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Energy
Article . 2017 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Modelling the rebound effect with network theory: An insight into the European freight transport sector

Authors: Riccardo Basosi; Franco Ruzzenenti; Franco Ruzzenenti;

Modelling the rebound effect with network theory: An insight into the European freight transport sector

Abstract

Abstract This paper presents a two pronged approach to the study of the rebound effect, with the aim of assessing the magnitude of the effect in the European freight transport sector and proposing a new modelling framework based on network theory. The (direct) rebound effect is assessed with: 1) an econometric regression; 2) a model based on network theory and statistical mechanics. According to the econometric model the European road freight transport sector undergone a negative rebound between of −74% between 1998 and 2007 and −146% between 1998 and 2011. The network analysis delivers an estimation of network rebound ranging between −29.37% and −7.25. Overall, these results indicate that energy efficiency in Europe, between 1998 and 2011, succeed in reducing the energy consumptions amid an increasing demand for transports. Results on rebound estimation depend on the decision of using GDP as an exogenous variable, an assumption that leaves questions open about the causality chain between growth and transports. Furthermore, the network analysis highlights a structural change –a migration of production factors offshore, that might partially explain this negative effect. In this view, rebound effect analysis on a local or regional scale is becoming more and more uncertain in a globally interconnected economic context.

Keywords

Pollution, Energy efficiency, European freight transport sector, Energy (all), 339, Network theory, Structural change, Rebound effect, Statistical mechanics of network

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Average
Top 10%
Green
bronze