Time-explicit life cycle assessment: a flexible framework for coherent consideration of temporal dynamics

Area of Science:

• Environmental Science
• Industrial Ecology
• Sustainability Science

Background:

• Conventional Life Cycle Assessment (LCA) lacks temporal considerations, limiting accuracy for evolving systems.
• Existing dynamic and prospective LCA methods do not fully integrate temporal distribution and evolution.
• A comprehensive approach is needed to address the temporal dimension in LCA.

Purpose of the Study:

• Introduce a novel time-explicit Life Cycle Assessment (LCA) framework.
• Integrate temporal distribution and evolution of product systems into the Life Cycle Inventory (LCI) phase.
• Support dynamic characterization of emissions in the Life Cycle Impact Assessment (LCIA) phase.

Main Methods:

• Expand LCA matrices to incorporate timing and time-based changes.
• Utilize best-first graph traversal for absolute timeline generation of intermediate flows.
• Integrate time-specific data into technology and biosphere matrices for dynamic LCIA.
• Implement the framework using the open-source Python package bw_timex within the Brightway ecosystem.

Main Results:

• Demonstrated a simplified electric vehicle (EV) case study.
• Time-explicit LCA showed EV Global Warming Impact as half of conventional LCA for a decarbonization scenario.
• Highlighted significant differences compared to conventional and prospective LCA due to time-specific inventory data.
• Showcased instantaneous and cumulative radiative forcing over the EV life cycle.

Conclusions:

• Time-explicit LCA provides more representative results by considering the timing of processes and emissions.
• The framework is particularly valuable for long-lived products in temporally variable or fast-evolving systems.
• Future work should address data gaps and integrate time-explicit LCA with spatial LCA and dynamic material flow analysis.