Tackling Climate Change Through Energy Efficiency: Mathematical Models to Offer Evidence-Based Recommendations for Public Policy
Promoting and increasing rates of energy efficiency is a promising method of reducing CO2 emissions and avoiding the potentially devastating effects of climate change. The question is: How do we induce a cultural or a behavioural change whereby people nationally and globally adopt more energy-efficient lifestyles?
We propose a new family of mathematical models, based on a statistical mechanics extension of discrete choice theory, that offer a set of formal tools to systematically analyse and quantify this problem. An application example is to predict the percentage of people choosing to buy new energy-efficient light bulbs instead of the old incandescent versions; in particular, through statistical evaluation of survey responses, the models can identify the key driving factors in the decision-making process, for example, the extent to which people imitate each other. These tools and models that allow us to account for social interactions could help us identify tipping points that may be used to trigger structural changes in our society. The results may provide tangible and deliverable evidence-based policy options to decision makers.
We believe that these models offer an opportunity for the research community, in both the social and the physical sciences, and decision makers, both in the private and the public sectors, to work together towards preventing the potentially devastating social, economic and environmental effects of climate change.
KeywordsEnergy Efficiency Utility Function Wind Farm Discrete Choice Increase Energy Efficiency
F.G. and A.C. would like to thank Bryony Worthington and James Fox for their contributions and the useful discussions. I.G. acknowledges partial support from the CULTAPTATION project of the European Commission (FP6-2004-NEST-PATH-043434).
- Ariely, D. (2008). Predictably irrational – the hidden forces that shape our decisions. London: Harper Collins.Google Scholar
- Bandura A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
- Ben-Akiva M. and Lerman, S. R. (1985). Discrete choice analysis. Cambridge MA: The MIT Press.Google Scholar
- Contucci, P., Gallo, I., and Ghirlanda, S. (2007). Equilibria of culture contact derived from ingroup and outgroup attitudes, arXiv: 0712.1119.Google Scholar
- Contucci, P. and Giardina, C. (2008). Mathematics and social sciences: A statistical mechanics approach to immigration. ERCIM News 73, 34–35.Google Scholar
- Creyts, J., Derkach, A., Nyquist, S., Ostrowski, K., and Stephenson, J. (2007). Reducing U.S. greenhouse emissions: how much and at what cost? McKinsey & Company Report, http://www.mckinsey.com/clientservice/ccsi/greenhousegas.asp
- DEFRA (2008). Household energy supplier obligations – The carbon emissions reduction target. Published on the DEFRA website: http://www.defra.gov.uk/environment/climatechange/uk/household/supplier/index.htm
- Fox, J., Daly, A. J, and Gunn, H. (2003). Review of RAND Europe’s transport demand model systems. Published on RAND’s website: http://rand.org/pubs/monograph_reports/MR1694
- IPCC (2007). Fourth assessment report: climate change 2007. Published on the IPCC’s website: http://www.ipcc.ch/ipccreports/assessments-reports.htm
- Knott, D., Muers, S., and Aldridge, S., (2007). Achieving cultural change: A policy framework. The strategy unit, Cabinet Office, UK Government.Google Scholar
- Luce, R. and Suppes, P. (1965). Preferences, utility and subjective probability. In R. Luce,, R. Bush, Galenter E. (Eds.) Handbook of mathematical psychology (Vol. 3) New York: Wiley.Google Scholar
- Ortuzar, J. and Wilumsen, L. (2001). Modell. Trans. Chichester, UK: Wiley.Google Scholar
- Paag, H., Daly, A.J., and Rohr, C. (2001). Predicting use of the Copenhagen harbour tunnel. In H. David (Ed.), Travel behaviour research: the leading edge. Pergamon.Google Scholar
- Ryan, M. and Gerard, K. (2003). Using discrete choice experiments to value health care programmes: Current practice and future research reflections. Appl. Health Econom. Health Pol., 21, 55–64.Google Scholar
- Scheinkman, J. A. (2008). Social interactions. The New Palgrave Dictionary of Economics (2nd edn.) Palgrave Macmillan.Google Scholar
- Schelling, T. (1978). Micromotives and macrobehavior. New York: W. W. Norton & Company.Google Scholar
- Stern, N. (2007). The economics of climate change – The stern review. Cambridge: Cambridge University Press.Google Scholar
- Verhaar, H. (2007). Reducing CO2 emissions by 555 Mton through Energy Efficiency Lighting. Presented at the UNFCCC Conference in Bali, December 8.Google Scholar
- Weiss, P. (1907). L’hypothèse du champ moléculaire et la propriété ferromagnétique. J. de Phys. 4 série VI, 661–690.Google Scholar