Life cycle assessment of prospective trajectories: A parametric approach for tailor‐made inventories and its computational implementation

Abstract

AbstractLife cycle assessment (LCA) is a standardized, holistic, and multi‐criteria approach to estimate the environmental impacts of a system over its entire lifecycle. Modeling the environmental impacts of future scenarios and evolving systems in LCA is challenging due to lack of knowledge around technological evolutions and changing backgrounds influencing the foreground system. The method proposed here tackles these issues by combining parameterized life cycle inventories (LCIs) of foreground and background systems to assess how background changes, for example in the energy sector, affect the environmental impacts of the foreground. First, parameterized LCIs for the heating and energy sector were developed to flexibly estimate the environmental impacts of heating, gas, and electricity trajectories. Assessing their environmental impacts is essential to avoid any burden shifting to other impacts. Thanks to their parameterization, these models can represent different geographical, temporal, or technological contexts. Second, the combination of these parameterized LCIs was implemented using the Python library lca_algebraic, designed to enhance computational efficiency and calculation speed of LCAs. lca_algebraic’s features were extended to link parameters to specific LCIs and, hence, ease their import and export and facilitate teamwork. In a final step, the developed LCIs and lca_algebraic library were used to estimate the environmental impacts of French heating scenarios from 2018 until 2050, as a test of the approach. The parameterized models developed within this work are available on a Git together with published reference datasets for comparison, so that the method can be adapted to evaluate similar energy trajectories for other countries or regions. This article met the requirements for a gold–gold JIE data openness badge described at http://jie.click/badges.