Resource-exhausted cities are cities where the ratio of exploited reserves to recoverable reserves exceeds 70%. Long-term energy extraction and consumption lead to weak economic growth, idle industrial land, and ecological imbalances. It is imperative to explore sustainable development paths that are green and low-carbon. The spatial characteristics of cities and the structure of energy networks are crucial foundations for low-carbon development and energy security in cities. The main research content includes three aspects: (1) The first involves the identification of the distribution characteristics of typical resource- exhausted cities worldwide. This mainly includes coal, oil, metallurgy, forestry, and non-metallic minerals. Among them, coal resource-exhausted cities are the most numerous, mainly distributed in China, Australia, the United States, etc. (2) The second includes an analysis of the spatial characteristics of resource-exhausted cities in China. This involves taking 24 resource-exhausted prefecture-level cities in China as the research objects, integrating geographic data such as Points of Interest (POIs), and using machine learning for accurate quantitative identification and spatial delineation of urban functions. The production space and ecological space of cities show an aggregated distribution pattern, while the living space is randomly distributed. (3) The third is based on urban energy consumption data, utilizing the modified gravity model and social network analysis (SNA), and analyzing the centrality/relevance, relationship density and frequency, and accessibility. The average degree of centrality of the 17 coal-related industries is 5.529, demonstrating the energy network structure of resource-exhausted cities. This paper provides data foundations and technical methods for achieving urban energy renewal, ecosystem stability, and optimized spatial structures.

With the active development of coal-related industries in Ningdong, problems such as land degradation caused by coal-based site expansion and soil pollution caused by coal-based solid waste discharge are getting progressively worse. The identification of space types and the proposal of risk management are the key basic issues of regional energy resource low-carbon development and ecological protection. This article proposes an index system for feature extraction at the regional and site scales. The spatial–temporal evolution trends and differences in Ningdong coal power base from 2003 to 2021 were identified and interpreted through feature recognition based on Landsat images at the regional scale. Accurate site type recognition was conducted based on 10 m resolution Sentinel-2 images from 2021. We utilized the PSR (pressure–state–response) model for a comprehensive assessment of risk management in Ningdong. The results of this study show that the coal site > the coal chemical site > the coal power site, and the risk of the coal power base is in a controllable state; thus, we put forward a zoning control strategy. Our data on the pollution risk management of large-scale coal-fired power generation complexes are of significant importance for site remediation and regional ecological restoration.