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dc.contributor.authorZhao, Y.
dc.contributor.authorAntunes, J.J.M.
dc.contributor.authorTan, Yong
dc.contributor.authorWanke, P.F.
dc.date.accessioned2023-03-24T16:19:17Z
dc.date.accessioned2023-03-29T15:26:24Z
dc.date.available2023-03-24T16:19:17Z
dc.date.available2023-03-29T15:26:24Z
dc.date.issued2023
dc.identifier.citationZhao Y, Antunes J, Tan Y et al (2023) Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi-activity network data envelopment analysis. International Journal of Finance and Economics. Accepted for Publication.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19387
dc.descriptionYesen_US
dc.description.abstractFor meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two-stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi-activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre-process and industry have a significant and positive impact on energy efficiency in China.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1002/ijfe.2765en_US
dc.rights© 2022 The Authors. International Journal of Finance & Economics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.subjectArtificial neural networksen_US
dc.subjectChinaen_US
dc.subjectData envelopment analysisen_US
dc.subjectDemographic efficiency driversen_US
dc.subjectEnergy efficiencyen_US
dc.titleDemographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi-activity network data envelopment analysisen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-12-17
dc.date.application2022-12-28
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.rights.licenseCC-BYen_US
dc.date.updated2023-03-24T16:19:20Z
refterms.dateFOA2023-03-29T15:27:10Z
dc.openaccess.statusopenAccessen_US


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