Revealing the spatiotemporal evolutionary trajectories of multi-scale territorial spatial conflicts in Yunnan: an interpretable and causal machine learning framework

Clarifying how Territorial Spatial Conflicts (TSC) evolve across space and time is fundamental to understanding the internal dynamics of territorial spatial systems and to advancing more effective regional spatial governance. Yet existing studies often rely on linear assumptions or correlation-based analyses, making it difficult to capture nonlinear transition processes and disentangle causal relationships among driving forces. Focusing on Yunnan Province, this study develops an integrated analytical framework that combines trajectory diagnostics, interpretable machine learning, and causal inference techniques. TSC evolutionary patterns from 2000 to 2020 were identified at three nested scales—county, township, and 1 km × 1 km grid—distinguishing positive linear, negative linear, positive curvilinear, and negative curvilinear trajectories. On this basis, the LightGBM-SHAP model was employed to uncover nonlinear relationships and threshold effects of “natural-social-economic” factors on the TSC evolutionary trajectories, while the CausalForestDML algorithm was introduced to move beyond correlation and quantify direct causal impacts. The results show that: (1) TSC intensity in Yunnan rose from 0.017 in 2000 to 0.022 in 2020, revealing a composite spatial configuration shaped by “vertical topographic differentiation” and “multi-scale coupling”; (2) Areas exhibiting increasing conflict trajectories accounted for 31.95%, 21.26%, and 17.08% at the county, township, and grid scales, respectively, forming spatially differentiated and multi-centered distribution patterns; (3) Changes in the aggregation index, elevation, and slope emerge as dominant drivers. Their effects are strongly nonlinear and characterized by identifiable thresholds. Elevation and slope exert stable and consistently negative causal influences across trajectory types, whereas change in aggregation index display bidirectional causal effects—reinforcing positive trajectories while mitigating negative ones. By explicitly linking evolutionary pathways with causal mechanisms, this study deepens the understanding of how TSC unfold in mountainous regions of China. The findings provide an empirical foundation for designing spatially differentiated and scale-sensitive governance strategies aimed at balancing development and ecological protection.