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    Agent-based modelling and socio-technical energy transitions: A systematic literature review

    Access Status
    Fulltext not available
    Authors
    Hansen, P.
    Liu, Xin
    Morrison, G.
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Hansen, P. and Liu, X. and Morrison, G. 2019. Agent-based modelling and socio-technical energy transitions: A systematic literature review. ENERGY RESEARCH & SOCIAL SCIENCE. 49: pp. 41-52.
    Source Title
    ENERGY RESEARCH & SOCIAL SCIENCE
    DOI
    10.1016/j.erss.2018.10.021
    ISSN
    2214-6296
    School
    Sustainability Policy Institute
    URI
    http://hdl.handle.net/20.500.11937/72967
    Collection
    • Curtin Research Publications
    Abstract

    © 2018 Elsevier Ltd Agent-based modelling has the potential to provide insight into complex energy transition dynamics. Despite a recent emphasis of research on agent-based modelling and on energy transitions, an overview of how the methodology may be of value to understanding transition processes is still missing from the literature. This systematic review evaluates the potential of agent-based modelling to understanding energy transitions from a social-scientific perspective, based on a set of 62 articles. Six topic areas were identified, addressing different components of the energy system: Electricity Market, Consumption Dynamics/ Consumer Behaviour, Policy and Planning, New Technologies/ Innovation, Energy System, Transitions. Distribution of articles across topic areas was indicative of a continuing interest in electricity market related enquiries, and an increasing number of studies in the realm of policy and planning. Based on the relevance of energy transition specific complexities to the choice of ABM as a methodology, four complexity categories (1–4) were identified. Indicating the degree of association between the complexity of energy transitions and ABM's ability to address these, the categorisation revealed that 35 of the 62 studies directly linked the choice of ABM to energy transition complexities (complexity category 1) or were set in the context of energy transitions (complexity category 2). The review further showed that the greatest potential contribution of ABM to energy transition studies lies in its practical application to decision-making in policy and planning. More interdisciplinary collaboration in model development is recommended to address the discrepancy between the relevance of social factors to modelling energy transitions and the ability of the social sciences to make effective use of ABM.

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