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An Agent-Based Model of Residential Energy Efficiency Adoption

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Abstract

This paper reports on an Agent-Based Model. The purpose of developing this model is to describe ‘the uptake of low carbon and energy efficient technologies and practices by households and under different interventions’. There is a particular focus on modelling non-financial incentives as well as the influence of social networks as well as the decision making by multiple types of agents in interaction, i.e. recommending agents and sales agents, not just households. The decision making model for householder agents is inspired by the Consumat approach, as well as some of those recently applied to electric vehicles. A feature that differentiates this model is that it also represents information agents that provide recommendations and sales agents that proactively sell energy efficient products. By applying the model to a number of scenarios with policies aimed at increasing the adoption of solar hot water systems, a range of questions are explored, including whether it is more effective to incentivise sales agents to promote solar hot water systems, or whether it is more effective to provide a subsidy directly to households; or in fact whether it is better to work with plumbers so that they can promote these systems. The resultant model should be viewed as a conceptual structure with a theoretical and empirical grounding, but which requires further data collection for rigorous analysis of policy options.

Suggested Citation

  • Magnus Moglia & Aneta Podkalicka & James McGregor, 2018. "An Agent-Based Model of Residential Energy Efficiency Adoption," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-3.
    • Handle: RePEc:jas:jasssj:2018-2-3
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    1. 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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    1. Amendola, Marco & Lamperti, Francesco & Roventini, Andrea & Sapio, Alessandro, 2024. "Energy efficiency policies in an agent-based macroeconomic model," Structural Change and Economic Dynamics, Elsevier, vol. 68(C), pages 116-132.
    2. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Chappin, Emile J.L. & Schleich, Joachim & Guetlein, Marie-Charlotte & Faure, Corinne & Bouwmans, Ivo, 2022. "Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Sara Ghaboulian Zare & Reza Hafezi & Mohammad Alipour & Reza Parsaei Tabar & Rodney A. Stewart, 2021. "Residential Solar Water Heater Adoption Behaviour: A Review of Economic and Technical Predictors and Their Correlation with the Adoption Decision," Energies, MDPI, vol. 14(20), pages 1-26, October.
    5. Magnus Moglia & John Hopkins & Anne Bardoel, 2021. "Telework, Hybrid Work and the United Nation’s Sustainable Development Goals: Towards Policy Coherence," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    6. Zhang, Qi & Wu, Xifeng & Chen, Yu, 2022. "Is economic crisis an opportunity for realizing the low-carbon transition? A simulation study on the interaction between economic cycle and energy regulation policy," Energy Policy, Elsevier, vol. 168(C).

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