Fallacies of energy efficiency indicators

The strategy of energy efficiency to save energy is deceptively simple: the idea is to use less input for the highest amount of useful output. However, on a practical and conceptual level, efficiency is an ambiguous and problematic concept to implement. Of particular concern is the lack of contextua...

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Detalhes bibliográficos
Autores: Velasco-Fernández, Raúl|||0000-0002-5438-1158, Dunlop, Tessa|||0000-0002-2265-9880, Giampietro, Mario|||0000-0002-5569-7023
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:215079
Acesso em linha:https://ddd.uab.cat/record/215079
https://dx.doi.org/urn:doi:10.1016/j.enpol.2019.111089
Access Level:acceso abierto
Palavra-chave:Energy efficiency
Energy performance
End-use matrix
Energy policy
Jevons paradox
Metabolic pattern
Descrição
Resumo:The strategy of energy efficiency to save energy is deceptively simple: the idea is to use less input for the highest amount of useful output. However, on a practical and conceptual level, efficiency is an ambiguous and problematic concept to implement. Of particular concern is the lack of contextual and qualitative information provided in energy efficiency measurements based on simple ratios. Oversimplification of efficiency measurements can have a detrimental effect on the choice of energy policies. Efficiency measurements are particularly problematic on a macroeconomic scale where a significant amount of meaningful information is lost through the aggregation of data into a simple ratio (economic energy intensity). First, practical examples are presented flagging conceptual problems with energy efficiency indicators, then an alternative accounting method-the end-use matrix-based on the concept of the metabolic pattern of social-ecological systems is illustrated to show the possibility of enriching efficiency indicators by adding qualitative and contextual information across multiple scales and dimensions. This method unpacks and structures salient energy input and output information in a meaningful and transparent way by generating a rich multi-level and multi-dimensional information space.