Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars.
Improvements in resource efficiency often underperform because of rebound effects. Calculations of the size of rebound effects are subject to various types of bias, among which methodological choices have received particular attention. Modellers have primarily focused on choices related to changes in demand, however, choices related to modeling the environmental burdens from such changes have received less attention. In this study, we analyze choices in the environmental assessment methods (life cycle assessment (LCA) and hybrid LCA) and environmental input-output databases (E3IOT, Exiobase and WIOD) used as a source of bias. The analysis is done for a case study on battery electric and hydrogen cars in Europe. The results describe moderate rebound effects for both technologies in the short term. Additionally, long-run scenarios are calculated by simulating the total cost of ownership, which describe notable rebound effect sizes-from 26 to 59% and from 18 to 28%, respectively, depending on the methodological choices-with favorable economic conditions. Relevant sources of bias are found to be related to incomplete background systems, technology assumptions and sectorial aggregation. These findings highlight the importance of the method setup and of sensitivity analyses of choices related to environmental modeling in rebound effect assessments.
Center for Industrial Ecology, School of Forestry and Environmental Studies, Yale University , New Haven, Connecticut 06511, United States.
David
D
Font Vivanco
Institute of Environmental Sciences (CML), Leiden University , 2300 RA Leiden, The Netherlands.
Arnold
A
Tukker
ICIS and UNU-MERIT, Maastricht University , 6200 MD Maastricht, The Netherlands.
René
R
Kemp
D001336:Automobiles; D004560:Electricity; D004777:Environment; D013672:Technology