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dc.contributor.author | Nieto Vega, Jaime | |
dc.contributor.author | Moyano Pesquera, Pedro Benito | |
dc.contributor.author | Moyano, Diego | |
dc.contributor.author | Miguel González, Luis Javier | |
dc.date.accessioned | 2023-01-09T13:22:42Z | |
dc.date.available | 2023-01-09T13:22:42Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Journal of Industrial Ecology, 2022. | es |
dc.identifier.issn | 1088-1980 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/57924 | |
dc.description | Producción Científica | es |
dc.description.abstract | Input–output tables (IOTs) provide a relevant picture of economic structure as they represent the composition and interindustry relationships of an economy. The technical coefficients matrix (A matrix) is considered to capture the technological status of an economy; so, it is of special relevance for the evaluation of long-term, structural transformations, such as sustainability transitions in integrated assessment models (IAMs). The A matrix has typically been considered either static or exogenous. Endogenous structural change has rarely been applied to models. The objective of this paper is to analyze energy intensity, a widely used variable in IAMs, and its role as a driver of structural change. We therefore identify the most relevant technical coefficients in the IOTs time series and estimate an econometric model based on the energy intensity of five different final end-use energy sources. The results of this analysis show that energy intensity has a significant influence on the evolution of the A matrix and should therefore be taken into consideration when analyzing endogenous structural change in models. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Wiley | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.classification | Energy efficiency | es |
dc.subject.classification | Industrial ecology | es |
dc.subject.classification | Industrial metabolism | es |
dc.subject.classification | input–output analysis (IOA) | es |
dc.subject.classification | Structural change | es |
dc.subject.classification | Technical coefficients | es |
dc.title | Is energy intensity a driver of structural change? Empirical evidence from the global economy | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2022 The Author(s) | es |
dc.identifier.doi | 10.1111/jiec.13352 | es |
dc.relation.publisherversion | https://onlinelibrary.wiley.com/doi/full/10.1111/jiec.13352 | es |
dc.identifier.publicationtitle | Journal of Industrial Ecology | es |
dc.peerreviewed | SI | es |
dc.description.project | Ministerio de Asuntos Económicos y Transformación Digital, project MODESLOW (grant ECO2017-85110-R) | es |
dc.description.project | European Union’s Horizon 2020 research and innovation program. Project “Low Carbon society: An enhanced modelling tool for the transition to sustainability (LOCOMOTION) grant award H2020-LC-CLA-01-2018, n. 821105 | es |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/821105 | |
dc.identifier.essn | 1530-9290 | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 53 Ciencias Económicas | es |
dc.subject.unesco | 33 Ciencias Tecnológicas | es |
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