Purpose Consequential Life Cycle Assessment (CLCA) and prospective LCA (pLCA) are both forward-looking approaches, yet current CLCA modeling often relies on simplified heuristics that restrict the scope of future consequences it can capture. This work introduces Dynamic-Counterfactual LCA (DC-LCA), a novel framework that integrates concepts from CLCA, pLCA, dynamic LCA, and counterfactual dynamic modeling to broaden the range of consequences typically considered in LCA. Methods DC-LCA is introduced and demonstrated via a System Dynamics (SD) model subject to a demand perturbation to explore the effects of the assessed decision or action. Consequences are then quantified as the time-integrated differences between the evolution of system variables under the perturbation and in an untouched counterfactual scenario. Tools from pLCA and dynamic LCA are adapted to operationalize this framework. The approach is demonstrated with a socio-ecological SD model simulating dietary changes in a city, perturbed to reflect functional units expressed in omnivore versus vegan diets, and measuring the impact on climate as global warming potential. Results and discussion In the studied example, DC-LCA produces results that differ substantially from current CLCA modeling, as it captures indirect, time-dependent dynamics such as the propagation of dietary change across individuals. For instance, reverting to omnivorous diets during a widespread vegan adoption trend substantially increases long-term emissions by delaying dietary shifts. The magnitude and direction of impacts strongly depend on the timing of decisions and the characteristics of the endogenous dynamic regime, illustrating how DC-LCA challenges the linear assumptions and generic heuristics of the common CLCA modeling framework. Conclusion DC-LCA establishes a new form of future-oriented LCA, in which future consequences unfold in a simulated dynamic counterfactual world. In the studied example, the decision is not modeled as a mere demand signal but is further qualified, within a socio-ecological modeling paradigm, as a role change accompanying the spread of vegan diets, leading to consequences far more substantial than those captured by classic market heuristics. The framework thus extends consequentialism, enabling richer and more realistic assessments of the causal chains linking decisions to environmental outcomes.

Dynamic counterfactual LCA: assessing the prospective consequences of diet changes

期刊论文
发表日期：

2026年4月14日

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Pierre Jouannais,
Thomas Elliot

摘要

Purpose

Consequential Life Cycle Assessment (CLCA) and prospective LCA (pLCA) are both forward-looking approaches, yet current CLCA modeling often relies on simplified heuristics that restrict the scope of future consequences it can capture. This work introduces Dynamic-Counterfactual LCA (DC-LCA), a novel framework that integrates concepts from CLCA, pLCA, dynamic LCA, and counterfactual dynamic modeling to broaden the range of consequences typically considered in LCA.

Methods

DC-LCA is introduced and demonstrated via a System Dynamics (SD) model subject to a demand perturbation to explore the effects of the assessed decision or action. Consequences are then quantified as the time-integrated differences between the evolution of system variables under the perturbation and in an untouched counterfactual scenario. Tools from pLCA and dynamic LCA are adapted to operationalize this framework. The approach is demonstrated with a socio-ecological SD model simulating dietary changes in a city, perturbed to reflect functional units expressed in omnivore versus vegan diets, and measuring the impact on climate as global warming potential.

Results and discussion

In the studied example, DC-LCA produces results that differ substantially from current CLCA modeling, as it captures indirect, time-dependent dynamics such as the propagation of dietary change across individuals. For instance, reverting to omnivorous diets during a widespread vegan adoption trend substantially increases long-term emissions by delaying dietary shifts. The magnitude and direction of impacts strongly depend on the timing of decisions and the characteristics of the endogenous dynamic regime, illustrating how DC-LCA challenges the linear assumptions and generic heuristics of the common CLCA modeling framework.

Conclusion

DC-LCA establishes a new form of future-oriented LCA, in which future consequences unfold in a simulated dynamic counterfactual world. In the studied example, the decision is not modeled as a mere demand signal but is further qualified, within a socio-ecological modeling paradigm, as a role change accompanying the spread of vegan diets, leading to consequences far more substantial than those captured by classic market heuristics. The framework thus extends consequentialism, enabling richer and more realistic assessments of the causal chains linking decisions to environmental outcomes.