Optimizing grassland biodiversity and carbon co-benefits under climate change: An integrated climate-ecological-economic modelling framework

Managing grasslands for both carbon sequestration and biodiversity conservation is increasingly challenging under climate change. We develop an integrated climate–ecological–economic modelling framework to evaluate biodiversity–carbon trade-offs in the Inner Mongolian typical steppe, where mowing and grazing are dominant management practices. Using butterflies as biodiversity indicators, carbon-sequestration estimates, regional opportunity-cost parameters, and downscaled climate projections under SSP1–2.6, SSP2–4.5, and SSP5–8.5, we applied the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify Pareto-optimal mowing–grazing configurations from 2025 to 2050. The simulations indicated that, within the parameterized water-limited response functions, projected precipitation differences were associated with larger shifts in biodiversity–carbon co-management potential than temperature differences. Under the assumptions of our modelling framework, optimized configurations were generally mowing-dominated, with mowing accounting for 77%–94% of the management-allocation matrix in representative 2030 solutions, although grazing-oriented patches remained important in biodiversity-priority outcomes by maintaining habitat heterogeneity. Opportunity-cost accounting provided a regional economic comparison layer for Pareto-optimal configurations, with temporal cost increases mainly reflecting the assumed annual escalation and cross-scenario differences reflecting the cost ranges of feasible Pareto-optimal solutions. Overall, the framework provides a transferable approach for linking climate scenarios, land-use allocation, ecological outcomes, and economic constraints in grassland biodiversity–carbon co-management, subject to recalibration with local ecological, management, and economic parameters.