Exploring land use evolution and its impact on carbon storage is crucial for mitigating climate change in resource-based cities and promoting green, low-carbon development. This study constructs a GMOP-PLUS-InVEST coupled model and utilizes remote sensing data from five phases of land use from 2000 to 2020. Four scenarios are established to simulate the future patterns of land use and carbon storage changes in Jining City. The results indicate that: (1) from 2000 to 2020, farmland, forest land, and grassland in Jining City show a declining trend; while construction and waters increase, resulting in a reduction of carbon storage from 167.35 × 10⁶ t in 2000 to 159.85 × 10⁶ t in 2020; (2) coal mining significantly influences nearby land utilization types and carbon storage, leading to a decline in nearby carbon reserves; (3) compared to the other three scenarios, the coordinated development scenario exhibits higher land use efficiency and carbon storage, with lower levels of human disturbance; balancing the local economy and environment, and serving as a sustainable pattern of land use for the area. The outcomes of this paper quantitatively reflect the relationship between land use, coal mining, and carbon storage in high-water-level resource-based cities; providing guidance for the local economy, urban development, and ecological environment protection.

Reasonable land use planning and management efficiently allocates land resources, promotes socio-economic development, protects the ecological environment, and fosters sustainable development. It is a crucial foundation for achieving harmonious coexistence between humans and nature. Optimizing land use is key to land use planning and management. Four scenarios are established: an economic development scenario (EDS), an ecological protection scenario (EPS), a natural development scenario (NDS), and a coordinated development scenario (CDS). This study simulates land use patterns under these scenarios through the coupling of the GMOP and PLUS models. It analyzes the land use efficiency transformation index, landscape ecological index, comprehensive land use benefits, and ecosystem service value (ESV) for each pattern. The optimal land use pattern is determined by balancing these factors. The results indicated that under the CDS, the areas of wasteland, grassland, forest land, water bodies, construction land, and unused land in Lvliang City were 6724.29 km², 6664.74 km², 6581.84 km², 126.94 km², 1017.33 km², and 0.42 km², respectively. This represented the optimal land use plan for Lvliang City. The plan minimized human interference with the landscape pattern, achieved the highest land use efficiency transformation index, and reached a reasonable balance between land use benefits and ESV. The research findings provide valuable insights and decision support for regional land use planning, territorial space planning, and related policy formulation.