Geologically constrained dynamic evolution and early warning of urban resource-environment carrying capacity: a case study of Jinan, China

Abstract

Urban resource environment carrying capacity (RECC) is increasingly challenged by nonlinear interactions among socioeconomic development, resource depletion, and geological environmental degradation. However, existing assessments often neglect the cumulative feedback effects of groundwater depletion and geological responses. This study develops a city-scale system dynamics (SD) model integrating six subsystems (population–economy, land, water resources, ecology, environment, and geological environment) to simulate RECC evolution in Jinan, China, during 2021–2031. A groundwater–geological feedback module is incorporated to explicitly represent the interactions among groundwater abstraction, groundwater depletion, land subsidence, and development constraints. A scenario-based early-warning framework is further developed to identify nonlinear degradation trajectories under alternative scenarios. Model validation indicates satisfactory performance, with R² and NSE values ranging from 0.87 to 0.97. Results reveal a transition from development-driven expansion to resource- and geo-environmental constraint regulation. Under the business-as-usual scenario, available water resources decline by 40%, the groundwater recovery index reaches − 0.35, land subsidence increases by 25%, and RECC declines by 28% by 2031. Water and geo-environmental constraints jointly contribute 71% of total RECC limitations, and a critical groundwater stress threshold is identified at approximately 1.15 (groundwater recovery index = − 0.15), beyond which degradation accelerates. Compared with business-as-usual, the resource-optimization scenario increases available water resources by 56%, reduces water pollution by 64% and land subsidence by 31%, delays high-level warnings by 2–3 years, and maintains 92% of per-capita GDP. These findings highlight the importance of groundwater–geological feedbacks for dynamic RECC assessment and sustainable urban planning.