Exploring sustainable pathways through AI-based simulation of China’s agricultural land-use transitions

Agricultural land systems globally face escalating pressures from rising food demands, climate change, environmental degradation, and biodiversity loss. China, as a critical case, exemplifies the urgent need for strategies that reconcile food security with ecological sustainability. Here, we demonstrate that adopting a systematic approach to spatially allocate existing land policy tools—such as cropland reforestation, agricultural intensification, non-grain cropland restoration, and agricultural expansion—has the potential to simultaneously achieve multiple sustainability goals. Using a predictive model based on a socio-ecological-technical framework and machine learning, we evaluated the outcomes of six counterfactual scenarios for China’s agricultural land-use transitions at the county level. Results indicate that under a maximum land-sparing scenario (maximizing intensification of exist cropland, restoring unstable cropland, and maintaining non-grain cropland), compared to the 2020 baseline, China could increase grain output by 8%, reduce crop carbon emission intensity by 1%, enhance carbon sequestration by 63%, while substantially mitigating biodiversity loss across key taxa. However, the spatial distribution of land policy tools remains uneven, leading to varying types and degrees of trade-offs across specific counties under any given scenario. This highlights the critical need for coordinated national leadership to achieve sustainable objectives at a broader scale, offering valuable insights for global land-use transitions.