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Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

Author

Listed:
  • Leila Niamir

    (University of Twente
    International Institute for Applied Systems Analysis (IIASA)
    Mercator Research Institute on Global Commons and Climate Change (MCC))

  • 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))

  • Tatiana Filatova

    (University of Twente
    University of Technology Sydney)

  • Alexey Voinov

    (University of Twente
    University of Technology Sydney)

  • Hans Bressers

    (University of Twente)

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.

Suggested Citation

  • Leila Niamir & Gregor Kiesewetter & Fabian Wagner & Wolfgang Schöpp & Tatiana Filatova & Alexey Voinov & Hans Bressers, 2020. "Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions," Climatic Change, Springer, vol. 158(2), pages 141-160, January.
  • Handle: RePEc:spr:climat:v:158:y:2020:i:2:d:10.1007_s10584-019-02566-8
    DOI: 10.1007/s10584-019-02566-8
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    1. Onwezen, Marleen C. & Antonides, Gerrit & Bartels, Jos, 2013. "The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 141-153.
    2. Ucok W.R. Siagian & Bintang B. Yuwono & Shinichiro Fujimori & Toshihiko Masui, 2017. "Low-Carbon Energy Development in Indonesia in Alignment with Intended Nationally Determined Contribution (INDC) by 2030," Energies, MDPI, vol. 10(1), pages 1-15, January.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. Gadenne, David & Sharma, Bishnu & Kerr, Don & Smith, Tim, 2011. "The influence of consumers' environmental beliefs and attitudes on energy saving behaviours," Energy Policy, Elsevier, vol. 39(12), pages 7684-7694.
    5. Nicholas M. Gotts & J. Gareth Polhill, 2017. "Experiments with a Model of Domestic Energy Demand," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(3), pages 1-11.
    6. Bin, Shui & Dowlatabadi, Hadi, 2005. "Consumer lifestyle approach to US energy use and the related CO2 emissions," Energy Policy, Elsevier, vol. 33(2), pages 197-208, January.
    7. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    8. Artis Kancs, 2001. "Predicting European Enlargement Impacts: A Framework of Interregional General Equilibrium," Eastern European Economics, Taylor & Francis Journals, vol. 39(5), pages 31-63, September.
    9. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
    10. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    11. Babatunde, Kazeem Alasinrin & Begum, Rawshan Ara & Said, Fathin Faizah, 2017. "Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 61-71.
    12. Sundt, Swantje & Rehdanz, Katrin, 2015. "Consumers' willingness to pay for green electricity: A meta-analysis of the literature," Energy Economics, Elsevier, vol. 51(C), pages 1-8.
    13. Anatolitis, Vasilios & Welisch, Marijke, 2017. "Putting renewable energy auctions into action – An agent-based model of onshore wind power auctions in Germany," Energy Policy, Elsevier, vol. 110(C), pages 394-402.
    14. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    15. Jackson, Jerry, 2010. "Improving energy efficiency and smart grid program analysis with agent-based end-use forecasting models," Energy Policy, Elsevier, vol. 38(7), pages 3771-3780, July.
    16. Giovanni Baiocchi & Jan Minx & Klaus Hubacek, 2010. "The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom," Journal of Industrial Ecology, Yale University, vol. 14(1), pages 50-72, January.
    17. Lee, Chul-Yong & Heo, Hyejin, 2016. "Estimating willingness to pay for renewable energy in South Korea using the contingent valuation method," Energy Policy, Elsevier, vol. 94(C), pages 150-156.
    18. Ju-Sung Lee & Tatiana Filatova & Arika Ligmann-Zielinska & Behrooz Hassani-Mahmooei & Forrest Stonedahl & Iris Lorscheid & Alexey Voinov & J. Gareth Polhill & Zhanli Sun & Dawn C. Parker, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
    19. Esmaeili Aliabadi, Danial & Kaya, Murat & Sahin, Guvenc, 2017. "Competition, risk and learning in electricity markets: An agent-based simulation study," Applied Energy, Elsevier, vol. 195(C), pages 1000-1011.
    20. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    21. Mills, Bradford & Schleich, Joachim, 2012. "Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries," Energy Policy, Elsevier, vol. 49(C), pages 616-628.
    22. Daniel Kahneman, 2003. "A Psychological Perspective on Economics," American Economic Review, American Economic Association, vol. 93(2), pages 162-168, May.
    23. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    24. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    25. Joanne Evans & Lester C. Hunt (ed.), 2009. "International Handbook on the Economics of Energy," Books, Edward Elgar Publishing, number 12764.
    26. Nicholas Stern, 2013. "The Structure of Economic Modeling of the Potential Impacts of Climate Change: Grafting Gross Underestimation of Risk onto Already Narrow Science Models," Journal of Economic Literature, American Economic Association, vol. 51(3), pages 838-859, September.
    27. Bin, Shui & Dowlatabadi, Hadi, 2005. "Corrigendum to "Consumer lifestyles approach to US energy use and the related CO2 emissions": [Energy Policy 33 (2005) 197-208]," Energy Policy, Elsevier, vol. 33(10), pages 1362-1363, July.
    28. Getachew F. Belete & Alexey Voinov & Iñaki Arto & Kishore Dhavala & Tatyana Bulavskaya & Leila Niamir & Saeed Moghayer & Tatiana Filatova, 2019. "Exploring Low-Carbon Futures: A Web Service Approach to Linking Diverse Climate-Energy-Economy Models," Energies, MDPI, vol. 12(15), pages 1-24, July.
    29. Varun Rai & Adam Douglas Henry, 2016. "Agent-based modelling of consumer energy choices," Nature Climate Change, Nature, vol. 6(6), pages 556-562, June.
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