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An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy. / van der Kam, Mart; Lagomarsino, Maria; Azar, Elie et al.
In: Cell Reports Sustainability, Vol. 1, No. 12, 100268, 20.12.2024.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

van der Kam, M, Lagomarsino, M, Azar, E, Hahnel, UJJ & Parra, D 2024, 'An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy', Cell Reports Sustainability, vol. 1, no. 12, 100268. https://doi.org/10.1016/j.crsus.2024.100268

APA

van der Kam, M., Lagomarsino, M., Azar, E., Hahnel, U. J. J., & Parra, D. (2024). An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy. Cell Reports Sustainability, 1(12), Article 100268. https://doi.org/10.1016/j.crsus.2024.100268

Vancouver

van der Kam M, Lagomarsino M, Azar E, Hahnel UJJ, Parra D. An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy. Cell Reports Sustainability. 2024 Dec 20;1(12):100268. doi: 10.1016/j.crsus.2024.100268

Bibtex

@article{078bf3b561ff49e78af71ee252a510a7,
title = "An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy",
abstract = "Identifying policy levers to accelerate the adoption of household energy technologies requires an integrative perspective, yet energy models have so far focused on the adoption of single technologies and single policies rather than co-adoption and policy mixes, respectively. Furthermore, experimental consumer data are underutilized in this field, limiting the capacity to study heterogeneous consumer responses to policies. Here, we report an interdisciplinary study addressing this gap by proposing an agent-based model on co-adoption of photovoltaic systems, electric vehicles, and heat pumps up to 2050. The model incorporates realistic consumer decision making and, importantly, is empirically grounded in experimental data of a large sample including 1,469 respondents. We simulate 16,834 policy mixes, which show that, even with decreasing investment costs, accelerating diffusion depends to a large extent on the specific policy mix. The findings moreover illustrate significant variation in adoption levels under identical policy conditions depending on income and political orientation.",
keywords = "agent-based modeling, co-adoption, discrete choice experiments, energy transition, policy mixes, social simulation, Psychology",
author = "{van der Kam}, Mart and Maria Lagomarsino and Elie Azar and Hahnel, {Ulf J.J.} and David Parra",
note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",
year = "2024",
month = dec,
day = "20",
doi = "10.1016/j.crsus.2024.100268",
language = "English",
volume = "1",
journal = "Cell Reports Sustainability",
issn = "2949-7906",
publisher = "Cell Press",
number = "12",

}

RIS

TY - JOUR

T1 - An empirical agent-based model of consumer co-adoption of low-carbon technologies to inform energy policy

AU - van der Kam, Mart

AU - Lagomarsino, Maria

AU - Azar, Elie

AU - Hahnel, Ulf J.J.

AU - Parra, David

N1 - Publisher Copyright: © 2024 The Author(s)

PY - 2024/12/20

Y1 - 2024/12/20

N2 - Identifying policy levers to accelerate the adoption of household energy technologies requires an integrative perspective, yet energy models have so far focused on the adoption of single technologies and single policies rather than co-adoption and policy mixes, respectively. Furthermore, experimental consumer data are underutilized in this field, limiting the capacity to study heterogeneous consumer responses to policies. Here, we report an interdisciplinary study addressing this gap by proposing an agent-based model on co-adoption of photovoltaic systems, electric vehicles, and heat pumps up to 2050. The model incorporates realistic consumer decision making and, importantly, is empirically grounded in experimental data of a large sample including 1,469 respondents. We simulate 16,834 policy mixes, which show that, even with decreasing investment costs, accelerating diffusion depends to a large extent on the specific policy mix. The findings moreover illustrate significant variation in adoption levels under identical policy conditions depending on income and political orientation.

AB - Identifying policy levers to accelerate the adoption of household energy technologies requires an integrative perspective, yet energy models have so far focused on the adoption of single technologies and single policies rather than co-adoption and policy mixes, respectively. Furthermore, experimental consumer data are underutilized in this field, limiting the capacity to study heterogeneous consumer responses to policies. Here, we report an interdisciplinary study addressing this gap by proposing an agent-based model on co-adoption of photovoltaic systems, electric vehicles, and heat pumps up to 2050. The model incorporates realistic consumer decision making and, importantly, is empirically grounded in experimental data of a large sample including 1,469 respondents. We simulate 16,834 policy mixes, which show that, even with decreasing investment costs, accelerating diffusion depends to a large extent on the specific policy mix. The findings moreover illustrate significant variation in adoption levels under identical policy conditions depending on income and political orientation.

KW - agent-based modeling

KW - co-adoption

KW - discrete choice experiments

KW - energy transition

KW - policy mixes

KW - social simulation

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85212310268&partnerID=8YFLogxK

U2 - 10.1016/j.crsus.2024.100268

DO - 10.1016/j.crsus.2024.100268

M3 - Journal articles

AN - SCOPUS:85212310268

VL - 1

JO - Cell Reports Sustainability

JF - Cell Reports Sustainability

SN - 2949-7906

IS - 12

M1 - 100268

ER -

DOI