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Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars
David Font Vivanco1, Arnold Tukker2, René Kemp3
1Center for Industrial Ecology, School of Forestry and Environmental Studies, Yale University , New Haven, Connecticut 06511, United States.
Environmental Science & Technology
|September 15, 2016
Summary
Rebound effects from resource efficiency can be biased by modeling choices. This study reveals significant rebound effects for electric and hydrogen cars in Europe, influenced by environmental assessment methods and data, highlighting the need for careful setup and sensitivity analysis.
Area of Science:
- Environmental Science
- Technological Assessment
- Economic Modeling
Background:
- Resource efficiency improvements often fall short due to rebound effects.
- Methodological choices in modeling rebound effects can introduce bias.
- Environmental burdens from demand changes have received less attention than demand changes themselves.
Purpose of the Study:
- To analyze bias sources in rebound effect calculations, focusing on environmental assessment methods and databases.
- To investigate rebound effects for battery electric and hydrogen cars in Europe.
- To highlight the impact of methodological choices on rebound effect assessments.
Main Methods:
- Life Cycle Assessment (LCA) and hybrid LCA were used for environmental assessment.
- Environmental input-output databases (E3IOT, Exiobase, WIOD) were analyzed for bias.
- Long-run scenarios were simulated using total cost of ownership calculations.
Main Results:
- Moderate rebound effects were observed for electric and hydrogen cars in the short term.
- Long-run rebound effects varied significantly (26-59% for electric, 18-28% for hydrogen cars) based on methodological choices and economic conditions.
- Incomplete background systems, technology assumptions, and sectorial aggregation were identified as key sources of bias.
Conclusions:
- Methodological choices in environmental modeling significantly impact rebound effect assessments.
- Sensitivity analyses are crucial for understanding the reliability of rebound effect calculations.
- Accurate modeling of environmental burdens is essential for evaluating resource efficiency policies.
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