Energy Planning Toward Absolute Environmental Sustainability: Key Decisions and Actionable Insights Through Interpretable Machine Learning

Abstract

Energy planning models traditionally support the energy transition by focusing on cost-optimized solutions that limit greenhouse gas emissions. However, this narrow focus risks burden-shifting, where reducing emissions increases other environmental pressures, such as freshwater use, solv-ing one problem while creating others. Therefore, we integrated Planetary Boundary-based Life Cycle Assessment (PB-LCA) into energy planning to identify solutions that respect absolute envi-ronmental sustainability limits. However, integrating PB-LCA into energy planning introduces chal-lenges, such as adopting distributive justice principles, interpreting trade-offs across PB indicator impacts, and managing subjective weighting in the objective function. To address these, we em-ployed weight screening and interpretable machine learning to extract key decisions and action-able insights from the numerous quantitative solutions generated. Preliminary results for a single weighting scenario show that the transition scenario exceeds several PB thresholds, particularly for ecosystem quality and mineral resource depletion, underscoring the need for a balanced weighting scheme. Next, we will apply screening and machine learning to pinpoint key decisions and provide actionable insights for achieving absolute environmental sustainability