A stochastic frontier analysis approach for estimating energy demand and efficiency in the transport sector of Latin America and the Caribbean

Llorca, M.; Baños, J.; Somoza, J.; Arbués, P.

Abstract

In this paper, a stochastic frontier analysis approach is applied to estimate energy demand functions in the transport sector. This approach allows us to obtain energy efficiency measures at country level that are a robust alternative to the energy intensity indicators commonly used for international comparisons. A transitive multilateral price index is constructed for aggregating the diverse energy components employed in the sector. Due to the likely unobserved heterogeneity among countries, the use of a random parameters model is proposed to accommodate these differences and to obtain different income and price elasticities per country. The estimated model is compared with alternative approaches of addressing this issue such as latent class, true fixed effects or true random effects models. This study is the first to use a random parameters stochastic frontier approach in the estimation of energy demand functions. The proposed procedure is applied to Latin America and the Caribbean, where the transport sector represents a large share of total energy consumption.

Citation

Llorca, M., Baños, J., Somoza, J., & Arbués, P. (2017). A stochastic frontier analysis approach for estimating energy demand and efficiency in the transport sector of Latin America and the Caribbean. The Energy Journal, 38(5), https://doi.org/10.5547/01956574.38.5.mllo

• Journal Article Type: Article
• Acceptance Date: Sep 20, 2016
• Online Publication Date: Sep 1, 2017
• Publication Date: Sep 1, 2017
• Deposit Date: Oct 31, 2016
• Publicly Available Date: Sep 1, 2020
• Journal: Energy Journal
• Print ISSN: 0195-6574
• Electronic ISSN: 1944-9089
• Publisher: International Association for Energy Economics
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: 5
• DOI: https://doi.org/10.5547/01956574.38.5.mllo
• Public URL: https://durham-repository.worktribe.com/output/1373231