In this study, an inexact stochastic fuzzy programming (ISFP) model is proposed for land-use allocation (LUA) and environmental/ecological planning at a watershed level, where uncertainties associated with land-use parameters, benefit functions, and environmental/ecological requirements are described as discrete intervals, probabilities and fuzzy sets. In this model, an interval stochastic fuzzy programming model is used to support quantitative optimization under uncertainty. Complexities in land-use planning systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The proposed method is applied to planning land use/ecological balance in Poyang Lake watershed, China. The objective of the ISFP is maximizing net benefit from the LUA system and the constraints including economic constraints, social constraints, land suitability constraints, environmental constraints, ecological constraints and technical constraints. Modeling results indicate that the desired system benefit will be between [15.17, 18.29] × 10¹² yuan under the minimum violating probabilities; the optimized areas of commercial land, industrial land, agricultural land, transportation land, residential land, water land, green land, landfill land and unused land will be optimized cultivated land, forest land, grass land, water land, urban land, unused land and landfill will be [228234, 237844] ha, [47228, 58451] ha, [20982, 23718] ha, [33897, 35280] ha, [15215, 15907] ha, [528, 879] ha and [1023, 1260] ha. These data can be used for generating decision alternatives under different scenarios and thus help decision makers identify desired policies under various system-reliability constraints of ecological requirement and environmental capacity. Tradeoffs between system benefits and constraint-violation risks can be tackled. They are helpful for supporting (a) decision of land-use allocation and government investment; (b) formulation of local policies regarding ecological protection, environment protection and economic development; (c) analysis of interactions among economic benefits, system reliability and ecological requirements.

Three Earth system models (ESMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6) were chosen to project ecosystem changes under 1.5 and 2 °C global warming targets in the Shared Socioeconomic Pathway 4.5 W m⁻² (SSP245) scenario. Annual terrestrial gross primary productivity (GPP) was taken as the representative ecological indicator of the ecosystem. Under 1.5 °C global warming, GPP in four climate zones—i.e., temperate continental; temperate monsoonal; subtropical–tropical monsoonal; high-cold Tibetan Plateau—showed a marked increase, the smallest magnitude of which was around 12.3%. The increase was greater under 2 °C of global warming, which suggests that from the perspective of ecosystem productivity, global warming poses no ecological risk in China. Specifically, in comparison with historical GPP (1986–2005), under 1.5 °C global warming GPP was projected to increase by 16.1–23.8% in the temperate continental zone, 12.3–16.1% in the temperate monsoonal zone, 12.5–14.7% in the subtropical–tropical monsoonal zone, and 20.0–37.0% on the Tibetan Plateau. Under 2 °C global warming, the projected GPP increase was 23.0–34.3% in the temperate continental zone, 21.2–24.4% in the temperate monsoonal zone, 16.1–28.4% in the subtropical–tropical monsoonal zone, and 28.4–63.0% on the Tibetan Plateau. The GPP increase contributed by climate change was further quantified and attributed. The ESM prediction from the Max Planck Institute suggested that the climate contribution could range from −12.8% in the temperate continental zone up to 61.1% on the Tibetan Plateau; however, the ESMs differed markedly regarding their climate contribution to GPP change. Although precipitation has a higher sensitivity coefficient, temperature generally plays a more important role in GPP change, primarily because of the larger relative change in temperature in comparison with that of precipitation.