Environmental–economic risk trade-offs in semi-arid grazing intensification: A stochastic bioeconomic optimization framework for Wulagai, China

CONTEXT Sustainable intensification in semi-arid grazing systems requires balancing farm profitability against environmental impacts under substantial climatic and market uncertainty. Stochastic approaches have already demonstrated the importance of climatic and economic variability in grazing and agricultural decision analysis, but a remaining challenge is to propagate these uncertainties through coupled plant–animal–soil processes and quantify the resulting distributional trade-offs between economic returns and environmental performance. OBJECTIVE We aimed to develop an integrated stochastic bioeconomic simulation–optimization framework that propagates climatic and market uncertainty through coupled plant–animal–soil processes, quantifies the resulting distributional trade-offs between farm profitability and environmental performance, and identifies risk-informed candidate grazing strategies. METHODS We developed an integrated stochastic bioeconomic simulation–optimization framework that couples forage production and defoliation, livestock intake and emissions, RothC soil‑carbon dynamics, and farm economics. For Wulagai meadow grasslands, stochastic weather and beef-price trajectories were propagated through 10,000 common 20-year scenarios. Candidate grazing strategies were evaluated using a five-quantile risk-aware multi-objective optimization that represented the profit distribution at five probability levels while minimizing mean net global warming potential (Net GWP). RESULTS AND CONCLUSIONS The stochastic optimization identified a set of non-dominated grazing strategies spanning distinct environmental–economic risk trade-offs rather than a single optimum. Increasing stocking rate generally increased profit potential but was accompanied by greater downside financial exposure and higher environmental costs, particularly at the more intensive end of the management range. The resulting frontier therefore defines a risk-informed candidate management range within which economic returns, downside risk, and environmental performance can be evaluated jointly. SIGNIFICANCE The framework extends grazing-management analysis beyond single expected-value outcomes by explicitly characterizing how climatic and market uncertainty propagate into economic and environmental performance. It provides a transparent basis for comparing candidate management strategies across alternative environmental–economic risk trade-offs, rather than prescribing a single optimal stocking rate.