Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions
Leila Niamir (),
Gregor Kiesewetter (),
Fabian Wagner (),
Wolfgang Schöpp (),
Tatiana Filatova,
Alexey Voinov () and
Hans Bressers ()
Additional contact information
Leila Niamir: University of Twente
Gregor Kiesewetter: International Institute for Applied Systems Analysis (IIASA)
Fabian Wagner: International Institute for Applied Systems Analysis (IIASA)
Wolfgang Schöpp: International Institute for Applied Systems Analysis (IIASA)
Alexey Voinov: University of Twente
Hans Bressers: University of Twente
Climatic Change, 2020, vol. 158, issue 2, No 3, 160 pages
Abstract:
Abstract In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.
Keywords: Behavioral change; Agent-based modeling; Carbon emissions; Macroeconomic impacts; Climate change mitigation; Energy economics; Residential energy (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)
Downloads: (external link)
http://link.springer.com/10.1007/s10584-019-02566-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:climat:v:158:y:2020:i:2:d:10.1007_s10584-019-02566-8
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10584
DOI: 10.1007/s10584-019-02566-8
Access Statistics for this article
Climatic Change is currently edited by M. Oppenheimer and G. Yohe
More articles in Climatic Change from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().