Impact of Smart Cities on Urban Resilience: The Roles of Land Green Utilization Efficiency and Industrial Structure Transformation

Abstract

Relying on information technologies such as the Internet, big data, and cloud computing, smart cities (SC) fully integrate urban resources, constantly strengthen the ability of urban economic systems, infrastructure systems, ecosystems, social systems, institutional systems, and other systems to withstand disaster disturbance and external risk shocks, and promote urban resilience (UR) construction. This study uses panel data from 254 prefecture-level cities in China from 2009 to 2021, and employs a multiperiod difference-in-differences method to examine the direct and heterogeneous effects of SC on UR. After a series of empirical tests, this study obtains the following results: (1) SC have a significant impact on the improvement of UR, which objectively demonstrates the reciprocity between SC and the level of UR construction, providing data support for promoting the in-depth practice of SC. (2) From the mechanism test of the impact of SC on UR, urban land green utilization efficiency and industrial structure transformation are intermediate mechanisms through which SC affect the improvement of UR. In addition, public environmental attention (PEA) has a positive regulatory effect on SC and UR, that is, PEA strengthens the role of SC in improving UR. (3) From the heterogeneity of urban characteristics that affect UR, SC have a more significant effect on improving UR in eastern cities and non-resource-based cities. This study provides new ideas for studying UR and provides useful insights for promoting SC construction and enhancing UR. This study proposes that the government should continue to promote the intelligent construction of Chinese cities, advance the industrial structure and improve the green land utilization efficiency, and strengthen their positive impact on UR.

1. Introduction

The current global economic recovery is weak, affecting the stable development of urban economy and society. For example, the credit crunch and housing price decline caused by the 2008 financial crisis in the United States have affected the lives of urban residents. In 2018, the trade friction between China and the United States increased tariffs and implemented technological blockades on high-end manufacturing industries in some regions and cities of China, affecting the development process of cities. In 2020, the global spread of COVID-19 impacted the urban labor market, causing the economic growth to slow down or even decline [1]. From this information, urban development is facing increasingly complex and changing risks and challenges, from natural disasters and sudden public events to socioeconomic fluctuations, and the uncertain impacts on cities are becoming considerably frequent. Traditionally, cities have responded to these crises and challenges by strengthening emergency plans and risk management, but this reactive governance model is insufficient to meet the complex and ever-changing urban challenges.

Faced with the impact of uncertain external factors, traditional thinking strategies are no longer able to meet the needs of sustainable urban development, and enhancing urban resilience (UR) has become a consensus in global urban construction [2,3]. In 2002, the United Nations Sustainable Development Summit first introduced the concept of resilience into the field of urban planning, sparking a global wave of resilient city construction and practice. In 2020, China officially upgraded the construction of UR to a national development strategy, clarifying the important role of resilience in urban development. UR refers to the ability of a city to maintain its basic functions, adapt to changes, and recover quickly in the face of crises or challenges. UR is of great significance for residents’ lives and safety, urban economic stability and sustainable development, urban ecosystem stability, and many other aspects [4].

The development of the digital economy is changing human society. The COVID-19 has further accelerated the pace of the digital era. Using digital technology to strengthen urban construction has become the new pursuit of many cities [5]. China’s 14th Five-Year Plan clarifies that strengthening the construction of digital government and digital society and enhancing the level of intelligence in urban services and social governance are inevitable choices for achieving modernization of social governance. The construction of smart cities (SC), as a bridge and link between digital government and intelligent society, has also become a key force in promoting the modernization of the national governance system and governance capacity [6]. To promote the smart transformation of cities, the Chinese government implemented a smart cities pilot (SCP) policy in 2012. The purpose of the SCP is to improve urban operational efficiency and enhance public service levels through the application of intelligent computing technologies such as the cloud computing, big data, and spatial geographic information integration in the fields of urban planning, design, construction, management, and operation. The SCP policy has attracted widespread attention and application to the construction of SC. The construction of SC utilizes advanced technological means to enhance the emergency response, disaster prevention, and management capabilities of cities, enabling them to effectively respond to emergencies and quickly restore normal order [7]. Based on such technologies as the Internet, big data, and cloud computing, SC can improve urban social, ecological, engineering, and organizational subsystems to achieve connectivity and information sharing through the establishment of urban information system platforms to improve efficiency and optimize urban resource allocation and UR. Since the first batch of SCP projects in 2012, China has gone through a relatively long practical process. Hence, can the construction of SC enhance UR? What transmission mechanism does the construction of SC use to affect UR? Under the constraint of heterogeneity in urban characteristics, will the impact of SC construction on UR show differences? However, exploration on the impact and transmission mechanism of China’s SC on UR is relatively lacking. On this basis, this study takes the issuance of the SCP policy as a quasi-natural experiment and uses the difference-in-difference (DID) method to identify the impact of SC on UR and the potential mechanism.

The potential marginal contribution of this study lies in the following aspects:

By clarifying the logical relationship between SC construction and UR, this study deeply explores the channel mechanism of SC construction in enhancing UR and explains the ways by which SC construction improves UR from two aspects: urban land green utilization efficiency (LGUE) and industrial structure transformation (ISU).

This study conducts heterogeneity analysis on the policy effects of improving UR through the construction of SC, revealing significant differences in the policy effects of cities in terms of geographical location, digital economy level, and human capital level. At the same time, reasonable explanations are provided for the analysis results which help enrich the theoretical explanation of the differential performance of UR in different types of cities and provide inspiration for personalized formulation of SC-related policies.

This study systematically evaluates the moderating effect of public environmental attention (PEA) within the same analytical framework. The regulatory effects of PEA can offer a new perspective for a comprehensive understanding of how SC contribute to improving UR.

This paper is structured into six parts. Following the introduction, Section 2 presents the literature review. Section 3 describes the research hypothesis. Section 4 outlines the model setting and variable explanation. Section 5 analyses and discusses the model results. Finally, Section 6 summarizes the conclusions and policy implications.

2. Literature Review

Based on the research topic of this paper, we conducted a literature review on the definition and influencing factors of UR, as well as the connotation of SC and its environmental and economic effects, in order to illustrate the theoretical basis of this study.

2.1. Research on Urban Resilience

The concept of UR originates from resilience, and given the complexity and intersectionality of resilience, different disciplines define the concept of resilience from their own research fields. Holin (1973) first introduced resilience into ecology, stating that resilience determines the persistence of relationships within ecosystems [8]. With the continuous deepening of scholars’ research on resilience, the concept of resilience has been extended to various disciplines, such as psychology, sociology, and economics. After continuous evolution and revision, three main definitions of resilience, namely, engineering resilience, ecological resilience, and socioecological resilience, currently exist. Among them, engineering resilience focuses on the field of engineering, representing the ability of a system to recover or maintain its original equilibrium state under external impacts [9], and its internal structure has not undergone substantial changes, presenting a single characteristic. Ecological resilience has revised the view of engineering resilience as a single equilibrium, emphasizing the ability of a system to undergo structural changes and enter a new equilibrium state after being subjected to pressure [10,11]. Socioecological resilience, also known as evolutionary resilience, no longer pursues the original equilibrium state. It is the dynamic adaptability exhibited by complex social ecological systems under external pressures [12].

Owing to differences in the concept of UR, multiple studies have proposed different research frameworks based on different UR concept to find feasible paths to enhance UR. Currently, the academic community mainly focuses on three perspectives: disaster risk, urban planning, and complex adaptive systems. First, the theoretical framework from the perspective of disaster risk mainly emphasizes the redundancy, effectiveness, and other property characteristics exhibited by cities in the face of risks. The representative local disaster resilience framework proposed by Cutter (2008) centers on natural disasters and regards UR as a continuous process of risk response capability [13]. Second, the theoretical framework from the perspective of urban planning mainly explores the solutions to urban prevention, regulation, and planning in response to risks. For example, Jabareen (2013) build resilient city planning framework, and analyzes the transformation path of resilient cities from vulnerability, urban governance, urban prevention, and other aspects [14]. Desouza (2012) constructed a resilient urban construction path around the management, design, and planning behaviors of cities [15]. Third, the theoretical framework from the perspective of complex adaptive systems emphasizes the dynamic evolution process of resilient city construction and focuses on the response strategies formulated by cities at different stages of development. Nystrom et al. (2011) studied the relationship between human and environmental resilience on the basis of feedback loop system theory and summarized the characteristics of urban sustainable development process [16].

Various methods for measuring UR, such as function modeling, social network analysis, maturity model, and evaluation index system method, which are becoming increasingly sophisticated, can be utilized. Scholars use the function modeling method, based on the theoretical framework and conceptual connotation of UR, to calculate the size of UR by constructing corresponding resilience models. The adopt the social network analysis method and digital information technology to analyze the relationships between nodes in different urban networks to summarize the evolutionary characteristics of urban network resilience [17,18]. Josune et al. (2019) found that urban development processes face complex external environments and are easily influenced by a series of factors [19]. Therefore, a resilience maturity model was constructed to provide a roadmap for overall urban planning and help cities accurately evaluate the resilience level in the mature stage. This model has also been recognized by some scholars, such as Raquel et al. (2017) [20]. With the continuous improvement of UR measurement methods, numerous scholars are using the comprehensive indicator system method to measure the level of UR on the basis of the basic components of urban society, economy, ecology, infrastructure, and other core indicators. The current exploration of factors influencing UR mainly focuses on the socioeconomic level. Scholars believe that in the process of building resilient cities, human factors such as population development, economic capital, social security, trade development, and policy intervention have a significant impact on the cities’ early warning response ability, postdisaster recovery ability, and learning ability, promoting sustainable and healthy urban development. As Dawley (2010) suggests, government policy intervention and institutional management have a significant impact on the construction of resilient cities [21]. In addition, building a good ecological environment provides possibilities for constructing livable cities, and environmental factors also play an important role in promoting UR. For example, Staddon et al. (2018) believe that the ecological environment affects UR by providing various ecological services for urban ecological processes [22].

2.2. Research on Smart Cities

SC belong to an advanced stage of urban development, utilizing the empowering technology of urban Internet of Things to open up a new model of information technology development [23]. The purpose of SC is to apply advanced technologies, such as the Internet of Things, to provide sustainable, efficient, and intelligent solutions for environmental protection, traffic management, and quality of life for residents and to safeguard the smooth operation of cities. The focus of modern SC is on how to efficiently utilize energy and address related challenges to meet the high demand of rapid urbanization [3,24]. The development and evolution of SC are accompanied with technological advancements, and, currently, cities urgently need to address efficiency and sustainability issues [25]. The concept of SC has been regarded as helpful in solving the dilemma of urban sustainable development since its inception and has quickly become a global focus, receiving considerable attention [26]. Solano et al. (2017) found that government environmental management, entrepreneurial enthusiasm, and citizen participation are the core factors for SC to promote sustainable urban development through a case study of three cities in Spain [27]. Yan et al. (2023) found that SC construction promotes urban green development through green technology innovation [28]. Okafor et al. (2023) found that emerging countries aimed at developing SC construction do not place much emphasis on the importance of SC in promoting social equity [29]. Alizadeh and Sharifi (2023) also found that in the construction of SC, policy makers overly focus on technological elements at the expense of social justice and democratic values [30]. The policy of SCP construction is an important policy in the Chinese government’s urban planning [31]. At present, China’s urban planning is actively learning from the new model of SC, which can be seen everywhere regardless of scale or administrative level. SC aim to solve urban development problems, and their specific expectations also include low-carbon and green aspects [7,24].

Scholars have empirically evaluated the economic and environmental benefits of SC construction on the basis of China’s SCP policy as a quasi-natural experiment. The construction of SC can help solve urban environmental problems, improve infrastructure construction to enhance public service levels [32], and promote local urban economic development [33]. Gu and Wang (2012) believed that the construction of SC can help achieve joint innovation in technology and finance, achieve a strong balance between the government and the market, and increase people’s attention to technology and legal norms [34]. Drawing lessons from the continuous promotion of SC construction is of great benefit to the future modernization development of Chinese cities. Wang and Deng (2022) believed that the implementation of SCP can strengthen the transformation of new urban infrastructure, and upgrading some data-driven and intelligent platform facilities can help enhance the innovation capabilities of enterprises [35]. Unlike the positive impact of SC analyzed by scholars above, some scholars have also studied the drawbacks of SC construction. For example, Hollands (2008) found some serious urban problems hidden in the booming SC construction, such as unequal public services, deepening urban aristocracy, and conflicts between sustainable development and urban economic growth [36].

SC can impact regional industrial development and urban land use. Zhang et al. (2023) found, based on data samples from prefecture-level cities, that the policy of SC construction can promote the upgrading of local legal industry structure [37]. Tang et al. (2023) determined that SCP construction can lead to the occurrence of industrial agglomeration [38]. Because most SCP can build industrial parks, as an integrated space that gathers technological resources and innovative talents, on the one hand, it provides a good innovation environment for technology-based enterprises in the industrial park; on the other hand, the technological innovation of enterprises in the industrial park can promote the development of SC. Zeng et al. (2023) believed that the use of new information technologies, such as big data and cloud computing, in SCP can help integrate resources and provide efficient services to enterprises and consumers in the future [39]. This advancement greatly helps enterprises improve business process efficiency, thereby enabling them to allocate considerable time and energy on enhancing core competitiveness and technological innovation. Innovation is a key factor affecting urban LGUE, and most literature has found that technological innovation can significantly enhance urban LGUE. SC construction plays an important driving role in the relationship between innovation and LGUE. Literature on the impact of SC on UR is limited, and the mechanism by which SC affect UR remains unclear. Therefore, this study investigates the impact mechanism of SC construction on UR from two perspectives: LGUE and ISU.

The above literature has provided useful discussions on SC and UR, which provides a theoretical basis for our study of the relationship between SC and UR. The existing research has not discussed the relationship between SC and UR from a holistic and comprehensive perspective, and the theoretical analysis of the mechanism of action between the two is not detailed enough. The sample range used in literature on SC and UR is mostly European cities, and there is a lack of empirical testing of the causal effects between SC and UR using econometric methods. In addition, there is still insufficient empirical evidence at the urban level in China. Therefore, this paper focuses on the impact of SC on UR, and on the basis of constructing a theoretical framework for mechanism analysis, conducts empirical research using panel data at the urban level, striving to make up for the shortcomings of the existing literature.

3. Research Hypothesis

Based on the literature review in the previous section, we propose corresponding theoretical hypotheses starting from the direct impact effect of SC on UR and the indirect impact effect of SC on UR.

3.1. Direct Impact of Smart Cities on Urban Resilience

SC are a new development model and important policy tool in China’s urban transformation. SC promote the green and efficient utilization of urban land and the healthy development of urban economy, society, and ecological environment. The level of SC construction is closely related to the technological innovation capability and the industrial upgrading effect driven by the information technology industry. The construction of SC is guided by the practice of updating the driving mechanism of urban economic development, improving the level of urban information infrastructure, enhancing the utilization of urban resources and environmental protection, and optimizing the governance capacity in the field of public services.

In the construction process of SC, the widespread application of digital technology and the comprehensive penetration of data elements can enhance the intelligence, interdependence, and redundancy of cities in the face of external risk shocks [40], thereby comprehensively improving UR. The widespread application of digital information technology has given rise to new industries, models, and services, promoting the overall restructuring and transformation of urban systems, thus absorbing and adapting to external risk shocks, and pushing the improvement of UR [41]. With the high penetration of the digital economy, data elements are deeply integrated and developed with the cities’ economic, ecological, governance, infrastructure, and other social material systems. Strengthening the comprehensive construction of urban systems helps cities resist external risks and promote resilient urban construction. The transformation of urban intelligence can empower the government with low-carbon governance, citizens with green and low-carbon lifestyles, and enterprises with low-carbon transformation [7]. At the same time, SC can apply environmental protection technologies, such as solid waste treatment and pollution emission monitoring, to urban ecosystems, reducing resource consumption and pollution emissions, alleviating the impact of high energy consumption and high pollution on urban systems, and thereby enhancing UR. SC can also promote the digitization of public service facilities, apply AI technology to public service facilities that are related to residents’ daily lives, such as water supply, power supply, and transportation, strengthen the construction of urban living systems, and thus enhance UR. On this basis, this study proposes research hypothesis H1.

Hypothesis H1. 

SC have a positive impact on enhancing UR.

3.2. Indirect Impact of Smart Cities on Urban Resilience

3.2.1. Smart Cities Affect Urban Resilience Through Industrial Structure Transformation

The construction of SC is considered an important driving force for ISU. First, the construction of SC utilizes the advantages of high penetration of information technology and increasing returns to scale to accelerate the integration of information technology and traditional industries, which helps improve the digitalization, intelligence, and informatization level of traditional industries and promote their transformation and upgrading [42]. Second, the construction of SC can optimize the proportional relationship between various industries, guide the gradual evolution of industrial structure from being labor intensive to being knowledge and technology intensive, force enterprises with low production efficiency and extensive production methods to shut down or transfer, compress their market share, and raise market access barriers [43]. Lastly, the construction of SC can stimulate emerging industries and drive the development of productive service industries, optimize the redistribution of production factors among industries, enterprises, and regions, guide the flow of production factors into high-efficiency sectors, further improve regional labor productivity, and promote ISU.

In fact, the industrial structure characteristics of a region, to some extent, determine the distribution pattern of energy consumption and whether the region can meet the objective requirements of sustainable development. in the process of ISU, green production factors, such as green technology and concepts, will gradually be embedded in various links of manufacturing production, transforming the factor input structure and business model of enterprises, which is conducive to reducing resource input intensity and pollution emission intensity [44]. In the practice of urban development, industrial structure is an important carrier for resource allocation and economic growth, determining the development mode of regional economy. ISU is not only the core content of optimizing urban economic structure but also an important mechanism for improving urban development resilience. Specifically, regions with overly single industrial structures or regions that excessively rely on local supply chain-related industries may experience longer periods of turbulence and higher costs in the process of restoring normal operations after external shocks. On the contrary, a rationalized, advanced, and diversified industrial structure can fully play its role as a stabilizer when the economy is subjected to external shocks, effectively alleviate economic waves, diversify risks, and reduce uncertainty, which is an important manifestation of regional resilience [45]. Accordingly, this study proposes research hypothesis H2.

Hypothesis H2. 

SC are conducive to promoting ISU, thereby enhancing UR.

3.2.2. Smart Cities Affect Urban Resilience Through Land Green Utilization Efficiency

SC establish connection channels between diverse subsystems, such as urban environment, energy, transportation, healthcare, and information, in their unique way, enabling real-time, efficient, and precise allocation of various urban resources and forming a comprehensive innovation effect of technological innovation, product innovation, and organizational innovation. SC have opened up a new era of urban sustainability innovation, endowing urban innovation with openness, accessibility, and synergy, thereby changing the development model of modern cities. Land resource management will benefit from their development dividends.

The improvement of urban technological innovation capabilities will bring high added value and high output efficiency of the same land resources, achieve intensive and efficient utilization of urban resources, reduce urban environmental pollution [46], and improve the LGUE in cities. In addition, SC have achieved the updating and upgrading of urban management methods and techniques through the comprehensive application of core information technologies, such as big data analysis, AI, and 5G, and intelligent communication terminal devices and integrated methods, such as information network platform systems, audio and video, and multimedia [47]. This advantage has been widely applied in the planning and management of land resources in the new era, bringing new development models for the implementation of national spatial planning and urban land intensive use [48]. This application is conducive to reducing land resource waste and ecological environment damage caused by improper use, improving the comprehensive output efficiency of land, and promoting the development of urban LGUE.

LGUE is determined by the degree of rational use and optimized allocation of land, which in turn determines UR pattern and its evolution. The higher the urban LGUE, the more reasonable its land use structure, and the corresponding urban functions and layout are more complete. This condition is conducive to the efficient operation of urban public transportation, communication, emergency, and other infrastructure and enhances the resilience of urban infrastructure [49]. In addition, improving the LGUE in cities can enhance urban sprawl and disorderly expansion, strengthen urban layout, force urban organizational management and network optimization, and boost the resilience of urban organizational management [50]. As the LGUE steadily improves, the economic, social, and ecological benefits of urban land become increasingly significant, providing a good economic, social, and ecological guarantee for UR construction. On this basis, this study proposes research hypothesis H3.

Hypothesis H3. 

SC are beneficial for improving LGUE, thereby enhancing UR.

4. Research Design

4.1. Model

The Chinese government issued a notice at the 2012 on carrying out comprehensive pilot programs for SC. The existing literature takes SC pilot policy as the starting point and uses the DID method to evaluate the economic benefits of SC [31,32]. The principle of DID is to simulate experimental research design using observational data. Its basic idea is to divide the survey sample into two groups: one group is the experimental group influenced by the policy, and the other group is the control group not influenced by the policy. Firstly, calculate the change in a certain indicator of the experimental group before and after the policy, then calculate the change in the same indicator of the control group before and after the policy, and finally calculate the difference between the two variables to reflect the net impact of the policy.

This provides methodological support for our research on the relationship between SC and UR. The establishment of comprehensive pilot programs for SC may result in differences in UR levels between cities within and outside the pilot scope at the same time point, as well as differences in UR levels within the same pilot city at different time spans. Based on this, the construction of SC within the pilot can be seen as a quasi-natural experiment. Based on the implementation time of SC policy and the availability of data, this paper takes 254 prefecture-level cities in China from 2009 to 2021 as the research sample. The data in this study are sourced from various city statistical bureaus and government websites, as well as from the China Urban Statistical Yearbook. This study uses the DID method to construct an evaluation model for SC to promote urban UR:

$${UR}_{it}={\alpha }_0+{\alpha }_1{SC}_{it}+{\alpha }_2{CV}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(1)

In Equation (1), i represents the city, t represents the year. The variable UR_it represents the UR level of city i in year t. The variable SC_it represents whether city i established a SCP policy in year t. If city i established a SCP experimental zone in year t, the value is 1, otherwise it is 0. CV_it represents the control variables (CV) at the city level. μ and γ represents the fixed effect of the time and city, respectively. ε is the random error term. ${\alpha }_0$ is the constant term coefficient. The coefficient ${\alpha }_1$ which we focus on can reflect the degree of impact of SC on urban UR. If ${\alpha }_1$ is significantly positive, it indicates that SC can enhance the UR level of the city.

4.2. Variable Selection

UR: The main purpose of this study is to examine the impact of SC on UR, so we need to determine the measurement indicator for UR. UR represents the ability of urban economic systems to withstand external shocks and recover from them [51]. Referring to Xu et al. (2025) [52], we constructed a comprehensive evaluation index system for urban resilience from five dimensions: economic resilience, social resilience, ecological resilience, infrastructure resilience, and organizational resilience, and calculated it using the entropy weight method. Entropy weight method is an objective weighting method based on information entropy theory, widely used in multi index comprehensive evaluation scenarios [52,53]. The core idea is to allocate weights to each indicator by measuring the degree of difference between samples. The specific evaluation indicators are shown in Table 1 below. This study first uses the entropy weight method to calculate the weight of each third-class indicator, and then standardizes each third-class indicator. Finally, we multiply the standardized variable values by the corresponding weights and add them together to obtain the UR of the cities.

Table 1. Urban resilience measurement index system.

First-Class	Second-Class Indicator	Third-Class Indicator
Urban Resilience	Urban economic resilience	Per capita GDP, the total retail sales of consumer goods, the general public budget revenues, the balance of deposits and loans of financial institutions
	Urban social resilience	Population density, per capita disposable income, number of college students, proportion of social security and employment expenditure to fiscal expenditure, urban unemployment rate
	Urban ecological resilience	Comprehensive utilization rate of general industrial solid waste, per capita green space in the park, rate of harmless treatment of domestic garbage, green coverage rate of built-up areas, industrial wastewater discharge, industrial SO2 emissions
	Urban infrastructure resilience	The number of Internet broadband users, the number of mobile phone users, road freight volume, drainage pipe density, per capita road area

SC: We use the SCP policy, a quasi-natural experiment, as a proxy variable for SC. This paper uses whether city i established SCP experimental zone in year t to represent SC. If city i established SCP experimental zone in year t, the value is 1, otherwise it is 0. CV: Considering that other urban characteristic factors may have an impact on UR, drawing on relevant research [54,55], this study controls for the following variables: economic development (EC), opening up (OU), R&D expenditure (RD), financial development (FIN), government intervention (GIN). Their calculation methods are shown in Table 2.

Table 1. Urban resilience measurement index system.

First-Class	Second-Class Indicator	Third-Class Indicator
Urban Resilience	Urban economic resilience	Per capita GDP, the total retail sales of consumer goods, the general public budget revenues, the balance of deposits and loans of financial institutions
	Urban social resilience	Population density, per capita disposable income, number of college students, proportion of social security and employment expenditure to fiscal expenditure, urban unemployment rate
	Urban ecological resilience	Comprehensive utilization rate of general industrial solid waste, per capita green space in the park, rate of harmless treatment of domestic garbage, green coverage rate of built-up areas, industrial wastewater discharge, industrial SO2 emissions
	Urban infrastructure resilience	The number of Internet broadband users, the number of mobile phone users, road freight volume, drainage pipe density, per capita road area

SC: We use the SCP policy, a quasi-natural experiment, as a proxy variable for SC. This paper uses whether city i established SCP experimental zone in year t to represent SC. If city i established SCP experimental zone in year t, the value is 1, otherwise it is 0. CV: Considering that other urban characteristic factors may have an impact on UR, drawing on relevant research [54,55], this study controls for the following variables: economic development (EC), opening up (OU), R&D expenditure (RD), financial development (FIN), government intervention (GIN). Their calculation methods are shown in Table 2.

Table 2. Descriptive statistics.

Variable	Obs	Definition	Mean	SD
UR	3302	UR index based on entropy method	0.081	0.075
EC	3302	The logarithm of per capita GDP	10.522	0.485
OU	3302	The logarithm of the proportion of foreign direct investment to GDP	−5.237	0.958
RD	3302	The ratio of scientific expenditure to general fiscal budget expenditure.	0.026	0.019
FD	3302	The ratio of the balance of deposits and loans of financial institutions to GDP	3.431	1.632
GI	3302	The general public budget expenditure to GDP	0.173	0.977

Table 2. Descriptive statistics.

Variable	Obs	Definition	Mean	SD
UR	3302	UR index based on entropy method	0.081	0.075
EC	3302	The logarithm of per capita GDP	10.522	0.485
OU	3302	The logarithm of the proportion of foreign direct investment to GDP	−5.237	0.958
RD	3302	The ratio of scientific expenditure to general fiscal budget expenditure.	0.026	0.019
FD	3302	The ratio of the balance of deposits and loans of financial institutions to GDP	3.431	1.632
GI	3302	The general public budget expenditure to GDP	0.173	0.977

5. Results and Discussion

5.1. Benchmark Regression

Table 3. Benchmark results.

	(1)	(2)	(3)
	UR	UR	UR
SC	0.136 ***	0.11 ***	0.121 ***
	(0.0245)	(0.0334)	(0.0302)
CV	N	Y	Y
City	N	Y	Y
Year	N	N	Y
R2	0.653	0.488	0.665
Obs	3302	3302	3302

Note: *** represents significance level at 1%, with robust stand errors in parentheses.

reports the empirical results of the impact of SC on the UR, regardless of the inclusion of control variables and fixed effects. The SC coefficient is significantly positive, which to some extent indicates that the SCP policy has a significant enhancement effect on the regional UR. Hypothesis H1 of this paper has been verified. This conclusion is consistent with the views of the existing literature and affirms the positive role of SC construction. For example, the implementation of pilot for SC can promote urban innovation, reduce urban pollution, and facilitate digital transformation of enterprises [24,31].

5.2. Robustness Tests

During the execution of quasi-natural experiment, there may be issues with the self-selection effect, where the selection of pilot cities for EGP is not random, but rather freely chosen by the state to become pilot cities. However, the selection of SCP pilot cities is not directly related to the urban LGUE situation, so it basically meets the problem of policy exogeneity. But in order to further verify the stability and reliability of the results, we adopt the following methods to test the results.

5.2.1. Parallel Trend

The parallel trend assumption is a prerequisite for the correct use of the DID model, which is mainly used to identify that the development trend of UR in all cities is consistent before policy shocks occur, indicating that non-systematic time trend differences between cities before being selected as SC pilot cities may not affect policy effectiveness. Based on the event study method, we conducted a parallel trend test, and the results are shown in Figure 1. The estimated coefficients for each period before the establishment of the SC policy are not significant, which indicates the effectiveness of the DID model used in this paper.

5.2.2. Placebo Test

This paper draws on the approach of La Ferrara et al. (2012) to make the selection of EGP pilot cities random, and then repeats this random process 500 times [56]. Such random processing does not have an impact on the corresponding LGUE. From Figure 2, it can be observed that the coefficients are concentrated around zero in all 500 random processes, which proves that other unobserved random factors in the city have almost no impact on the estimation results, and the previous estimation results are robust.

5.2.3. Propensity Score Matching

In the previous analysis of the impact of SC policy on UR, it was assumed that the government’s selection of pilot cities was random. However, in practice, the government pays more attention to the economic development foundation of these cities when determining the list of SC pilot cities. Therefore, to further reduce estimation bias, this paper is subjected to robustness testing using the PSM-DID method. This method can minimize the differences in features between cities as much as possible, thereby making the evaluation results more robust. The regression result of PSM-DID is shown in the first column of

Table 4. Robustness test results.

	(1)	(2)	(3)
	UR	UR	UR
SC	0.119 ***	0.053 ***	0.114 *
	(0.041)	(0.017)	(0.063)
CV	Y	Y	Y
City	Y	Y	Y
Year	Y	Y	Y
R2	0.555	0.613	0.663
Obs	2860	3302	3302

Note: *, *** represents significance level at 10%, 1% respectively, with robust stand errors in parentheses.

, and the coefficient of SC is significantly positive, which verifies the robustness of our results.

5.2.4. Eliminate Interference from Environmental Policy

During the analysis period of this paper, the Chinese government implemented a low-carbon city pilot (LCCP) policy in 2011. This LCCP promotes enterprises to increase their investment in green technology R&D, reduce pollution emissions during the production process, and thus contribute to improving technological innovation through administrative orders and intense market competition. Technological innovation has a significant positive driving effect on enhancing UR. Therefore, the policy effects of LCCP may affect the estimation results of this paper and need to be excluded. We add a dummy variable of LCCP to the benchmark regression model to eliminate the interference of LCCP policy. Specifically, if city i implements a LCCP policy in year t, the corresponding dummy variable is assigned a value of 1, otherwise it is assigned a value of 0. The regression result is shown in the second column of Table 4. The coefficient of SC is still significant, and the estimated result is similar to the baseline regression results, indicating that the core result is still robust after excluding the influence of relevant policy.

5.2.5. Using Clustering Robust Standard Error

Considering that cities within the same province may exhibit similarities in UR, as well as differences in UR between cities in different provinces, this paper uses robust clustering standard errors at the provincial level to regress. The regression result is shown in the third column of Table 4, indicating that even with the use of province level clustering robust standard errors, the regression coefficient of SC remains significant.

5.3. Mechanism Analysis

By examining the impact of SC on UR, it is concluded that SC has a positive effect on improving the UR. So what is the reason for this positive effect? Based on the theoretical analysis in the previous text, this paper examines the mechanism of SC from the perspective of LGUE and ISU. We construct the following mediation effect model for testing:

$${MV}_{it}={\beta }_0+{\beta }_1{SC}_{it}+{\beta }_2{CV}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(2)

The MV refers mechanism variable, representing ISU and LGUE, respectively. We measured ISU using the ratio of the output value of the tertiary industry to that of the secondary industry [57]. The definition of LGUE refers to the process of utilizing land resources to generate maximum benefits at the lowest land use cost while minimizing negative impacts on the environment. Scholars have conducted extensive research on the indicator system and measurement methods of LGUE. Referring to existing studies [57,58], we constructed an indicator system from three dimensions: input, expected output, and unexpected output, and measured it using a super efficiency SBM model that included unexpected output. The estimated results of the mechanism analysis in

Table 5. Mediation effect.

	(1)	(2)	(3)	(4)
	ISU	ISU	LGUE	LGUE
SC	0.288 ***	0.117 ***	0.089 *	0.101 *
	(0.072)	(0.038)	(0.048)	(0.056)
CV	N	Y	N	Y
City	Y	Y	Y	Y
Year	Y	Y	Y	Y
R2	0.321	0.767	0.417	0.632
Obs	3302	3302	3302	3302

Note: *, *** represents significance level at 10%, 1% respectively, with robust stand errors in parentheses.

show that SC can improve the levels of ISU and LGUE in cities. Existing research has demonstrated that ISU and LGUE can significantly enhance UR. Therefore, SC can improve urban UR through two channels: ISU and LGUE.

5.4. Moderating Effect of Public Environmental Attention

As a supplement to government environmental regulations, how does public concern and participation in the environment affect local government governance behavior? We constructed a moderation effect model to test the moderation effect of PEA.

$${UR}_{it}={\rho }_0+{\rho }_1{SC}_{it}+{\rho }_2{PEA}_{it}+{\rho }_3{(PEA\ast SC}_{it})+{\rho }_4{CV}_{it}+{\mu }_i+{\gamma }_t+{\epsilon }_{it},$$

(3)

The PEA is public environmental attention. PEA is an important manifestation of the public’s preference and participation in environmental governance, reflecting their environmental behavior. Drawing on the research ideas of Zheng et al. (2012) [59], this paper uses the Baidu Haze Search Index to characterize PEA. The larger the index, the higher the public’s attention to haze control.

Table 6. Moderating effect.

	(1)	(2)
	UR	UR
SC * PEA	0.0338 ***	0.0276 **
	(0.0130)	(0.0129)
CV	N	Y
City	Y	Y
Year	Y	Y
R2	0.651	0.667
Obs	3302	3302

Note: **, *** represents significance level at 5%, 1% respectively, with robust stand errors in parentheses.

reports the moderating effect of PEA, and regardless of whether CV are included, the coefficients of PEA are significantly positive. This indicates that PEA has a regulatory effect, that is, it amplifies the enhancement effect of SC on UR.

Based on the complexity of environmental governance in China, public participation influences government environmental governance behavior [60]. With the development of AI technology, the public can pay attention to environmental information through the Internet and public service platforms. SC helps the public express their opinions and comments on environmental governance issues in regions, and thereby reflect their demands for environmental quality improvement to the government. The government’s environmental governance behavior may be influenced by local public opinions; the government will respond accordingly and make environmental policies meet public preferences. Therefore, to respond to the public’s demands for the environment and to assume social responsibility toward the public, the government will reduce urban land pollution by increasing environmental governance. In addition, public satisfaction is an important indicator for evaluating the performance of local governments. The development of SC promotes public demand, participation, and supervision of the environment, and forces higher-level governments to incentivize and supervise lower level governments’ environmental governance policies, which helps improve local land environmental quality [61]. A stronger PEA facilitates the use of digital technology to influence local governments’ environmental governance behavior and thereby reduces land pollution. Therefore, the public’s attention and participation in environmental governance can amplify the improvement effect of SC on urban UR.

5.5. Heterogeneity Analysis

5.5.1. Geographic Location

Geographical differences have profound, complex effects on economic culture, natural resources, and industrial layout. This paper explores the influence of SC development on regional heterogeneity of UR from the perspective of geographical differences. The sample is divided into the eastern region and the mid-west region, and regression analysis is conducted separately, as shown in

Table 7. Heterogeneity analysis.

	(1)	(2)	(3)	(4)
	Mid-west region	Eastern region	Resource-based	No-resource-based
	UR	UR	UR	UR
SC	0.084 *	0.161 ***	0.103 ***	0.149 ***
	(0.0466)	(0.0503)	(0.0360)	(0.0425)
CV	Y	Y	Y	Y
City	Y	Y	Y	Y
Year	Y	Y	Y	Y
R2	0.601	0.615	0.654	0.662
Obs	2067	1235	1365	1937

Note: *, *** represents significance level at 10%, 1% respectively, with robust stand errors in parentheses.

. The absolute value of the SC coefficient in the sample of the eastern region is considerably higher than that in the mid-west region, and indicates that the role of SC in improving UR is reflected in the eastern region. The development of SC not only relies on policy support in the later stage but also on the economic development foundation of the city itself in the early stage. The SCP pilot cities emphasize the application of new generation information technology, which requires the updating of a large amount of infrastructure. This goal cannot be achieved without the city’s economic strength and advanced industrial structure. Cities in the eastern region developed earlier and have strong economic strength, more technological talents, and advanced, complete infrastructure, which is more conducive to the development of SC. Therefore, in the eastern region, the improvement effect of SC on UR is better than that in the central and western regions.

5.5.2. Resource Type

Drawing on the existing literature [60], the sample cities are divided into resource-based and non-resource-based cities. Table 7 shows the effectiveness of SC in improving UR in two types of cities. The regression results reveal that the absolute value of SC coefficient in non-resource-based cities is substantially higher than that in resource-based cities, and SC has a more considerable effect on improving UR in non-resource-based cities. A possible reason for this outcome is that resource-based cities have a single type of industry, mainly focused on high energy consuming mineral resource industries. To a certain extent, they sacrifice the ecological environment for economic growth, and path dependence leads to greater resistance in promoting the green transformation of development mode. Therefore, resource-based cities find it difficult to adapt to changes and challenges, and are prone to falling into difficulties when faced with risks. Non-resource-based cities have stronger resilience and adaptability when facing various challenges, and can better cope with risks and pressures. So non-resource-based cities are more effective in using SC to improve UR than resource-based cities.

6. Conclusions and Policy Implications

UR is a considerable global challenge, and its discussion has a long history with rich achievements, which provides a theoretical basis for later research. Most discussions on UR focus on its sources and transmission pathways, which are influenced by natural and social factors. However, few papers have conducted in-depth research on the development of SC and UR. In the context of the continuous advancement of digitalization, UR is not only influenced by the characteristics and attributes of the city itself but also closely related to government behavior. The construction of SC is an important lever for the current government’s governance reform. How to take the construction of SC as an opportunity to enhance UR and achieve urban green development has become an urgent issue that needs attention. On the basis of the theoretical analysis of the impact of SC construction on UR, this study empirically tests the effect and mechanism of SC construction on UR. The main conclusions are as follows: (1) A remarkable positive correlation exists between SC and UR; thus, SC can improve UR. (2) SC can improve UR by promoting ISU, enhancing the level of LGUE. Meanwhile, PEA can enhance the improvement effect of SC on UR. (3) The enhancement effect of SC on UR is mainly reflected in eastern cities and non-resource-based cities. This research conclusion is similar to the research on SC in some countries around the world. Apostu et al. (2022) found that the development of SC in European countries is highly positively related to UR after the COVID-19 [62]. Kutty et al. (2022) also found that cities in Europe such as Copenhagen, Geneva, and Stockholm have also improved UR when building SC based on machine learning methods [63]. Building SC to enhance UR is an effective approach [64]. The results of this study enrich the relevant research on SC and UR, providing practical reference suggestions for China and other countries around the world to promote UR through digitization and intelligence.

The following policy recommendations are proposed.

The government should actively summarize the experience of SCP projects and form a realistic and typical model for promoting UR through SC construction. As an important exploration of the future direction of urban development, the construction of SC has significantly promoted the improvement of UR. Therefore, local governments should have a correct understanding of the relationship between SC construction and UR development, increase investment in SC construction, and strengthen the overall planning and top-level design of SC. The government should further expand the number and scale of policy pilot projects, promote the high integration of SC with their internal economic, social, population, resource, and ecological environment subsystems, and leverage the positive role of policies in reducing ecological pollution and improving the utilization efficiency of limited urban resources.

The government needs to explore in depth the promotion path and long-term mechanism for enhancing UR to continuously release the sustainable development dividends of SC construction. They should fully leverage the intermediary roles of ISU and LGUE to promote the construction of SC as an important lever for enhancing UR. Specifically, local governments should increase policy support for green technology innovation in enterprises, formulate preferential and incentive policies that are conducive to green industry development, and create favorable innovation environment and atmosphere for innovation entities. Local governments should formulate scientific and reasonable industrial policies and continue to promote the deep integration of smart technology and traditional industries. Investment should be increased to continuously cultivate emerging service industries, such as e-commerce and modern logistics for upgrading and development, promote smart communities and smart transportation, gradually increase the proportion of green and low-carbon industries, create green industry clusters and industrial ecosystems that can support sustainable urban development, and strengthen UR through industrial greening. In the context of ecological civilization construction, the government should incorporate environmental protection into the performance evaluation system of local officials, effectively collect, integrate, and share environmental pollution monitoring data through AI technology, and accurately evaluate government environmental governance performance. In addition, the government can guide residents to form environmental awareness and encourage them to participate in pollution control. The government also needs to use AI public service platforms to guide public attention and participation in environmental governance.

With regard to the heterogeneity of the impact of SC on UR, SC have a greater impact on UR in eastern cities and non-resource-based cities. Cities in the eastern region should fully leverage their technological, talent, and financial advantages by establishing regional cooperation mechanisms and building resource sharing platforms, providing digital technology support and experience sharing to cities in the mid-west regions, and promoting the formation of regional synergies for the development of SC. The government and enterprises in mid-west cities should increase the investment in digital infrastructure, overcome various problems in digital technology such as the Internet, smart grids, and data centers, comprehensively improve the innovation ability of digital technology, promote the digital transformation of life, society, economy, and other urban areas, and help the urban system to make rapid response decisions in emergencies. In addition, the government should encourage financial institutions and enterprises to tilt resources towards resource-based cities through financial support, policy guidance, and other means, helping resource-based cities accelerate their digital construction process, achieve industrial upgrading, reduce dependence on natural resources.