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

Eric Buah, Lassi Linnanen and Huapeng Wu

Energy, 2020, vol. 190, issue C

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.

Keywords: Artificial intelligence; CO2 capture and stoarge; Deep neural network algorithm; Environmental social science; Fuzzy logic; Fuzzy deep learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:190:y:2020:i:c:s036054421931905x

DOI: 10.1016/j.energy.2019.116210

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