Stochastic Technology Choice Model for Consequential Life Cycle Assessment

Discussions
on Consequential Life Cycle Assessment (CLCA) have
relied largely on partial or general equilibrium models. Such models
are useful for integrating market effects into CLCA, but also have
well-recognized limitations such as the poor granularity of the sectoral
definition and the assumption of perfect oversight by all economic
agents. Building on the Rectangular-Choice-of-Technology (RCOT) model,
this study proposes a new modeling approach for CLCA, the Technology
Choice Model (TCM). In this approach, the RCOT model is adapted for
its use in CLCA and extended to incorporate parameter uncertainties
and suboptimal decisions due to market imperfections and information
asymmetry in a stochastic setting. In a case study on rice production,
we demonstrate that the proposed approach allows modeling of complex
production technology mixes and their expected environmental outcomes
under uncertainty, at a high level of detail. Incorporating the effect
of production constraints, uncertainty, and suboptimal decisions by
economic agents significantly affects technology mixes and associated
greenhouse gas (GHG) emissions of the system under study. The case
study also shows the model’s ability to determine both the
average and marginal environmental impacts of a product in response
to changes in the quantity of final demand.