Abstract
Original language | English |
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Pages (from-to) | 11366-11376 |
Number of pages | 11 |
Journal | Environmental Science & Technology |
Volume | 50 |
Issue number | 20 |
DOIs | |
Publication status | Published - 18 Oct 2016 |
Keywords
- Sensitivity analysis
- Background systems
- Economic condition
- Environmental assessment methods
- Environmental burdens
- Environmental model
- Life Cycle Assessment (LCA)
- Resource efficiencies
- Total cost of ownership
- Life cycle
- LIFE-CYCLE ASSESSMENT
- EXTENSIONS
- EMISSIONS
- ENERGY EFFICIENCY
- US HOUSEHOLDS
- EUROPE
- UK HOUSEHOLDS
- INPUT-OUTPUT-ANALYSIS
- IMPACTS
- CONSUMPTION
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In: Environmental Science & Technology, Vol. 50, No. 20, 18.10.2016, p. 11366-11376.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Do Methodological Choices in Environmental Modeling Bias Rebound Effects?
T2 - A Case Study on Electric Cars
AU - Font Vivanco, D.
AU - Tukker, A.
AU - Kemp, R.
N1 - Export Date: 8 December 2016 CODEN: ESTHA Correspondence Address: Font Vivanco, D.; Center for Industrial Ecology, School of Forestry and Environmental Studies, Yale UniversityUnited States; email: david.fontvivanco@yale.edu References: Khazzoom, J.D., Economic implications of mandated efficiency in standards for household appliances (1980) Energy Journal, 1 (4), pp. 21-40; Brookes, L., The greenhouse effect: The fallacies in the energy efficiency solution (1990) Energy Policy, 18 (2), pp. 199-201; Binswanger, M., Technological progress and sustainable development: What about the rebound effect? 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PY - 2016/10/18
Y1 - 2016/10/18
N2 - 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. © 2016 American Chemical Society.
AB - 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. © 2016 American Chemical Society.
KW - Sensitivity analysis
KW - Background systems
KW - Economic condition
KW - Environmental assessment methods
KW - Environmental burdens
KW - Environmental model
KW - Life Cycle Assessment (LCA)
KW - Resource efficiencies
KW - Total cost of ownership
KW - Life cycle
KW - LIFE-CYCLE ASSESSMENT
KW - EXTENSIONS
KW - EMISSIONS
KW - ENERGY EFFICIENCY
KW - US HOUSEHOLDS
KW - EUROPE
KW - UK HOUSEHOLDS
KW - INPUT-OUTPUT-ANALYSIS
KW - IMPACTS
KW - CONSUMPTION
U2 - 10.1021/acs.est.6b01871
DO - 10.1021/acs.est.6b01871
M3 - Article
C2 - 27626810
SN - 0013-936X
VL - 50
SP - 11366
EP - 11376
JO - Environmental Science & Technology
JF - Environmental Science & Technology
IS - 20
ER -