Electricity demand in industrial and service sectors in Taiwan

Original Article
  • 31 Downloads

Abstract

The electricity demand in the industrial and service sectors in Taiwan is estimated using a panel dataset, covering 23 industries in the industrial and 9 in the service sectors from 1998 to 2015. Static and dynamic models are studied. Industries are reclassified based on the national accounts and the energy balance sheets. Estimated results indicate the price elasticity is − 0.14 in the short term and − 0.82 in the long term, while the income elasticity is 0.08 in the short term and 0.47 in the long term. The influence of cooling degree days was positive, and substitution effect of electricity with respect to petroleum was proven. In addition, the scenario analysis reveals the gap between the current situation and the policy target of electrical efficiency. This gap can be bridged by economic development and adjustment of industrial structure if Taiwan chooses to stick with low electricity prices.

Keywords

Panel data model Price elasticity Income elasticity Electrical efficiency Industrial structure 

JEL classification

Q41 C23 

Notes

Acknowledgments

I thank the editor and six anonymous reviewers for constructive comments. The errors, idiocies, and inconsistencies remain my own.

Compliance with ethical standards

Conflict of interest

The author declares that she has no conflict of interest.

References

  1. Alberini, A., & Filippini, M. (2011). Response of residential electricity demand to price: the effect of measurement error. Energy Economics, 33(5), 889–895.  https://doi.org/10.1016/j.eneco.2011.03.009 CrossRefGoogle Scholar
  2. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.  https://doi.org/10.2307/2297968 CrossRefMATHGoogle Scholar
  3. Berndt, E. R. (1994). The practice of econometrics: classic and contemporary. Reading: Addison-Wesley.Google Scholar
  4. Bjørner, T. B., & Jensen, H. H. (2002). Energy taxes, voluntary agreements and investment subsidies—a micro-panel analysis of the effect on Danish industrial companies’ energy demand. Resource and Energy Economics, 24(3), 229–249.  https://doi.org/10.1016/S0928-7655(01)00049-5 CrossRefGoogle Scholar
  5. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.  https://doi.org/10.1016/S0304-4076(98)00009-8 CrossRefMATHGoogle Scholar
  6. Bohi, D. R. (1982). Price elasticities of demand for energy: evaluating the estimates. (No. EPRI-EA-2612). Washington, DC: Resources for the Future, Inc..Google Scholar
  7. Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrika, 47, 1287–1294.MathSciNetCrossRefMATHGoogle Scholar
  8. Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239–253.  https://doi.org/10.2307/2297111 MathSciNetCrossRefMATHGoogle Scholar
  9. Caloghirou, Y. D., Mourelatos, A. G., & Thompson, H. (1997). Industrial energy substitution during the 1980s in the Greek economy. Energy Economics, 19(4), 476–491.  https://doi.org/10.1016/S0140-9883(97)01026-8 CrossRefGoogle Scholar
  10. Chamberlain, G. (1984). Panel data. In Handbook of econometrics (Vol. 2, pp. 1247–1318). Amsterdam: North Holland.Google Scholar
  11. Chaudhry, A. A. (2016). A panel data analysis of electricity demand in the Pakistani industrial sector. Energy Sources Part B: Economics, Planning, and Policy, 11(1), 73–79.  https://doi.org/10.1080/15567249.2011.576376 MathSciNetCrossRefGoogle Scholar
  12. Cheng, B. S., & Lai, T. W. (1997). An investigation of co-integration and causality between energy consumption and economic activity in Taiwan. Energy Economics, 19(4), 435–444.  https://doi.org/10.1016/S0140-9883(97)01023-2 MathSciNetCrossRefGoogle Scholar
  13. Cook, R. D., & Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika, 70(1), 1–10.  https://doi.org/10.1093/biomet/70.1.1 MathSciNetCrossRefMATHGoogle Scholar
  14. Dubin, J. A. (1985). Consumer durable choice and the demand for electricity (Vol. 155). Amsterdam: North-Holland.Google Scholar
  15. Filippini, M. (2011). Short- and long-run time-of-use price elasticities in Swiss residential electricity demand. Energy Policy, 39(10), 5811–5817.  https://doi.org/10.1016/j.enpol.2011.06.002 CrossRefGoogle Scholar
  16. Fisher, F. M. (1962). A study in econometrics: the demand for electricity in the United States (Vol. 27). Amsterdam: North-Holland.Google Scholar
  17. Halvorsen, R. (1978). Econometric models of US energy demand. Lexington, MA: D.C. Heath.Google Scholar
  18. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 46, 1251–1271.MathSciNetCrossRefMATHGoogle Scholar
  19. Hayashi, F. (2000). Panel data. In Econometrics (pp. 323–364). Princeton: Princeton University Press.Google Scholar
  20. Hesse, D. M., & Tarkka, H. (1986). The demand for capital, labor and energy in European manufacturing industry before and after the oil price shocks. The Scandinavian Journal of Economics, 88, 529–546.CrossRefGoogle Scholar
  21. Holtedahl, P., & Joutz, F. L. (2004). Residential electricity demand in Taiwan. Energy Economics, 26(2), 201–224.  https://doi.org/10.1016/j.eneco.2003.11.001 CrossRefGoogle Scholar
  22. Houthakker, J. E., & Taylor, L. D. (1970). Consumer demand in the United States: analyses and projections. Cambridge: Harvard University Press.Google Scholar
  23. Hung, M.-F., & Huang, T.-H. (2015). Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing. Energy Economics, 48, 168–177.  https://doi.org/10.1016/j.eneco.2015.01.010 CrossRefGoogle Scholar
  24. Kamerschen, D. R., & Porter, D. V. (2004). The demand for residential, industrial and total electricity, 1973–1998. Energy Economics, 26(1), 87–100.  https://doi.org/10.1016/S0140-9883(03)00033-1 CrossRefGoogle Scholar
  25. OECD/IEA. (2014). Energy efficiency indicators: fundamentals on statistics.Google Scholar
  26. Roodman, D. (2009). How to do xtabond2: an introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.Google Scholar
  27. Roy, J., Sanstad, A. H., Sathaye, J. A., & Khaddaria, R. (2006). Substitution and price elasticity estimates using inter-country pooled data in a translog cost model. Energy Economics, 28(5), 706–719.  https://doi.org/10.1016/j.eneco.2006.05.008 CrossRefGoogle Scholar
  28. Taheri, A. A. (1994). Oil shocks and the dynamics of substitution adjustments of industrial fuels in the US. Applied Economics, 26(8), 751–756.  https://doi.org/10.1080/00036849400000089 CrossRefGoogle Scholar
  29. Taylor, L. D. (1975). The demand for electricity: a survey. The Bell Journal of Economics, 6(1), 74–110.  https://doi.org/10.2307/3003216 CrossRefGoogle Scholar
  30. Taylor, L. D., Blattenberger, G. R., & Rennhack, R. K. (1984). Residential energy demand in the United States: introduction and overview of alternative models. In Advances in the Economics of Energy and Resources, 5, 85–102.Google Scholar
  31. Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709–748.  https://doi.org/10.1111/j.1468-0084.2007.00477.x CrossRefGoogle Scholar
  32. White, H. (1980). A heteroskedasticity consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.  https://doi.org/10.2307/1912934 MathSciNetCrossRefMATHGoogle Scholar
  33. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.  https://doi.org/10.1016/j.jeconom.2004.02.005 MathSciNetCrossRefMATHGoogle Scholar
  34. Woodland, A. D. (1993). A micro-econometric analysis of the industrial demand for energy in NSW. The Energy Journal, 14(2), 57–89.Google Scholar
  35. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press.MATHGoogle Scholar
  36. Yang, H.-Y. (2000). A note on the causal relationship between energy and GDP in Taiwan. Energy Economics, 22(3), 309–317.  https://doi.org/10.1016/S0140-9883(99)00044-4 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018
corrected publication March/2018

Authors and Affiliations

  1. 1.Industrial Economics and Knowledge CenterIndustrial Technology Research InstituteHsinchuTaiwan

Personalised recommendations