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<p>Integrated assessment and energy system models are challenged to account for societal transformation dynamics, but empirical evidence is lacking on which factors to incorporate, how and to what extent this would improve the relevance of modelled pathways. Here we include six societal factors related to infrastructure dynamics, actors and decision-making, and social and institutional context into an open-source simulation model of the national power system transition. We apply this model in 31 European countries and, using hindcasting (1990–2019), quantify which societal factors improved the modelled pathways. We find that, if well-chosen and in most cases, incorporating societal factors can improve the hindcasting performance by up to 27% for modelled installed capacity of individual technologies. Public acceptance, investment risks and infrastructure lock<i>-</i>in contribute the most to model performance improvement. Our study paves the way to a systematic and objective selection of societal factors to be included in energy transition modelling.</p>

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Multi-country evidence on societal factors to include in energy transition modelling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月21日

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  • Vivien Fisch-Romito,
  • Marc Jaxa-Rozen,
  • Xin Wen,
  • Evelina Trutnevyte

摘要

Integrated assessment and energy system models are challenged to account for societal transformation dynamics, but empirical evidence is lacking on which factors to incorporate, how and to what extent this would improve the relevance of modelled pathways. Here we include six societal factors related to infrastructure dynamics, actors and decision-making, and social and institutional context into an open-source simulation model of the national power system transition. We apply this model in 31 European countries and, using hindcasting (1990–2019), quantify which societal factors improved the modelled pathways. We find that, if well-chosen and in most cases, incorporating societal factors can improve the hindcasting performance by up to 27% for modelled installed capacity of individual technologies. Public acceptance, investment risks and infrastructure lock-in contribute the most to model performance improvement. Our study paves the way to a systematic and objective selection of societal factors to be included in energy transition modelling.