Participatory Policy Development with Agent-Based Modelling: Overcoming the Building Energy-Efficiency Gap

  • Eva HalwachsEmail author
  • Anne von Streit
  • Christof Knoeri
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


To support the energy transition, making buildings, and in particular residential buildings, more energy-efficient is a central issue. To develop effective policies for achieving an increase of the renovation rate, regional and communal initiatives and policy structures play an important role. Decisions regarding energy-efficient renovations are characterized by the local interaction of homeowners, construction experts and regulators with the physical properties of the building stock. Agent-based modelling is an ideal tool, to display such complex interplay of socio-technical systems and the interacting actors. Regional-specific models can therefore support regional decision-makers in the policy development process. This paper describes (i) how agent-based modelling can contribute to a common system understanding by simulating different pathways of the regional building stock and its future energy demand and (ii) how social simulation can support policy actors in the development of regional policies concerning buildings’ energy efficiency.


Participation Policy Building stock model Energy efficiency 


  1. 1.
    Patwardhan, A.P., Gomez-Echeverri, L., Nakićenović, N., et al. (eds.): Global Energy Assessment (GEA). Cambridge University Press, Cambridge (2012)Google Scholar
  2. 2.
    Bundesministerium für Wirtschaft und Energie.: Energieeffizienzstrategie Gebäude: Wege zu einem nahezu klimaneutralen Gebäudebestand (2015)Google Scholar
  3. 3.
    Binder, C.R., Knoeri, C., Hecher, M.: Modeling transition paths towards de-centralized regional energy autonomy: the role of legislation, technology adoption, and resource availability. Raumforsch. Raumordn. 74(3), 273–284 (2016). CrossRefGoogle Scholar
  4. 4.
    Chapman, R., Howden-Chapman, P., Viggers, H., et al.: Retrofitting houses with insulation: a cost-benefit analysis of a randomised community trial. J. Epidemiol. Community Health. 63(4), 271–277 (2009). CrossRefGoogle Scholar
  5. 5.
    Deutsche Energie-Agentur GmbH.: Der dena-Gebäudereport 2012: Statistiken und Analysen zur Energieeffizienz im Gebäudebestand (2012)Google Scholar
  6. 6.
    Weiss, J., Dunkelberg, E., Vogelpohl, T.: Improving policy instruments to better tap into homeowner refurbishment potential: lessons learned from a case study in Germany. Energy Policy. 44, 406–415 (2012). CrossRefGoogle Scholar
  7. 7.
    Friege, J., Chappin, E.: Modelling decisions on energy-efficient renovations: a review. Renew. Sust. Energ. Rev. 39, 196–208 (2014). CrossRefGoogle Scholar
  8. 8.
    Hecher, M., Vilsmaier, U., Akhavan, R., et al.: An integrative analysis of energy transitions in energy regions: a case study of ökoEnergieland in Austria. Ecol. Econ. 121, 40–53 (2016). CrossRefGoogle Scholar
  9. 9.
    Späth, P., Rohracher, H.: ‘Energy regions’: the transformative power of regional discourses on socio-technical futures. Res. Policy. 39(4), 449–458 (2010). CrossRefGoogle Scholar
  10. 10.
    Grimm, V., Railsback, S.F.: Individual-Based Modeling and Ecology. Princeton Series in Theoretical and Computational Biology. Princeton University Press, Princeton (2013)Google Scholar
  11. 11.
    Friege, J.: Increasing homeowners’ insulation activity in Germany: an empirically grounded agent-based model analysis. Energ. Buildings. 128, 756–771 (2016). CrossRefGoogle Scholar
  12. 12.
    Knoeri, C., Goetz, A.: Generic bottom-up Generic bottom-up building-energy models for developing regional energy transition scenarios. Paper presented at the 9th Conference of the European Social Simulation Association. Barcelona (2014)Google Scholar
  13. 13.
    Mendoza, G.A., Prabhu, R.: Combining participatory modeling and multi-criteria analysis for community-based forest management. For. Ecol. Manag. 207(1–2), 145–156 (2005). CrossRefGoogle Scholar
  14. 14.
    Gaddis, E.J.B., Falk, H.H., Ginger, C., et al.: Effectiveness of a participatory modeling effort to identify and advance community water resource goals in St. Albans, Vermont. Environ. Model Softw. 25(11), 1428–1438 (2010). CrossRefGoogle Scholar
  15. 15.
    Röckmann, C., Ulrich, C., Dreyer, M., et al.: The added value of participatory modelling in fisheries management – what has been learnt? Mar. Policy. 36(5), 1072–1085 (2012). CrossRefGoogle Scholar
  16. 16.
    Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U. S. A. 99(Suppl 3), 7280–7287 (2002). CrossRefGoogle Scholar
  17. 17.
    Johnson, P.: Agent-based models as “interested amateurs”. Land. 4(2), 281–299 (2015). CrossRefGoogle Scholar
  18. 18.
    Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., et al.: Computational model-ling of public policy: reflections on practice. JASSS. 21(1), (2018).
  19. 19.
    Böhme, S., Eigenhüller, L., Werner, D., et al.: Demografie und Arbeitsmarkt in Bayern: Entwicklung, aktuelle Lage und Ausblick. IAB-Regional - Berichte und Analysen aus dem Regionalen Foschungsnetz (2012)Google Scholar
  20. 20.
    Hofer, V., Süß, A., Prasch, M., et al.: Das naturräumliche und technische Potential für Erneuerbare Energien in der Modellregion Oberland. INOLA Working Paper (2016)Google Scholar
  21. 21.
    Knoeri, C., Binder, C.R., Althaus, H.: An agent operationalization approach for context specific agent-based modeling. JASSS. 14(2), (2011).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Eva Halwachs
    • 1
    Email author
  • Anne von Streit
    • 1
  • Christof Knoeri
    • 2
  1. 1.Human-Environment Relations, Department of GeographyLudwig Maximilian University of MunichMunichGermany
  2. 2.Group for Sustainability and Technology, Department of ManagementTechnology and Economics, ETH ZurichZürichSwitzerland

Personalised recommendations