Mining-resource-based cities, as distinctive human–environment systems, face urgent challenges from intensified urbanization and mining, leading to land imbalance and ecosystem service degradation. To enhance resilience, it is essential to identify the evolution and drivers of ecosystem services and construct targeted ecological compensation models. This study focuses on Xinzhou, a representative mining city in China, and systematically analyzes three aspects: (1) spatiotemporal dynamics of land use and ecosystem service value (ESV) from 2000 to 2023 using Markov chains, equivalent factor method, hotspot and sensitivity analyses; (2) identification of ESV driving mechanisms through an integrated “stepwise regression + geographical detector” framework; and (3) formulation of ecological compensation models via quantification of priority indices, demand intensity coefficients, and compensation standards. Key findings indicate that land conversion was concentrated in coalfield zones and surrounding built-up areas, involving 2,518,341.75 hm² (35.76% of total area), primarily characterized by a reduction in farmland and expansion of forest, grassland, and construction land. ESV showed a striped spatial pattern, with higher values in mountainous zones and lower values in valleys and basins with frequent human activity. The northwest coalfield region experienced an initial decline followed by a recovery in ESV. Annual mean temperature emerged as the dominant driver, while DEM influence increased annually. All factor interactions exhibited synergistic effects, with natural variables exerting greater influence than socio-economic ones. Ecological compensation demand was high overall, especially in Wutai, Kelan, and Pianguan counties, with high-value compensation areas mainly distributed in the eastern and central parts of Xinzhou. Looking ahead, a compensation framework prioritizing ecological–economic optimization should be developed, guided by zoned, typological, and dynamic configurations. By analyzing ecosystem governance from the perspective of a mining-resource-based city, this study enhances global ecosystem service evaluation frameworks and offers a replicable model to advance transnational ecological cooperation and green urban transformation.

Scientific identification of ecological sources and corridors is crucial in constructing an ecological security pattern (ESP). To develop an ESP tailored to the scale of central urban areas in megacities, this study takes Central Beijing as the research object. It innovatively integrates the integrated valuation of ecosystem services and tradeoffs (InVEST), the morphological spatial pattern analysis (MSPA), and the Conefor software to identify ecological sources. Seven indicators related to topographic, natural conditions, and human disturbance factors are selected to build the ecological resistance surface, which is then combined with circuit theory to construct the ESP. The results show the following: (1) Central Beijing contains 157 ecological sources, primarily distributed in the western, northern, and eastern regions, with woodland as the dominant land type. (2) A total of 439 ecological corridors were extracted, including 317 key ecological corridors and 122 inactive ecological corridors. (3) The identified ecological pinch points are mainly the Jingmi Diversion Canal and the West Moat. (4) The identified ecological barriers are spread throughout the entire study area. The results of this study are highly significant for improving the quality of ecological security and protecting biodiversity in the study area and other urban centers.

Balancing economic growth with the decline in ecosystem service functions presents a critical global challenge in the 21st century. In response to the United Nations Development Programme’s emphasis on localizing Sustainable Development Goals, China has devised policies aimed at synergistically advancing high-quality economic development (HQED) and high-level ecological environment protection. However, research exploring the interplay between regional HQED and green space ecosystem services (GSES) remains limited. Consequently, this study examines Shanxi Province, a prototypical resource-based region in China, to develop localized metrics for green total factor productivity (GTFP) and the valuation of green space ecosystem services (GSESV). It quantifies the efficiency of HQED and the growth rate of green space ecological benefits (GSEB). Employing coupling coordination models, Theil indices, cluster analysis, and enhanced grey relational techniques, the research scrutinizes the evolutionary patterns and driving factors of coupling and coordination between HQED and GSEB across various spatial scales from 2007 to 2020. Significant findings include (1) an overall increase in HQED efficiency across all spatial scales, accompanied by a decline in green technological progress (GTC); (2) a sustained growth trend in GSEB per hectare across the province, with the southeastern region, particularly Jincheng, leading; (3) a level of coordination between HQED and GSEB across the province that surpasses the preliminary stage, albeit with pronounced regional disparities, notably the lagging and unstable coordination in the southern regions; (4) significant driving effects of industrial “three wastes” and industrial transformation on the coordinated evolution between HQED and GSEB across all spatial dimensions, with notable impacts of innovation input–output in central, southern, and southeastern Shanxi. This research offers new strategic insights for the synergistic development of the economy and ecology in Shanxi Province and contributes novel theoretical foundations for formulating sustainable development policies globally.

To promote the economic revitalization and cultural prosperity of abandoned coal mine clusters and facilitate regional sustainable development, this study involved the construction of a ranking system for coal mines suitable for cultural tourism transformation and a regional cultural tourism resource coordination planning framework. The research findings and innovations are as follows: (1) Through a combination of subjective judgment and quantitative analysis, an AHP–entropy–TOPSIS evaluation model for the transformation of abandoned coal mine clusters was developed. This model significantly enhances the scientific and precise nature of the decision-making process. (2) By integrating cultural tourism land use indicators, a ranking system for the suitability of coal mines for transformation into cultural tourism destinations was established, enabling the identification of the most suitable coal mines for transformation. (3) The most suitable coal mines for transformation were then integrated with regional historical cultural resources. An innovative application of circuit theory was used to optimize the regional road network, while a kernel density analysis was employed to perform the functional zoning of the study area. This resulted in a comprehensive regional cultural tourism resource coordination planning framework. This study offers a valuable reference for transforming abandoned coal mines and integrating cultural tourism, contributing to regional sustainable development.

The acceleration of global urbanization and the rapid growth of urban populations have intensified the complexity and urgency of parking demand. In megacities with limited land resources, efficiently addressing diverse parking needs has become a critical issue for sustainable urban development. Multi-objective optimization methods are widely applied to tackle such challenges, providing decision-makers with a set of optimal solutions that balance multiple objectives. However, existing studies often lack quantitative analyses of the relationships among these solutions, limiting their applicability in accommodating decision-makers with varying preferences. This study focuses on Jing’an District in Shanghai, a representative region of a Chinese megacity, to address this global issue. Based on real-world data, a multi-objective optimization model is constructed considering convenience, coverage, and cost-efficiency. The model is solved using an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), which dynamically adjusts crossover and mutation rates. Furthermore, the Pareto solution set is quantitatively analyzed from a cost-benefit perspective by integrating marginal benefit theory. This approach provides robust support for decision-makers seeking an optimal balance between cost and benefit, offering scenario-specific strategies. The findings of this study not only present an innovative, systematic, and flexible solution to the “parking dilemma” in high-density residential areas but also provide practical guidance and insights for other large cities in the planning and implementation of smart underground parking facilities.