Open Access
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
Article Number 03014
Number of page(s) 7
Section High Energy Performance and Sustainable Buildings
Published online 13 August 2019
  1. Artola, I., Rademaekers, K., Williams, R. & Yearwood, J. Boosting Building Renovation: What potential and value for Europe? (2016). doi: 10.2861/331360 [Google Scholar]
  2. Government of the Netherlands. Dutch Goals within the EU for Climate Change. Government of the Netherlands (2016). Available at: (Accessed: 6th February 2018) [Google Scholar]
  3. Ministry of the Interior and Kingdom. Investing in the Dutch housing market. (2014). [Google Scholar]
  4. Government of the Netherlands. Measures to reduce greenhouse gas emissions,. (2017). Available at: (Accessed: 23rd October 2017) [Google Scholar]
  5. Aune, M. Energy comes home. Energy Policy 35, 5457–5465 (2007). [CrossRef] [Google Scholar]
  6. Mlecnik, E. & Straub, A. Experiences of Homeowners Regarding Nearly Zero-Energy Renovations and Consequences for Business Models. Plea 2015 Bol. Archit. (R)evolution (2015). [Google Scholar]
  7. Wilson, C., Crane, L. & Chryssochoidis, G. Why do homeowners renovate energy efficiently? Contrasting perspectives and implications for policy. Energy Res. Soc. Sci. 7, 12–22 (2015). [Google Scholar]
  8. Schilder, F., van Middelkoop, M. & van den Wijngaart, R. Energiebesparing in de woningvoorraad: financiële consequenties voor corporaties, huurders, eigenaren-bewoners en Rijksoverheid. 50 (2016). [Google Scholar]
  9. Fan, K., Qian, Q. K. & W Chan, E. H. Transaction Costs (TCs) in Building Regulations and Control for Green Buildings. 818–828 (2016). [Google Scholar]
  10. Mundaca, L., Mansoz, M., Neij, L. & Timilsina, G. Transaction costs analysis of low carbon technologies. Clim. policy 13, 1–24 (2013). [CrossRef] [Google Scholar]
  11. Wilson, C., Pettifor, H. & Chryssochoidis, G. Quantitative modelling of why and how homeowners decide to renovate energy efficiently. Appl. Energy 212, 1333–1344 (2018). [Google Scholar]
  12. Coase, R. H. The problem of social cost. J. Law Econ. 3, 351–376 (1960). [Google Scholar]
  13. Ostertag, K. Transaction costs of raising energy efficiency. Eng. Anal. Conserv. Energy 18 (1999). [Google Scholar]
  14. Fan, K., Qian, Q. & Chan, E. Transaction costs (TCs) in building regulations and control for green buildings : case study of Hong Kong. (2016). [Google Scholar]
  15. Williamson, O. E. The Economics of Organization: The Transaction Cost Approach. American Journal of Sociology 87, 548–577 (1981). [CrossRef] [Google Scholar]
  16. Williamson, O. Transaction cost economics: the governance of contractual relations. Economic Organization 101–130 (1979). doi: 10.1046/j.1365-2702.2003.00683.x [Google Scholar]
  17. Coase, R. H. The Nature of the Firm. Economica 4, 386–405 (1937). [Google Scholar]
  18. Matthews, R. C. O. The Economics of Institutions and the Sources of Growth. Econ. J. 96, 903 (1986). [CrossRef] [Google Scholar]
  19. Furubotn, E. G. & Richter, R. The New Institutional Economics of Markets: An Introduction. New Institutional Econ. Mark. 1–29 (2010). doi: 10.1007/s12273-011-0024-9 [Google Scholar]
  20. Björkqvist, O. & Wene, C. A study of transaction costs for energy investments in the residential sector. in Proceedings of the eceee 1993 Summer Study conference(The European Council for an Energy Efficient Economy Stockholm, 1993). [Google Scholar]
  21. Haurin, D. R. & Gill, H. L. The impact of transaction costs and the expected length of stay on homeownership. J. Urban Econ. 51, 563–584 (2002). [Google Scholar]
  22. Qian, Q. K., Lehmann, S., Ghani, A., Khalid B., & Chan, E. H. W. Transaction Costs (Tcs) Framework to Understand the Concerns of Building Energy Efficiency (BEE) Investment in Hong Kong. Int. J. Waste Resour. 4, 1–7 (2014). [Google Scholar]
  23. Qian, Q. K., Chan, E. H. W., Visscher, H. & Lehmann, S. Modeling the green building (GB) investment decisions of developers and endusers with transaction costs (TCs) considerations. J. Clean. Prod. 109, 315–325 (2015). [Google Scholar]
  24. Pettifor, H., Wilson, C. & Chryssochoidis, G. The appeal of the green deal: Empirical evidence for the influence of energy efficiency policy on renovating homeowners. Energy Policy 79, 161– 176 (2015). [Google Scholar]
  25. Itard, L. & Meijer, F. Towards a sustainable Northern European Figures, facts and future. (2008). [Google Scholar]
  26. Brown, M. A. Market failures and barriers as a basis for clean energy policies. Energy Policy 29, 1197–1207 (2001). [Google Scholar]
  27. Stieß, I. & Dunkelberg, E. Objectives, barriers and occasions for energy efficient refurbishment by private homeowners. in Journal of Cleaner Production 48, 250–259 (2013). [Google Scholar]
  28. Mundaca, L. Transaction costs of energy efficiency policy instruments. in (International Institute for Industrial Environmental Economics at Lund University, 2007). [Google Scholar]
  29. Baginski, J. P. & Weber, C. A Consumer Decision-Making Process? Unfolding Energy Efficiency Decisions of German Owner-Occupiers. HEMF Work. Pap. 08, (2017). [Google Scholar]
  30. Murphy, L. C. Policy Instruments to Improve Energy Performance of Existing Owner Occupied Dwellings Understanding and Insight Lorraine. (Delft University of Technology, 2016). [Google Scholar]
  31. Wilson, C. & Dowlatabadi, H. Models of Decision Making and Residential Energy Use. Annu. Rev. Environ. Resour. 32, 169–203 (2007). [Google Scholar]
  32. Midi, H., Sarkar, S. K. & Rana, S. Collinearity diagnostics of binary logistic regression model. J. Interdiscip. Math. 13, 253–267 (2010). [CrossRef] [Google Scholar]
  33. Brant, R. Assessing Proportionality in the Proportional Odds Model for Ordinal Logistic Regression. Biometrics 46, 1171 (1990). [Google Scholar]
  34. Gigerenzer, G. & Selten, R. Bounded rationality: The adaptive toolbox. (2001). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.