Unveiling the spatial and temporal distribution of urban ecological resilience and analyzing the configuration paths for enhancing its levels are crucial for promoting sustainable development in China. Our study integrates the DPSIR and EES models, considering the causal relationships between systems affecting urban ecological resilience while also examining their internal structures. Based on this, we construct an evaluation system for urban ecological resilience indicators. Utilizing the entropy-TOPSIS method, we assess the ecological resilience index (ERI) across 280 Chinese cities from 2011 to 2021, and the kernel density estimation and Markov chain are used to study the evolution process while the magnitude and source of spatial–regional differences are examined by the Dagum Gini coefficient decomposition method. Additionally, we empirically investigate the driving mechanisms toward high ERI with the focused stepwise quantitative case analysis (fsQCA) method based on the technology–organization–environment (TOE) framework. The results find that the ERI in China shows a tendency of moderate growth in variability, with an obvious gradient distribution: higher levels in the eastern and southern and lower levels in the western and northern regions. Also, ERI exhibits evolutionary features of increasing polarization and inter-regional differentiation. Spatial disparities gradually increase with fluctuations, driven primarily by transvariation density and intra-regional differences, contributing to a dual non-equilibrium state of east–west and north–south directions. Achieving a high ERI is influenced by various antecedent variables interacting with each other, and there are three predominant driving paths among these variables, with the level of informatization playing a central role in each pathway.

Understanding the multidimensional sources and key drivers of differences in green transition performance (GTP) among resource-based cities is vital for accomplishing national sustainable development objectives and facilitating regional coordination. This study proposes a “Carbon Reduction–Pollution Control–Greening–Growth” evaluation framework and utilizes the entropy method to assess the GTP of China’s resource-based cities from 2013 to 2022. The Dagum Gini coefficient and variance decomposition methods are employed to investigate the GTP differences, and the Optimal Parameters-Based Geographical Detector and the Geographically and Temporally Weighted Regression model are applied to identify the driving factors. The results indicate the following trends: (1) GTP exhibits a fluctuating upward trend, accompanied by pronounced regional imbalances. A pattern of “club convergence” is observed, with cities showing a tendency to shift positively toward adjacent types. (2) Spatial differences in GTP have widened over time, with transvariation density emerging as the dominant contributor. (3) Greening differences represent the primary structural source, with an average annual contribution exceeding 60%. (4) The impact of digital economy, the level of financial development, the degree of openness, industrial structure, and urbanization level on GTP differences declines sequentially. These factors exhibit notable spatiotemporal heterogeneity, and their interactions display nonlinear enhancement effects.

Clarifying the sources of disparity and the systemic structure of influencing factors behind China’s economic resilience is crucial for promoting regional coordinated development and ensuring national security. This study constructs an evaluation index system based on the DPSIR model and employs the entropy method to measure China’s economic resilience from 2008 to 2023, examining its temporal evolution and spatial distribution. A bi-dimensional decomposition method of Gini coefficient is applied to examine disparities from both spatial and structural perspectives. Furthermore, the DEMATEL-ISM model is employed to reveal the systemic structure of influencing factors. The findings reveal that: (1) China’s economic resilience steadily improved during the study period, showing a spatial gradient of “Eastern > Central > Northeastern > Western,” with its geographic center shifting southeastward, reflecting strong spatial dependence. (2) Disparities in economic resilience have generally widened. Inter-regional differences are the main source of spatial disparities, while variations in response dominate the structural disparities. Initially, disparities were mainly due to differences in influence between eastern and western regions, but by the end of the period, disparities in driving forces became the key contributor. (3) Influencing factors follow a four-level, three-stage hierarchical structure. Foreign capital withdrawal risks, innovation investment, technological progress, factor supply, and the output of opening-up constitute deep-level factors influencing economic resilience. This study refines the evaluation framework of economic resilience and provides important references for understanding the disparities in China’s economic resilience and developing targeted improvement strategies.