IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2015-72-2.html
   My bibliography  Save this article

Exploring Homeowners’ Insulation Activity

Author

Abstract

Insulating existing buildings offers great potential for reducing greenhouse gas emissions and meeting Germany’s climate protection targets. Previous research suggests that, since homeowners’ decision-making processes are inadequately understood as yet, today’s incentives aiming at increasing insulation activity lead to unsatisfactory results. We developed an agent-based model to foster the understanding of homeowners’ decision-making processes regarding insulation and to explore how situational factors, such as the structural condition of houses and social interaction, influence their insulation activity. Simulation experiments allow us furthermore to study the influence of socio-spatial structures such as residential segregation and population density on the diffusion of renovation behavior among homeowners. Based on the insights gained, we derive recommendations for designing innovative policy instruments. We conclude that the success of particular policy instruments aiming at increasing homeowners’ insulation activity in a specific region depends on the socio-spatial structure at hand, and that reducing financial constraints only has a relatively low potential for increasing Germany’s insulation rate. Policy instruments should also target the fact that specific renovation occasions are used to undertake additional insulation activities, e.g. by incentivizing lenders and craftsmen to advise homeowners to have insulation installed.

Suggested Citation

  • Jonas Friege & Georg Holtz & Emile Chappin, 2016. "Exploring Homeowners’ Insulation Activity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-4.
  • Handle: RePEc:jas:jasssj:2015-72-2
    as

    Download full text from publisher

    File URL: https://www.jasss.org/19/1/4/4.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Werker, C. & Brenner, T., 2004. "Empirical calibration of simulation models," Working Papers 04.13, Eindhoven Center for Innovation Studies.
    2. Richard Ball & Ross Cullen & Christopher Gan, 1999. "The diffusion of energy efficiency innovations among residential energy consumers," New Zealand Economic Papers, Taylor & Francis Journals, vol. 33(1), pages 115-135.
    3. McMichael, Megan & Shipworth, David, 2013. "The value of social networks in the diffusion of energy-efficiency innovations in UK households," Energy Policy, Elsevier, vol. 53(C), pages 159-168.
    4. Sascha Holzhauer & Friedrich Krebs & Andreas Ernst, 2013. "Considering baseline homophily when generating spatial social networks for agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 19(2), pages 128-150, June.
    5. William Rand & Jeffrey Herrmann & Brandon Schein & Neža Vodopivec, 2015. "An Agent-Based Model of Urgent Diffusion in Social Media," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-1.
    6. Friege, Jonas & Chappin, Emile, 2014. "Modelling decisions on energy-efficient renovations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 196-208.
    7. Martin Jakob, 2007. "The drivers of and barriers to energy efficiency in renovation decisions of single-family home-owners," CEPE Working paper series 07-56, CEPE Center for Energy Policy and Economics, ETH Zurich.
    8. Weiss, Julika & Dunkelberg, Elisa & Vogelpohl, Thomas, 2012. "Improving policy instruments to better tap into homeowner refurbishment potential: Lessons learned from a case study in Germany," Energy Policy, Elsevier, vol. 44(C), pages 406-415.
    9. Jakob, Martin, 2006. "Marginal costs and co-benefits of energy efficiency investments: The case of the Swiss residential sector," Energy Policy, Elsevier, vol. 34(2), pages 172-187, January.
    10. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    11. Stefan Zundel & Immanuel Stieß, 2011. "Beyond Profitability of Energy-Saving Measures—Attitudes Towards Energy Saving," Journal of Consumer Policy, Springer, vol. 34(1), pages 91-105, March.
    12. Olonscheck, Mady & Holsten, Anne & Kropp, Jürgen P., 2011. "Heating and cooling energy demand and related emissions of the German residential building stock under climate change," Energy Policy, Elsevier, vol. 39(9), pages 4795-4806, September.
    13. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    14. Handi Chandra Putra & Haiyan Zhang & Clinton Andrews, 2015. "Modeling Real Estate Market Responses to Climate Change in the Coastal Zone," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-18.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Nägeli, Claudio & Jakob, Martin & Catenazzi, Giacomo & Ostermeyer, York, 2020. "Policies to decarbonize the Swiss residential building stock: An agent-based building stock modeling assessment," Energy Policy, Elsevier, vol. 146(C).
    3. Georg Holtz & Christian Schnülle & Malcolm Yadack & Jonas Friege & Thorben Jensen & Pablo Thier & Peter Viebahn & Émile J. L. Chappin, 2020. "Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies," Energies, MDPI, vol. 13(22), pages 1-26, November.

    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. Paul Baginski & Christoph Weber, 2017. "A Consumer Decision-making Process? Unfolding Energy Efficiency Decisions of German Owner-occupiers," EWL Working Papers 1708, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Aug 2017.
    2. Friege, Jonas & Chappin, Emile, 2014. "Modelling decisions on energy-efficient renovations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 196-208.
    3. Robert Baumhof & Thomas Decker & Klaus Menrad, 2019. "A Comparative Analysis of House Owners in Need of Energy Efficiency Measures but with Different Intentions," Energies, MDPI, vol. 12(12), pages 1-19, June.
    4. Shoaib Azizi & Gireesh Nair & Thomas Olofsson, 2020. "Adoption of Energy Efficiency Measures in Renovation of Single-Family Houses: A Comparative Approach," Energies, MDPI, vol. 13(22), pages 1-16, November.
    5. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    6. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    7. Mária Csutora & Ágnes Zsóka, 2011. "Maximizing the Efficiency of Greenhouse Gas Related Consumer Policy," Journal of Consumer Policy, Springer, vol. 34(1), pages 67-90, March.
    8. Pacilly, Francine C.A. & Hofstede, Gert Jan & Lammerts van Bueren, Edith T. & Kessel, Geert J.T. & Groot, Jeroen C.J., 2018. "Simulating crop-disease interactions in agricultural landscapes to analyse the effectiveness of host resistance in disease control: The case of potato late blight," Ecological Modelling, Elsevier, vol. 378(C), pages 1-12.
    9. Leigh Tesfatsion, 2017. "Elements of Dynamic Economic Modeling: Presentation and Analysis," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 192-216, March.
    10. Anshuka Anshuka & Floris F. Ogtrop & David Sanderson & Simone Z. Leao, 2022. "A systematic review of agent-based model for flood risk management and assessment using the ODD protocol," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 2739-2771, July.
    11. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    12. Handi Chandra‐Putra & Clinton J. Andrews, 2020. "An integrated model of real estate market responses to coastal flooding," Journal of Industrial Ecology, Yale University, vol. 24(2), pages 424-435, April.
    13. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    14. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201704300700001022, Iowa State University, Department of Economics.
    15. Achtnicht, Martin & Madlener, Reinhard, 2014. "Factors influencing German house owners' preferences on energy retrofits," Energy Policy, Elsevier, vol. 68(C), pages 254-263.
    16. Leigh Tesfatsion, 2017. "Modeling economic systems as locally-constructive sequential games," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(4), pages 384-409, October.
    17. Wilson, C. & Pettifor, H. & Chryssochoidis, G., 2018. "Quantitative modelling of why and how homeowners decide to renovate energy efficiently," Applied Energy, Elsevier, vol. 212(C), pages 1333-1344.
    18. Bauermann, Klaas, 2016. "German Energiewende and the heating market – Impact and limits of policy," Energy Policy, Elsevier, vol. 94(C), pages 235-246.
    19. Egger, Claudine & Plutzar, Christoph & Mayer, Andreas & Dullinger, Iwona & Dullinger, Stefan & Essl, Franz & Gattringer, Andreas & Bohner, Andreas & Haberl, Helmut & Gaube, Veronika, 2022. "Using the SECLAND model to project future land-use until 2050 under climate and socioeconomic change in the LTSER region Eisenwurzen (Austria)," Ecological Economics, Elsevier, vol. 201(C).
    20. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201703280700001022, Iowa State University, Department of Economics.

    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:jas:jasssj:2015-72-2. 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: Francesco Renzini (email available below). General contact details of provider: .

    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.