Coupling Agent-based Modeling and Life Cycle Assessment to Analyze Trade-offs in Resilient Energy Transitions

TL;DR

The paper presents a bidirectionally coupled agent-based modeling and life cycle assessment (ABM-LCA) framework to analyze trade-offs in resilient energy transitions. It couples portfolio-level environmental impacts with site-level siting decisions, enabling feedback loops between resource availability, pathway deployment, and local constraints. A Southern California case study demonstrates how water, land, and community burdens shape the spatial distribution of hydrogen, geothermal, waste-to-energy, direct lithium extraction, and direct air capture technologies, revealing spatial heterogeneity and cumulative impacts. The work offers a decision-ready, multiscale tool for adaptive energy transition planning with potential to reduce unintended environmental and social consequences.

Abstract

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.

Figures (5)

• Figure 1: Coupling ABM and LCA to model trade-offs in the siting of energy transition pathways and to evaluate environmental impacts at both portfolio and site-specific scales.
• Figure 2: Environmental impact assessment for the proposed portfolio in Southern California. Abbreviations: Ag&Fo, agricultural and forest residues; AW, animal waste; MSW, municipal solid waste; DC, direct combustion; AD, anaerobic digestion; LFG, landfill gas.
• Figure 3: Spatial distribution of the cumulative environmental impacts from all deployed pathways.
• Figure A1: The ABM-LCA model workflow illustrating the interactions between agents, LCA module, and environmental layers.
• Figure A2: Spatial distribution of the deployed energy transition pathways under the baseline scenario.