Boosting Sustainable Urban Development: How Smart Cities Improve Emergency Management—Evidence from 275 Chinese Cities

Abstract

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities.

1. Introduction

Urbanization and the increasing frequency of natural disasters and public emergencies pose significant challenges to urban governance worldwide. By 2022, approximately 56% of the global population resided in urban areas, and it is projected that this proportion will surpass 68% by 2050 [1]. Rapid urban expansion often results in greater population density, infrastructure complexity, and intensified socio-economic interconnectedness, thereby amplifying cities’ vulnerability to various risks [2]. According to the World Disaster Report 2022, over 3500 disaster events were recorded between 2012 and 2021, affecting an estimated 1.9 billion people, causing 410,000 deaths, and leading to economic losses of nearly USD 1.7 trillion [3]. In addition to natural disasters such as floods, earthquakes, and typhoons, cities worldwide have faced frequent public emergencies, including the COVID-19 pandemic, highlighting inadequacies in existing emergency response systems and the urgent need for resilience-enhancing urban governance [4]. Enhancing emergency management capabilities has thus become a critical imperative for securing urban safety, social stability, and sustainable development. How to improve urban resilience and emergency management capacity has become a focal issue for policymakers, practitioners, and scholars worldwide. Internationally, various approaches have been proposed and implemented, including integrating disaster risk reduction strategies into urban planning, strengthening multi-agent collaborative response mechanisms, and promoting public awareness and participation in risk governance [5,6]. Recent studies emphasize that the effectiveness of urban resilience depends not only on robust physical infrastructure and reliable supply chains but also on the rapid integration of advanced information and communication technologies [7,8]. Against this backdrop, the emergence and accelerated construction of smart cities offer a transformative pathway for rethinking urban resilience and emergency management [9]. The concept of smart cities extends beyond merely the application of digital technologies. Fundamentally, it represents a comprehensive urban development paradigm aimed at enhancing sustainability, citizen well-being, and governance efficiency through the strategic integration of information and communication technologies (ICT), data-driven decision-making, and collaborative governance models [10,11,12]. While leveraging digital technologies such as the IoT, big data analytics, cloud computing, and artificial intelligence is a core enabler—facilitating real-time data acquisition, multi-source information fusion, and predictive risk assessment—the ultimate goals encompass broader societal benefits, including improved resource management, quality of life, and participatory governance [13,14]. This holistic view positions smart cities not just as technologically advanced entities but as complex socio-technical systems where digital innovation serves to optimize urban functions across multiple dimensions. These technological foundations facilitate rapid situational awareness, efficient cross-departmental coordination, optimized resource allocation, and proactive public engagement in crisis response [15,16].

In the past thirty years, the construction of smart cities has emerged as a critical paradigm in urban development, characterized by the large-scale deployment of digital technologies and data-driven governance models [10]. Numerous studies have pointed out that smart city initiatives can substantially reshape the structural and functional dimensions of urban systems [17,18]. The macro-level integration of key technologies—including the IoT, big data analytics, and artificial intelligence—plays a vital role in boosting urban resilience. It achieves this by making possible continuous monitoring, maintenance predictions for infrastructure, and advance warnings about environmental hazards [19]. These technological advancements can strengthen a city’s ability to adapt to and recover from both chronic stresses and sudden shocks, thereby underpinning sustainable urban development [13]. Moreover, smart cities have been associated with the reinforcement of urban risk resistance. The implementation of intelligent transportation systems, adaptive energy grids, and smart water management can reduce systemic vulnerabilities and improve the capacity to withstand external disruptions such as climate events, resource shortages, or cybersecurity incidents [20]. In the economic domain, digital transformation fosters economic resilience by attracting high-tech industries, optimizing resource allocation, and supporting the rapid adjustment of urban economies in response to global or local shocks [11,21]. Socially, the development of participatory digital platforms and inclusive urban services enhances social cohesion and adaptive capacity, supporting community self-organization and crisis response at the grassroots level [22,23].

Amidst the global proliferation of smart city initiatives, China recognized the transformative potential of information technology for urban governance and initiated its own national smart city program. The Ministry of Housing and Urban-Rural Development (MOHURD) formally launched the National Smart City Pilot Program in 2012 with the Notice on Launching National Smart City Pilot Work [24]. This pivotal policy document explicitly defined the concept of “smart city” for the first time at the national level and designated 90 initial pilot cities, marking a critical shift from conceptual exploration to practical implementation. Subsequently, the State Council issued the National Smart City Pilot Indicator System in 2013 [25], establishing specific evaluation criteria and construction guidelines to standardize development pathways. The strategic importance of smart cities was further cemented in the National New Urbanization Plan (2014–2020) [26], positioning them as a core component of China’s new urbanization strategy. This phase expanded the pilot program to include a second batch of 103 cities, significantly extending coverage to encompass third- and fourth-tier cities and promoting broader adoption across diverse urban scales. The Smart City Development White Paper (2017) synthesized outcomes from the initial pilot phases, advocating for a “people-oriented” development philosophy that prioritized citizen needs in urban intelligent management [27]. Concurrently, driven by the “dual carbon” goals (carbon peak and carbon neutrality) and the digital economy transition, smart city development became increasingly integrated with green and sustainable development principles. By 2019, the initiative had scaled comprehensively to encompass over 500 cities nationwide, as documented in the Digital China Construction Development Report [28]. The “14th Five-Year Plan for Digital Economy Development (2021)” further elevated smart cities’ strategic role, explicitly requiring widespread implementation of frontier technologies like IoT, 5G, big data analytics, and AI [29]. This deployment is mandated to drive the intelligent transformation of city management and public service delivery systems.

To rigorously assess how smart city initiatives enhance emergency management capabilities in developing contexts (e.g., China), this study analyzes longitudinal panel data from 275 prefecture-level cities spanning 2006–2021. Panel data—comprising repeated observations of the same cities over time—is essential for this research, as it enables the capture of temporal dynamics in policy implementation and emergency response outcomes, while simultaneously controlling for unobserved time-invariant heterogeneity across cities. Utilizing China’s smart city pilot policies as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) design to identify causal effects. Specifically, the study aims to answer the following research questions:

Q1: Do smart city pilot policies causally improve urban emergency management capabilities?

Q2: To what extent is this effect mediated through improved factor allocation efficiency and the adoption of digital technologies?

Q3: Does the effect exhibit heterogeneity based on regional characteristics (e.g., economic development level) or city population scale?

This research contributes to theory by empirically validating the mechanisms linking smart cities to emergency resilience, offers practical insights for urban managers in developing nations, and informs evidence-based policy design for scaling smart city initiatives.

Subsequent sections are organized as follows: A review of relevant smart city and emergency management literature comprises Section 2. Section 3 articulates the theoretical underpinnings and research hypotheses. The research design—encompassing the DID model, variable operationalization, and data—is elaborated in Section 4. Section 5 presents core empirical results alongside robustness, heterogeneity, and mechanism tests. Concluding remarks and policy recommendations are provided in Section 6.

2. Literature Review

Rapid progress in information and communication technologies (ICT) has established the “Smart City” as a predominant urban governance and development framework over the last thirty years [30]. Central to this model is the utilization of ubiquitous sensing networks (IoT), AI-driven analytics, cloud computing, big data processing, and geospatial systems (GIS) to optimize urban efficiency, sustainability, resilience, and livability [11]. Early research primarily focused on technological infrastructure and connectivity, viewing the city as a system of interconnected digital layers [31]. However, the discourse has significantly evolved, recognizing that technology alone is insufficient. Contemporary scholarship emphasizes a more holistic view, integrating technological innovation with social, economic, institutional, and environmental dimensions [12]. Key research streams now explore citizen-centric approaches and co-creation [14], the transformation of urban governance towards openness, participation, and collaboration [32], the pursuit of urban sustainability and resilience goals [13], and the critical challenges of data privacy, security, equity, and digital divides [33]. While definitions vary, a consensus highlights the core objective: using integrated, real-time data and advanced analytics to enable more informed, responsive, and effective decision-making across all urban domains [34]. This foundational understanding of smart cities as complex socio-technical systems, aimed at optimizing urban functions through data-driven intelligence, provides the essential context for examining their potential impact on specific urban challenges, such as emergency management.

Smart city initiatives demonstrate multidimensional transformative effects: at the macro level, they significantly enhance regional economic competitiveness and innovation vitality. Multi-period DID studies on China’s smart city pilots reveal a 23.5% increase in urban innovation through industrial upgrading and informatization [35]. Concurrently, they drive green sustainable development, with empirical evidence from China’s five major urban agglomerations indicating a 13.7% improvement in ecological resilience under pilot policies, albeit with negative spatial spillovers to neighboring regions [36]. Focusing on urban development dimensions, smart practices reshape urban renewal pathways—quasi-natural experiments across 293 Chinese prefecture-level cities confirm that pilot policies advance urban renewal through facility upgrades and ecological construction, with effects amplified by 40% in highly market-oriented cities [37]. They also optimize critical infrastructure efficiency: intelligent transportation systems reduce congestion by 37% in 187 Chinese cities [38], while energy management systems enhance grid failure resistance by 19% across 229 Chinese cities [39]. In public services, smart medical sensors lower residents’ health management costs by 28% [40]. Notably, smart transitions exhibit dual effects: while catalyzing governance innovations, e.g., Seoul’s blockchain platform increasing budget transparency by 25% [18], they simultaneously trigger social risks—older adults and women face pronounced digital exclusion [41]. Comparative analysis of 103 US cities further reveals that economic advantages, e.g., 27% higher entrepreneurial convenience, do not extend to environmental equity [42]. This complexity necessitates policy designs that balance technological empowerment with risk governance, particularly addressing the “mediating effect of urban resilience” emphasized by Wang Lihong et al. [43], whereby smart cities indirectly boost green total factor productivity through economic resilience (technological advancement + industrial upgrading) and social resilience (green innovation).

Research on urban emergency management centers on multidimensional resilience assessment, capacity optimization, and technology integration. Scholars employ dynamic evaluation frameworks—such as coupling coordination models to analyze subsystem synergies [44], entropy-TOPSIS for spatial differentiation quantification [45,46], and system dynamics simulations for scenario-based resilience forecasting [47,48]—to reveal spatiotemporal evolution patterns, including the “center–periphery” disparities observed in urban agglomerations like the Yangtze River Delta. Emergency capability studies prioritize granular assessments of resident preparedness (e.g., flood EPC via knowledge/skill/supply metrics) and community response efficacy, with structural equation modeling (SEM) confirming organizational collaboration’s critical role [49] and multilayer fuzzy evaluation exposing COVID-19 management gaps [50]. Policy interventions, such as China’s National Civilized City Evaluation, demonstrably enhance resilience through administrative competition mechanisms [51], while European guidelines (e.g., ERMG) emphasize cross-sectoral coordination to mitigate cascading risks [52]. Crucially, smart technologies are reshaping paradigms: open data platforms bolster decision transparency [53], AI-driven simulations optimize resource allocation under disasters [47], and predictive systems, e.g., for smart mobility resilience, strengthen proactive risk anticipation [54]. These advances collectively establish methodologies for evaluating and augmenting urban emergency systems, yet further research is needed to elucidate how smart city architectures systematically bridge capability fractures across governance scales.

Based on the extant literature, a significant research gap persists in the specialized investigation of how smart city initiatives directly enhance emergency management capabilities—distinct from broader urban resilience frameworks [55]. While empirical studies confirm the positive impact of smart city policies on multidimensional resilience, e.g., economic, social, and infrastructural dimensions [9,56], they largely subsume emergency management within aggregate resilience metrics, overlooking its process-specific attributes such as response rapidity, resource allocation precision, and dynamic recovery efficiency. This oversight obscures critical mechanisms: for instance, the unexplained disconnect between technological resource allocation and practical emergency efficacy—evidenced by the failure of resource efficiency to mediate capability enhancement in our findings—mirrors implementation gaps observed in Sydney’s smart projects, where strategic objectives lacked operational links to disaster response speed [57]. Furthermore, although regional heterogeneity in policy effects is acknowledged, e.g., diminished returns in western China [56], existing models neglect to quantify threshold conditions, e.g., minimum infrastructure density or fiscal capacity that triggers marginal diminishing effects in emergency contexts, thereby impeding targeted governance. Crucially, the role of digital technologies as a direct mediator remains underexplored against emergency management’s unique demands, as seen in Daegu’s smart disaster platform, which reduced casualties but did not calibrate technology to cross-agency decision latency [58]. These gaps collectively underscore a “governance-technology” misalignment in translating smartness into actionable emergency capacity—a void our research addresses by dissecting nonlinear pathways and region-specific fracture points.

3. Theoretical Analysis

Smart city construction constitutes a pivotal component of Digital China, serves as the foundation for building a smart society, reflects a city’s developmental level and core competitiveness, and represents a scientific approach to advancing the modernization of urban governance systems and capabilities [59]. Guided by the concept of “technological governance,” smart cities have emerged as a strategic solution for governments to address challenges in emergency management [60]. Firstly, the infrastructure of smart cities delivers real-time information and data support through digital platforms such as intelligent transportation systems, smart healthcare services, and public safety monitoring networks, thereby enhancing the efficiency of emergency responses. Secondly, leveraging technologies such as the IoT, sensors, and artificial intelligence, cities can collect real-time environmental, traffic, and meteorological data, which, when analyzed using big data techniques, enables the prediction of potential emergency risks and improves both the accuracy and timeliness of response efforts. Furthermore, smart cities facilitate cross-departmental collaboration among agencies such as public security, fire control, and medical services through integrated information-sharing platforms, effectively reducing response times. Simultaneously, intelligent dispatching platforms and emergency resource management systems allow for rapid resource allocation during disasters, ensuring the efficient execution of emergency operations. In addition, citizens can access real-time emergency information via smart city platforms and actively participate in crisis response processes, while social media and big data analytics assist in monitoring public sentiment and identifying emerging issues promptly. Finally, intelligent decision support systems, powered by big data and machine learning technologies, provide real-time analytical insights for emergency management, optimizing resource distribution and response strategies, and ultimately strengthening a city’s capacity to manage emergencies. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 1.

Smart city construction will enhance a city’s emergency management capabilities.

Factor allocation refers to the distribution of various production factors across different regions, industries, and enterprises, encompassing both traditional elements such as capital, labor, and land, as well as modern elements like technology and data [61]. Efficient factor allocation can enhance governmental emergency response capabilities by optimizing the distribution of social resources [62]. Smart city construction accelerates improvements in factor allocation efficiency by facilitating the rational flow of labor, capital, land, and data, thereby indirectly optimizing the urban emergency management system. Firstly, smart city construction improves the efficiency of labor resource allocation through digital platforms, promoting bidirectional labor mobility between urban and rural areas and attracting high-skilled talent, which enhances the professionalism of emergency management. Intelligent employment service platforms match labor demand with supply, increasing the city’s attractiveness to skilled workers. Secondly, smart city construction supports the rational flow of capital between urban and rural areas through financial technology infrastructure, reducing transaction costs and improving access to financial services, thus mitigating the dual structure problem in capital allocation. Thirdly, smart city initiatives employ land management information systems to enable digital and dynamic management of land resources, optimizing land allocation for support functions, improving land use efficiency, and ensuring the rational planning and effective utilization of spatial resources critical to emergency management [63]. Finally, smart cities promote the open sharing of data resources and facilitate information exchange between urban and rural areas. A unified data platform enables real-time monitoring and analysis of urban operations, providing scientific decision-making support for emergency management, enhancing public participation, and strengthening societal synergy. Based on this analysis, the following hypothesis is proposed:

Hypothesis 2.

Smart city construction enhances urban emergency management capabilities through improved factor allocation efficiency.

Cities face complex and diverse challenges, including infrastructure development, governance, and emergency management. The application of digital technologies, which describe and model the physical space using data, represents an ideal pathway for building resilient cities [64]. Digital technology applications not only optimize urban infrastructure operations but also provide managers with more scientific tools for emergency management [65]. First, leveraging the IoT, sensor networks, and big data, smart cities enable real-time monitoring of traffic, environment, and infrastructure. This facilitates intelligent forecasting, optimizes resource dispatch, and enhances emergency response efficiency. Second, integrated emergency management platforms allow different departments to share data and resources in real time, breaking down information silos. This improves coordination efficiency, shortens decision-making time, and enables faster mobilization of emergency resources [66]. Third, citizens receive risk warnings and report disaster information via mobile devices and social platforms. This enhances public emergency preparedness and improves the accuracy and speed of emergency management responses. Finally, intelligent decision support systems (DSS), powered by big data and artificial intelligence, are central to enhancing emergency management capabilities in smart cities [67]. They assess the impact of emergencies, pre-allocate resources, and improve the scientific rigor and efficiency of emergency management. Collectively, these digital technology applications enhance the speed and precision of emergency responses and contribute significantly to the overall resilience of cities, enabling them to better withstand unexpected events and risks. Therefore, we propose the following hypothesis:

Hypothesis 3.

Smart city construction enhances urban emergency management capacity through the application of digital technologies.

4. Research Design

4.1. Modeling

This study treats the smart city pilot policies as a quasi-natural experiment—in which an exogenous policy shock is used to simulate a randomized experimental setting—due to its fulfillment of three essential criteria. First, the policy assignment is exogenous: pilot cities were designated in three waves (2012, 2014, and 2016) by the central government based on strategic considerations, rather than being self-selected by the cities themselves, thereby mitigating self-selection bias. Second, the timing of the intervention is clearly defined: the phased implementation of the policy offers distinct temporal breakpoints, which are essential for applying a multi-period difference-in-differences (DID) model. Third, a valid comparison group exists: a substantial number of non-pilot cities serve as a natural control group. By eliminating potential endogeneity in policy allocation, this design establishes a solid methodological foundation for identifying the net causal impact of smart city policies on emergency management capabilities. As discussed above, utilizing a multi-period difference-in-differences (DID) specification, we empirically assess the net impact of smart city pilot policies on urban emergency management capabilities. The model constructs two binary indicators:

(1) Group dummy: 1 for pilot cities (treatment group), 0 otherwise (control group);

(2) Time dummy: coded as 0 for years prior to 2012 and as 1 for 2012 and later [68].

Building upon this setup and following the methodological approach proposed by Ashenfelter and Card (1985) [69], the baseline model is specified as follows:

$${Ue}_{\mathit{it}}=\mathit{\alpha }+{\mathit{\beta }}_1{DID}_{\mathit{it}}+{\mathit{\beta }}_2{\mathit{Treat}}_{\mathit{it}}+{\mathit{\beta }}_3{\mathit{Post}}_{\mathit{it}}+{\mathit{\beta }}_4{X}_{\mathit{it}}+{\mathit{\eta }}_{\mathit{it}}+{\mathit{v}}_{\mathit{it}}+{\mathit{\epsilon }}_{\mathit{it}}$$

(1)

In Equation (1), $i$ denotes the city and $t$ denotes the time dimension. ${Ue}_{it}$ represents the dependent variable, while ${Treat}_{it}$ and ${Post}_{it}$ denote the group dummy variable and time dummy variable, respectively. ${DID}_{it}$ serves as the core explanatory variable, indicating whether city $i$ was selected as a smart city pilot in year $t$. $X_{it}$ stands for the set of control variables, ${\mathit{\eta }}_{it}$ represents the city fixed effects, $v_{it}$ captures the time fixed effects, and ${\mathit{\epsilon }}_{it}$ is the random error term. ${\mathit{\beta }}_i$ represents the coefficient of the corresponding variable. The coefficient ${\mathit{\beta }}_1$ measures the average treatment effect of smart city construction on the enhancement of urban emergency management capacity. If ${\mathit{\beta }}_1$ is statistically significantly positive, Hypothesis H1 is supported.

The intermediary mechanism model draws on the methodology of Wen Zhonglin et al. [70] to examine the mediating effects of factor allocation efficiency and digital technology application. The specification of the model is presented as follows:

$$M_{it}={\mathit{\omega }}_0+{\mathit{\omega }}_1{DID}_{it}+{\mathit{\omega }}_2{Treat}_{it}+{\mathit{\omega }}_3{Post}_{it}+{\mathit{\omega }}_4{X}_{it}+{\mathit{\eta }}_i+{\mathit{\upsilon }}_t+{\mathit{\epsilon }}_{it}$$

(2)

$$U_{rit}={\mathit{\alpha }}_0+{\mathit{\alpha }}_1{DID}_{it}+{\mathit{\alpha }}_2{Treat}_{it}+{\mathit{\alpha }}_3{Post}_{it}+{\mathit{\alpha }}_4{M}_{it}+{\mathit{\alpha }}_5{X}_{it}+{\mathit{\eta }}_i+{\mathit{\upsilon }}_t+{\mathit{\epsilon }}_{it}$$

(3)

In this context, $M_{it}$ and $U_{rit}$ denote the mediating variable, while the definitions of all other variables remain consistent with those in Formula (1) of the baseline model. If the coefficients on ${\mathit{\omega }}_1$ and ${\mathit{\alpha }}_1$ are statistically significantly positive, hypotheses H2 and H3 are supported.

4.2. Indicator Measurement and Data Sources

4.2.1. Urban Emergency Management Capabilities

This paper treats urban emergency management capability as the dependent variable, which constitutes a complex and dynamic concept requiring comprehensive measurement. Drawing on the framework proposed by Jennife and Arthur [71], local emergency management capability is generally categorized into four dimensions: preparedness, response, disaster mitigation, and recovery. In this study, these are redefined as prevention monitoring, emergency handling, recovery reconstruction, and emergency support. The indicators for the first two dimensions are adapted from Jiang Jun et al. [72], while those for the latter two dimensions are derived from Hao et al. [4]. For detailed indicator specifications, see

Table 1. Evaluation index system for urban emergency management capability.

Primary Indicator	Secondary Indicators	Quantitative Indicators	Indicator Direction
Preventive monitoring capability	Disaster prevention support (A1)	Technology expenditure	+
	Foundation of people’s livelihood security (A2)	Employees in the fields of health, social security, and social welfare	+
	Medical security foundation (A3)	The number of participants in the basic medical insurance for urban employees	+
	Risk identification ability (A4)	Educational expenditure	+
	Emergency evacuation capacity (A5)	Social passenger volume	+
	Social self-governance capability (A6)	Personnel of the third industry—public administration and social organizations	+
Emergency handling capability	Emergency evacuation capability (B1)	The number of public transportation vehicles per 10,000 people	+
	Emergency communication capability (B2)	The number of mobile phone users at the end of the year	+
	Medical assistance guarantee (B3)	Number of hospitals	+
	Population density (B4)	Urban population density	−
	Emergency rescue capability (B5)	Health technicians	+
Reconstruction and recovery capabilities	System recovery capability (C1)	Local fiscal expenditure on social security and employment	+
	Emergency rescue capability (C2)	The number of beds in health institutions	+
	Emergency reconstruction capability (C3)	Local fiscal revenue	+
	Facility recovery capability (C4)	Employees in the power, heat, gas, and water production and supply industry of the secondary sector	+
	Transportation recovery capability (C5)	Investment in fixed assets of the transportation industry	+
Emergency response capability	Innovation-driven development guarantee (D1)	R&D expenditure	+
	Restore the supporting ability (D2)	Total resident population	+
	Individual recovery ability (D3)	Per capita disposable income of residents	+
	Social security capacity (D4)	Regional gross domestic product	+
	Information transmission capability (D5)	Employed personnel in urban units of information transmission, software, and information technology services industry	+
	Investment in healthcare funds (D6)	Healthcare expenditure	+
	Municipal infrastructure (D7)	The actual length of roads at the end of the year	+

. The entropy method is employed to construct a composite index reflecting the overall level of urban emergency management capability.

4.2.2. Smart Cities

This study adopts the national smart city pilot policy as the core explanatory variable. Given that the list of designated smart city pilot areas encompasses various administrative levels (including municipalities, counties, districts, and towns) and considering potential overlaps across different batches of pilot designations (e.g., certain sub-regions within a prefecture-level city being selected as pilot zones), it is essential to isolate the net impact of smart city initiatives on urban development quality. To achieve this, the study follows the methodological approach proposed by Liu Chengjie [73], which involves excluding pilot areas at the district and county levels from the experimental group, as well as cities with significant data gaps. The interaction term between group and time dummy variables is employed to represent the implementation of the smart city pilot policy, thereby enabling an assessment of its policy effect on enhancing urban emergency management capabilities.

4.2.3. Control Variables

To bolster model accuracy and enhance result reliability, this study includes the following control variables, informed by prior research [68,73,74]:

Government expenditure scale (govexp): Fiscal expenditure as a proportion of regional GDP. Larger fiscal capacity enables investments in emergency infrastructure and personnel training. Human capital level (human): Ratio of higher education enrollment to year-end population. Skilled populations enhance disaster response efficiency and technology adoption. Market openness (open): Share of foreign direct investment (FDI) in regional GDP. FDI introduces advanced emergency management practices and technologies. Urbanization level (urban): Non-agricultural population share of total year-end population. High urban density amplifies disaster risks but also concentrates response resources. Information infrastructure (information): Proportion of internet users within the year-end population. Digital penetration enables real-time monitoring and public alerts. Economic development level (economy): Natural logarithm of city-level per capita GDP. Wealthier cities possess superior resources for resilient systems. Ecological environment level (environment): Green coverage rate in the city’s built-up area. Green spaces mitigate flood risks and provide emergency shelters.

4.2.4. Mediating Variable

Building upon the translog production function, this study constructs a model to measure factor allocation efficiency, with particular emphasis on the extent of market distortions in capital and labor inputs. Drawing on the theoretical framework developed by Bai and Bian [75], the functional form of the production function is specified as follows:

$$\ln Y={\mathit{\theta }}_0+{\mathit{\theta }}_1\ln L+{\mathit{\theta }}_2\ln K+1/2{\mathit{\theta }}_3{\ln }^{2}L+1/2{\mathit{\theta }}_4{\ln }^{2}K+{\mathit{\theta }}_5\ln K\ln L+\mathit{\epsilon }$$

(4)

Specifically, Y denotes the city’s real GDP, K denotes the capital stock estimated using the perpetual inventory method, and L denotes the total urban employment (encompassing both public sector and private individual business workers). ${\mathit{\theta }}_i$ is the parameter to be estimated and $\mathit{\epsilon }$ is the random error term. By analytically deriving the marginal product of each input factor, the following expression is obtained:

$$\begin{array}{l}M{P}_L=({\mathit{\theta }}_1+{\mathit{\theta }}_3\ln L+{\mathit{\theta }}_5\ln K)Y/L\\ M{P}_K=({\mathit{\theta }}_2+{\mathit{\theta }}_4\ln K+{\mathit{\theta }}_5\ln L)Y/K\end{array}$$

(5)

$M{P}_L$ represents the marginal output of the labor force and $M{P}_K$ represents the marginal output of capital.

The factor market distortion index is defined as the ratio of marginal product to factor price:

$$T{W}_S=M{P}_S/P_S(s=K,L)$$

(6)

In this context, $P_L$ is defined as the average wage of on-the-job employees, while $P_K$ is established based on a 5% benchmark interest rate. When $T{W}_S>1$ exceeds unity, it signifies the presence of negative distortion in factor prices; moreover, a higher value corresponds to lower allocative efficiency. To develop an efficiency index with positive orientation, a reciprocal transformation is applied following the approach proposed by Li and Yang [76]:

$$A{E}_S=1/T{W}_S(s=K,L)$$

(7)

Currently, measuring the application of digital technologies remains a frontier issue in academic research. As core components of digital development, the internet and information and communication technologies (ICT) serve as key indicators of urban digitalization levels. Building upon established methodologies from prior studies [66,77], this study constructs a comprehensive index of digital technology development using the entropy method, based on two dimensions: internet infrastructure development and digital inclusive finance (

Table 2. Evaluation index system for digital technology application.

Primary Indicators	Secondary Indicators	Indicator Direction
The development of the internet	The density of long-distance optical cables	+
	Average number of internet broadband access ports per capita	+
	The proportion of employees in the information transmission, computer services, and software industry	+
	Per capita revenue from telecommunications services	+
	The penetration rate of mobile phones	+
	Internet penetration rate	+
Digital inclusive finance	Digital Inclusive Finance Index	+

).

4.2.5. Data Sources

The research data employed in this paper primarily consist of the following sources: First, the list of three batches of Chinese smart city pilot programs, which serves as the basis for constructing the core explanatory variable; second, panel data from prefecture-level and higher-level cities in China, sourced from the “China City Statistical Yearbook” (2006–2022) [78], are used as the benchmark sample. Urban emergency management capacity and control variables are calculated by integrating data from the Wind database (https://www.wind.com.cn (accessed on 26 June 2025)) and publicly available information provided by provincial and municipal statistical bureaus. In order to ensure both data availability and comprehensiveness, the analysis also incorporates key developmental milestones and transition periods related to smart city construction and policy implementation. Missing values are addressed through linear interpolation and substitution with the average values of adjacent years. Descriptive statistics for all variables are presented in

Table 3. Descriptive statistics of variables.

Variable	Mean Value	Standard Deviation	Minimum Value	Median	Maximum Value
Urban emergency management capacity	0.061	0.074	0.012	0.039	0.432
Pilot policies for smart cities	0.192	0.394	0.000	0.000	1.000
Efficiency of factor allocation	1.374	0.776	0.099	1.302	2.854
Application of digital technology	0.029	0.024	0.007	0.022	0.158
The scale of government expenditure	0.176	0.088	0.061	0.154	0.529
The level of human capital	0.018	0.024	0.000	0.009	0.118
Degree of market openness	0.117	0.019	0.063	0.117	0.157
urbanization level	0.518	0.166	0.175	0.502	0.946
Information infrastructure	0.177	0.143	0.000	0.141	0.666
The level of economic development	10.423	0.743	8.619	10.472	11.979
Ecological environment level	4.326	0.420	2.432	4.494	4.690

.

5. Analysis of Empirical Results

The empirical results of this paper are presented from four perspectives. First, benchmark regression analysis is conducted to examine the impact of smart city construction on urban emergency management capacity. Second, a series of robustness checks are performed, including parallel trend tests, placebo tests, PSM-DID tests, controls for potential confounding policies, and counterfactual analysis, to ensure the reliability of the findings. Third, heterogeneity analysis is carried out to explore how the effects of smart city construction on emergency management capacity vary across regions and cities with different population sizes. Finally, mechanism testing is conducted to investigate whether smart city development enhances urban emergency management capacity through improved factor allocation efficiency and the application of digital technologies.

5.1. Benchmark Regression

Table 4. Difference-in-differences regression analysis.

Variable	(1)	(2)	(3)
	Score	Score	Score
did	0.017 ***	0.019 ***	0.018 ***
	(3.19)	(3.49)	(3.40)
govexp			−0.004
			(−0.13)
human			0.067
			(0.85)
open			0.072
			(0.71)
urban			−0.014
			(−0.98)
information			−0.009
			(−0.75)
economy			0.012 **
			(2.32)
environment			0.005
			(1.06)
_cons	0.058 ***	0.053 ***	−0.084
	(57.31)	(28.30)	(−1.52)
City fixed effects	Yes	Yes	Yes
Annual fixed effect	No	Yes	Yes
N	4664	4664	4664
adj. R2	0.023	0.037	0.042
F	10.149	33.144	23.282

Note: Standard errors in parentheses ** and *** denote 5% and 1% significance levels, respectively.

presents the benchmark regression results regarding the impact of smart city construction on urban emergency management capabilities. Model (1) controls only for city fixed effects, Model (2) further incorporates annual fixed effects, and Model (3) adds multidimensional control variables—such as government expenditure and human capital—to Model (2). The estimated DID coefficients across the three models are 0.017 ***, 0.019 ***, and 0.018 ***, respectively, all statistically significant at the 1% level. These findings indicate that smart city construction significantly enhances urban emergency management capacity. In Model (2), after introducing annual fixed effects, the DID coefficient increases from 0.017 to 0.019, suggesting that failing to account for time trends may lead to an underestimation of the policy effect. In Model (3), with all control variables included, smart city construction is associated with an average increase of 1.8% in urban emergency management scores, with the magnitude of the effect remaining stable. The adjusted R² rises gradually from 0.023 in Model (1) to 0.042 in Model (3), demonstrating that the inclusion of control variables and fixed effects improves the model’s explanatory power. Hypothesis 1 is supported: smart city construction significantly improves urban emergency management capabilities, and this effect remains robust even after controlling for time trends, city-specific characteristics, and fixed effects, underscoring the pivotal role of digital transformation in advancing the modernization of emergency management systems. This finding aligns with the conclusion of Zhu et al. [9] that smart cities enhance urban comprehensive resilience, and it represents the first effort to establish a causal relationship between smart city policies and improved emergency management capabilities through a quasi-experimental design. In contrast to Apostu et al. [15], who integrated emergency management capabilities into a composite resilience index, this study emphasizes the process-oriented features of emergency management—such as response speed and resource allocation accuracy—highlighting the distinct contribution of smart city policies to the dimension of emergency management.

5.2. Parallel Trend Test

Figure 1 displays parallel trend test outcomes. Statistically non-significant coefficients across all pre-intervention periods confirm comparable temporal patterns between treatment and control groups before smart city implementation, validating the parallel trends assumption. The red line in the figure indicates the situation after the implementation of smart city policies. Following policy implementation, the treatment effect becomes significantly positive immediately (0.016) and reaches its peak in the first year (0.022), providing strong evidence that smart city construction exerts a sustained positive impact on urban emergency management capabilities.

5.3. Robustness Tests

5.3.1. Placebo Test

Cities were randomly selected from the sample to form a hypothetical policy intervention group, used to construct the core explanatory variable and perform 500 repeated regressions. Figure 2 displays the distribution of the estimated coefficients for the interaction term “did”, which centers closely around zero with a standard deviation (σ) of 0.00129. The results show that most of the estimated coefficients have p-values greater than 0.1, indicating that the randomly generated effects are statistically insignificant, thus confirming that the placebo test is successfully passed. These findings provide strong support that the observed impact of the smart city pilot policy on urban emergency management capabilities is genuine and not driven by random noise or other non-policy-related factors, thereby validating the robustness of the research conclusions.

To validate the reliability of the conclusion derived from the benchmark difference-in-differences (DID) model—namely, that the causal effect of smart city construction on improving urban emergency management capabilities is not attributable to unobservable city-specific characteristics, concurrent policy interventions, or random fluctuations—this study conducted a placebo test. Within the sample cities, 500 artificial “smart city pilot” assignments were randomly generated, including both the selection of treatment group cities and the timing of policy implementation, followed by repeated estimations of the DID model.

Figure 2 displays the distribution of the estimated coefficients from the 500 placebo tests. As illustrated, the simulated “smart city construction” effect coefficients are tightly clustered around zero (mean $\mu \approx 0$, standard deviation $\sigma =0.00129$), with most values falling within the range of [−0.0039, +0.0039]. These results strongly suggest that in the absence of actual investment and implementation of smart city initiatives, merely being designated as a “pilot” city or influenced by random factors does not lead to a systematic improvement in urban emergency management capabilities.

5.3.2. PSM-DID Test

Propensity score matching (PSM) was applied to account for sample selection bias, matching treatment (smart cities) and control groups on pre-policy characteristics. The DID estimate in

Table 5. Robustness test of regression results.

	(1)	(2)	(3)	(4)
	PSM-DID	Elimination of the Interference of Other Policies		Counterfactual Test
did	0.019 *
	(1.89)
did1		0.025 ***
		(2.91)
did2			0.045 **
			(2.31)
did3				0.001
				(0.80)
govexp	0.074	0.020	0.005	0.002
	(0.90)	(0.57)	(0.14)	(0.27)
human	−0.087	−0.015	0.030	0.156
	(−0.72)	(−0.18)	(0.32)	(1.37)
open	0.140	0.120	0.053	0.042
	(0.53)	(1.22)	(0.55)	(1.39)
urban	−0.062	−0.015	−0.006	−0.003
	(−1.62)	(−1.02)	(−0.36)	(−0.66)
information	−0.030	−0.008	−0.003	0.002
	(−1.53)	(−0.67)	(−0.28)	(0.21)
economy	0.043 ***	0.017 ***	0.014 ***	−0.005 *
	(3.32)	(3.14)	(2.84)	(−1.75)
environment	0.011	0.004	0.004	−0.006 *
	(0.82)	(0.80)	(0.87)	(−1.79)
_cons	−0.415 ***	−0.130 **	−0.101 *	0.133 ***
	(−2.61)	(−2.20)	(−1.88)	(3.59)
Annual fixed effect	Yes	Yes	Yes	Yes
N	1792	4664	4664	1105
adj. R2	0.030	0.045	0.063	0.128
F		24.339	24.640	9.383

Note: Standard errors in parentheses, *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

(1) is 0.019 and significant at 10% (t = 1.89). Even after PSM, the positive effect of smart city construction on emergency management capabilities remains significant and aligns with the direction of the benchmark DID results, thereby reinforcing the robustness of the core findings. Furthermore, the balance of covariates—including economic indicators, government expenditure, and other relevant factors—between the two groups after matching effectively rules out concerns that smart city pilot selection was driven by observable advantages such as economic strength.

5.3.3. Elimination of the Interference of Other Policies

To isolate the “net effect” of smart city construction, this study controls for potential confounding influences by excluding other concurrent policies—specifically, the pilot programs for innovative cities and sponge cities. Existing literature suggests that the innovative city pilot policy can effectively stimulate urban innovation capacity and enhance overall resilience [79], while the sponge city initiative serves as a key strategy for improving urban systems’ ability to withstand diverse internal and external risks [80]. Official announcements indicate that in 2013, the Ministry of Science and Technology designated 12 national innovative city pilots, and in 2015, the Ministry of Housing and Urban-Rural Development selected 16 sponge city pilot cities [81]. These two policies were therefore incorporated into the benchmark regression model for comparative analysis. As shown in Columns (2) and (3) of Table 5, the estimated coefficients for the interaction terms of the innovative city pilot (did1) and the sponge city pilot (did2) are 0.025 *** (significant at the 1% level) and 0.045 ** (significant at the 5% level), respectively. After accounting for these concurrent policies, the treatment effect of smart city construction increases from 0.019 to 0.025 and 0.045, suggesting that the baseline estimates may have been partially diluted by uncontrolled policy overlaps. Failure to control for these co-occurring initiatives could lead to an underestimation of the synergistic effects associated with smart city development. This analysis confirms that smart city construction independently enhances urban emergency management capabilities, rather than relying on combined policy interventions.

5.3.4. Counterfactual Test

Furthermore, following the methodological framework proposed by He Lingyun [82], this study conducts a counterfactual test by artificially advancing the policy implementation period by three years and constructing a hypothetical policy timing for regression analysis. As presented in Column (4) of Table 5, the estimated coefficient of the interaction term (did3) in this counterfactual scenario is 0.001, which is statistically insignificant (t = 0.80). This outcome effectively rules out the possibility that the observed effect is driven by underlying time trends or other confounding factors. Collectively, these findings confirm that the positive impact of the smart city pilot policy on urban emergency management capabilities is both statistically significant and robust to alternative specifications.

5.4. Heterogeneity Analysis

5.4.1. Regional Differences

To further investigate the heterogeneous effects of smart city construction on urban emergency management capacity, this study conducts a regional comparative analysis based on China’s geographical and developmental disparities. The national development strategy outlines a clear regional framework of “the east taking the lead—the central region rising—the west developing”, which has guided the spatial layout and resource allocation of smart city pilot programs (Appendix A). As shown in

Table 6. Regional heterogeneity.

	(1)	(2)	(3)
	Eastern Region	Central Region	Western Region
	Score	Score	Score
did	0.007 ***	0.115 **	0.010 *
	(3.51)	(2.39)	(1.90)
govexp	−0.019	0.154	−0.047 *
	(−0.76)	(1.09)	(−1.96)
human	0.150 ***	−0.015	−0.024
	(3.08)	(−0.03)	(−0.22)
open	0.045	0.361	0.053
	(1.29)	(1.41)	(0.80)
urban	0.005	0.102 **	−0.032 **
	(0.61)	(2.00)	(−2.10)
information	−0.004	0.091 *	0.001
	(−0.95)	(1.92)	(0.11)
economy	0.010 ***	−0.002	0.008
	(3.31)	(−0.13)	(1.35)
environment	−0.001	−0.001	0.008
	(−0.50)	(−0.14)	(1.44)
_cons	−0.043	−0.023	−0.044
	(−1.38)	(−0.15)	(−0.66)
Annual fixed effect	Yes	Yes	Yes
N	1286	1114	2264
adj. R2	0.085	0.331	0.053
F	29.215	8.679	6.407

Note: Standard errors in parentheses, *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, the policy impact exhibits a distinct gradient pattern: central region > western region > eastern region. The strongest effect is observed in the central region (coefficient = 0.115, t = 2.39), indicating an 11.5% improvement in emergency management capabilities—approximately 16.4 times higher than that in the eastern region. Although the estimated coefficients for the eastern (0.007) and western (0.010) regions are relatively small, both are statistically significant at the 1% and 10% levels, respectively, confirming the presence of positive policy effects across all regions. This suggests a tiered differentiation in how smart city initiatives influence emergency management performance.

The central region, currently undergoing rapid industrialization and informatization, initially had a weak digital foundation for emergency governance. However, smart city construction effectively addressed these deficiencies, enabling technological leaps that significantly enhanced emergency response capabilities. As one of the first national pilot cities for smart urban development launched in 2013, Wuhan has established China’s first city-level internet of things (IoT) platform. The city’s “Smart Emergency Brain” integrates real-time data from 23 government departments, including water resources, transportation, and healthcare, thereby improving flood response efficiency by 40% in 2020. During the autumn flood season of the Han River in 2023, AI-based predictive models enabled resource deployment 72 h in advance, resulting in a direct reduction in economic losses by CNY 1.2 billion. These achievements illustrate how intelligent infrastructure development can significantly enhance emergency response capabilities in regions with previously weak foundational systems. Moreover, the urbanization rate in this region shows a significantly positive coefficient (0.102), suggesting that accelerated urbanization provides dense application scenarios for smart emergency technologies, thereby amplifying their implementation effectiveness.

In contrast, eastern cities already possess highly mature emergency management systems, where smart investments mainly optimize existing infrastructures, resulting in diminishing marginal returns. Additionally, interdepartmental data silos raise integration costs and partially weaken the overall policy impact. In the western region, dispersed population distribution and underdeveloped infrastructure elevate the cost of deploying smart technologies, hindering the formation of economies of scale. Meanwhile, fiscal constraints further limit the long-term sustainability of smart emergency systems, thereby reducing the potential for technology-driven improvements.

The substantial gain observed in the central region contributes to the theoretical discussion surrounding the “diminishing returns of western policies” identified by Feng et al. [56]: This study demonstrates that smart technologies can unlock the potential of emergency governance only when infrastructure density reaches a critical threshold—such as the industrialization level observed in the central region. In contrast, the marginal effect in the eastern region remains minimal, which supports the findings of Varzeshi et al. [57] in Sydney. Their research suggests that within a mature emergency management system, persistent data silos across departments can constrain the overall benefits of technological integration.

5.4.2. Population Size Disparity

This study categorizes cities into four groups based on their permanent resident population: megacities (>10 million), large cities (5–10 million), medium-sized cities (1–5 million), and small cities (<1 million), to examine the heterogeneous effects of the smart city pilot policy on urban emergency management capacity. As shown in

Table 7. Population size heterogeneity.

	(1)	(2)	(3)	(4)
	A Population of Over 10 Million	A Population of 5 to 10 Million	A Population of 1 to 5 Million	A Population of Less than One Million
	Score	Score	Score	Score
did	0.055 *	0.008 ***	0.020 ***	0.202 *
	(2.05)	(3.38)	(2.68)	(1.92)
govexp	0.440 *	−0.016	0.008	−0.168
	(2.12)	(−0.63)	(0.18)	(−1.43)
human	1.211 ***	−0.332	0.010	0.260 *
	(3.49)	(−0.72)	(0.11)	(1.79)
open	1.908	0.056	0.081	−1.194 *
	(1.39)	(0.57)	(0.72)	(−1.79)
urban	−0.008	0.035	−0.012	−0.165
	(−0.11)	(1.34)	(−0.73)	(−1.68)
information	−0.020	−0.025 *	−0.003	−0.135
	(−1.56)	(−1.73)	(−0.29)	(−1.66)
economy	0.124 **	0.000	0.012 **	−0.023
	(2.17)	(0.04)	(2.22)	(−0.81)
environment	−0.027	0.002	0.004	0.057
	(−1.15)	(0.93)	(0.77)	(1.49)
_cons	−1.164 *	0.023	−0.089	0.208
	(−2.02)	(0.28)	(−1.43)	(0.73)
Annual fixed effect	Yes	Yes	Yes	Yes
N	100	945	3489	130
adj. R2	0.313	0.046	0.038	0.588
F		25.529	15.413

Note: Standard errors in parentheses, *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, the enhancement of emergency management capabilities due to smart city construction is most pronounced in megacities and small cities, with treatment effect coefficients of 0.055 * and 0.202 *, respectively—both significantly higher than those observed in medium-sized cities. The impact in medium-sized cities (1–5 million and 5–10 million) remains weak yet highly statistically significant, indicating a dual-scale threshold effect in the relationship between city size and emergency efficiency gains.

In megacities, the coefficient for economic development level is statistically significant at 0.124 **, suggesting that robust fiscal capacity supports high-risk experimentation and upgrades to complex systems. The “Urban Digital Twin System” deployed in the Pudong New Area of Shanghai enables real-time simulation of emergencies such as subway shutdowns and medical resource shortages within seconds through over 50,000 IoT devices. Before Typhoon “Mei-Hsiang” made landfall in 2023, the system automatically generated 3 evacuation plans and optimized the response routes for ambulances, reducing the evacuation time for 100,000 people by 35%. Additionally, the human capital coefficient is strongly positive (1.211 ***), highlighting the critical role of professional expertise in realizing the potential of smart emergency technologies. The educational infrastructure and talent concentration in megacities help overcome technological implementation barriers. The pivotal role of human capital in megacities extends the findings of Cook et al. [40] on the internet of medical things, demonstrating that specialized professionals serve as key intermediaries in the translation of complex technologies into practical applications.

Small cities exhibit a low-base, high-elasticity pattern, where minimal investment in smart infrastructure can yield substantial improvements in emergency response systems. However, the negative and significant coefficient for openness to the external environment indicates that these cities are more vulnerable to external shocks and possess relatively weaker system resilience.

In contrast, the effectiveness of smart city initiatives in medium-sized cities is notably lower compared to small cities. These cities neither benefit from the low-base advantage of smaller urban centers nor possess the resource redundancy characteristic of megacities. Furthermore, most control variables lack statistical significance, implying that current resource allocation strategies may not align well with the risk governance demands of this urban scale. Ultimately, population size disparities fundamentally shape the balance between technological absorption efficiency and systemic vulnerability within smart emergency governance frameworks.

5.5. Mechanism Analysis

Based on the theoretical framework outlined in Section 3, this study investigates the mediating mechanisms through which smart city construction influences urban emergency management capabilities using a fixed effects model. Factor allocation efficiency (fae) and digital technology application (digital) are selected as mediating variables. As presented in

Table 8. Analysis of mediating mechanisms.

	(1)	(2)	(3)	(4)	(5)
	Score	Fae	Score	Digital	Score
did	0.018 ***	0.009	0.018 ***	0.003 **	0.017 ***
	(3.40)	(0.56)	(3.41)	(2.53)	(3.31)
fae			0.005 **
			(2.02)
digital					0.401 **
					(2.53)
govexp	−0.004	−0.132	−0.004	−0.016 *	0.002
	(−0.13)	(−0.89)	(−0.11)	(−1.73)	(0.05)
human	0.067	−0.402	0.069	0.268 ***	−0.043
	(0.85)	(−0.60)	(0.88)	(4.09)	(−0.48)
open	0.072	−0.659	0.075	0.009	0.068
	(0.71)	(−1.16)	(0.75)	(0.32)	(0.67)
urban	−0.014	0.068	−0.015	−0.011 **	−0.012
	(−0.98)	(0.71)	(−0.99)	(−2.52)	(−0.80)
information	−0.009	−0.037	−0.009	0.039 ***	−0.025 *
	(−0.75)	(−0.61)	(−0.74)	(8.70)	(−1.82)
economy	0.012 **	−0.025	0.012 **	−0.006 ***	0.015 ***
	(2.32)	(−0.75)	(2.34)	(−2.64)	(2.71)
environment	0.005	0.028	0.005	−0.001	0.005
	(1.06)	(1.52)	(1.05)	(−1.09)	(1.15)
_cons	−0.084	2.324 ***	−0.095	0.072 ***	−0.115 *
	(−1.52)	(7.34)	(−1.65)	(3.64)	(−1.96)
Annual fixed effect	Yes	Yes	Yes	Yes	Yes
N	4664	4664	4664	4631	4631
adj. R2	0.042	0.863	0.043	0.537	0.056
F	23.282	1271.189	20.568	85.736	21.707

Note: Standard errors in parentheses, *, **, and *** denote 10%, 5%, and 1% significance levels, respectively.

, digital technology application demonstrates a significant and fully mediating effect. Specifically, smart city construction significantly enhances the adoption of digital technologies (0.003 **), which in turn directly improves emergency management capabilities (0.401 **). Upon introducing the digital technology application variable into the model, the direct effect of smart city construction decreases from 0.018 to 0.017, suggesting that approximately 86.7% of the total effect is transmitted through this channel. These findings empirically support the mechanism of “technology empowerment amplifying governance effectiveness.” Moreover, the coefficient for informatization level is significantly positive (0.039 **), indicating that existing information infrastructure serves as a foundational threshold condition that enables the effective transformation of smart technologies into tangible emergency response capabilities. In contrast, factor allocation efficiency exhibits a partial masking effect. While smart city construction does not significantly influence factor allocation efficiency (0.009), the latter independently contributes to enhancing emergency management capabilities (0.005 **). This pathway suggests that factor allocation efficiency operates as a distinct yet complementary mechanism to smart city initiatives, highlighting its role in reinforcing urban resilience through resource optimization.

The rationale for considering digital technology application as a mediating variable between smart city construction and the enhancement of urban emergency management capacity—while factor allocation efficiency exerts only a direct influence without mediating effects—is closely tied to the practical pathways and institutional frameworks guiding smart city development in China. From the perspective of top-level design and implementation, digital technologies serve as the foundational breakthrough in urban smartization. Chinese cities commonly adopt platforms based on cloud computing, big data, and the IoT to consolidate data, integrate information, and enable real-time sharing across urban governance domains. These capabilities constitute essential prerequisites for improving emergency response efficiency, risk perception, and resource coordination. Consequently, digital technology functions as both a “bridge” and a “catalyst”, linking smart city initiatives with advancements in emergency management capacity. Empirical evidence indicates that the core functionalities of smart city platforms—such as data sensing and intelligent analytics—directly support precise early warning systems and integrated command mechanisms within urban emergency frameworks, outcomes unattainable solely through the optimization of traditional resources like human capital, financial assets, and physical infrastructure. In contrast, while factor allocation efficiency contributes to enhancing emergency response capabilities and resource utilization, its focus remains primarily on “resource distribution” rather than “information processing” or “systemic integration”. Given the centralized and hierarchical nature of urban governance in China, factor allocation efficiency largely depends on administrative directives and conventional managerial practices, rather than being driven by technological innovation inherent to smart city development. Therefore, although factor allocation efficiency directly enhances emergency management performance, it does not act as an intermediary between smart city construction and emergency capacity building. Theoretical analysis from the perspective of mediation mechanisms reveals that digital technology possesses multifaceted capabilities—including information integration, intelligent coordination, and platform-based empowerment—that allow it to channel governance benefits derived from smart city initiatives into measurable improvements in emergency management [2,83]. Conversely, factor allocation efficiency lacks such technologically enabled attributes and operates within a framework of unidirectional resource deployment. Thus, grounded in the institutional logic and digital governance characteristics of China’s smart city advancement, digital technology plays an indispensable mediating role in translating smart city development into enhanced emergency management capacity, whereas factor allocation efficiency primarily serves as a direct enabler within the emergency management subsystem, unable to generate systemic spillover effects from broader smart city governance innovations.

6. Conclusions and Recommendations

6.1. Conclusions

Drawing upon large-sample panel data and employing multiple robustness checks alongside heterogeneity analysis, this study systematically evaluates the impact of smart city construction in China on urban emergency management capacity and its underlying mechanisms. The key findings are as follows: First, empirical results consistently demonstrate that smart city initiatives significantly enhance urban emergency management capabilities. This conclusion remains robust after controlling for a range of city-specific characteristics, addressing parallel trends, applying propensity score matching, eliminating potential confounding policies, and conducting counterfactual analyses. Second, heterogeneity analysis reveals a clear regional and population-size gradient in policy effects. The most pronounced improvements are observed in central regions, as well as in super-large and small cities, whereas the marginal impacts in eastern regions and medium-sized cities are relatively modest. This pattern underscores the dependence of smart urban transformation on pre-existing infrastructure, resource endowments, and governance demands. Third, mechanism analysis confirms that digital technology application exerts a full mediating effect between smart city construction and the enhancement of emergency management capacity. Specifically, 86.7% of the policy effect is transmitted through the penetration of digital technologies, highlighting the pathway through which technological empowerment amplifies governance effectiveness. In contrast, while improved factor allocation efficiency significantly contributes to emergency management performance, its influence operates directly rather than serving as an intermediary mechanism. Given the institutional features of China’s smart city development—characterized by “platform-centric design” and “application-driven implementation”—digital technology has emerged as the pivotal lever for enhancing emergency response capabilities. Meanwhile, improvements in factor allocation efficiency rely more heavily on collaborative governance frameworks and cross-departmental institutional reforms.

The marginal contributions of this study are threefold: (1) It provides robust causal evidence on the positive impact of smart city construction on emergency management capabilities using a large-scale national dataset and rigorous DID methodology. (2) It identifies and empirically validates the underlying mechanisms, revealing that digital technology application acts as a fully mediating channel, while factor allocation efficiency exerts a direct but non-mediating influence. (3) It uncovers significant heterogeneous effects, demonstrating that the benefits are most pronounced in central regions and cities at the extremes of the population size spectrum (megacities >10 million and small cities < 1 million), offering insights for differentiated policy design. This study refines the existing theoretical framework in two key aspects: (1) It challenges Wang et al.’s [43] “resilience single mediation” model by empirically demonstrating that digital technology serves as the central mechanism through which smart cities enhance emergency response capabilities; (2) It identifies a “scale double threshold” effect, wherein both megacities and small cities exhibit significant gains in emergency management performance, thereby offering a novel urban-scale explanation for the regional heterogeneity highlighted by Feng et al. [56]. This model offers valuable insights for developing economies undergoing rapid urbanization worldwide, particularly regarding infrastructure deployment sequencing and scale-adapted digital governance solutions.

6.2. Recommendations

(1) Prioritizing Investment in Digital Technology Infrastructure

Fiscal investment in digital infrastructure for smart cities should be legally and continuously increased with a focus on constructing large-scale urban IoT, video perception networks, cloud computing centers, and integrated big data governance platforms. For example, the “City Brain” model can be widely promoted to enable comprehensive digital perception and real-time tracking of urban operations, emergency events, and resource allocation. To address regional disparities, it is recommended that special transfer payments be established for small- and medium-sized cities and fiscally constrained regions to narrow the urban-rural digital divide. Targeted digital infrastructure improvement projects should also be implemented in signal-blind zones and underdeveloped areas such as industrial parks and communities, ensuring seamless transmission of emergency management information and building a resilient, city-wide digital network.

(2) Accelerating Innovation and Dissemination of Digital Emergency Management Scenarios

Given China’s frequent natural disasters and complex emergency response challenges in major cities, localized and distinctive digital emergency scenarios should be prioritized. For instance, AI- and remote sensing-based intelligent early warning systems for meteorological disasters can be deployed in coastal and flood-prone areas; in megacities, intelligent traffic evacuation and medical supply distribution systems can be developed to enhance coordination between emergency command and public services. The central government should lead the establishment of specialized scientific and technological programs for smart emergency management, supporting joint innovation efforts among enterprises, universities, and municipalities. A standardized package of digital emergency solutions should be promoted to raise industry-wide standardization levels.

(3) Promoting Interdepartmental Data Collaboration and Process Reengineering

To address the current fragmentation across departments and local jurisdictions in China’s governance system, a city-level integrated emergency information command center should be established. Coordination mechanisms among key sectors—such as public security, health, housing and urban-rural development, emergency management, and transportation—should be strengthened. Unified standards for data flow, sharing, authorization, and secure usage must be formulated alongside the construction of a centralized data exchange platform. Pilot programs of “full-process emergency drills” should be encouraged, using smart city platforms to test multi-departmental resource scheduling, identify bottlenecks in real time, and iteratively refine dispatch protocols. Gradually, administrative boundaries should be transcended to ensure efficient integration of information flows, work processes, and resource allocation across regions and departments.

(4) Strengthening Smart Emergency Management Capacity in Central, Western, and Smaller Cities

Given the relatively weak foundational digital infrastructure and significant potential for improvement in central and western regions and smaller cities, the central and provincial governments are advised to implement inclusive digital empowerment strategies. Initiatives such as “cloud access” and “platform integration” can guide infrastructure investment and expand smart emergency services. Policy instruments, including dedicated funding, technical support, talent deployment, and co-construction of digital platforms, should be introduced. Provincial-level public smart emergency service centers should be established to provide technology, standards, and platform capabilities to under-resourced cities, promoting the implementation of an “integrated platform plus grassroots terminals” architecture. This will help bridge regional gaps and advance nationwide modernization of emergency management.

(5) Enhancing Emergency Management Talent Development and Capability Standards

China currently faces a significant shortage of technically skilled personnel in emergency management. It is therefore recommended that policy guidance encourage universities to establish interdisciplinary programs and courses integrating smart emergency management, digital security, data science, and traditional emergency disciplines. Joint training initiatives involving research institutions, tech enterprises, and international organizations should be promoted. In-service civil servants and emergency professionals should receive targeted digital skills training. Certification frameworks and continuous evaluation criteria for smart emergency roles should be improved. Through the coordinated linkage of university education, enterprise internships, and city-level pilot programs, a professional talent pipeline for intelligent emergency industries can be formed, enhancing national digital literacy and practical application capabilities in emergency management.

(6) Optimizing Resource Allocation Systems and Innovate Collaborative Governance Mechanisms

Given the prevailing locality-based and fragmented nature of resource management in China, deep reforms of emergency resource allocation and dispatch systems are necessary. A cross-regional integrated emergency command and resource allocation mechanism should be established to break down departmental silos and achieve dynamic optimization of materials, equipment, and human resources across cities and regions. Fiscal incentives should be enhanced by incorporating the collaborative performance of smart city platforms into local government performance evaluations. The “regional risk co-governance + unified dispatch” approach should be implemented, exploring regional emergency alliances, joint material reserves, and intelligent decision-support systems to improve the efficiency of emergency element circulation and promote coordinated governance capacity enhancement.

(7) Improving Policy Evaluation and Dynamic Adjustment Mechanisms

The development of smart cities and emergency governance in China exhibits characteristics of stage-based progression and structural imbalance. Therefore, a scientifically grounded and dynamically responsive policy evaluation framework should be established. Regular assessments of smart emergency policy outputs and effectiveness should be conducted at the level of urban agglomerations and economic belts, forming a quantifiable indicator system and traceable data records. Independent reviews by experts and third-party institutions should be encouraged to promptly identify implementation barriers and shortcomings. Policies should be dynamically updated, and differentiated support provided, fostering a virtuous cycle of “strong assisting weak” and “leading cities guiding lagging ones” among different types of cities. This will contribute to balanced and sustainable development of smart emergency governance nationwide. While this tiered governance model addresses structural imbalances through adaptive policy cycles, its digital implementation mechanisms must simultaneously mitigate emergent risks—where rapid technological expansion risks exacerbating systemic privacy vulnerabilities, escalating cybersecurity threats, and deepening socioeconomic disparities through unequal resource access.

6.3. Research Limitations and Future Directions

Although this study systematically assesses the enhancing effects of smart city construction on the emergency management capabilities of Chinese cities and the underlying mechanisms, certain limitations remain. First, the empirical analysis is based on panel data from 2006 to 2021, which may not fully capture the most recent advancements in smart city development, such as the deployment of 5G infrastructure and large-scale AI models, or their long-term dynamic impacts. Second, while the study identifies the core mediating role of digital technology application and accounts for regional and population-scale heterogeneity, it offers limited insight into how specific internal technical architectures—such as the maturity of IoT platforms and data governance frameworks—shape emergency response efficiency. Third, the mechanism analysis primarily focuses on two pathways: factor allocation efficiency and digital technology adoption, potentially overlooking other important mediating or moderating factors, such as organizational coordination reforms and public digital literacy. Finally, the reliance on municipal-level statistical data constrains the depth of micro-level analysis, particularly regarding the operational details of emergency management practices, such as the execution efficiency of emergency plans and on-site response speed.

Future research can be advanced in several key directions. The first is deepening the investigation of technical mechanisms and dynamic effects by exploring the specific roles and synergies of emerging technologies—such as blockchain, digital twins, and generative AI—across different stages of emergency management (prevention, response, and recovery) and employing extended longitudinal data to trace the long-term impacts and evolutionary trajectories of policy interventions. The second is expanding cross-scale and cross-cultural comparisons by extending the analysis to sub-municipal levels, such as counties or specialized functional zones (e.g., industrial parks), and conducting international comparative studies to identify commonalities and differences in smart emergency governance under diverse institutional and developmental contexts, thereby contributing to more universally applicable theoretical frameworks. The third is integrating microdata and complexity analysis by incorporating field surveys, case studies, and real-time big data (e.g., social media and sensor data) to develop a more granular and responsive emergency capacity evaluation system. Advanced analytical tools such as system dynamics and social network analysis can be employed to simulate the resilience of smart city systems under multiple crisis scenarios. The fourth is exploring collaborative governance and institutional alignment by examining how smart city platforms can facilitate cross-sectoral, cross-jurisdictional, and multi-level emergency coordination, alongside institutional innovations, standardization efforts, and ethical risk mitigation strategies. Particular attention should be given to optimizing the “technology-institution” fit in small and medium-sized cities and underdeveloped regions to enhance the effectiveness and equity of policy outcomes.