This study parametrically and non-parametrically estimates the TFEE
scores for 47 regions in Japan and the determinants of inefficiency for
1996–2008. We extend the SFA approach employed by Zhou et al. (2012) and
Hu (2013) and incorporate the technical inefficiency effects model proposed
by Battese and Coelli (1995). Our two technical inefficiency effects models
exclusively include the manufacturing industry share and the five
energy-intensive industry shares as environmental variables that influence
inefficiency, the scores of which are referred to as TFEESFA,M and TFEESFA,E,
respectively. For comparison, a stochastic TFEE without environmental
variables, TFEESFA,O, is also computed. In addition, we use the DEA technique
41
The four TFEEs are highly correlated with one another, especially
TFEESFA,O, TFEESFA,M, and TFEESFA,E. The trend of the mean TFEEs suggests
that energy efficiency improved during the sample period. However, there is
considerable potential for further savings on energy consumption in the
Japanese regions. For the bottom three regions—Chiba, Okayama, and
Oita—the TFEE scores in all four models are very low (<0.2). This suggests
the possibility of conserving more than 80% of energy in all three regions.
We compare not only the TFEEs but also the determinants of inefficiency
between SFA and DEA. In SFA, the determinants are estimated simultaneously
with inefficiency. This simultaneous estimation suitably incorporates
influences from environmental variables into inefficiency. This is SFA’s
advantage over the two-step estimation in DEA. But in the SFA estimation, we
must introduce additional assumptions regarding a functional form of the
frontier and a distribution of inefficiency and error terms compared to DEA.
On the other hand, the inefficiency of the DEA TFEEs is regressed on
environmental factors via a Tobit approach. The signs of the Tobit model are
consistent with those of the SFA estimation. For both SFA and DEA, the
42
manufacturing and wholesale and retail trade shares, the lower the energy
efficiency. The results that include energy-intensive industry’s shares show
that the higher shares of chemicals; iron and steel; non-metallic mineral
products; and pulp, paper, and paper products industries, the significantly
lower the levels of efficiency.
Our study has two limitations. First, in our SFA model, all inefficiencies
are attributed to energy input. This may lead to overestimating energy
inefficiency. On this point, DEA is superior to SFA because the DEA TFEE
takes into account both radial and non-radial slack with respect to energy
inputs. The second limitation is that the estimates do not consider unobserved
heterogeneity.
Our approach can be extended in various directions. First, the
environmental variables that affect energy efficiency should be further
explored. It is important not only to measure efficiency but also to examine the
determinants of inefficiency. Second, the functional form can be easily
changed from the Cobb-Douglas function to more general functional forms
such as a translog function. Third, undesirable outputs should be added to the
43
in the form of emissions and waste.
Acknowledgments
The authors thank the participants at the 2013 APEC Conference on Low
Carbon Town and Physical Energy Storage. This work was supported by the
Grant-in-Aid for Scientific Research from the Japan Society for the Promotion
of Science (#25380346) and Taiwan’s National Science Council (NSC
102-2410-H-009-045).
References
Aigner, D., Lovell, C.A.K., Schmidt, P., 1977. Formulation and estimation of
stochastic frontier production function models. Journal of Econometrics
6, 21-37.
Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating
technical and scale efficiencies in data development analysis.
Management Science 30, 1078-1092.
44
efficiency and panel data: With application to paddy farmers in India.
Journal of Productive Analysis 3, 153-169.
Battese, G.E., Coelli, T.J., 1995. A model for technical inefficiency effects in a
stochastic frontier production function for panel data. Empirical
Economics 20, 325-332.
Battese, G.E., Rao, D.S.P., O’Donnell, C.J., 2004. A metafrontier production
function for estimation of technical efficiencies and technology gaps for
firms operating under different technologies. Journal of Productivity
Analysis 21, 91-103.
Coelli, T.J., 1996. A Guide to FRONTIER Version 4.1: A Computer Program
for Stochastic Frontier Production and Cost Function Estimation, CEPA
Working Paper 96/7, University of New England, Armidale, NSW,
Australia.
Coelli, T.J., Rao, D.S.P., O’Donnell, C.J., Battese, G.E., 2005. An Introduction
to Efficiency and Productivity Analysis, 2nd ed., New York: Springer.
Filippini, M., Hunt, L.C., 2011. Energy demand and energy efficiency in the
OECD countries: A stochastic demand frontier approach. Energy Journal
45
Fried, H., Lovell, K., Schmidt, S., (eds.) 2008. The Measurement of
Productive Efficiency and Productivity Change. New York: Oxford
University Press.
Greene, W., 2005a. Fixed and random effects in stochastic frontier models.
Journal of Productivity Analysis 23, 7-32.
Greene, W. H., 2005b. Reconsidering heterogeneity in panel data estimators of
the stochastic frontier model, Journal of Econometrics 126, 269-303.
Herrala, R., Goel, R.K., 2012. Global CO2 efficiency: Country-wise estimates
using a stochastic cost frontier. Energy Policy 45, 762-770.
Hjalmarsson, L., Kumbhakar, S., Heshmati, A., 1996. DEA, DFA and SFA: A
comparison. Journal of Productivity Analysis 7, 303-327.
Honma, S., Hu, J.L., 2008. Total-factor energy efficiency of regions in Japan.
Energy Policy 36, 821-833.
Honma, S., Hu, J.L., 2013. Total-factor energy efficiency for sectors in Japan.
Energy Sources, Part B 8(2), 130-136.
Honma, S., Hu, J.L., 2014. Industry-level total-factor energy efficiency in
developed countries. Applied Energy 119, 37-78.
46
application of the stochastic frontier analysis. Paper Presented at the
2013 APEC Conference on Low Carbon Town and Physical Energy
Storage, Chanhsha, China.
Hu, J.L., Kao, C.H., 2007. Efficient energy-saving targets for APEC
economies. Energy Policy 35, 373-382.
Hu, J.L., Lio, M.C., Kao, C.H., Lin, Y.L., 2012. Total-factor energy efficiency
for regions in Taiwan. Energy Sources, Part B 7(3), 292-300.
Hu, J.L., Wang, S.C., 2006. Total-factor energy efficiency of regions in China.
Energy Policy 34, 3206-3217.
Iglesias, G., Castellanos, P., Seijas, A., 2010. Measurement of productive
efficiency with frontier methods: a case study for wind farms. Energy
Economics 32, 1199-1208.
Le Pen, Y., Sévi, B., 2010. On the non-convergence of energy intensities:
Evidence from a pair-wise econometric approach. Ecological Economics
69, 641-650.
Lin, B., Du, K., 2013. Technology gap and China’s regional energy efficiency:
A stochastic metafrontier approach. Energy Economics 40, 529–536.
47
differences. Applied Energy 87, 3218-3225.
Lozano, S., Gutiérreza, E., 2008. Non-parametric frontier approach to
modeling the relationships among population, GDP, energy
consumption and CO2 emissions. Ecological Economics 66, 687-699.
Meeusen, W., van den Broeck, J., 1977. Efficiency estimation from
Cobb-Douglas production function with composed errors. International
Economic Review 18, 435-444.
Miketa, A., Mulder, P., 2005. Energy productivity across developed and
developing countries in 10 manufacturing sectors: Patterns of growth
and convergence. Energy Economics 27, 429-453.
Ministry of Economy, Trade and Industry, 2013. Annual Report on Energy (in
Japanese), Tokyo, Japan.
Morikawa, M., 2012. The effects of the Great East Japan Earthquake and
policy priorities for restoring economic growth: Evidence from a survey
of Japanese firms. RIETI Policy Discussion Paper Series 12-P-010 (in
Japanese).
Mulder, P., De Groot, H., 2007. Sectoral energy- and labor-productivity
48
Mukherjee, K., 2008. Energy use efficiency in U.S. manufacturing: A
nonparametric analysis. Energy Economics 30, 76-96.
Nilsson, L.J., 1993. Energy intensity trends in 31 industrial and developing
countries 1950–1988. Energy 18, 309-322.
Olesen, O.B., Petersen, N.C., 2009. Target and technical efficiency in DEA:
Controlling for environmental characteristics. Journal of Productivity
Analysis 32, 27-40.
Patterson, M.G., 1996. What is energy efficiency? Concepts, indicators and
methodological issues. Energy Policy 24, 377-390.
Schmidt, P., 2011. One-step and two-step estimation in SFA models. Journal of
Productivity Analysis 36, 201-203.
Sözen, A., Alp, I., 2009. Comparison of Turkey’s performance of greenhouse
gas emissions and local/regional pollutants with EU countries. Energy
Policy 37, 5007-5018.
Stern, D.I., 2012. Modeling international trends in energy efficiency.
Ecological Economics 34, 2200-2208.
Vivoda, V., 2012. Japan’s energy security predicament post-Fukushima.
49
Zhou, P., Ang, B.W., Zhou, D.Q., 2012. Measuring economy-wide energy
efficiency performance: a parametric frontier approach. Applied Energy