Abstract
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.
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Acknowledegments
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).
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Gallo, F., Contucci, P., Coutts, A., Gallo, I. (2010). Tackling Climate Change Through Energy Efficiency: Mathematical Models to Offer Evidence-Based Recommendations for Public Policy. In: Capecchi, V., Buscema, M., Contucci, P., D'Amore, B. (eds) Applications of Mathematics in Models, Artificial Neural Networks and Arts. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8581-8_7
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