融合智能体与生命周期评估，模拟能源转型中的资源竞争与区域权衡。
Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions
- 用智能体模型模拟社区行为与资源竞争互动
- 揭示不同情景下空间分布的环境与社会代价
- 适合政策制定者和能源规划者参考
向可持续且有韧性的能源系统转型，需权衡环境、社会与资源等多重维度的复杂交互。现有评估常孤立分析路径与影响，忽视区域资源竞争与累积效应。本文提出一种耦合智能体建模与生命周期评估（LCA）的综合框架，模拟能源转型路径如何与区域资源竞争、生态约束及社区负担相互作用。以南加州为例，结果表明集成多尺度决策可显著影响路径部署，并揭示情景驱动下的空间显性权衡。该框架有助于在空间与制度层面支持更适应性强的能源转型规划。
原文摘要 · Abstract (English)
Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences across sectors. However, existing assessments often evaluate emerging energy pathways and their impacts in silos, overlooking critical interactions such as regional resource competition and cumulative impacts. We present an integrated modeling framework that couples agent-based modeling and Life Cycle Assessment (LCA) to simulate how energy transition pathways interact with regional resource competition, ecological constraints, and community-level burdens. We apply the model to a case study in Southern California. The results demonstrate how integrated and multiscale decision making can shape energy pathway deployment and reveal spatially explicit trade-offs under scenario-driven constraints. This modeling framework can further support more adaptive and resilient energy transition planning on spatial and institutional scales.
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