Reviewing the scope and thematic focus of 100 000 publications on energy consumption, services and social aspects of climate change: a big data approach to demand-side mitigation*

Felix Creutzig, Max Callaghan, Anjali Ramakrishnan, Aneeque Javaid, Leila Niamir, Jan Minx, Finn Müller-Hansen, Benjamin Sovacool, Zakia Afroz, Mark Andor, Miklos Antal, Victor Court, Nandini Das, Julio Díaz-José, Friederike Döbbe, Maria J Figueroa, Andrew Gouldson, Helmut Haberl, Andrew Hook, Diana IvanovaWilliam F Lamb, Nadia Maïzi, Érika Mata, Kristian S Nielsen, Chioma Daisy Onyige, Lucia A Reisch, Joyashree Roy, Pauline Scheelbeek, Mahendra Sethi, Shreya Some, Steven Sorrell, Mathilde Tessier, Tania Urmee, Doris Virág, Can Wan, Dominik Wiedenhofer, Charlie Wilson

Research output: Contribution to journalReview article

2 Citations (Scopus)
6 Downloads (Pure)

Abstract

As current action remains insufficient to meet the goals of the Paris agreement let alone to stabilize the climate, there is increasing hope that solutions related to demand, services and social aspects of climate change mitigation can close the gap. However, given these topics are not investigated by a single epistemic community, the literature base underpinning the associated research continues to be undefined. Here, we aim to delineate a plausible body of literature capturing a comprehensive spectrum of demand, services and social aspects of climate change mitigation. As method we use a novel double-stacked expert—machine learning research architecture and expert evaluation to develop a typology and map key messages relevant for climate change mitigation within this body of literature. First, relying on the official key words provided to the Intergovernmental Panel on Climate Change by governments (across 17 queries), and on specific investigations of domain experts (27 queries), we identify 121 165 non-unique and 99 065 unique academic publications covering issues relevant for demand-side mitigation. Second, we identify a literature typology with four key clusters: policy, housing, mobility, and food/consumption. Third, we systematically extract key content-based insights finding that the housing literature emphasizes social and collective action, whereas the food/consumption literatures highlight behavioral change, but insights also demonstrate the dynamic relationship between behavioral change and social norms. All clusters point to the possibility of improved public health as a result of demand-side solutions. The centrality of the policy cluster suggests that political actions are what bring the different specific approaches together. Fourth, by mapping the underlying epistemic communities we find that researchers are already highly interconnected, glued together by common interests in sustainability and energy demand. We conclude by outlining avenues for interdisciplinary collaboration, synthetic analysis, community building, and by suggesting next steps for evaluating this body of literature.
Original languageEnglish
Article number033001
JournalEnvironmental Research Letters
Volume16
Issue number3
Early online date19 Feb 2021
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Behavior
  • Climate change mitigation
  • Demand
  • IPCC
  • Machine learning
  • Services
  • Social norm

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