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Emotional responses to energy projects: A new method for modeling and prediction beyond self-reported emotion measure

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  • Buah, Eric
  • Linnanen, Lassi
  • Wu, Huapeng

Abstract

A considerable amount of studies report that negative emotions evoked by Wind Energy, Nuclear Energy and CO2 Capture and Storage (CCS) can lead to cancellation of the energy project or a delay in policy decisions for its implementation if not adequately addressed. Earlier studies have attempted to study this problem using self-reported emotion measurements to identify the emotions the participants felt. As an alternative, we propose the use of an emotional artificial intelligence (AI) algorithm for improved modeling and prediction of the participants’ emotional behaviour to guide decision-making. We have validated the system using emotional responses to a hypothetical CCS project as a case study. Running our simulation on the experimental dataset (thus 40% of the 72,105), we obtained an average validation accuracy of 98.81%. We challenged the algorithm further with 84 test samples (unseen cases), and it predicted 75 feelings correctly when the stakeholders took a definite position on how they felt. Although there are few limitations to this study, we did find, in a sensitivity experiment, that it was challenging for the algorithm to predict indecisive feelings. The method is adaptable to study emotional responses to other projects, including Wind Energy, Nuclear Energy and Hydrogen Technology.

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  • Buah, Eric & Linnanen, Lassi & Wu, Huapeng, 2020. "Emotional responses to energy projects: A new method for modeling and prediction beyond self-reported emotion measure," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s036054421931905x
    DOI: 10.1016/j.energy.2019.116210
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    References listed on IDEAS

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    1. Bart W. Terwel & Fieke Harinck & Naomi Ellemers & Dancker D. L. Daamen, 2009. "Competence‐Based and Integrity‐Based Trust as Predictors of Acceptance of Carbon Dioxide Capture and Storage (CCS)," Risk Analysis, John Wiley & Sons, vol. 29(8), pages 1129-1140, August.
    2. Yang, Lin & Zhang, Xian & McAlinden, Karl J., 2016. "The effect of trust on people's acceptance of CCS (carbon capture and storage) technologies: Evidence from a survey in the People's Republic of China," Energy, Elsevier, vol. 96(C), pages 69-79.
    3. Huijts, Nicole M.A. & Midden, Cees J.H. & Meijnders, Anneloes L., 2007. "Social acceptance of carbon dioxide storage," Energy Policy, Elsevier, vol. 35(5), pages 2780-2789, May.
    4. Goda Perlaviciute & Linda Steg & Nadja Contzen & Sabine Roeser & Nicole Huijts, 2018. "Emotional Responses to Energy Projects: Insights for Responsible Decision Making in a Sustainable Energy Transition," Sustainability, MDPI, vol. 10(7), pages 1-12, July.
    5. Sabine Roeser, 2012. "Risk Communication, Public Engagement, and Climate Change: A Role for Emotions," Risk Analysis, John Wiley & Sons, vol. 32(6), pages 1033-1040, June.
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    Cited by:

    1. Mark K. McBeth & Megan Warnement Wrobel & Irene van Woerden, 2023. "Political ideology and nuclear energy: Perception, proximity, and trust," Review of Policy Research, Policy Studies Organization, vol. 40(1), pages 88-118, January.
    2. Robert G. Boutilier & Kyle Bahr, 2020. "A Natural Language Processing Approach to Social License Management," Sustainability, MDPI, vol. 12(20), pages 1-12, October.
    3. Eric Buah & Lassi Linnanen & Huapeng Wu & Martin A. Kesse, 2020. "Can Artificial Intelligence Assist Project Developers in Long-Term Management of Energy Projects? The Case of CO 2 Capture and Storage," Energies, MDPI, vol. 13(23), pages 1-15, November.

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