Spatiotemporal Patterns of Synergies and Trade-Offs Among Sustainable Development Goals in the Former Central Soviet Area (Jiangxi, China)

Understanding how Sustainable Development Goals (SDGs) interact—through synergies or trade-offs—is critical for coordinating economic growth, social equity, and environmental protection at the regional scale. However, empirical evidence on the structure, directionality, and spatial heterogeneity of SDG interactions remains limited, particularly in policy-supported regions undergoing development transitions. This study addresses this gap by examining SDG interactions in the former Central Soviet Area of Jiangxi Province, China. Using panel data from eight prefecture-level cities spanning 2001–2022, we construct a multi-dimensional SDG evaluation framework encompassing economic development, social equity and livelihood security, resource utilization and environmental protection, and sustainable cities and communities. A two-stage analytical approach is employed: Spearman’s rank correlation analysis is used to identify synergistic and trade-off relationships among SDGs, and a Geographically and Temporally Weighted Regression (GTWR) model is applied to estimate directional influences and their spatiotemporal heterogeneity among development dimensions. The results indicate that synergistic relationships dominate the regional SDG interaction network, while trade-offs are comparatively limited and selectively concentrated in specific goal pairings. Marked spatial heterogeneity is observed, with stronger synergies in the east than in the west. Functional-zone analysis reveals that ecological and cultural conservation zones exhibit the strongest synergies, whereas industrial transformation zones face pronounced trade-offs, particularly between food security (SDG2) and income inequality (SDG10). GTWR results further demonstrate directional asymmetry among development dimensions, with social equity exerting a stronger influence on economic development than the reverse, and relatively weaker feedback from economic growth to resource and environmental outcomes. Overall, this study provides a systematic, spatiotemporally explicit assessment of SDG interactions in a policy-supported regional context. By integrating interaction analysis with spatiotemporal modeling, it offers a robust empirical basis for understanding where, how, and in which direction SDGs interact, thereby contributing to more context-sensitive approaches to regional sustainable development.