People-centric Emission Reduction in Buildings: A Data-driven and Network Topology-based Investigation
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Authors
Debnath, R.
Bardhan, R.
Mohaddes, K.
Shah, D. U.
Ramage, M. H.
Alvarez, R. M.
Publication Date
2022-01-05Series
Cambridge Working Papers in Economics
Janeway Institute Working Paper Series
Publisher
Faculty of Economics, University of Cambridge
Type
Working Paper
Metadata
Show full item recordCitation
Debnath, R., Bardhan, R., Mohaddes, K., Shah, D. U., Ramage, M. H., & Alvarez, R. M. (2022). People-centric Emission Reduction in Buildings: A Data-driven and Network Topology-based Investigation. https://doi.org/10.17863/CAM.81919
Abstract
There is a growing consensus among policymakers that we need a human-centric low-carbon transition. There are few studies on how to do it effectively, especially in the context of emissions reduction in the building sector. It is critical to investigate public sentiment and attitudes towards this aspect of climate action, as the building and construction sector accounts for 40% of global carbon emissions. Our methodology involves a multi-method approach, using a data-driven exploration of public sentiment using 256,717 tweets containing #emission and #building between 2009 - 2021. Using graph theory-led metrics, a network topology-based investigation of hashtag co-occurrences was used to extract highly influential hashtags. Our results show that public sentiment is reactive to global climate policy events. Between 2009-2012, #greenbuilding, #emissions were highly influential, shaping the public discourse towards climate action. In 2013-2016, #lowcarbon, #construction and #energyefficiency had high centrality scores, which were replaced by hashtags like #climatetec, #netzero, #climateaction, #circulareconomy, and #masstimber, #climatejustice in 2017-2021. Results suggest that the current building emission reduction context emphasises the social and environmental justice dimensions, which is pivotal to an effective people-centric policymaking.
Keywords
Emission, climate change, building, computational social science, people-centric transition, Twitter
Identifiers
CWPE2202, JIWP2201
This record's DOI: https://doi.org/10.17863/CAM.81919
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334501
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