Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111286
Title: A Review of Agent-Based Modeling of Climate-Energy Policy
Authors: Castro, J.
Drews, S.
Exadaktylos, F.
Foramitti, J.
Klein, F.
Konc, T.
Savin, I.
van den Bergh, J.
Issue Date: 2020
Publisher: Wiley-Blackwell
Wiley
Citation: A Review of Agent-Based Modeling of Climate-Energy Policy / J. Castro, S. Drews, F. Exadaktylos et al. // Wiley Interdisciplinary Reviews: Climate Change. — 2020. — Vol. 11. — Iss. 4. — e647.
Abstract: Agent-based models (ABMs) have recently seen much application to the field of climate mitigation policies. They offer a more realistic description of micro behavior than traditional climate policy models by allowing for agent heterogeneity, bounded rationality and nonmarket interactions over social networks. This enables the analysis of a broader spectrum of policies. Here, we review 61 ABM studies addressing climate-energy policy aimed at emissions reduction, product and technology diffusion, and energy conservation. This covers a broad set of instruments of climate policy, ranging from carbon taxation, and emissions trading through adoption subsidies to information provision tools such as smart meters and eco-labels. Our treatment pays specific attention to behavioral assumptions and the structure of social networks. We offer suggestions for future research with ABMs to answer neglected policy questions. This article is categorized under: Climate Economics > Economics of Mitigation. © 2020 Wiley Periodicals, Inc.
Keywords: AGENT-BASED MODELS
BOUNDED RATIONALITY
CLIMATE POLICY
OTHER-REGARDING PREFERENCES
SOCIAL INTERACTIONS
AUTONOMOUS AGENTS
COMPUTATIONAL METHODS
EMISSION CONTROL
ENERGY POLICY
SIMULATION PLATFORM
TAXATION
AGENT-BASED MODEL
BOUNDED RATIONALITY
CLIMATE POLICY
OTHER-REGARDING PREFERENCES
SOCIAL INTERACTIONS
CLIMATE MODELS
ENERGY POLICY
ENVIRONMENTAL POLICY
MODELING
SOCIAL NETWORK
URI: http://hdl.handle.net/10995/111286
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85082760051
PURE ID: 13142983
ISSN: 1757-7780
metadata.dc.description.sponsorship: This study has received funding through an ERC Advanced Grant from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 741087). I.S. acknowledges financial support from the Russian Science Foundation [RSF grant number 19-18-00262].
RSCF project card: 19-18-00262
CORDIS project card: H2020: 741087
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85082760051.pdf2,97 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.