Assessing the behavioral realism of energy system models in light of the consumer adoption literature

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and non-monetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking. • Behaviorally realistic consumer adoption models help inform decarbonization policy. • The determinants of consumer adoption vary across low-carbon technologies. • Consumer preferences are heterogeneous and include non-monetary considerations. • Existing models tend to oversimplify adoption decisions. • They capture the most important adoption factors but neglect many others.

Authors

Institutions

Publication Details

Journal
Renewable and Sustainable Energy Reviews
Published
2024-12-24
DOI
https://doi.org/10.1016/j.rser.2024.115184
Citations
5
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
1.04

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing the behavioral realism of energy system models in light of the consumer adoption literature

Ari Ball-Burack, Ruixiao Sun, Ranjan Bose, Hung-Chia Yang et al.
5 citations
Renewable and Sustainable Energy Reviews
Electric Vehicles and Infrastructure
1.04
article

Assessing the behavioral realism of energy system models in light of the consumer adoption literature

Ari Ball-Burack, Ruixiao Sun, Ranjan Bose, Hung-Chia Yang, Shiqi Ou, Stephen Stack
article en
5 citations

Abstract

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and non-monetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking. • Behaviorally realistic consumer adoption models help inform decarbonization policy. • The determinants of consumer adoption vary across low-carbon technologies. • Consumer preferences are heterogeneous and include non-monetary considerations. • Existing models tend to oversimplify adoption decisions. • They capture the most important adoption factors but neglect many others.

Renewable and Sustainable Energy ReviewsVol. 211
Oak Ridge National Laboratory (US), Lawrence Berkeley National Laboratory (US), University of California, Berkeley (US)
National Science Foundation, U.S. Department of Energy, University of California, Office of Energy Efficiency and Renewable Energy, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory
Affordable and clean energy
Openalex Percentile: Top 22%
Electric Vehicles and Infrastructure
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.