The new perspective of future multi-scenario analysis: Decoding impact pathways of land use dynamics on sustainable development goals

Abstract

Achieving Sustainable Development Goals (SDGs) is often challenged by escalating land use conflicts and climate change. Existing research has overlooked the mediating effects of climatic factors and the nonlinear relationships between land use changes and SDGs, leading to gaps in understanding the synergistic mechanisms of these interactions. This study incorporates key climatic variables into a time-series forecasting framework for SDGs using a transformer-based model, with the goal to systematically revealing the relationships between land use dynamics and SDGs in the Yangtze River Economic Belt (YREB), as well as the impact mechanisms of different land use structures on SDGs. Predictions based on the Transformer model indicate that the spatial distribution of SDGs exhibits persistent heterogeneity in YREB in 2030. Land use dynamic changes exert nonlinear impacts on SDGs under different scenarios. The strongest nonlinear relationship is observed under the Sustainable Development scenario (P_non–linear = 0.0003, P_overall < 0.001). The impact of built-up land on SDGs exhibits varying effects under different scenarios: 0.649 (p < 0.001) in the Economic Priority scenario; −0.702 (p < 0.001) and −0.549 (p < 0.001) in the Natural Development and Farmland Protection scenarios, respectively. This study demonstrates a framework for forecasting SDGs incorporating the influence of climatic variables, emphasizing the need to formulate scenario-specific policies to achieve synergies between land use change and SDGs against the backdrop of climate change.