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Modifying the rebound: It depends! Explaining mobility behavior on the basis of the German socio-economic panel

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  • Matiaske, Wenzel
  • Menges, Roland
  • Spiess, Martin

Abstract

We address the empirical question of the extent to which higher fuel efficiency of cars affects additional travel and the way this behavioral aspect is modified by additional variables. The data set used to estimate a theoretical model of the rebound effect covers two panel waves, 1998 and 2003, taken from the German Socio-Economic Panel (SOEP). To take full advantage of the information in the data available, and to avoid problems due to possible selection effects, we estimated an unbalanced two-wave random effects panel model. Our results suggest that in line with the rebound hypothesis, car efficiency has a negative effect on the kilometers driven. That is, the lower the fuel consumption, the greater the distance driven. However, contrasting recent empirical literature about the rebound effect in the transportation sector, this seems to be true only for cars with a consumption of more than roughly 8l per 100km. In addition, we find a positive diesel effect, which implies that owning a diesel engine car is positively correlated with the distance driven. Both effects can be interpreted as support for the rebound hypothesis, although not in a simple linear way. Moreover, it can be shown that some “soft” variables such as certain attitudes towards the environment tend to amplify this non-linear rebound effect.

Suggested Citation

  • Matiaske, Wenzel & Menges, Roland & Spiess, Martin, 2012. "Modifying the rebound: It depends! Explaining mobility behavior on the basis of the German socio-economic panel," Energy Policy, Elsevier, vol. 41(C), pages 29-35.
    • Handle: RePEc:eee:enepol:v:41:y:2012:i:c:p:29-35
    DOI: 10.1016/j.enpol.2010.11.044
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    2. Biying Yu & Junyi Zhang & Akimasa Fujiwara, 2016. "Who rebounds in the private transport sector? A comparative analysis between Beijing and Tokyo," Environment and Planning B, , vol. 43(3), pages 561-579, May.
    3. ten Dam, Chris Djie & Kramer, Gert Jan & Ettema, Dick & Koning, Vinzenz, 2022. "Spatial and sociodemographic determinants of energy consumption for personal mobility in the Netherlands," Journal of Transport Geography, Elsevier, vol. 98(C).
    4. Weber, Sylvain, 2019. "Consumers' preferences on the Swiss car market: A revealed preference approach," Transport Policy, Elsevier, vol. 75(C), pages 109-118.
    5. Yu, Biying & Zhang, Junyi & Fujiwara, Akimasa, 2013. "Evaluating the direct and indirect rebound effects in household energy consumption behavior: A case study of Beijing," Energy Policy, Elsevier, vol. 57(C), pages 441-453.
    6. Frondel, Manuel & Martinez Flores, Fernanda & Vance, Colin, 2016. "Heterogeneous rebound effects: Comparing estimates from discrete-continuous models," Ruhr Economic Papers 601, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Wen, Fenghua & Ye, Zhengke & Yang, Huaidong & Li, Ke, 2018. "Exploring the rebound effect from the perspective of household: An analysis of China's provincial level," Energy Economics, Elsevier, vol. 75(C), pages 345-356.
    8. Ritter, Nolan, 2012. "Beyond the Average Elasticity – Applying Quantile Panel Regression to German Household Mobility Data," Ruhr Economic Papers 392, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Johannes Buhl, 2014. "Revisiting Rebound Effects from Material Resource Use. Indications for Germany Considering Social Heterogeneity," Resources, MDPI, vol. 3(1), pages 1-17, February.
    10. Siskos, Pelopidas & Capros, Pantelis & De Vita, Alessia, 2015. "CO2 and energy efficiency car standards in the EU in the context of a decarbonisation strategy: A model-based policy assessment," Energy Policy, Elsevier, vol. 84(C), pages 22-34.
    11. Arpita Asha Khanna & Ilka Dubernet & Patrick Jochem, 2025. "Do car drivers respond differently to fuel price changes? Evidence from German household data," Transportation, Springer, vol. 52(2), pages 579-613, April.
    12. Rosal, Ignacio del, 2022. "European dieselization: Policy insights from EU car trade," Transport Policy, Elsevier, vol. 115(C), pages 181-194.
    13. Carlena Cochi Ficano & Patrick Thompson, 2014. "Estimating Rebound Effects in Personal Automotive Transport: Gas Price and the Presence of Hybrids," The American Economist, Sage Publications, vol. 59(2), pages 167-175, November.
    14. Nolan Ritter, 2012. "Beyond the Average Elasticity – Applying Quantile Panel Regression to German Household Mobility Data," Ruhr Economic Papers 0392, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    15. Schall, Dominik L. & Wolf, Menas & Mohnen, Alwine, 2016. "Do effects of theoretical training and rewards for energy-efficient behavior persist over time and interact? A natural field experiment on eco-driving in a company fleet," Energy Policy, Elsevier, vol. 97(C), pages 291-300.
    16. Murtaza Haider & Randall Shannon & George P. Moschis, 2022. "Sustainable Consumption Research and the Role of Marketing: A Review of the Literature (1976–2021)," Sustainability, MDPI, vol. 14(7), pages 1-36, March.
    17. Dimitropoulos, Alexandros & Oueslati, Walid & Sintek, Christina, 2018. "The rebound effect in road transport: A meta-analysis of empirical studies," Energy Economics, Elsevier, vol. 75(C), pages 163-179.

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    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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