Modeling the Land-Use-Driven Energy Consumption Nexus in Shaanxi Province, China: A Digital Approach Integrating Machine Learning and Spatial Simulation

Within the context of regional energy governance, land use has emerged as a critical regulatory interface for managing energy demand. Clarifying the land-use–energy nexus is a technical prerequisite for evidence-based and spatially explicit energy planning. This study develops a digital modeling framework that integrates machine learning (Random Forest, achieving R² = 0.95/0.91 for training/testing) and spatial simulation (Patch-generating Land Use Simulation model, with 82.5% accuracy for industrial land) to quantify land-use-driven energy dynamics in Shaanxi Province, China (2005–2030). Key findings reveal: (1) socioeconomic factors dominate land-use expansion, with service industries (14.8–22.4%) and infrastructure (13.5–18.9%) acting as primary drivers, leading to a projected 94.2% growth in urban built-up areas and a tripling of total energy consumption; (2) structural transitions indicate a declining industrial energy share (from 68% to 54%) and reduced coal dependency (from 78% to 62%), though with significant regional disparities; (3) spatial analysis identifies critical energy path-dependency risks in Xi’an City and Yulin City, which are projected to account for 70% of provincial consumption by 2030. These results demonstrate that land-use structure constitutes a direct physical interface linking regional development with energy demand trajectories. The findings underscore the necessity of transitioning from generalized energy policies toward data-driven, land-use-based energy constraints, providing a digital evidentiary base for more precise and stable regional energy governance.