaluation studies. By incorporating both individual and time fixed effects, the model effectively controls for unobserved time-invariant heterogeneity across cities and common temporal shocks that might otherwise confound the estimation of policy impacts. Furthermore, the multi-period specification captures the dynamic nature of policy implementation, allowing for the identification of both immediate and evolving effects as cities adapt to new regulatory frameworks. This design is particularly well suited for evaluating policies with potential heterogeneous treatment effects across time, a critical consideration when examining complex environmental and economic outcomes such as ULGUE. The model is set up as follows:

$${ULGUE}_{it}={\alpha }_0+{\alpha }_1{DID}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(3)

In Equation (3), ${ULGUE}_{it}$ denotes the green land use efficiency of city $i$ in year $t$, ${DID}_{it}$ is a policy dummy variable, ${\alpha }_0$ is a constant term, and ${\alpha }_1$ is a double-differential impact coefficient. If ${\alpha }_1$ is significant, it indicates that the low-carbon construction policy can promote ULGUE; if ${\alpha }_1$ is not significant, it indicates that the low-carbon construction policy does not have a promotional effect on ULGUE. ${\mu }_i$ and ${\nu }_t$ denote individual fixed effects and time fixed effects, respectively, which are used to control for individual heterogeneity that does not change over time, and the overall trend of the treatment group distribution that changes over time, but does not affect the treatment group distribution; ${\epsilon }_{it}$ is the error term. This is the same below.

4.4.2. Mechanism Model

To test the two potential influence mechanisms, two channels are considered: green innovation technology and industrial agglomeration effects. The objective is to explore how the “dual-pilot” policy of low-carbon construction promotes green land use efficiency in urban areas. Estimations are conducted using Equation (4).

$$M_{it}={\beta }_0+{\beta }_1{DID}_{it}+{\beta }_2{X}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(4)

Among them, $M_{it}$ represents the mechanism variables, specifically including green technology innovation and the industrial agglomeration effect. The rest of the variables are consistent with Equation (3).

4.4.3. Spatial Econometric Analysis

Finally, this paper employs a spatial econometric model to assess the spatial dependencies and regional variations in the impact of low-carbon policies on green land utilization efficiency. The selection of the spatial weight matrix is based on geographic proximity, to capture potential spillover effects of policy implementation across neighboring regions.

$${ULGUE}_{it}=\rho W{ULGUE}_{it}+{\delta }_1{DID}_{it}+{\delta }_{2}W{DID}_{it}+{\delta }_3{X}_{it}+{\delta }_{4}W{X}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(5)

where $\rho$ is the spatial autoregressive coefficient, $W_{it}$ is the spatial weight matrix, and other variables have the same meaning as in Equation (3). Possible spatial effects were investigated using the geographic distance matrix. Subsequently, for robustness testing, the anti-geographic distance square matrix and the economic–geographic nested matrix were employed. This step-by-step approach helped in comprehensively understanding the spatial relationships and validating the reliability of the results.

5. Results

5.1. Parallel Trend Test

To validate the effectiveness of the DID approach, it was crucial to assess whether the parallel trends assumption held prior to the implementation of the low-carbon policies. The parallel trends assumption posits that, in the absence of treatment, the average outcomes for both the treatment and control groups would have followed the same trajectory over time. This assumption is fundamental for attributing any observed differences in outcomes post-treatment to the policy intervention rather than to pre-existing trends, following the research framework of Jacobson et al. [78]. The model is constructed as follows:

$${ULGUE}_{it}={\theta }_0+{\theta }_1{\sum }_{k=-5}^{k=3}{D}_{c,t0+k}+{\theta }_2{X}_{it}+{\mu }_i+{\nu }_t+{\epsilon }_{it}$$

(6)

In Equation (6), $D_{c,t0+k}$ is a series of dummy variables. These variables represent the specific implementation status of the “dual-pilot” policy across different years. The offset of year k relative to the policy’s official implementation year (t0) is captured. The first set of dummy variables ($k=-5$) represents the five periods before the policy implementation. This set assesses the pre-policy effects, also known as the pre-processing trend. The second set of dummy variables ($k=3$) represents the three periods following the policy implementation. This set captures the dynamic effects of the policy over time. The year marking the policy intervention is the reference point. This year is used to analyze changes in policy effects. The remaining variables are consistent with those in Equation (3). The parallel trend test is shown in Figure 4.

As illustrated in Figure 4, the results of the parallel trend test reveal that, in the five observation periods prior to the policy’s formal implementation, the impact coefficients on land green utilization efficiency remain statistically insignificant and exhibit a stable trend. This indicates that, in the absence of policy intervention, the treatment and control groups followed parallel trajectories in terms of land green utilization efficiency. Following the policy’s implementation, the impact coefficient gradually increased in the subsequent year, though it did not reach statistical significance, reflecting the delayed nature of the policy’s effect. This delay can be attributed to the incubation period required for policy mechanisms to activate, resource allocation to adjust, and behavioral patterns to shift.

From the second year post-implementation, the impact coefficient became significantly positive, confirming the effectiveness of the “dual-pilot” policy in enhancing land green utilization efficiency. This suggests that the policy’s effects are not only significant, but also sustainable over the long term. The results underscore the importance of allowing sufficient time for policy mechanisms to mature and for their effects to manifest fully.

In summary, the parallel trend test supports the validity of the DID approach by demonstrating that the treatment and control groups exhibited similar trends prior to the policy intervention. This finding strengthens the causal inference that the observed improvements in land green utilization efficiency can be attributed to the dual-pilot policy, rather than to pre-existing differences between the groups. The test also highlights the policy’s delayed yet sustained impact, providing valuable insights for future policy design and implementation.

5.2. Benchmark Regression

The benchmark regression analysis results, presented in

Table 4. Benchmark regression analysis results.

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	ULGUE	ULGUE	ULGUE	ULGUE	ULGUE	ULGUE	ULGUE
DID	0.048 ***	0.047 ***	0.053 ***	0.053 ***	0.054 ***	0.054 ***	0.060 ***
	(0.011)	(0.011)	(0.012)	(0.012)	(0.012)	(0.012)	(0.012)
Urban		−0.127 ***	−0.127 ***	−0.128 ***	−0.129 ***	−0.128 ***	−0.122 ***
		(0.038)	(0.038)	(0.038)	(0.038)	(0.038)	(0.037)
FDI			0.591 ***	0.595 ***	0.411 ***	0.409 ***	0.339 *
			(0.186)	(0.186)	(0.187)	(0.187)	(0.183)
POP				−0.003	−0.003	−0.003	−0.003
				(0.005)	(0.005)	(0.005)	(0.005)
GOV					0.178 ***	0.177 ***	0.166 ***
					(0.029)	(0.029)	(0.029)
Road						0.002	−0.009
						(0.009)	(0.009)
UE							0.462 ***
							(0.052)
_Cons	0.501 ***	0.551 ***	0.541 ***	0.563 ***	0.484 ***	0.478 ***	0.334 ***
	(0.002)	(0.015)	(0.015)	(0.044)	(0.046)	(0.053)	(0.054)
City FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES
Observations	5076	5076	5076	5076	5076	5076	5076
R2	0.738	0.739	0.740	0.740	0.742	0.742	0.746

Note: * and *** indicate significance at the level of 10% and 1%, respectively.

, provide critical insights into the impact of the dual-pilot low-carbon policies on green land use efficiency. The regression coefficient for the dual-pilot policy in column (1) is 0.048, which is statistically significant at the 1% level, indicating that the policy significantly enhances green land use efficiency. This suggests that the implementation of low-carbon policies can lead to a 4.8% increase in green land use efficiency, holding other factors constant. This finding is consistent with hypothesis 1, which posits that the dual-pilot policy promotes green land use efficiency. In columns (2) to (7), the estimated coefficients for the dual-pilot policy variable are consistently positive and statistically significant across all model specifications. This indicates that even after considering other influencing factors, the positive impact of the dual-pilot policy on green space utilization efficiency still exists. This reinforces the conclusion that the dual-pilot policy itself is responsible for improving land use efficiency. The coefficient of ULGUE obtained from column (7) is 0.060, which is statistically significant at the 1% level, further confirming the policy’s effectiveness. This consistency across different model specifications suggests that the dual-pilot policy’s impact is not driven by omitted variable bias or other confounding factors.

The economic significance of these results is substantial. A 6.0% increase in green land use efficiency implies that cities implementing the dual-pilot policy can achieve more sustainable land use practices, reducing resource waste and environmental degradation. This is particularly important in the context of China’s rapid urbanization, where efficient land use is critical for balancing economic growth with environmental sustainability.

The results also highlight the synergistic effect of combining LCCP initiatives with CETP. The dual-pilot policy leverages both policy guidance and market mechanisms, creating a more comprehensive approach to promoting green land use. This synergy not only enhances the efficiency of land use, but also contributes to broader environmental and economic benefits, such as reduced carbon emissions and improved resource allocation.

In summary, the benchmark regression results provide strong empirical support for the effectiveness of the dual-pilot policy in improving green land use efficiency. The findings underscore the importance of integrating multiple policy tools to achieve sustainable urban development. Future research should explore the long-term impacts of these policies and their potential for scaling up to other regions.

5.3. Robustness Tests

5.3.1. Placebo Test

To assess the causal effect of the “dual-pilot” policy on green land use efficiency, a placebo test was conducted to ensure robustness. This approach helps to eliminate potential biases from confounding variables. First, 40 cities were randomly selected from 282 cities nationwide, and were designated as “pseudo” dual-pilot areas, while the rest formed the control group. A fictitious policy implementation year was then assigned to these cities, simulating a scenario where the policy was not applied. To enhance reliability and reduce random errors, the placebo test was independently repeated 1000 times using a Monte Carlo simulation approach. This iterative process generated a distribution of estimated treatment effects under the null hypothesis of no effect, allowing us to evaluate the statistical significance of our actual estimate.

The results, shown in Figure 5, indicate that the regression coefficients for the hypothetical experimental group fluctuate around zero, following a normal distribution. The mean of these coefficients is 0.002, with a standard deviation of 0.011, and none of the placebo estimates exceed the magnitude of our actual estimate (0.060). The absence of extreme values in the placebo distribution provides strong evidence that our findings are not driven by chance or selection bias. This confirms that the positive effect of the “dual-pilot” policy on green land use efficiency is both robust and reliable, excluding the influence of unobservable factors or random events.

The placebo test methodology provides several advantages over simpler robustness checks. First, it directly addressed potential concerns about endogeneity in policy implementation by creating counterfactual scenarios that mimicked the structure of our data, but removed the true treatment effect. Second, by repeating the test 1000 times, we minimized the probability of drawing conclusions based on a single anomalous random assignment. Third, the resulting distribution of coefficients allowed us to calculate the exact probability of obtaining our result by chance, which, in this case, was less than 0.1%.

The placebo test results confirm that the observed improvements in green land use efficiency are indeed attributable to the dual-pilot policy, rather than to other unobserved factors. The normal distribution of the coefficients around zero suggests that the policy’s impact is not driven by chance or external variables, reinforcing the validity of our findings. This robustness check is crucial for establishing the causal relationship between the dual-pilot policy and green land use efficiency, ensuring that the policy’s benefits are accurately measured and not overstated.

From an economic perspective, the placebo test underscores the importance of policy design and implementation in achieving sustainable urban development. The dual-pilot policy’s ability to enhance green land use efficiency without being influenced by external factors highlights its potential as a scalable and replicable model for other regions. This finding is particularly significant for policymakers aiming to balance economic growth with environmental sustainability, as it provides empirical evidence that targeted low-carbon policies can yield measurable and reliable improvements in land use efficiency.

5.3.2. Dynamic Effects Test

To address potential heterogeneous treatment effects arising from variations in economic conditions, social structures, and cultural practices [79], this study used a dynamic effects test for validation [80]. Unlike traditional DID models that may produce biased estimates when treatment effects vary over time, this methodology explicitly accounts for treatment effect heterogeneity across both units and time periods. The Sun and Abraham approach implements an interaction-weighted estimator that delivers unbiased estimates even in the presence of treatment effect heterogeneity.

The test constructed dynamic effects maps for different time windows, allowing for a detailed examination of the temporal evolution of policy impacts. We implemented this by interacting treatment status with a full set of dummies for each relative time period, omitting the period immediately before treatment as the reference point. This specification allowed us to trace the evolution of treatment effects over time, while controlling for unit-specific and time-specific fixed effects. The key advantage of this approach is that it avoids the contamination of estimates that can occur in staggered DID designs when previously treated units serve as controls for newly treated units.

The results, as illustrated in Figure 6, demonstrate a high degree of consistency with the parallel trend test (Figure 4), particularly in the pre-treatment period. Both the treatment and control groups exhibit similar trends before the policy intervention, further validating the robustness of the two-way fixed effects model.

The dynamic effects test reveals that the dual-pilot policy’s impact on green land use efficiency became statistically significant in the second year post-implementation, with a coefficient of 0.049 (p < 0.01). This delayed effect aligns with theoretical expectations, as policy mechanisms often require time to activate, and resource allocation adjustments may take several years to materialize. The sustained positive impact in subsequent years, with coefficients ranging from 0.052 to 0.056, underscores the long-term effectiveness of the dual-pilot policy in promoting green land use efficiency.

From an economic perspective, the dynamic effects test highlights the importance of policy persistence in achieving sustainable urban development. The gradual yet sustained improvement in green land use efficiency suggests that the dual-pilot policy not only addresses immediate environmental challenges, but also fosters long-term structural changes in land use patterns. This finding is particularly relevant for policymakers aiming to balance economic growth with environmental sustainability, as it demonstrates the potential of integrated low-carbon policies to drive continuous improvements in land use efficiency.

5.3.3. PSM-DID

To address potential selection bias in the estimation of the dual-pilot policy’s impact, this study employs the Propensity Score Matching Difference-in-Differences (PSM-DID) method. This two-stage approach first matches treated and control units based on observable characteristics using propensity score matching, then applies the DID framework to the matched sample. The key advantage of this combined approach is that it accounts for both observable differences between treatment and control groups through matching, and unobservable time-invariant differences through the DID component.

The propensity scores, calculated based on key covariates such as economic development level, industrial structure, and environmental regulations, were used to match treatment and control groups with similar characteristics. As shown in Figure 7, the kernel density plots of propensity scores before and after matching reveal a significant improvement in the balance between the two groups. Before matching, the distribution of propensity scores for the treatment and control groups exhibited substantial differences, indicating potential selection bias. After matching, the distributions became highly overlapping, suggesting that the matching process effectively balanced the covariates between the groups.

The PSM-DID results confirm the robustness of the baseline regression findings. The coefficient for the dual-pilot policy remains significantly positive at 0.056 (p < 0.01), consistent with the benchmark regression results. This suggests that the observed improvement in green land use efficiency is indeed attributable to the policy, rather than to pre-existing differences between the treatment and control groups. The matching process effectively mitigates selection bias, enhancing the credibility of the causal inference. The results underscore the importance of controlling for observable confounders when evaluating policy impacts. The significant and positive effect of the dual-pilot policy highlights its potential as an effective tool for promoting sustainable land use practices. By ensuring that the treatment and control groups are comparable in terms of key economic and environmental characteristics, the PSM-DID approach provides a more accurate assessment of the policy’s true impact.

5.3.4. Excluding the Impact of Similar Policies

The dual-pilot region also includes the “innovative city” pilot. The implementation of the innovative city policy may impact the dual-pilot low-carbon construction policy. It could influence both the policy’s effectiveness and green land use efficiency, thereby acting as a potential source of interference in the evaluation process. To address this potential interference, an “innovative city” dummy variable was introduced and included as a control variable in the baseline regression model. The results of this model are presented in column (2) of

Table 5. Robustness test results.

Variable	PSM-DID	Excluding Impact of Similar Policies	Lagged Variables	Excluding Outliers	Dependent Variable Substitution
	(1) ULGUE	(2) ULGUE	(3) ULGUE	(4) ULGUE	(5) ULGUE_PM2.5
DID	0.056 ***	0.060 ***	0.055 ***	0.061 ***	0.039 ***
	(0.012)	(0.012)	(0.012)	(0.012)	(0.011)
_Cons	0.337 ***	0.334 ***	0.457 ***	0.334 ***	0.280 ***
	(0.054)	(0.054)	(0.053)	(0.056)	(0.052)
Controls	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observations	5055	5076	4794	5076	5076
R2	0.747	0.746	0.750	0.745	0.772

Note: *** indicate significance at the level of 1%, respectively.

. The findings indicate that the coefficient for the dual-pilot low-carbon construction policy remains significant. This holds true even after controlling for the potential interference of the “innovative city” policy. This confirms that the positive impact of the low-carbon construction policy on green land use efficiency is robust and not affected by the concurrent implementation of the “innovative city” policy.

5.3.5. Lagged Variables

To account for potential time lags in policy effects, the control variables were lagged by one period. This approach captures delayed responses and gradual policy impacts. The results in column (3) of Table 5 show that the implementation effects of the dual-pilot policy on low-carbon construction remain significant. This robustness test further strengthens the conclusion that the policy’s impact is not distorted by temporal delays.

5.3.6. Exclude Outliers

To mitigate the influence of extreme values, all variables underwent upper and lower 1% bilateral tailing. As shown in column (4) of Table 5, the coefficient for the impact of low-carbon policies on land green utilization efficiency remains significant at the 1% level. This consistency with the benchmark regression results confirms that the policy effect is not driven by outliers, ensuring the reliability of the findings.

5.3.7. Dependent Variable Substitution

PM2.5 concentration, a critical indicator of environmental quality, was used to replace the original SBM model’s comprehensive environmental pollution index. The land green utilization efficiency index was recalculated using PM2.5 data [10]. As shown in column (5) of Table 5, the coefficient for the effect of low-carbon construction policies remains significantly positive. This substitution underscores the policy’s positive influence on both air quality and land green utilization efficiency, further validating the robustness of the original results.

6. Mechanism Test Regression

This section investigates the mechanisms through which dual-pilot low-carbon policy enhances ULGUE.

6.1. Green Technology Innovation Effects

The number of green patents granted serves as a key indicator of green innovation output. It is commonly used to assess the level of green innovation. Both the quantity and quality of these patents reflect the activity and effectiveness of green technological innovation [81]. This study adopts the number of green patents granted per 10,000 people as a benchmark measure of green technological innovation. Column (1) of

Table 6. Mechanism test regression results.

Variable	Green Technology Innovation Effects			Industrial Agglomeration Enhancement
	(1) GT	(2) GT_Q	(3) GT_N	(4) Service Agglomeration	(5) Manufacturing Agglomeration	(6) Collaborative Agglomeration
DID	0.263 ***	0.103 ***	0.160 ***	1.707 ***	0.197 ***	1.843 ***
	(0.063)	(0.030)	(0.035)	(0.659)	(0.026)	(0.660)
_Cons	1.202 ***	0.309 ***	0.893 ***	7.138 ***	0.447 ***	8.153 ***
	(0.121)	(0.054)	(0.075)	(2.752)	(0.108)	(2.757)
Controls	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	5076	5076	5076	5076	5076	5076
R2	0.644	0.559	0.673	0.933	0.826	0.932

Note: *** indicate significance at the level of 1%, respectively.

presents the regression analysis results. The results show that the low-carbon construction policy has a significant positive effect on green technological innovation. The coefficient is 0.263, and the significance level is 1%. This indicates that the low-carbon construction policy has effectively stimulated a rise in green patent applications. It has further fostered the vigorous development of green technological innovation.

Technological innovation encompasses both substantive and strategic dimensions, which complement each other [82]. Invention patents, known for their high creativity and technological depth, present a higher threshold compared to utility model patents. These patents more accurately represent the core competitiveness and deep quality of green technological innovation. In this context, the number of granted green invention patents serves as a measure of substantive green innovation. On the other hand, the number of granted green utility patents acts as an indicator of strategic green innovation. Columns (2) and (3) of Table 6 show the regression analysis results. These results are based on two dependent variables: the number of green invention patents granted per 10,000 people and the number of green utility patents granted per 10,000 people. The analysis reveals that the low-carbon construction policy significantly promotes substantive green innovation. A coefficient of 0.103 is observed, which passes the 1% significance level. This result strongly affirms the positive role of the policy in enhancing the quality of green technological innovation. Furthermore, the policy also stimulates strategic green innovation. The corresponding coefficient is 0.160. This indicates that the low-carbon construction policy effectively increases the number of green innovations and broadens the scope and flexibility of innovation activities.

In conclusion, the low-carbon construction policy plays a significant role in stimulating green technological innovation. It not only boosts the number of green patent applications, but also enhances the quality of green technological innovation. This demonstrates a dual leap, moving from an expansion in quantity to an improvement in quality. These results provide strong support for the validity of hypothesis H2.

6.2. Industrial Agglomeration Enhancement

Industrial agglomeration can be categorized into specialized and synergistic types. Both forms foster cooperation and resource sharing among enterprises. This cooperation leads to economies of scale and synergistic effects, enhancing the efficiency of land green utilization. The industrial agglomeration effect is assessed from the perspectives of both the productive service and manufacturing industries. To measure the degree of agglomeration in these sectors, the method developed by Yang et al. and other relevant studies was applied [83,84]. This approach calculates the level of industrial synergy agglomeration. The calculation is as follows:

$${magg}_i=\left(E_{mi}/E_m\right)/\left(E_i/E\right)$$

(7)

$${pagg}_i=\left(E_{pi}/E_p\right)/\left(E_i/E\right)$$

(8)

where ${\mathrm{m}\mathrm{a}\mathrm{g}\mathrm{g}}_{\mathrm{i}}$ represents the location entropy index of the manufacturing industry in region $i$; ${\mathrm{p}\mathrm{a}\mathrm{g}\mathrm{g}}_{\mathrm{i}}$ represents the location entropy index of the productive service industry in region $i$; and $E_{mi}$ and $E_{pi}$ represent the amount of employment in the manufacturing industry and the productive service industry in region $i$. $E_i$ represents the total employment in the region, and $\mathrm{E}$ represents the total employment in the whole country.

The degree of synergistic agglomeration between the manufacturing and productive service industries is further assessed. This measurement reflects the strength of the connection between upstream and downstream segments of the industrial chain. A higher level of agglomeration indicates greater efficiency in resource coordination and knowledge spillovers. The analysis captures the extent to which these industries integrate, fostering enhanced collaboration and technological advancement.

$${coagg}_i=1-{\displaystyle \frac{\left|{magg}_i-{pagg}_i\right|}{{magg}_i+{pagg}_i}}+({magg}_i+{pagg}_i)$$

(9)

${coagg}_i$ represents the index of synergistic agglomeration of the productive service industry and manufacturing industry in region $i$. The larger the value of ${coagg}_i$, the higher the level of synergistic agglomeration of the two industries.

The regression results are presented in columns (4) and (5) of Table 6. The findings in column (4) indicate that the low-carbon construction policy significantly promotes the development of the productive service industry. A significantly positive coefficient confirms this effect. This suggests that policy implementation stimulates growth within the productive service sector. Similarly, the results in column (5) show a significant positive impact of the low-carbon construction policy on the manufacturing industry. The coefficient remains statistically significant at the 1% level. This finding demonstrates that the policy does not negatively affect the manufacturing sector. Instead, it enhances competitiveness and promotes sustainable development. The stimulation of technological innovation and the optimization of resource allocation further reinforce this positive impact. Column (6) of Table 6 reveals the industrial agglomeration effect of the low-carbon construction policy. The policy strengthens the linkage between the manufacturing and productive service industries. This finding highlights a new model of inter-industry interaction under a low-carbon economy. The low-carbon construction policy creates broader opportunities for industrial collaboration. It establishes a more solid foundation for synergy between these two industries. As a result, industrial agglomeration accelerates, facilitating the green transformation and upgrading of land use.

7. Further Analysis

7.1. Heterogeneity Analysis

7.1.1. Regional Heterogeneity

China’s vast geographical landscape exhibits significant regional heterogeneity, driven by variations in economic development, industrial structure, resource endowment, and environmental carrying capacity. To examine the region-specific effects of low-carbon construction policies, this study categorizes 282 prefecture-level cities into three major regions: eastern, central, and western. The results, presented in columns (1) to (3) of

Table 7. Regional- and urban-scale heterogeneity analysis results.

Variable	East	Central	West	Small- and Medium-Sized Cities	Large Cities
	(1)	(2)	(3)	(4)	(5)
DID	0.056 ***	0.050 ***	−0.010	0.045 ***	0.103 ***
	(0.014)	(0.011)	(0.082)	(0.010)	(0.038)
_Cons	0.240 **	−0.052	0.730 ***	0.297 ***	0.895 ***
	(0.109)	(0.064)	(0.109)	(0.053)	(0.271)
Controls	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observations	1800	1782	1494	4446	630
R2	0.747	0.697	0.750	0.772	0.643

Note: ** and *** indicate significance at the level of 5% and 1%, respectively.

, reveal a significant positive effect of low-carbon construction policies in the eastern and central regions. In these regions, policy implementation effectively stimulates the green transformation of land resources, improving the efficient and sustainable utilization of land and contributing to the overall green development of the regional economy. These findings suggest that low-carbon policies in developed regions provide a solid foundation for achieving national low-carbon transformation goals.

In contrast, the impact in the western region shows a negative trend, though it remains statistically insignificant. This result underscores the challenges in balancing economic growth and environmental protection in less-developed areas. Multiple constraints hinder the effectiveness of low-carbon policies in the western region, including a lower level of economic development, a homogeneous industrial structure, and limited resource endowment. These structural limitations weaken the capacity of low-carbon policies to generate significant improvements in green land utilization efficiency. Additionally, the western region’s relatively weak environmental carrying capacity exacerbates challenges in the low-carbon transition process, reducing the adaptability and responsiveness to low-carbon policies and making short-term policy effects less pronounced.

The economic implications of these findings are profound. In the eastern and central regions, the positive impact of low-carbon policies highlights the importance of leveraging existing economic and industrial strengths to drive sustainable land use practices. However, the western region’s challenges emphasize the need for targeted policy interventions that address structural limitations and enhance environmental capacity. These insights underscore the importance of region-specific policy design to maximize the effectiveness of low-carbon initiatives across diverse geographical contexts.

7.1.2. Urban-Scale Heterogeneity

To analyze the effect of heterogeneity in city size on the impact of low-carbon construction policies on green land use efficiency, cities are classified into two categories based on population size: small- and medium-sized cities (fewer than 3 million residents) and large cities (exceeding 3 million residents). This classification ensures accuracy and rationality in evaluating policy effects across different urban scales. Regression analyses, presented in columns (4) and (5) of Table 7, reveal significant variations in policy impact between city sizes. In small and medium-sized cities, the low-carbon construction policy exerts a significantly positive effect on green land use efficiency, promoting the economical and intensive use of land resources and facilitating the green transformation of urban development. In contrast, the impact coefficient is larger in large cities, indicating distinct advantages in the low-carbon transition process.

Several factors contribute to the superior performance of large cities. First, their higher levels of economic development and diversified industrial structures enable faster adaptation to market demands and policy orientations, accelerating the efficient allocation and utilization of land resources. Second, large cities possess stronger infrastructure, public service capabilities, and environmental management experience, which enhance overall land resource utilization and increase environmental carrying capacity [85]. Finally, their strengths in technological innovation, capital investment, and policy execution allow for a more effective response to low-carbon policy requirements, resulting in significant improvements in green land use efficiency.

Future low-carbon policies should adopt a differentiated approach tailored to each city’s specific characteristics. Refining urban classifications will help to better align policies with local needs. Small- and medium-sized cities require enhanced policy support to overcome unique challenges and barriers, while large cities should focus on optimizing their capacities for low-carbon transitions. Data-driven decision-making will play a crucial role in guiding these efforts, enabling the design of targeted interventions that accelerate sustainable urban transformation and ensure both economic growth and environmental sustainability.

7.1.3. Human Capital Heterogeneity

Human capital plays a pivotal role in the successful implementation of low-carbon policies and the advancement of green development. The student population in higher education serves as a critical reserve of human capital, directly influencing a region’s potential for sustainable development. To quantify human capital levels, this study employs the ratio of students enrolled in higher education to the total regional population. Using the quartile division method, regions are categorized into three groups: low-level (below the 25th percentile), medium-level (25th–75th percentile), and high-level (above the 75th percentile) human capital areas. Low-level regions often face challenges such as educational resource shortages and significant brain drain, while high-level regions benefit from abundant higher education resources and a concentration of skilled talent.

Regression results for regions with varying human capital levels (

Table 8. Human capital and resource endowment heterogeneity analysis results.

Variable	Low Human Capital	Medium Human Capital	High Human Capital	Non-Resource Based	Resource Based
	(1)	(2)	(3)	(4)	(5)
DID	0.027	0.047 ***	0.180 ***	0.034 **	0.134 ***
	(0.017)	(0.017)	(0.036)	(0.014)	(0.022)
_Cons	0.394 ***	0.359 ***	0.610 ***	0.444 ***	0.199 ***
	(0.087)	(0.076)	(0.153)	(0.079)	(0.073)
Controls	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES
Observations	1228	2560	1261	3024	2052
R2	0.827	0.788	0.660	0.740	0.768

Note: ** and *** indicate significance at the level of 5, and 1%, respectively.

, columns 1 to 3) indicate a clear pattern: the higher the human capital level, the stronger the positive effect of low-carbon construction policies on green land use efficiency. The influence coefficients follow a descending order from high-level to low-level regions. These findings underscore the critical role of human capital in advancing green development and improving policy implementation efficiency.

Despite these results, a notable discrepancy emerges. Low- and medium-level human capital regions should theoretically be the primary focus of policy interventions; however, the regression results reveal that the impact of low-carbon construction policies on green land use efficiency remains statistically insignificant in these regions. Several factors may explain this outcome. First, regions with low human capital levels often lack adequate educational resources and technological innovation capacity, hindering the effective absorption and application of low-carbon technologies and management concepts. Second, medium-level human capital regions possess certain educational resources and talent pools; however, policy effects may remain unrealized due to weak policy transmission mechanisms, insufficient implementation, or inadequate market incentives. Third, economic pressures may drive policy preferences in low- and medium-level human capital regions, leading to a stronger focus on short-term economic benefits and neglect of long-term green development goals.

To address these challenges, future efforts must prioritize targeted investments in education, talent retention, and innovation in low- and medium-level human capital regions. Strengthening policy implementation capacity and adapting strategies to regional needs will be critical for improving policy success. High-level human capital regions, with their established strengths, should serve as models for development, fostering collaboration and knowledge sharing with low- and medium-level regions. Such efforts will not only enhance regional development, but also contribute to the broader promotion of sustainable development across the nation.

7.1.4. Resource Endowment Heterogeneity

China’s vast territory and uneven resource distribution have led to the emergence of resource-based cities, which are rich in natural resources such as minerals and forests. Early economic growth in these regions relied heavily on low-end industrial models, with crude oil extraction, mining, and primary processing industries dominating local economies. As a result, there was an over-reliance on resource trading revenues, and the sustainable use of land resources was often overlooked. Inefficient land utilization increased environmental pressure, exacerbating ecological degradation. Given this dependence on natural resources, integrating low-carbon construction into the economic framework of these cities presents significant challenges, with numerous obstacles and uncertainties complicating short-term implementation.

To evaluate the effects of the dual-pilot low-carbon construction policy across different city types, this study classifies cities into two major groups: resource-based and non-resource-based, following the National Sustainable Development Plan for Resource Cities [86]. The regression results in columns (4) and (5) of Table 8 reveal substantial differences in policy impact between these two groups. The effect of low-carbon construction policies on green land use efficiency is more pronounced in resource-based cities compared to non-resource-based cities.

Several factors may explain this outcome. Non-resource-based cities tend to have more diversified economies, which often require longer periods of policy accumulation and broader institutional support to promote efficient land use and environmental improvement. Additionally, the economic and social transformation pressures in non-resource-based cities may slow the effectiveness of low-carbon policies. In contrast, the implementation of low-carbon construction policies in resource-based cities yields significantly positive impacts, largely due to the strong interaction with the transformation and upgrading of resource-dependent industries. Policy incentives accelerate the elimination of outdated production capacity, optimize industrial structures, and improve resource utilization efficiency.

Furthermore, low-carbon construction offers new economic growth opportunities for resource-based cities. This transition generates employment and reduces economic pressures associated with resource depletion. Additionally, a focus on ecological protection and restoration during development enhances the conditions for sustainable land use, creating a foundation for long-term economic and environmental stability. By integrating low-carbon policies, resource cities can achieve both economic growth and sustainability.

7.2. Dual-Pilot Synergies

This paper initially employed the traditional DID approach to compare green land use efficiency between pilot and non-pilot cities. To further explore the advantages of the “dual-pilot” policy over the “single-pilot” approach, this study additionally examines how the combination of policies enhances land green utilization efficiency. This analysis provides critical insights into the effectiveness of the dual-pilot strategy compared to other policy frameworks.

To test the enabling effect of the “single-pilot” policy on green land use efficiency, this study first evaluated the impact of the LCCP. All data related to the CETP were excluded, leaving only the samples of LCCP and non-pilot cities for regression analysis. In this evaluation, the estimated coefficients of the multi-period double-difference variables in model (1) reflect the net impact of the LCCP. The regression results, shown in columns (1) and (2) of

Table 9. Dual-pilot synergy analysis.

Variables	LCCP		CETP		Dual-Pilot
	(1)	(2)	(3)	(4)	(5)	(6)
DID	0.014 *	0.031 ***	0.104 ***	0.120 ***	0.041 ***	0.039 **
	(0.009)	(0.009)	(0.016)	(0.020)	(0.012)	(0.013)
Constant	0.498 ***	0.176 ***	0.502 ***	0.216 ***	0.503 ***	0.603 ***
	(0.003)	(0.063)	(0.002)	(0.071)	(0.003)	(0.073)
Controls	NO	YES	NO	YES	NO	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
N	3924	3924	2898	2898	2592	2592
R2	0.753	0.765	0.746	0.758	0.727	0.735

Note: *, **, and *** indicate significance at the level of 10%, 5%, and 1%, respectively.

, reveal that the coefficients for the LCCP remain significantly positive, regardless of whether control variables are included. These findings confirm the positive effect of the LCCP in promoting green land use efficiency, suggesting that this policy facilitates a transition towards more low-carbon, efficient, and sustainable land use patterns.

Next, the study assessed the effects of the CETP using a similar analytical framework. Samples of the LCCP were excluded, and only CETP and non-pilot cities were included in regression analysis. The regression results, presented in columns (3) and (4) of Table 9, indicate that the estimated coefficients for the CETP are also significant before and after the inclusion of control variables. These results demonstrate that the CETP positively impacts green land use efficiency.

Based on this analysis, it can be deduced that the “dual-pilot” policy likely has advantages over the “single-pilot” policy in enhancing green land utilization efficiency. The LCCP establishes clear development goals and pathway planning for cities, facilitating their low-carbon transformation in land use, while the CETP encourages enterprises to reduce carbon emissions through market mechanisms, indirectly promoting green land use efficiency. When implemented together, these two policies complement each other, leading to synergistic improvements in green land use efficiency.

To evaluate whether the “dual-pilot” policy outperforms the “single-pilot” policy in improving green land use efficiency, all sample data not related to LCCP or CETP were excluded. This approach ensured that the sample only included cities that had transitioned from a single-pilot to a dual-pilot approach, allowing for a clearer understanding of the unique impacts on green land use efficiency. Regression analyses were then conducted using the remaining data, with results presented in columns (5) and (6) of Table 9. The estimated coefficients of the differential variables on land green use efficiency are all significantly positive, aligning with expectations and confirming that upgrading from a single-pilot to a dual-pilot policy leads to a notable improvement in green land utilization efficiency. This result highlights the effectiveness of the “dual-pilot” policy over the single-pilot approach in promoting green land use efficiency. These findings align with the broader literature on policy synergies in environmental governance. Research consistently demonstrates that coordinated policy implementation yields greater environmental benefits than isolated interventions and produces more robust outcomes for sustainable development [52,53,54,87]. The superior performance of the dual-pilot approach can be attributed to the complementary nature of the LCCP and CETP policies, where the LCCP provides the strategic framework and development objectives, while the CETP introduces market incentives to accelerate implementation and resource optimization. This policy integration creates a more comprehensive governance system that addresses both the structural and behavioral dimensions of urban development, resulting in enhanced green land utilization efficiency compared to either policy implemented in isolation.

The dual-pilot policy combines the strengths of both the LCCP and CETP policy tools, creating a more comprehensive and powerful policy synergy. Such synergy can drive the economical and intensive utilization of land resources, while supporting the sustainable enhancement of the ecological environment. Building on earlier tests of the “single-pilot” policy’s enabling effects, a more holistic conclusion emerges: to fully capitalize on the CETP potential, it is essential to implement and align it with the LCCP. In other words, maximizing green land use efficiency relies on the coordinated and synergistic implementation of both policies.

Therefore, the “dual-pilot” policy, which combines the LCCP and CETP, represents the optimal policy combination for improving green land use efficiency, and is a crucial direction for developing future urban sustainable development strategies.

7.3. Spatial Spillover Effect

In the previous sections, this study employed the traditional DID approach to compare land green use efficiency between pilot and non-pilot cities, assuming that the establishment of a dual-pilot for low-carbon construction would not impact neighboring non-pilot cities. However, the influence of the policy may extend beyond the pilot areas, potentially affecting adjacent regions. To capture these spatial effects, this study adopts the Spatial Durbin Model with Difference-in-Differences (SDM-DID) to assess the broader spatial impact of the low-carbon construction dual-pilot policy.

7.3.1. Spatial Econometric Model

This study utilized several tests, including the LM test, Hausman test, Wald test, and LR test, to ensure the rationality and accuracy of the spatial econometric model. The results of these tests, presented in

Table 10. Spatial measurement model selection.

Model Test		Statistical Result	p-Value
LM test	Moran’s I	77.628	0.000
	LM_erorr	834.822	0.000
	Robust_LM_erorr	1046.124	0.000
	LM_lag	40.459	0.000
	Robust_LM_lag	251.662	0.000
Wald test	Wald_lag	108.65	0.000
	Wald_error	105.23	0.000
LR test	LR_lag	108.53	0.000
	LR_error	105.81	0.000
Hausman test	Fixed or random effects of SDM	88.27	0.000
LR test	Individual or mixed fixed effect SDM	99.94	0.000
	Time or mixed fixed effects of SDM	6440.5	0.000

, indicate that the model successfully passed all evaluations. Consequently, the double-fixed-double-difference Spatial Durbin Model was employed to examine the spatial correlation of the dual-pilot low-carbon construction policy.

7.3.2. Spatial Spillover Effects

The results in columns (1) to (3) of

Table 11. Spatial Durbin Model regression results.

Variables	Geographic Distance Matrix	Inverse Geographic Distance Square Matrix	Economic Geography Nested Matrix
	(1)	(2)	(3)
DID	0.057 ***	0.057 ***	0.052 ***
	(0.014)	(0.015)	(0.013)
W × DID	−0.107 *	−0.024	−0.125 ***
	(0.059)	(0.024)	(0.048)
rho	0.436 ***	0.014 ***	0.300 ***
	(0.087)	(0.003)	(0.085)
Sigma2_e	0.014 ***	0.245 ***	0.014 ***
	(0.003)	(0.031)	(0.000)
Controls	Control	Control	Control
Observations	5076	5076	5076
R2	0.010	0.040	0.014

Note: * and *** indicate significance at the level of 10% and 1%, respectively.

show that low-carbon building policies have a positive impact on the green space utilization efficiency of local land, which is consistent with the results of a previous study [88]. This further validates the policy’s effectiveness in enhancing land and green space utilization efficiency. However, the coefficients of spatial effects in each column are significantly negative, suggesting that the dual-pilot low-carbon construction policies create a “siphon effect” on the green space utilization efficiency of neighboring cities. This results in an uneven distribution of policy benefits across regions.

The “siphon effect” arises primarily from the unique advantages of dual-pilot low-carbon construction regions, which attract substantial external production factors through preferential policies, such as tax incentives, innovation subsidies, and advanced environmental infrastructure. This concentration of resources, including enterprises, capital, advanced technologies, and high-end talent, boosts the economic vitality and competitiveness of pilot regions. However, neighboring cities face increasing difficulties in acquiring key production factors, leading to a “resource depression” effect that limits their ability to improve green space utilization efficiency.

Additionally, the implementation of low-carbon pilot policies intensifies regional market segmentation and inter-regional competition. Pilot regions often adopt protective measures to maintain their competitive edge, restricting the outflow of resources or limiting market entry for external competitors. This “fortress mindset” further hinders neighboring cities’ access to resources and impedes their progress in improving green space utilization efficiency. Moreover, insufficient coordination mechanisms and information asymmetries exacerbate the siphon effect, preventing collaborative green development across regions.

To mitigate these negative spatial spillover effects, it is essential to incorporate spatial dimensions into the design and implementation of low-carbon construction policies. A coordinated approach will ensure that neighboring cities also benefit, fostering more balanced and equitable regional development [89].

7.3.3. Spatial Attenuation Boundary Analysis

To examine the influence of geographic distance on the spatial spillover effects of low-carbon policies, a geographic distance matrix based on latitude and longitude was constructed. The matrix was divided into multiple distance threshold intervals, with the initial base distance threshold set at 100 km. This threshold was incrementally increased by 100 km, up to a maximum of 1500 km. For each distance threshold, the corresponding spatial spillover coefficients and their p-values were recorded and used to construct Figure 8. The results reveal a distinct non-linear pattern in the spatial transmission of policy effects, which can be categorized into three critical spatial zones based on distance thresholds.

The first zone (100–600 km) exhibits a strong but diminishing positive influence, wherein the magnitude of spillover effects decreases significantly with distance, until approaching zero at approximately 600 km. This distance notably corresponds to typical provincial boundaries in China, suggesting that policy benefits primarily remain concentrated within provincial administrative regions. The second zone (600–1000 km) demonstrates a gradual transition phase, where spatial correlation coefficients continue to decline, but at a decelerated rate, indicating weakening yet persistent inter-provincial policy transmission. The third zone (beyond 1000 km) shows a counterintuitive upward trend in spatial correlation, potentially attributable to strengthened inter-regional economic integration and technological advancements in information dissemination that facilitate long-distance policy diffusion, despite geographical separation. This finding underscores the spatial limits of policy effects, and highlights the need for policymakers to consider geographic factors to achieve optimal policy distribution and effective transmission on a larger scale.

The second interval spans a medium-distance range of 600 to 1000 km. Here, the spatial correlation coefficient shows a gradual decline, signifying that the spatial influence of low-carbon policies extends beyond provincial boundaries, but weakens as the distance increases. This weakening may be attributed to several factors, including administrative barriers that create differences in policy implementation and effectiveness across provinces, as well as variations in economic development and resource endowments that affect the adaptability of low-carbon policies in different regions. Thus, the decline in the spatial correlation coefficient at this stage reflects the challenges and limitations faced by low-carbon policies when crossing provincial boundaries.

The final interval covers the long-distance range of 1000 to 1500 km. In this range, the spatial correlation coefficient begins to rise as the distance increases. This upward trend may stem from two key factors. First, the deepening of regional economic integration strengthens inter-provincial economic ties, making the transmission of low-carbon policy effects across provinces more efficient. Second, technological advancements and innovations in information dissemination reduce the cost and difficulty of transferring policy information, enhancing transparency, accessibility, and efficiency in cross-regional policy communication. These technological improvements are likely important drivers of the upward trend in the spatial correlation coefficient during this stage.

8. Conclusions and Implications

8.1. Conclusions

This paper presents a theoretical and empirical analysis of the impact of low-carbon construction policies on green land use efficiency. The “dual-pilot” policy, encompassing LCCP initiatives and the CTTP, is treated as a quasi-natural experiment. The study utilizes balanced panel data from Chinese prefecture-level cities from 2006 to 2023, and employs a multi-period Difference-in-Differences model to assess the effects of the dual-pilot low-carbon construction policy on green land use efficiency and its underlying mechanisms. Several key findings emerge from the analysis. First, the low-carbon construction policy significantly enhances the efficiency of green land utilization. Compared to single-pilot projects, the simultaneous implementation of both the “LCCP” and “CETP” proves more effective in improving green land use efficiency. Second, technological innovation and industrial agglomeration are identified as critical mechanisms driving improvements in green land utilization efficiency. Third, the policy’s impact on green land use efficiency is more pronounced in specific regions, including western areas of China, regions with high human capital, large cities, cities with strong environmental regulations, and non-resource cities. Finally, while the low-carbon construction policy enhances local land green use efficiency, it also induces a spatial siphoning effect on neighboring areas. The spatial decay boundary analysis empirically confirms that policy benefits diffuse unevenly across geographic space, with positive effects extending within a 100 to 600 km radius, but turning negative beyond this threshold. This finding provides robust evidence of a center–periphery dynamic in urban sustainability transitions, reflecting both the opportunities and challenges of implementing targeted environmental policies in highly heterogeneous urban systems.

The findings of this study directly support several key conclusions with significant implications for sustainable urban development policy. First, our empirical evidence demonstrates that policy integration—specifically the coordination of regulatory frameworks (LCCP) with market-based instruments (CETP)—creates synergistic effects that exceed the sum of individual policy impacts. This finding challenges conventional single-policy approaches, and suggests that multi-dimensional environmental governance strategies are more effective for addressing complex sustainability challenges. Second, our identification of specific transmission mechanisms (innovation and agglomeration) provides policymakers with concrete intervention pathways to enhance policy effectiveness. Finally, the documented spatial heterogeneity in policy outcomes underscores the necessity of place-based policy design that accounts for local contextual factors, rather than one-size-fits-all implementation approaches. These insights offer practical guidance for policymakers seeking to optimize the design and implementation of low-carbon initiatives across diverse urban contexts.

8.2. Policy Implications

Based on the above conclusions, this paper puts forward the following recommendations:

• Promoting the LCCP and CETP: Expanding LCCP initiatives should encompass not only developed regions, but also underdeveloped ones. Development paths must be tailored to regional industrial structures, resources, and development levels, ensuring the widespread adoption of the low-carbon concept across the country. In parallel, the carbon emissions trading market needs further development. The allocation mechanism for emission rights must be improved, using methods such as historical emissions and baseline approaches, to ensure fairness in distribution. Additionally, the introduction of financial derivatives, such as carbon futures and options, can enhance market function, optimize resource allocation, and further incentivize low-carbon development. Equally important is the establishment of a nationwide low-carbon development network. This network would break down regional barriers, enabling the cross-regional flow and optimal allocation of resources like talent, technology, and capital, thus fostering mutual benefits across regions. Synergy between LCCP construction and carbon emissions trading policies is also critical. The government must ensure alignment between these policies to prevent conflicts and ensure seamless integration. This alignment will drive low-carbon development more effectively, creating a cohesive and efficient framework. The government should establish dedicated research funds, encourage collaboration between industry and academia, and accelerate the transformation of research into practical applications. This will be key in driving the low-carbon transition in industries. Finally, strengthening the regulatory framework for carbon emissions trading is vital for the smooth operation of the market. By improving information disclosure, the government can enhance market transparency and fairness, ensuring that the low-carbon transition progresses efficiently and effectively.
• Incentivizing green technology innovation and application: The government should prioritize a clear, systematic, and forward-looking incentive policy to drive innovation in green technology. This framework should enhance the innovation capabilities of enterprises and research institutions. Targeted funding strategies must be implemented at different stages of green technology development. In the basic research stage, special funds should provide long-term investment guarantees. In the application development stage, funding should support technology optimization and feasibility verification, accelerating the transfer of research outcomes to practical use. In the achievement transformation stage, dedicated funds should support the industrialization of technologies, integrating green technologies into the market for both economic and environmental benefits. To address capital turnover issues in the commercialization phase, preferential loan policies should be introduced for green technology enterprises. Furthermore, the government and financial institutions must collaborate to establish a bridge for green financial services. This partnership will provide essential support to enterprises, helping them to overcome funding barriers and successfully promote technological innovations to the market, unlocking their economic and social value.
• Promoting industrial agglomeration and synergistic development: Targeted industrial support policies should be accelerated to foster the coordinated development of productive services and manufacturing industries. The synergy between these sectors can boost the added value, innovation capacity, and overall competitiveness of the manufacturing industry. A scientifically planned industrial layout will promote closer cooperation between upstream and downstream enterprises within the value chain. Enterprises should optimize resource allocation based on their strengths and collaborate in areas such as technology, information, and talent. Building a virtuous cycle within the industrial ecosystem requires cooperation to reduce production costs, improve efficiency, and enhance productivity. Efforts should also focus on encouraging the green transformation of the industrial chain by adopting clean production technologies and resource recycling methods. This will improve resource utilization and reduce environmental pollution. Enterprises should be encouraged to form long-term, stable strategic partnerships to tackle evolving market challenges. By integrating resources, strong synergies can be created, driving joint efforts in technology development and market expansion. This will enhance the green competitiveness of enterprises, ensure sustainable progress across the industrial chain, and lay a solid foundation for long-term economic stability.
• Focus on differentiated policy formulation and implementation: When formulating policies, governments must consider the diversity between cities and implement differentiated measures based on each city’s unique needs. For cities with low-to-medium-level human capital, investment in education and human resources is essential. This will enhance the city’s capacity to absorb and apply green and low-carbon technologies. Additionally, external professionals and intellectual resources should be brought in to support the city’s low-carbon transition. Small- and medium-sized cities require additional financial support and technical guidance to overcome their financial and technological constraints in low-carbon development. Special funds, low-interest loans, or tax incentives can help to reduce the costs of these transitions. For cities with weak environmental regulations, the focus should be on improving environmental laws and enforcement. A strong system of environmental protection regulations should be established, clearly defining the responsibilities of enterprises and individuals. Penalties for environmental violations should also be increased. Resource-dependent cities must develop rational industrial transformation plans. These plans should guide resource-based industries toward green and low-carbon alternatives. Furthermore, the development of high-tech industries should be encouraged to foster new economic growth areas and drive long-term sustainable development.
• Reducing the negative spatial effects of “factor siphoning”: The government should prioritize improving inter-connectivity between cities. This involves upgrading transportation infrastructure and establishing efficient networks, including highways, railways, and aviation, to promote economic integration. Addressing policy disparities, market fragmentation, and information asymmetry is crucial. This will create a favorable environment for the free flow of production factors, optimizing resource allocation and improving efficiency. For underdeveloped cities, exploring a unique development path offers an opportunity for economic catch-up. These cities should assess their resources, industrial foundations, and market demands, focusing on specialized industries and building competitive advantages. By capitalizing on policy-driven opportunities, these cities can invest in green industries, innovate land use models, and integrate green principles into development. This will improve land use efficiency, drive sustainable growth, and enhance economic quality.

8.3. Limitations

This study acknowledges several inherent limitations in examining the role of dual-pilot low-carbon construction policies in improving urban land green use efficiency. First, data limitations restrict the analysis to macro-level evaluations of cities, overlooking micro-level impacts that may provide key insights into the mechanisms of policy effects and their heterogeneity. Second, caution is warranted when interpreting the external validity of the findings; while this study confirms the positive impact of low-carbon construction policies on green land use efficiency in Chinese cities, these conclusions may not directly apply to other countries. Countries adopting similar policies should consider their own national conditions and policy environments. However, the analytical framework and empirical methods proposed in this study offer valuable tools for evaluating the effectiveness of local policies in other regions.