Cited by 335Innovation Diffusion and ForecastingSocial Acceptance of Renewable EnergyEnvironmental Education and Sustainability

Agent-based modeling of energy technology adoption: Empirical integration of social, behavioral, economic, and environmental factors

Open access
Rai, Varun, Robinson, Scott A.·2015·Environmental Modelling & Software

Integrity signals

Registry

Crossref · 10.1016/j.envsoft.2015.04.014

Cites

119 of 119 references verified0 citation errors · 0 not checkable

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  • ABMs have been applied to a multitude of problems relevant to climate change, in particular to study low-carbon technology adoption.

    “ABMs have been applied to a multitude of problems relevant to climate change, in particular to study low-carbon technology adoption [320,584,656,878].”

    From Tackling climate change with machine learning (2023) · cites this paper as [656]

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Authors
Rai, V., & Robinson, S. A.
Affiliations
The University of Texas at Austin (US)
Corresponding author
Rai, Varun
Venue
Environmental Modelling & Software
Publisher
Elsevier BV
Published
2015-08
Volume
70
Pages
163-177
ISSN
  • 1556-5068 (ISSN-L)
  • 1364-8152
OpenAlex
W1519386789
Cited by
335
References
119
Citations by year
2026: 33 · 2025: 33 · 2024: 31 · 2023: 27 · 2022: 48 · 2021: 30 · 2020: 30 · 2019: 35 · 2018: 31 · 2017: 21 · 2016: 11 · 2015: 3 · 2014: 2
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (publishedVersion)
  • Environmental Modelling & Software (acceptedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
U.S. Department of Energy (DE-EE0006129)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
  • Social Sciences › Sociology and Political Science › Social Acceptance of Renewable Energy
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
Keywords
agent-based modeling, technology adoption, residential photovoltaic systems, rebates, policy evaluation, social networks, household attitudes, behavioral rules, model validation

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