Prospective consequential Life Cycle Assessment: Identifying the future marginal suppliers using Integrated Assessment Models

While previous research has focused on developing prospective LCI databases that build upon projections from integrated assessment models (IAMs), until now only attributional databases have been developed. To construct consequential LCI databases, a novel approach is required that can be applied consistently on a large scale. To this end, the heuristic approach from Bo Weidema was selected as a basis for this study. This approach has been validated before with historical data and was adapted in this study to identify the marginal suppliers in a prospective context. The different steps within the approach were analyzed and alternative techniques for each step within the heuristic method were proposed. The techniques were tested out on the future electricity sector using projections from two IAMs (IMAGE and REMIND). Results showed how sensitive the results are to which technique is selected in each step. The most sensitive step is the selection of the time interval, with even small changes resulting in a noticeable difference. In addition the results also showed a substantial difference between the IAM projections. The relevance and goals of the alternative techniques for each step were discussed to guide users on forming the heuristic method for their study.