Climate‐Resilient Energy Policies for Degraded Ecosystems: An AI and MCDA Approach to Balance Land Restoration and Regional Economic Development

Abstract

This study develops climate‐resilient energy policies for China's Loess Plateau, a region plagued by severe land degradation and economic challenges. It aims to balance ecological restoration with economic development under SSP2‐4.5 and SSP5‐8.5 climate scenarios, hypothesizing that integrating Artificial Intelligence (AI) and Multi‐Criteria Decision Analysis (MCDA) can effectively manage these issues. The goal is to formulate sustainable policies that reduce degradation while promoting growth. Utilizing Sentinel‐2 imagery (2015–2022), CMIP6 projections (2025–2050), socioeconomic data (2010–2022), and energy infrastructure details, the methodology involves three phases: predictive modeling via deep learning (CNN for land degradation classification at 92.3% accuracy; LSTM for energy demand forecasting), policy generation using reinforcement learning, and evaluation with a hybrid fuzzy‐VIKOR framework. Results feature a land degradation map showing severe issues in central and northern areas, energy demand rises (74% under SSP2‐4.5; 90% under SSP5‐8.5 by 2050), and five policy scenarios. Scenario 4, ranked highest (Qᵢ = 0.12), allocates 30% budget to solar, 20% to wind, and 7200 km2 to afforestation, yielding 22% degradation reduction, 2.2% annual GDP growth, 18% GHG emissions cut by 2030, and ecosystem recovery (94.10% carbon fixation; 87.59% sand fixation). It supports SDGs 7 and 15.3, enhances social equity via community cooperatives, and aligns with China's 14th Five‐Year Plan (15% non‐fossil energy by 2025).