TY - JOUR
T1 - Prospective consequential life cycle assessment
T2 - Identifying the future marginal suppliers using integrated assessment models
AU - Maes, Ben
AU - Sacchi, Romain
AU - Steubing, Bernhard
AU - Pizzol, Massimo
AU - Audenaert, Amaryllis
AU - Craeye, Bart
AU - Buyle, Matthias
N1 - Funding Information:
We thank the Research Foundation Flanders (FWO-Vlaanderen) for supporting Matthias Buyle with a post-doctoral fellowship (Postdoctoral Fellow - junior; 1207520N) and for supporting Ben Maes with a PhD fellowship (PhD fellow strategic basic research; 1S81222N). Romain Sacchi would like to acknowledge the funding from the SFOE's SWEET-SURE program.
Funding Information:
We thank the Research Foundation Flanders (FWO- Vlaanderen ) for supporting Matthias Buyle with a post-doctoral fellowship (Postdoctoral Fellow - junior; 1207520N ) and for supporting Ben Maes with a PhD fellowship (PhD fellow strategic basic research; 1S81222N ). Romain Sacchi would like to acknowledge the funding from the SFOE's SWEET-SURE program.
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12
Y1 - 2023/12
N2 - Previous research efforts have focused on developing prospective life cycle inventory databases that build upon projections from integrated assessment models but were limited to attributional system models. A novel approach is required to construct consequential LCI databases 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 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 on the future electricity sector using projections from two integrated assessment models (IMAGE and REMIND). Results show the sensitivity of results on the modelling technique 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 projections of the two models. The relevance and goals of the alternative techniques for each step were discussed to guide users in forming the heuristic method for their study.
AB - Previous research efforts have focused on developing prospective life cycle inventory databases that build upon projections from integrated assessment models but were limited to attributional system models. A novel approach is required to construct consequential LCI databases 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 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 on the future electricity sector using projections from two integrated assessment models (IMAGE and REMIND). Results show the sensitivity of results on the modelling technique 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 projections of the two models. The relevance and goals of the alternative techniques for each step were discussed to guide users in forming the heuristic method for their study.
KW - Consequential LCA
KW - Electricity sector
KW - Integrated assessment model
KW - Prospective LCA
UR - http://www.scopus.com/inward/record.url?scp=85173227449&partnerID=8YFLogxK
U2 - 10.1016/j.rser.2023.113830
DO - 10.1016/j.rser.2023.113830
M3 - Review article
AN - SCOPUS:85173227449
SN - 1364-0321
VL - 188
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
M1 - 113830
ER -