Future demand for energy services through a quantitative approach of lifestyles. (15th December 2017)

• Record Type: Journal Article
• Authors: Le Gallic, Thomas; Assoumou, Edi; Maïzi, Nadia
• Is Part Of: Energy. Volume 141(2017)
• Page Start: 2613
• Page End: 2627
• Publication Date: 2017-12-15
• Subjects: Lifestyles -- Energy system -- Energy demand -- Foresight -- Modelling -- Transition; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.07.065
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Among the tools and processes that are used to inform decisions makers on the long-term challenges raised by energy transition, numerical models are at the forefront. Whether led at the global, continental, country or local level, they help projecting the future operational conditions of our energy systems. However the possibility of addressing the sustainability challenge by changes in our lifestyles rather than technical solutions often remains outside the scope of such models whereas lifestyles contain a set of key determinants of mobility, housing, spatial planning or the organization terms of the productive sectors (industry, agriculture, services). Energy is not consumed for itself and understanding how the future demand of energy services could be framed is an important issue. This paper makes proposals to improve the consideration of lifestyles in the quantitative foresight exercises. Our methodology includes the development of a statistical model of the dynamic of changes in lifestyle patterns to derive energy service demands. The use of this model provides a more coherent framework for the formulation of lifestyle change scenarios. A set of three lifestyles anticipated for France are then designed and discussed up to 2072. Highlights: Lifestyle changes are decisive for our future demand of energy services. A new statistical approach is proposed to include them in energy foresight studies. Three lifestyle scenarios are investigated for France by 2072. The framework increases assumptions' consistency compared to classical methods.