Data-driven understanding of low-carbon lifestyles
Abstract
The most recent IPCC report has stressed one more time that strong, rapid and sustained reductions in global GHG emissions are needed in order to limit global warming to 1.5 �C. Such sustained emission reductions require a widespread and rapid societal transformation towards low-carbon lifestyles in the developed and developing world. The feasible rate of societal change, though, is uncertain, not only due to the unknown future dynamics of human behavior in relation to the natural systems, but also because of the lack of data on a global scale about the current state and drivers of low-carbon lifestyles. LOW-AI will address this data gap by utilizing publicly available content and audience size data of the major social media platforms. By using machine learning techniques such as feature importance based on regression trees, topic modelling and sentiment analysis, LOW-AI will identify the key socio-economic and behavioral drivers of low-carbon lifestyles on a global scale. The resulting datasets will be published and presented in online interactive dashboards, where users can explore the current state of interest in low-carbon options in different consumption domains and the drivers of these low-carbon choices across the world. The results will inform policymaking about the public opinion on and the uptake potential of demand-side mitigation solutions, and will be used in future research to develop bottom-up scenarios of food and energy demand so that the feasible mitigation potential of low-carbon lifestyles can be quantified.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 03, 2023
- Source ID
- N629092312022
Entities
People
- Sibel Eker
Organizations
- Office of Naval Research
- United States Navy