What are the general public's needs, concerns and views about energy efficiency retrofitting of existing building stock? A sentiment analysis of social media data

Abstract

Energy efficiency retrofitting of existing buildings (EEREB) is critical to combating climate change. While policy interventions that seek to encourage widespread EEREB adoption benefit from a deeper understanding of public retrofit decision behaviour, studies that comprehensively evaluate the general public's sentiments towards EEREB are lacking, contributing to a lack of understanding of the public's needs, concerns and views. This study aims to evaluate the general public's needs, concerns and views about EEREB. A total of 3,306 data of the general public's views were collected from the social media platform YouTube, pre-processed and analyzed using the Model-based clustering and a text mining technique. Results showed nine areas of public concerns: ventilation, energy efficiency, indoor environment quality, comfort and occupant behaviour, cost involved, community engagement, technology use, implementation knowledge, and social impact. The general intention to retrofit is mostly driven by personal choices, not regulations per se, although some climate-dependant factors strongly impact public sentiments. Moreover, the recognition of benefits and public support for EEREB was attached to the understanding of cost saving and payback period and finding reliable supply-side actors for retrofit works, respectively. Overall, the public reported positive sentiments (56%) toward EEREB. Novel insights into the general public's needs, concerns and views have been uncovered in this study, which policymakers can utilize to better advocate for EEREB.

Publication DOI: https://doi.org/10.1016/j.enbuild.2023.113721
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
Funding Information: The work described in this paper forms part of a major research project entitled “Developing an intelligent decision support system for green retrofitting of aged existing buildings in Hong Kong”, fully funded by the Postdoc Matching Fund Scheme of The Ho
Additional Information: Copyright © 2023, Elsevier. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Energy efficiency,Existing buildings stock,Retrofitting,Sentiment analysis,Social media,Civil and Structural Engineering,Building and Construction,Mechanical Engineering,Electrical and Electronic Engineering
Publication ISSN: 0378-7788
Last Modified: 02 May 2024 07:28
Date Deposited: 12 Dec 2023 09:18
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 9519?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-12-15
Published Online Date: 2023-11-03
Accepted Date: 2023-11-02
Authors: Tetteh, Mershack O. (ORCID Profile 0000-0002-9226-3451)
Boateng, Emmanuel B.
Darko, Amos
Chan, Albert P.C.

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 3 November 2024.

License: Creative Commons Attribution Non-commercial No Derivatives


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