A1 Journal article (refereed), original research

Augmenting the communication and engagement toolkit for CO2 capture and storage projects

Open Access publication

Publication Details

Authors: Buah Eric, Linnanen Lassi, Wu Huapeng

Publisher: Elsevier

Publication year: 2021

Language: English

Related journal or series: International Journal of Greenhouse Gas Control

Volume number: 107

ISSN: 1750-5836

eISSN: 1878-0148

JUFO level of this publication: 2

Digital Object Identifier (DOI): http://dx.doi.org/10.1016/j.ijggc.2021.103269

Permanent website address: https://www.sciencedirect.com/science/article/abs/pii/S1750583621000219

Open Access: Open Access publication

Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe202102053802


This paper revisits the Communication and Engagement Toolkit for CO2 Capture and Storage (CCS) projects proposed by Ashworth and colleagues in collaboration with the Global CCS Institute. The paper proposes a new method for understanding the social context where CCS will be deployed based on the toolkit. In practice, the proposed method can be used to harness social data collected on the CCS project. The outcome of this application is a development of a predictive tool for gaining insight into the future, to guide strategic decisions that may enhance deployment. Methodologically, the proposed predictive tool is an artificial intelligence (AI) tool. It uses fuzzy deep neural network to develop computational ability to reason about the social behavior. The hybridization of fuzzy logic and deep neural network algorithms make the predictive tool an explainable AI system. It means that the prediction of the algorithm is interpretable using fuzzy logical rules. The practical feasibility of the proposed system has been demonstrated using an experimental sample of 198 volunteers. Their perceptions, emotions and sentiments were tested using a standard questionnaire from the literature, on a hypothetical CCS project based on 26 predictors. The generalizability of the algorithm to predict future reactions was tested on, 84 out-of-sample respondents. In the simulation experiment, we observed an approximately 90 % performance. This performance was measured when the algorithm's predictions were compared to the self- reported reactions of the out of sample subjects. The implication of the proposed tool to enhance the predictive power of the conventional CCS Communication and Engagement tool is discussed

Last updated on 2021-10-02 at 07:46