Integrating Knowledge Graphs and Bayesian Inference to Balance Ecological Security, Carbon Sinks, and Development: A Case Study of Land Use Zoning in Yunnan

Balancing ecological protection, carbon sinks, and development is a practical challenge in mountainous regions. Using Yunnan Province, China, as a case study, this paper develops a knowledge-guided probabilistic framework for carbon-oriented territorial zoning. The framework combines an indicator system, corridor analysis of pattern, risk and potential, knowledge-graph rule encoding, Bayesian mechanism calibration, and constrained posterior decoding on 11,853 effective planning cells. The results show a clear conservation–development gradient in the carbon sink priority surface: high-priority areas are concentrated in western and southwestern Yunnan, whereas low-priority areas cluster around major urban centers. Corridor analysis identifies a central resistance belt and several urban–rural bottlenecks, indicating that connectivity constraints are concentrated in a limited number of critical links. The final zoning assigns 35.4% of grids to integrated development, 25.9% to emergency intervention, 14.5% to long-term conservation, 13.8% to priority restoration, and 10.4% to risk control. Zone separability is generally strong, with one-versus-rest AUC values ranging from 0.777 to 0.995. Land use enrichment further supports the zoning results: integrated development contains 78.85% of built-up land and 45.93% of cropland, whereas Emergency intervention, priority restoration, and long-term conservation together contain 70.01% of forest area.