IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v151y2015icp273-284.html
   My bibliography  Save this article

Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach

Author

Listed:
  • Robinson, Scott A.
  • Rai, Varun

Abstract

Energy technology adoption is a complex process, involving social, behavioral, and economic factors that impact individual decision-making. This paper uses an empirical, geographic information system (GIS)-integrated agent-based model of residential solar photovoltaic (PV) adoption to explore the importance of using empirical household-level data and of incorporating economic as well as social and behavioral factors on model outcomes. Our goal is to identify features of the model that are most critical to successful prediction of the temporal, spatial, and demographic patterns that characterize the technology adoption process for solar PV. Agent variables, topology, and environment are derived from detailed and comprehensive real-world data between 2004 and 2013 in Austin (Texas, USA). Four variations of the model are developed, each with a different level of complexity and empirical characterization. We find that while an explicit focus only on the financial aspects of the solar PV adoption decision performs well in predicting the rate and scale of adoption, accounting for agent-level attitude and social interactions are critical for predicting spatial and demographic patterns of adoption with high accuracy.

Suggested Citation

  • Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    • Handle: RePEc:eee:appene:v:151:y:2015:i:c:p:273-284
    DOI: 10.1016/j.apenergy.2015.04.071
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915005346
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.04.071?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. G. Fagiolo & C. Birchenhall & P. Windrum, 2007. "Empirical Validation in Agent-based Models: Introduction to the Special Issue," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 189-194, October.
    2. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    3. Popp, David & Newell, Richard G. & Jaffe, Adam B., 2010. "Energy, the Environment, and Technological Change," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 873-937, Elsevier.
    4. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    5. Drury, Easan & Miller, Mackay & Macal, Charles M. & Graziano, Diane J. & Heimiller, Donna & Ozik, Jonathan & Perry IV, Thomas D., 2012. "The transformation of southern California's residential photovoltaics market through third-party ownership," Energy Policy, Elsevier, vol. 42(C), pages 681-690.
    6. Domenico Gatti & Edoardo Gaffeo & Mauro Gallegati, 2010. "Complex agent-based macroeconomics: a manifesto for a new paradigm," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(2), pages 111-135, December.
    7. Alison Heppenstall & Andrew Evans & Mark Birkin, 2006. "Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-2.
    8. Wilkerson, Jordan T. & Cullenward, Danny & Davidian, Danielle & Weyant, John P., 2013. "End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors," Energy Economics, Elsevier, vol. 40(C), pages 773-784.
    9. Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
    10. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    11. 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.
    12. Malte Schwoon, 2005. "Simulating the Adoption of Fuel Cell Vehicles," Working Papers FNU-59, Research unit Sustainability and Global Change, Hamburg University, revised Feb 2006.
    13. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    14. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
    15. Claudia Werker & Thomas Brenner, 2004. "Empirical Calibration of Simulation Models," Papers on Economics and Evolution 2004-10, Philipps University Marburg, Department of Geography.
    16. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
    17. Steven N. Durlauf, 2012. "Complexity, economics, and public policy," Politics, Philosophy & Economics, , vol. 11(1), pages 45-75, February.
    18. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    19. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    20. Zhang, Tao & Zhang, David, 2007. "Agent-based simulation of consumer purchase decision-making and the decoy effect," Journal of Business Research, Elsevier, vol. 60(8), pages 912-922, August.
    21. Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
    22. Kaufmann, Peter & Stagl, Sigrid & Franks, Daniel W., 2009. "Simulating the diffusion of organic farming practices in two New EU Member States," Ecological Economics, Elsevier, vol. 68(10), pages 2580-2593, August.
    23. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    24. Ashraf, Nava & Jack, B. Kelsey & Kamenica, Emir, 2013. "Information and subsidies: Complements or substitutes?," Journal of Economic Behavior & Organization, Elsevier, vol. 88(C), pages 133-139.
    25. Faiers, Adam & Neame, Charles, 2006. "Consumer attitudes towards domestic solar power systems," Energy Policy, Elsevier, vol. 34(14), pages 1797-1806, September.
    26. Tim Nelson & Paul Simshauser & Simon Kelley, 2011. "Australian Residential Solar Feed-in Tariffs: Industry Stimulus or Regressive Form of Taxation?," Economic Analysis and Policy, Elsevier, vol. 41(2), pages 113-129, September.
    27. Macintosh, Andrew & Wilkinson, Deb, 2011. "Searching for public benefits in solar subsidies: A case study on the Australian government's residential photovoltaic rebate program," Energy Policy, Elsevier, vol. 39(6), pages 3199-3209, June.
    28. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    29. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, December.
    30. Hsu, Chiung-Wen, 2012. "Using a system dynamics model to assess the effects of capital subsidies and feed-in tariffs on solar PV installations," Applied Energy, Elsevier, vol. 100(C), pages 205-217.
    31. Krause, Rachel M. & Carley, Sanya R. & Lane, Bradley W. & Graham, John D., 2013. "Perception and reality: Public knowledge of plug-in electric vehicles in 21 U.S. cities," Energy Policy, Elsevier, vol. 63(C), pages 433-440.
    32. Zhao, Jiayun & Kucuksari, Sadik & Mazhari, Esfandyar & Son, Young-Jun, 2013. "Integrated analysis of high-penetration PV and PHEV with energy storage and demand response," Applied Energy, Elsevier, vol. 112(C), pages 35-51.
    33. Laciana, Carlos E. & Rovere, Santiago L., 2011. "Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1139-1149.
    34. Hoff, Thomas E & Wenger, Howard J & Farmer, Brian K, 1996. "Distributed generation : An alternative to electric utility investments in system capacity," Energy Policy, Elsevier, vol. 24(2), pages 137-147, February.
    35. Michael Meadows & Dave Cliff, 2012. "Reexamining the Relative Agreement Model of Opinion Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(4), pages 1-4.
    36. Lee, Timothy & Yao, Runming & Coker, Phil, 2014. "An analysis of UK policies for domestic energy reduction using an agent based tool," Energy Policy, Elsevier, vol. 66(C), pages 267-279.
    37. Maya Sopha, Bertha & Klöckner, Christian A. & Hertwich, Edgar G., 2011. "Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation," Energy Policy, Elsevier, vol. 39(5), pages 2722-2729, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hossein Sabzian & Mohammad Ali Shafia & Mehdi Ghazanfari & Ali Bonyadi Naeini, 2020. "Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory," Sustainability, MDPI, vol. 12(7), pages 1-36, April.
    2. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    3. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    4. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    5. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    6. repec:hal:spmain:info:hdl:2441/1nlv566svi86iqtetenms15tc4 is not listed on IDEAS
    7. repec:hal:spmain:info:hdl:2441/5qr7f0k4sk8rbq4do5u6v70rm0 is not listed on IDEAS
    8. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    9. 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.
    10. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    11. Simpson, Genevieve & Clifton, Julian, 2016. "Subsidies for residential solar photovoltaic energy systems in Western Australia: Distributional, procedural and outcome justice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 262-273.
    12. Kaufmann, Peter & Stagl, Sigrid & Franks, Daniel W., 2009. "Simulating the diffusion of organic farming practices in two New EU Member States," Ecological Economics, Elsevier, vol. 68(10), pages 2580-2593, August.
    13. Martin Zsifkovits & Markus Günther, 2015. "Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 501-522, June.
    14. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    15. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    16. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    17. Nils ROLOFF & Ulrike LEHR & Wolfram KREWITT & Gerhard FUCHS & Sandra WASSERMANN & Wolfganf WEIMER-JEHLE & Bernd SCHMIDT, 2008. "Success Determinants for Technological Innovations in the Energy Sector - The Case of Photovoltaics," EcoMod2008 23800118, EcoMod.
    18. Zhang, Tianyu & Dong, Peiwu & Zeng, Yongchao & Ju, Yanbing, 2022. "Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model," Journal of Business Research, Elsevier, vol. 139(C), pages 90-105.
    19. Petschnig, Martin & Heidenreich, Sven & Spieth, Patrick, 2014. "Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 68-83.
    20. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    21. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    22. Fatouh, Mahmoud & Markose, Sheri & Giansante, Simone, 2021. "The impact of quantitative easing on UK bank lending: Why banks do not lend to businesses?," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 928-953.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:151:y:2015:i:c:p:273-284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.