A Framework for Time-Explicit Life Cycle Optimization

Abstract Life cycle optimization (LCO) selects optimal processes and supply chains from a set of alternatives given environmental constraints and objectives, supporting the design of sustainable systems and their transitions. However, current approaches rely on static assumptions, overlooking two interacting temporal dimensions: temporal distribution (flows are spread over time) and temporal evolution (processes and supply chains change over time). Because distribution and evolution jointly dictate the magnitude of a flow at a given time, both must be considered simultaneously to accurately optimize against cumulative environmental impacts and time-specific environmental constraints. We present a framework for time-explicit LCO that captures these interdependencies to determine optimal transition pathways. The framework tracks flow timing relative to process installation and dynamically determines flow magnitudes from time-specific process parameters and background supply chains. A case study on integrated methanol and pig iron production demonstrates how time-explicit LCO enables optimization against time-resolved environmental impacts and constraints, revealing mitigation strategies inaccessible to static approaches: prioritizing early high-efficiency technology installation to idle older processes as operational savings outweigh embodied impacts of stranded assets, and avoiding transient resource bottlenecks through diversification. To facilitate widespread application, we provide the open-source Python package optimex, enabling practitioners to design transition pathways that rigorously respect environmental limits.