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Annual Review of Environment and Resources
Article . 2010 . Peer-reviewed
Data sources: Crossref
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Evaluating Energy Efficiency Policies with Energy-Economy Models

Authors: Mundaca, Luis; Neij, Lena; Worrell, Ernst; McNeil, Michael A.;

Evaluating Energy Efficiency Policies with Energy-Economy Models

Abstract

The growing complexities of energy systems, environmental problems, and technology markets are driving and testing most energy-economy models to their limits. To further advance bottom-up models from a multidisciplinary energy efficiency policy evaluation perspective, we review and critically analyze bottom-up energy-economy models and corresponding evaluation studies on energy efficiency policies to induce technological change. We use the household sector as a case study. Our analysis focuses on decision frameworks for technology choice, type of evaluation being carried out, treatment of market and behavioral failures, evaluated policy instruments, and key determinants used to mimic policy instruments. Although the review confirms criticism related to energy-economy models (e.g., unrealistic representation of decision making by consumers when choosing technologies), they provide valuable guidance for policy evaluation related to energy efficiency. Different areas to further advance models remain open, particularly related to modeling issues, techno-economic and environmental aspects, behavioral determinants, and policy considerations.

Country
United States
Keywords

Policy Evaluation, Energy Efficiency, Decision Making, Energy Efficiency Policy, Bottom-Up Energy-Economy Models, 29, Induced Technological Change, Household Sector, Energy Systems, Evaluation, policy instruments, technology choice determinants, Testing Bottom-Up Energy-Economy Models, policy evaluation, 32, Market, Households, household sector, induced technological change, Simulation

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    114
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
114
Top 1%
Top 10%
Top 10%
gold