Analyzing the energy performance of manufacturing across levels using the end-use matrix
Within the context of the controversial use of the concept energy intensity to assess national energy performance, this paper proposes an innovative accounting framework: the energy end-use matrix. This tool integrates quantitative assessments of energy use of the various constituent compartments of...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:195143 |
| Acceso en línea: | https://ddd.uab.cat/record/195143 https://dx.doi.org/urn:doi:10.1016/j.energy.2018.07.122 |
| Access Level: | acceso abierto |
| Palabra clave: | Energy intensity Energy efficiency End-use matrix Industrial sector Manufacturing MuSIASEM |
| Sumario: | Within the context of the controversial use of the concept energy intensity to assess national energy performance, this paper proposes an innovative accounting framework: the energy end-use matrix. This tool integrates quantitative assessments of energy use of the various constituent compartments of socioeconomic systems. More specifically it identifies, moving across levels of analysis, what compartments (or sub-compartments) are using what type of energy carriers for what type of end-use. This analysis is integrated with an assessment of labor requirements and the associated flows of value added. The enduse matrix thus integrates in a coherent way quantitative assessments across different dimensions and hierarchical scales and facilitates the development of integrated sets of indicators. In this way it contributes to a multi-criteria characterization of national or sectoral energy performance. The tool is illustrated with an analysis of three EU countries: Bulgaria, Finland and Spain. Challenges to improving the usefulness of biophysical analysis of the efficiency of the industrial sector are identified and discussed. Increasing the discriminatory power of quantitative analysis through better data standardization by statistical offices is the major challenge. |
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