Tackling climate change through energy efficiency: mathematical models for evidence-based public policy recommendations

Federico Gallo; Pierluigi Contucci; Adam Coutts; Ignacio Gallo

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arXiv · 0804.3319

Tackling climate change through energy efficiency: mathematical models for evidence-based public policy recommendations

Federico Gallo·Pierluigi Contucci·Adam Coutts·Ignacio Gallo

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Abstract

Promoting and increasing 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 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 could be to predict the percentage of people choosing to buy new energy efficient light bulbs instead of the traditional incandescent versions. 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 models allow us to incorporate the effect of social interactions could help us identify 'tipping points' at a societal level. This knowledge could 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 physical sciences-and decision-makers in the private and public sectors to work together towards preventing the potentially devastating social, economic and environmental effects of climate change.

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Federico Gallo, Pierluigi Contucci, Adam Coutts, Ignacio Gallo. 2008-08-02. Tackling climate change through energy efficiency: mathematical models for evidence-based public policy recommendations. https://arxiv.org/abs/0804.3319

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