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An Agent-Based Model of Consumer Lighting

  • E. J. L. Chappin
  • M. R. Afman
Part of the Agent-Based Social Systems book series (ABSS, volume 9)

Abstract

With the aim of better understanding the consequences of the EU ban on incandescent lamps, an agent-based model has been developed in which consumer behaviour in the purchasing of lamps is simulated. In this model, consumers are modelled based on heterogeneous preferences, and they develop opinions (based on memories and perceptions) about lamps and share these in a social network structure. Lighting technology is modelled using lamps of many different technology types. The results of the simulations indicate that the ban on bulbs will be effective in realising an energy-efficient sector, albeit at significant expense to consumers. An alternative policy, introducing a tax on incandescent lamps, is also shown to be effective, given a sufficient level of taxation.

Keywords

Electricity Consumption Light Bulb Purchase Decision Policy Scenario Incandescent Lamp 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Afman, M. (2010). Modelling transitions in consumer lighting—consequences of the E.U. ban on light bulbs. Master’s thesis, Delft University of Technology, Faculty of Technology, Policy and Management. Google Scholar
  2. Azevedo, I. L., Morgan, G., & Morgan, F. (2009). The transition to solid-state lighting. Proceedings of the IEEE, 97(3), 481–510. CrossRefGoogle Scholar
  3. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. MathSciNetCrossRefGoogle Scholar
  4. Bertoldi, P., & Atanasiu, B. (2007). Electricity consumption and efficiency trends in the enlarged European Union—status report 2006. Google Scholar
  5. CEC (2009). Directive 2008/101/ec of the European Parliament and of the Council of 19 November 2008 amending directive 2003/87/ec so as to include aviation activities in the scheme for greenhouse gas emission allowance trading within the community. Official Journal of the European Union, 8(L076), 3–21. Google Scholar
  6. Chappin, E. J. L. (2011). Simulating energy transitions. PhD thesis, Delft University of Technology, Delft, the Netherlands. ISBN: 978-90-79787-30-2. Google Scholar
  7. Curtis, S. (2005). Efficiency gains boost high-power LED performance. Compound Semiconductor, 11(11), 27–30. Google Scholar
  8. Dictionary.com (2010). Lamp, dictionary.com unabridged. http://dictionary.reference.com/browse/lamp.
  9. Dupuis, R., & Krames, M. (2008). History, development, and applications of high-brightness visible light-emitting diodes. Journal of Lightwave Technology, 26(9), 1154–1171. CrossRefGoogle Scholar
  10. Forbes, G. (1889). Electric-lighting stations in Europe, and their lessons. Science, 13(326), 337. Google Scholar
  11. Fouquet, R., & Pearson, P. J. G. (2006). Seven centuries of energy services: the price and use of light in the United Kingdom (1300–2000). Energy Journal, 27(1), 139–177. Google Scholar
  12. Gendre, M. F. (2003). Two centuries of electric light source innovations. URL: http://www.einlightred.tue.nl/lightsources/history/light_history.pdf.
  13. Hausman, J. A. (1979). Individual discount rates and the purchase and utilization of energy-using durables. The Bell Journal of Economics, 10(1), 33–54. CrossRefGoogle Scholar
  14. Holonyak, J. N. (2005). From transistors to lasers and light-emitting diodes. Materials Research Society Bulletin, 30, 509–515. CrossRefGoogle Scholar
  15. Kooreman, P. (1996). Individual discounting, energy conservation, and household demand for lighting. Resource and Energy Economics, 18(1), 103–114. CrossRefGoogle Scholar
  16. Martinot, E., & Borg, N. (1998). Energy-efficient lighting programs. Experience and lessons from eight countries. Energy Policy, 26(14), 1071–1081. CrossRefGoogle Scholar
  17. Menanteau, P., & Lefebvre, H. (2000). Competing technologies and the diffusion of innovations: the emergence of energy-efficient lamps in the residential sector. Research Policy, 29(3), 375–389. CrossRefGoogle Scholar
  18. Mills, E. (1993). Efficient lighting programs in Europe: cost effectiveness, consumer response, and market dynamics. Energy, 18(2), 131–144. IN1. Google Scholar
  19. Mills, E. (2002). The $230-billion global lighting energy bill. In Proceedings of the fifth European conference on energy-efficient lighting (pp. 368–385). Google Scholar
  20. Nekovee, M., Moreno, Y., Bianconi, G., & Marsili, M. (2007). Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and Its Applications, 374(1), 457–470. CrossRefGoogle Scholar
  21. NPS (2007). Edison biography. Website. Accessed: May 19th, 2009. URL: http://www.nps.gov/edis/historyculture/edison-biography.htm.
  22. Peifer, D. (2007). The CFL myth. Lighting Design and Application LD and A, 37(10), 87–90. Google Scholar
  23. Sylvania (2009). Business products: Glossary, Website. Accessed: November 2009. http://www.sylvania.com/BusinessProducts/Glossary/.
  24. Taskforce Verlichting (2008). Groen licht voor energiebesparing—eindrapport van de taskforce verlichting. Report, SenterNovem. Google Scholar
  25. U.S. Department of Energy (2009). Multi-year program plan fy’09-fy’15—solid-state lighting research and development. Technical report, prepared for: lighting research and development, building technologies program, office of energy efficiency and renewable energy. U.S. Department of Energy, Prepared by: Navigant Consulting, Inc., Radcliffe Advisors, Inc., and SSLS, Inc. Google Scholar
  26. Wang, X. F., & Chen, G. (2003). Complex networks: small-world, scale-free and beyond. IEEE Circuits and Systems Magazine, 3(1), 6–20. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.

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