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Energy and Social Practice: From Abstractions to Dynamic Processes

  • Elizabeth ShoveEmail author
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

Energy policies are typically organised around the supply, management and reduction of energy conceptualised as a singular resource and measured in standardised units like KWh or Mtoe. This kind of abstraction enables national and international institutions to collect and compare data on per capita consumption, the effect of efficiency measures, progress towards emissions targets and the like. The problem is that such approaches treat energy, and energy consumers, as topics of analysis in their own right, stripped from the historically and culturally specific situations in which demand arises. In this chapter I make the case for seeing energy demand as something that is intimately related to the conduct of social practices, and thus inseparable from the spatial and temporal ordering of society, and from the infrastructures and institutions involved. I argue that better understanding of the dynamic and recursive relation between supply, demand and social practice is both necessary and important, particularly given the increasing significance of renewable energy and related challenges of matching peaks in provision with those of consumption. This way of thinking has policy implications. Rather than seeking to maintain present ways of life (but with lower carbon energy supplies), I suggest that the longer term goal could and should be that of imagining and promoting technologies, practices and socio-temporal orders that are compatible with greater reliance on renewables and reduced demand, accepting that this is likely to entail the emergence of ways of living that are really very different from those with which we are familiar today.

Keywords

Energy Efficiency Renewable Energy Social Practice Smart Grid Energy Consumer 
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.

Notes

Acknowledgements

This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/K011723/1] as part of the RCUK Energy Programme and by EDF as part of the R&D ECLEER Programme.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.DEMAND Centre/Department of SociologyLancaster UniversityLancasterUK

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