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Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response

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  • Rodrigues, Niágara
  • Losekann, Luciano
  • Silveira Filho, Getulio

Abstract

This paper analyzes the role of Asymmetric Price Response (APR) and Underlying Energy Demand Trend (UEDT) in the Brazilian automotive fuel demand from June 2001 to December 2016. The demand functions of automotive gasoline, ethanol and compressed natural gas (CNG) were estimated by employing the autoregressive distributed lag (ARDL) model and Harvey's Structural Time Series Model (STSM). The importance of considering a more flexible approach incorporating both UEDT and APR was confirmed by the data. We identified that consumer response to changes in price is not linear. The model also inferred a high substitutability between gasoline and ethanol. Both in the short and the long term, demand for ethanol is more price elastic than demand for gasoline. Empirical analysis suggests that the decision to refuel a vehicle with CNG is not influenced by price variations in ethanol, which indicates that competition only occurs between CNG and gasoline. Therefore, the inclusion of UEDT and APR provides more precise information on the effect of price and income changes on automotive fuel demand. Such information is relevant for establishing public policies, as refinery expansion plannning and CO2 emission mitigation.

Suggested Citation

  • Rodrigues, Niágara & Losekann, Luciano & Silveira Filho, Getulio, 2018. "Demand of automotive fuels in Brazil: Underlying energy demand trend and asymmetric price response," Energy Economics, Elsevier, vol. 74(C), pages 644-655.
    • Handle: RePEc:eee:eneeco:v:74:y:2018:i:c:p:644-655
    DOI: 10.1016/j.eneco.2018.07.005
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    Cited by:

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    2. Pei-Hsuan Tsai & Chih-Jou Chen & Ho-Chin Yang, 2021. "Using Porter’s Diamond Model to Assess the Competitiveness of Taiwan’s Solar Photovoltaic Industry," SAGE Open, , vol. 11(1), pages 21582440209, January.
    3. Colmenar, J.M. & Hidalgo, J.I. & Salcedo-Sanz, S., 2018. "Automatic generation of models for energy demand estimation using Grammatical Evolution," Energy, Elsevier, vol. 164(C), pages 183-193.
    4. Aloisio S. Nascimento Filho & Hugo Saba & Rafael G. O. dos Santos & João Gabriel A. Calmon & Marcio L. V. Araújo & Eduardo M. F. Jorge & Thiago B. Murari, 2021. "Analysis of Hydrous Ethanol Price Competitiveness after the Implementation of the Fossil Fuel Import Price Parity Policy in Brazil," Sustainability, MDPI, vol. 13(17), pages 1-12, September.
    5. Wang, Banban & Wei, Jie & Tan, Xiujie & Su, Bin, 2021. "The sectorally heterogeneous and time-varying price elasticities of energy demand in China," Energy Economics, Elsevier, vol. 102(C).
    6. Dilaver, Zafer & Hunt, Lester C., 2021. "Modelling U.S. gasoline demand: A structural time series analysis with asymmetric price responses," Energy Policy, Elsevier, vol. 156(C).
    7. Cassiano A. Isler & Yesid Asaff & Marin Marinov, 2020. "Designing a Geo-Strategic Railway Freight Network in Brazil Using GIS," Sustainability, MDPI, vol. 13(1), pages 1-21, December.

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    More about this item

    Keywords

    Energy demand; Gasoline; Ethanol; Compressed natural gas; Underlying energy demand trend (UEDT); Asymmetric price responses (APR);
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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