Emergency response capability evaluation is an essential means to strengthen emergency response capacity-building and improve the level of government administration. Based on the whole life cycle of emergency management, the emergency capability evaluation index system is constructed from four aspects: prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, and recovery and reconstruction. Firstly, the entropy method is applied to measure the emergency response capability level of 31 Chinese provinces from 2011 to 2020. Second, the Theil index and ESDA (Exploratory Spatial Data Analysis) are applied in exploring the regional differences and spatial-temporal distribution characteristics of China’s emergency response capacity. Finally, the obstacle degree model is used to explore the obstacle factors and obstacle degrees that affect the emergency response capability. The results show that: (1) The average value of China’s emergency response capacity is 0.277, with a steady growth trend and a gradient distribution of “high in the east, low in the west, and average in center and northeast” in the four major regions. (2) From the perspective of spatial distribution characteristics, the unbalanced regional development leads to the obvious aggregation effect of “high-efficiency aggregation and low-efficiency aggregation”, and the interaction of the “centripetal effect” and “centrifugal effect” finally forms the spatial clustering result of emergency response capability level in China. (3) Examining the source of regional differences, inter-regional differences are the decisive factor affecting the overall differences in emergency response capability, and the inter-regional differences show a reciprocating fluctuation of narrowing–widening–narrowing from 2011 to 2020. (4) Main obstacles restricting the improvement of China’s emergency response capabilities are “the business volume of postal and telecommunication services per capita”, “the daily disposal capacity of city sewage” and “the general public budget revenue by region”. The extent of the obstacles’ impacts in 2020 are 12.19%, 7.48%, and 7.08%, respectively. Based on the evaluation results, the following countermeasures are proposed: to realize the balance of each stage of emergency management during the holistic process; to strengthen emergency coordination and balanced regional development; and to implement precise measures to make up for the shortcomings of emergency response capabilities.

Building resilient cities is playing an increasingly important role in enhancing urban safety and promoting sustainable urban development. However, few scholars pay attention to urban resilience in inland provinces. Choosing Henan Province, as it is a typical representative of China’s major inland economic provinces, has practical guiding significance. This study aims to provide a systematic indicator system and evaluation tool to measure the cuity’s resilience level. Therefore, based on a multidimensional perspective, this paper dissects the urban resilience spatial and temporal evolution characteristics of 18 Henan Province cities with the entropy method, Thiel index, and ESDA (Exploratory Spatial Data Analysis) and explores influencing factors with a spatial econometric model. The main results are as follows: (1) the overall resilience in Henan Province continuously grows, and the resilience level of the Zhengzhou metropolitan area is the highest. In the urban resilience subsystem, economic and social resilience notably drive urban resilience improvement in Henan Province. (2) The spatial difference of urban resilience has been significantly reduced, but the inner metropolitan area presents the characteristics of “core–periphery”. Urban resilience presents a positive spatial correlation, and local spatial agglomeration is relatively stable. (3) Under the state of spatial interaction, urbanization rate, administrative, innovation, market, and industrial structure factors all have significant direct effects and spatial spillover effects on overall resilience, but openness exerts downward pressure on local resilience. (4) On this basis, strategies have been proposed to continuously promote the development of new urbanization, improve the regional coordinated development mechanism, increase market activity, optimize the environment for scientific and educational innovation, and promote the optimization and upgrading of industrial structure. The approach taken in this research may also be useful for developing urban resilience assessment tools in other central plains cities as well as in other cities in the interior of the world with similar conditions.

Urban resilience assessment is crucial to guarantee the safe and stable operation of our cities. Looking at the whole process of emergencies from the occurrence, development, causing disaster losses to taking emergency response measures, we combine the danger of urban accident and disaster, the capacity of disaster-bearing carriers, and the emergency management capacity as the influencing factors. Taking Beijing as an example, considering the completeness of the relevant indicators and the difficulty of obtaining data, TOPSIS is used to construct a multi-factor comprehensive assessment model of urban safety resilience; At the same time, the important factors constraining the improvement of safety resilience are further analyzed by using the obstacle degree model. The results show that: among the 16 districts in Beijing, Chaoyang and Haidian districts have the highest level of safety resilience; the level of the degree of disaster tolerance among the multi-factors has the greatest impact on the level of urban safety resilience in Beijing; and the multifactorial comprehensive assessment model of urban resilience is able to reflect the urban resilience distribution, discover the weaknesses that exist in the development of urban safety and provide decision-making aids for the relevant city managers to carry out urban resilience construction.

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions.

Weak links in modern power grids can lead to cascading failures from isolated incidents, making the scientific assessment of grid and transmission line vulnerability essential for preventing large-scale blackouts. This study conceptualizes transmission lines as functionally critical infrastructure and develops a four-dimensional vulnerability assessment framework encompassing Danger, Susceptibility, Resistance, and Recoverability, taking into account natural environmental conditions, human disturbances, grid operational states, and emergency response capabilities. Distinct from conventional classification methods that primarily rely on structural topology or subjective judgment, this study adopts a hazard index-based approach and introduces an objective weighting mechanism using the entropy method to quantitatively evaluate and classify indicators across the four dimensions. A case study of 500 kV transmission lines in Henan Province, China, reveals that the composite vulnerability index of the province's critical power infrastructure reaches 57.2, indicating a high level of vulnerability. Among the transmission towers, 19.8% are classified at the "Higher" level and 40.6% at the "High" level. Considerable variation in Recoverability among towers significantly influences the overall vulnerability assessment results. Moreover, the distribution of susceptibility to various hazards and accidents shows strong spatial and temporal heterogeneity, closely related to seasonal factors and tower locations. Based on these findings, targeted strategies are proposed from the perspectives of line inspection and differentiated management, providing technical support for enhancing power grid resilience and improving disaster response efficiency.

In the context of increasingly frequent and severe natural disasters, scientifically measuring and analyzing the efficiency of natural disaster emergency management in China is of great practical significance for enhancing the performance of the emergency management system and promoting its systematic and high-quality development. This study first applies a super-efficiency SBM-DEA model with undesirable outputs to systematically measure the efficiency of China’s natural disaster emergency management system during the period 2019–2023. Subsequently, the Dagum Gini coefficient and Kernel Density estimation are employed to examine regional disparities and dynamic evolution across eastern, central, western, and northeastern China. Finally, the coefficient of variation and spatial econometric models are applied to test the spatial convergence characteristics of emergency management efficiency. The results indicate that: (1) China’s overall disaster emergency management efficiency remains at a relatively low level and exhibits a fluctuating trend characterized by an initial increase followed by a decline. The regional distribution pattern of emergency efficiency is ranked as “Northeast > Central > West > East”. (2) The average annual contributions of intra-regional disparities, inter-regional disparities, and transvariation density to the overall variation in national emergency management efficiency are 27.58%, 39.90%, and 32.53%, respectively, indicating that inter-regional disparities and transvariation density are the dominant sources of systemic differences among regional subsystems. (3) The national distribution of emergency management efficiency displays a bimodal pattern, indicating polarization; however, the secondary peak is relatively flat, suggesting a weakening trend of provincial-level polarization and a gradual narrowing gap with high-efficiency regions. (4) σ-divergence is observed at the national level and in the central region, while both absolute and conditional β-convergence exist to varying degrees at the national level and across all four regions. Nevertheless, the enhancement of natural disaster emergency management efficiency has not yet realized a system-level transition from convergence in growth rates to convergence in efficiency gaps. In addition, economic development, technological progress, urbanization, and industrial structure exert significantly heterogeneous effects on disaster emergency management efficiency across different regions.