A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation
•Oil product consumption and CO2 emissions sharply influence the transport logistics indexes.•A feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance.•We found empirical sustain for the “feedback hypothesis” f...
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| Published in | Structural change and economic dynamics Vol. 60; pp. 315 - 328 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.03.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0954-349X 1873-6017 1873-6017 |
| DOI | 10.1016/j.strueco.2021.11.018 |
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| Abstract | •Oil product consumption and CO2 emissions sharply influence the transport logistics indexes.•A feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance.•We found empirical sustain for the “feedback hypothesis” for Germany and Japan.
This paper critically assesses the effect of fossil fuel dependence and polluting emissions from the transport sector on the performance of logistics operations in the context of Green Supply Chain Management (GSCM). We collected macro-level time-series data for a sample of 27 European Union (EU) countries over the period 2007–2018. A new Artificial Neural Networks (ANNs) algorithm is adopted in a multivariate framework to investigate the dynamic interactions among a range of Logistics Performance Indexes (LPI), the demand for oil products, and carbon dioxide (CO2) emissions from fuel combustion in the transport sector. Empirical findings show that oil product consumption and CO2 emissions sharply influence the transport logistics indexes. However, a feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. Based on our empirical insights, adequate policy recommendations are provided to help turning the logistics sector towards a more sustainable path in the European area. |
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| AbstractList | •Oil product consumption and CO2 emissions sharply influence the transport logistics indexes.•A feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance.•We found empirical sustain for the “feedback hypothesis” for Germany and Japan.
This paper critically assesses the effect of fossil fuel dependence and polluting emissions from the transport sector on the performance of logistics operations in the context of Green Supply Chain Management (GSCM). We collected macro-level time-series data for a sample of 27 European Union (EU) countries over the period 2007–2018. A new Artificial Neural Networks (ANNs) algorithm is adopted in a multivariate framework to investigate the dynamic interactions among a range of Logistics Performance Indexes (LPI), the demand for oil products, and carbon dioxide (CO2) emissions from fuel combustion in the transport sector. Empirical findings show that oil product consumption and CO2 emissions sharply influence the transport logistics indexes. However, a feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. Based on our empirical insights, adequate policy recommendations are provided to help turning the logistics sector towards a more sustainable path in the European area. |
| Author | Mele, Marco Magazzino, Cosimo Schneider, Nicolas |
| Author_xml | – sequence: 1 givenname: Cosimo surname: Magazzino fullname: Magazzino, Cosimo email: cosimo.magazzino@uniroma3.it organization: Roma Tre University, Italy – sequence: 2 givenname: Marco surname: Mele fullname: Mele, Marco email: marco.mele@uniroma3.it organization: Roma Tre University, Italy – sequence: 3 givenname: Nicolas surname: Schneider fullname: Schneider, Nicolas email: N.Schneider2@lse.ac.uk organization: London School of Economics and Political Science, United Kingdom |
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| Keywords | European union Green supply chain management Logistics performance CO2 emissions Artificial neural networks |
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| Title | A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation |
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