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.
AignerD.LovellK.SchmidtP. (1977). “Formulation and estimation of stochasticfrontierproductionfunctionmodels.” Journal of Econometrics6: 21-37. http://dx.doi.org/10.1016/0304-4076(77)90052-5.
2.
Al-GhandoorA.SamhouriM.Al-HintiI.JaberJ.Al-RawashdehM. (2012). “Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique.” Energy38(1): 128-135. http://dx.doi.org/10.1016/j.energy.2011.12.023.
3.
AngB.W. (2006). “Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index.” Energy Policy34: 574-582. http://dx.doi.org/10.1016/j.enpol.2005.11.011.
4.
BauerM.MarE.ElizaldeA. (2003). “Transport and energy demand in Mexico: The personal income shock.” Energy Policy31(14): 1475-1480. http://dx.doi.org/10.1016/S0301-4215(02)00203-3.
5.
BonillaD.FoxonT. (2009). “Demand for new car fuel economy in the UK, 1970-2005.” Journal of Transport Economics and Policy43(1): 55-83.
6.
CaudillS.B.FordJ.M. (1993). “Biases in frontier estimation due to heteroscedasticity.” Economic Letters41: 17-20. http://dx.doi.org/10.1016/0165-1765(93)90104-K.
7.
CaudillS.B.FordJ.M.GropperD.M. (1995). “Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity.” Journal of Business &Economic Statistics13: 105-11.
8.
CavesD.W.ChristensenL.R.DiewertW.E. (1982). “The economic theory of index numbers and the measurement of input, output, and productivity.” Econometrica50(6): 1393-1414. http://dx.doi.org/10.2307/1913388.
9.
CeylanH.CeylanH.HaldenbilenS.BaskanO. (2008). “Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey.” Energy Policy36(7): 2527-2535. http://dx.doi.org/10.1016/j.enpol.2008.03.019.
10.
ChowG.C. (1960). “Tests of equality between sets of coefficients in two linear regressions.” Econometrica, 28(3): 591605. http://dx.doi.org/10.2307/1910133.
11.
CoelliT.J.RaoD.S.P.O’DonnellC.J.BatteseG.E. (2005). An introduction to efficiency and productivity analysis. 2nd ed. Springer, New York.
12.
DahlC. (1995). “Demand for transportation fuels: A survey of demand elasticities and their components.” The Journal of Energy Literature1: 3-27.
13.
Di BellaG.NortonL.NtamatungiroJ.OgawaS.SamakeI.SantoroM. (2015). Energy Subsidies in Latin America and the Caribbean: Stocktaking and Policy Challenges. IMF Working Paper, 15/30, International Monetary Fund, Washington. http://dx.doi.org/10.5089/9781484365366.001.
14.
DreherM.WietschelM.GobeltM.RentzO (1999). “Energy price elasticities of energy service demand for passenger traffic in the Federal Republic of Germany.” Energy24(2): 133-140. http://dx.doi.org/10.1016/S0360-5442(98)00075-9.
15.
ECLAC (2005). The millennium development goals: A Latin American and Caribbean perspective. United Nations Publications. Santiago de Chile.
16.
ECLAC (2010). Sustainable development in Latin America and the Caribbean: Trends, progress, and challenges in sustainable consumption and production, mining, transport, chemicals and waste management. United Nations Publications. Santiago de Chile.
17.
EIA (1995). Measuring energy efficiency in the United States’ economy: A beginning. Energy Information Administration. Washington, DC, USA.
18.
EltetoO.KovesP. (1964). “On a problem of index number computation relating to international comparison.” Sta- tisztikai Szemle42: 507-18.
19.
EvansJ.FilippiniM.HuntL.C. (2013). “The contribution of energy efficiency towards meeting CO2 targets.” In: R.Fouquet (Ed.) Edward Elgar, Handbook of Energy and Climate Change, Cheltenham, UK and Northampton, MA, USA, 175-223 (Chapter 8). http://dx.doi.org/10.4337/9780857933690.00016.
20.
FarsiM.FilippiniM.KuenzleM. (2005). “Unobserved heterogeneity in stochastic frontier models: An application to Swiss nursing homes.” Applied Economics37: 2127-2141. http://dx.doi.org/10.1080/00036840500293201.
21.
FilippiniM.HuntL.C. (2011). “Energy demand and energy efficiency in the OECD countries: A stochastic demand frontier approach.” The Energy Journal32(2): 59-80. http://dx.doi.org/10.5547/ISSN0195-6574-EJ-Vol32-No2-3.
22.
FilippiniM.HuntL.C. (2012). “US residential energy demand and energy efficiency: A stochastic demand frontier approach.” Energy Economics34(5): 1484-1491. http://dx.doi.org/10.1016/j.eneco.2012.06.013.
23.
FilippiniM.HuntL.C. (2015a). “Measurement of energy efficiency based on economic foundations.” Energy Economics52(S1), S5-S16. http://dx.doi.org/10.1016/j.eneco.2015.08.023.
24.
FilippiniM.HuntL.C. (2015b). “Measuring persistent and transient energy efficiency in the US.” Energy Efficiency, 1-13.
25.
FilippiniM.OreaL. (2014). “Applications of the stochastic frontier approach in the analysis of energy issues.” Economics and Business Letters3 (1), 35-42. http://dx.doi.org/10.17811/ebl.3.1.2014.35-42.
26.
FilippiniM.ZhangL. (2016). “Estimation of the energy efficiency in Chinese provinces”. Energy Efficiency, 1-14. http://dx.doi.org/10.1007/s12053-016-9425-z.
27.
FilippiniM.HuntL.C.ZoricJ. (2014). “Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector.” Energy Policy69, 73-81. http://dx.doi.org/10.1016/j.enpol.2014.01.047.
28.
FonsecaJ.R.S.CardosoM.G.M.S. (2007). “Mixture-model cluster analysis using information theoretical criteria.” Intelligent Data Analysis11(2): 155-173.
29.
FouquetR. (2012). “Trends in income and price elasticities of transport demand (1850-2010).” Energy Policy, 50, 62-71. http://dx.doi.org/10.1016/j.enpol.2012.03.001.
30.
GalindoL.M. (2005). “Short-and long-run demand for energy in Mexico: A cointegration approach.” Energy Policy33(9): 1179-85. http://dx.doi.org/10.1016/j.enpol.2003.11.015.
31.
GrahamD.GlaisterS. (2002). “The demand for automobile fuel: A survey of elasticities.” Journal of Transport Economics and Policy36: 1-26.
32.
GreeneW. (2002). Alternative panel data estimators for stochastic frontier models. Working Paper, Stern School of Business, New York University.
33.
GreeneW. (2004). “Distinguishing between heterogeneity and inefficiency: Stochastic frontier analysis of the World Health Organization’s panel data on national health care systems.” Health Economics13: 959-980. http://dx.doi.org/10.1002/hec.938.
34.
GreeneW. (2005a). “Fixed and random effects in stochastic frontier models.” Journal of Productivity Analysis23: 7-32. http://dx.doi.org/10.1007/s11123-004-8545-1.
35.
GreeneW. (2005b). “Reconsidering heterogeneity in panel data estimator of the stochastic frontier model.” Journal of Econometrics126: 269-303. http://dx.doi.org/10.1016/j.jeconom.2004.05.003.
36.
HaldenbilenS.CeylanH. (2005). “Genetic algorithm approach to estimate transport energy demand in Turkey.” Energy Policy33(1): 89-98. http://dx.doi.org/10.1016/S0301-4215(03)00202-7.
37.
HaoH.WangH.YiR. (2011). “Hybrid modeling of China’s vehicle ownership and projection through 2050.” Energy36(2): 1351-1361. http://dx.doi.org/10.1016/j.energy.2010.10.055.
38.
HestonA.SummersR.AtenB. (2012). Penn World Table Version 7.1. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.
39.
HuntingtonH.G. (1994). “Been top down so long it looks like bottom up to me.” Energy Policy22(10), 833-838. http://dx.doi.org/10.1016/0301-4215(94)90142-2.
40.
IEA (2014). Energy efficiency indicators: Essentials for policy making. International Energy Agency Publishing. Paris.
41.
IslasJ.ManziniF.MaseraO. (2007). “A prospective study of bioenergy use in Mexico.” Energy32(12): 2306-2320. http://dx.doi.org/10.1016/j.energy.2007.07.012.
42.
JondrowJ.LovellC.A.K.MaterovI.S.SchmidtP. (1982). “On the estimation of technical efficiency in the stochastic frontier production function model.” Journal of Econometrics19: 233-238. http://dx.doi.org/10.1016/0304-4076(82)90004-5.
43.
LimanondT.JomnonkwaoS.SrikaewA. (2011). “Projection of future transport energy demand of Thailand.” Energy Policy39: 2754-2763. http://dx.doi.org/10.1016/j.enpol.2011.02.045.
44.
LuI.JLewisC.LinS.J. (2009). “The forecast of motor vehicle, energy demand and CO2 emission from Taiwan’s road transportation sector.” Energy Policy37(8): 2956-2961. http://dx.doi.org/10.1016/j.enpol.2009.03.039.
45.
LuI.J.LinS.J.LewisC. (2008). “Grey relation analysis of motor vehicular energy consumption in Taiwan.” Energy Policy36(7): 2556-2561. http://dx.doi.org/10.1016/j.enpol.2008.03.015.
46.
LundgrenT.MarklundP.-OZhangS. (2016). “Industrial energy demand and energy efficiency - Evidence from Sweden.” Resource and Energy Economics43, 130-152. http://dx.doi.org/10.1016/j.reseneeco.2016.01.003.
47.
ManziniF. (2006). “Inserting renewable fuels and technologies for transport in Mexico City Metropolitan Area.” Hydrogen Energy31: 327-335. http://dx.doi.org/10.1016/j.ijhydene.2005.06.024.
48.
MundlakY (1978). “On the pooling of time series and cross section data.” Econometrica64: 69-85. http://dx.doi.org/10.2307/1913646.
49.
MuratY.S.CeylanH. (2006). “Use of artificial neural networks for transport energy demand modelling.” Energy Policy34(17): 3165-3172. http://dx.doi.org/10.1016/j.enpol.2005.02.010.
50.
OreaL.KumbhakarS. (2004). “Efficiency measurement using stochastic frontier latent class model.” Empirical Economics29: 169-183. http://dx.doi.org/10.1007/s00181-003-0184-2.
51.
OreaL.LlorcaM.FilippiniM. (2015). “A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand.” Energy Economics49: 599-609. http://dx.doi.org/10.1016/j.eneco.2015.03.016.
52.
PradhanS.BahadurB.BhusanV. (2006). “Mitigation potential of greenhouse gas emission and implications on fuel consumption due to clean energy vehicles as public passenger transport in Kathmandu Valley of Nepal: A case study of trolley buses in ring road.” Energy31(12): 1748-1760. http://dx.doi.org/10.1016/j.energy.2005.10.013.
53.
ReifschneiderD.StevensonR. (1991). “Systematic departures from the frontier: A framework for the analysis of firm inefficiency.” International Economic Review32: 715-723. http://dx.doi.org/10.2307/2527115.
54.
RogatJ. (2007). “The politics of fuel pricing in Latin America and their implications for the environment.” Energy &Environment, 18(1):1-12. http://dx.doi.org/10.1260/095830507780157195.
55.
RogatJ.SternerT. (1998). “The determinants of gasoline demand in some Latin American countries.” International Journal of Global Energy Issues11(1-4): 162-170.
56.
Sa’adS. (2010). “Improved technical efficiency and exogenous factors in transportation demand for energy: An application of structural time series analysis to South Korean data.” Energy35(7): 2745-2751. http://dx.doi.org/10.1016/j.energy.2010.01.038.
57.
SaminiR. (2003). “Road transport energy demand modelling. A cointegration approach.” Energy Economics17(4): 329339. http://dx.doi.org/10.1016/0140-9883(95)00035-S.
58.
SteadD. (2001). “Transport intensity in Europe - indicators and trends.” Transport Policy8: 29-46. http://dx.doi.org/10.1016/S0967-070X(00)00034-2.
59.
SuganthiL.SamuelA.A. (2012). “Energy models for demand forecasting-A review.” Renewable and Sustainable Energy Reviews16(2): 1223-1240. http://dx.doi.org/10.1016Zj.rser.2011.08.014.
WohlgemuthN. (1997). “World transport energy demand modeling: Methodology and elasticities.” Energy Policy25 (1415): 1109-1119. http://dx.doi.org/10.1016/S0301-4215(97)00103-1.
62.
ZhangM.MuH.LiG.NingY. (2009). “Forecasting the transport energy demand based on PLSR method in China.” Energy34(9): 1396-1400. http://dx.doi.org/10.1016/j.energy.2009.06.032.
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