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Energy Efficiency in the Passenger Transport Sectors of Germany and the Netherlands

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  • Elsenberger, Sebastian

Abstract

This thesis examines, quantifies, and ranks the influence of various factors of activity, structure, and intensity on passenger transport energy consumption to assess the progression of energy efficiency. For this purpose, four logarithmic mean Divisia index (LMDI) decomposition analyses are conducted employing continuous data from 2000 to 2016, one each for the land passenger transport sector and one for the LDV sector. In particular, the question of to what extent gross efficiency gains can be attributed to technical efficiency improvements versus behavioral factors is answered. The analyses on land passenger transport solely feature gross energy intensity, whereas the subsequent analyses on LDV energy consumption further decompose gross efficiency into fuel share, average occupancy, and technical energy intensity. Beyond that, population factors are introduced to obtain results normalized per capita. The results of the full decomposition analyses highlight that technical energy efficiency enhancements are always substantially offset by behavioral factors, such as passenger activity per capita or LDV average occupancy. What is more, modal split is of the least significance even though it holds an enormous energy savings potential. However, passenger transport energy consumption per capita decreases throughout all scenarios. Ultimately, a successful policy response must address behavioral and personal utility factors since measures exclusively focused on technical energy efficiency improvements are likely to induce rebound effects.

Suggested Citation

  • Elsenberger, Sebastian, 2023. "Energy Efficiency in the Passenger Transport Sectors of Germany and the Netherlands," MPRA Paper 122147, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:122147
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    References listed on IDEAS

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    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other

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