Mining activities have significantly altered the land use patterns of mining areas, exacerbated the degree of landscape fragmentation, and thereby led to the loss of biodiversity. Ecological networks have been recognized as an essential component for enhancing habitat connectivity and protecting biodiversity. However, existing studies lack dynamic analysis at the landscape scale under multiple future scenarios for mining areas, which is adverse to the identification of ecological conservation regions. This study used the MOP-PLUS (multi-objective optimization problem and patch-level land use simulation) model to simulate the land use patterns in the balance of ecology and economy (EEB) scenario and ecological development priority (EDP) scenario for the Shendong coal base. Then, climate change and land use patterns were integrated into ecosystem models to analyze the dynamic changes in the ecological networks. Finally, the conservation priorities were constructed, and dynamic conservation hotspots were identified using landscape mapping methods. The following results were obtained: (1) From 2000 to 2020, large grassland areas were replaced by mining areas, while cultivated land was replenished. By 2030, the forest and grassland areas (967.00 km², 8989.70 km²) will reach their peaks and the coal mine area (356.15 km²) will reach its nadir in the EDP scenario. (2) The fragmentation of ecological sources intensified (MPS decreased from 19.81 km² to 18.68 km²) and ecological connectivity declined (in particular, $\alpha$ decreased by 6.58%) from 2000 to 2020. In 2030, the connectivity in the EDP scenario will increase, while the connectivity in the EEB scenario will be close to that of 2020. (3) The central and southeastern parts of the Shendong coal base have higher conservation priorities, which urgently need to be strengthened. This study offers guidance on addressing the challenges of habitat and biodiversity conservation in mining areas.

Urban sprawl does not invariably impede factor agglomeration; rather, it can foster polycentric urban configurations, thereby enhancing productivity and encouraging enterprise innovation. This study investigates the effect of urban sprawl on enterprise innovation using data for A-share listed Chinese companies from 2010 to 2020. The results reveal a significant inverted U-shaped relationship between urban sprawl and enterprise innovation, particularly among large enterprises, well-established entities, non-state-owned enterprises, and those operating in non-manufacturing sectors. Additionally, the effects of urban sprawl on the inverted U-shaped relationship are more pronounced in the north-eastern regions and small cities. Regional integration significantly moderates the inverted U-shaped relationship between urban sprawl and enterprise innovation. This research contributes new insights to the field of enterprise innovation, offering theoretical and empirical support for analyzing the economic implications of urban sprawl.