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.
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Change history
21 April 2018
In table 1 columns 10 and 16, the value of the average price was updated. In Fig. 2b, some parts of the line graph were removed by mistake during production.
Notes
According to the Bureau of Energy of Taiwan.
According to the Directorate-General of Budget, Accounting, and Statistics (DGBAS) of Taiwan.
Average nominal electricity price, according to the Bureau of Energy of Taiwan.
Summer months (June, July, August, and September) and nonsummer months.
The amount of electricity consumption is divided by 300, 700, and 1500 kWh per month.
Contractual capacity is divided by 100 and 1000 kW and counted an average in every 15 min.
The percentage of energy consumption was 48% for electricity, 39% for petroleum, 9% for coal, and 3% for gas in Taiwan in 2016.
The endogeneity was checked through the correlations between explanatory variables and error terms, Corr(X, ε). The correlation coefficients were all nearly zero and showed that there is no evidence of endogeneity.
The cointegration often leads to very high R2, but the adjusted R2 for the general models are between 0.0236 and 0.5922, and those for the sectoral models are from 0.3522 to 0.5961. Moreover, the Westerlund error-correction-based panel cointegration tests (Westerlund 2007) do not reject the hypothesis that the series are not cointegrated.
Even though the electrical intensity has some drawbacks as an indicator across countries, it is still considered valid to evaluate the electrical efficiency over time (OECD/IEA 2014).
This proportion is calculated by the percentage of value added in the industrial sector divided by the total of value added in the industrial and service sectors. The agriculture sector was not included because it consumes little electricity (nearly 1%) and was not considered in the panel data model.
The relation between growth rates is as follows: (YI + YS) (1 + r)n = YI (1 + rI)n + YS (1 + rS)n, where r represents the annual growth rate, Y represents the value added, n is the time period, and the superscript I and S stand for the industrial and service sectors, respectively.
References
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
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
Berndt, E. R. (1994). The practice of econometrics: classic and contemporary. Reading: Addison-Wesley.
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
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
Bohi, D. R. (1982). Price elasticities of demand for energy: evaluating the estimates. (No. EPRI-EA-2612). Washington, DC: Resources for the Future, Inc..
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrika, 47, 1287–1294.
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
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
Chamberlain, G. (1984). Panel data. In Handbook of econometrics (Vol. 2, pp. 1247–1318). Amsterdam: North Holland.
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
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
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
Dubin, J. A. (1985). Consumer durable choice and the demand for electricity (Vol. 155). Amsterdam: North-Holland.
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
Fisher, F. M. (1962). A study in econometrics: the demand for electricity in the United States (Vol. 27). Amsterdam: North-Holland.
Halvorsen, R. (1978). Econometric models of US energy demand. Lexington, MA: D.C. Heath.
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 46, 1251–1271.
Hayashi, F. (2000). Panel data. In Econometrics (pp. 323–364). Princeton: Princeton University Press.
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.
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
Houthakker, J. E., & Taylor, L. D. (1970). Consumer demand in the United States: analyses and projections. Cambridge: Harvard University Press.
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
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
OECD/IEA. (2014). Energy efficiency indicators: fundamentals on statistics.
Roodman, D. (2009). How to do xtabond2: an introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.
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
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
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
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.
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
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
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
Woodland, A. D. (1993). A micro-econometric analysis of the industrial demand for energy in NSW. The Energy Journal, 14(2), 57–89.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press.
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
Acknowledgments
I thank the editor and six anonymous reviewers for constructive comments. The errors, idiocies, and inconsistencies remain my own.
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I thank the Bureau of Energy in Taiwan for funding this project.
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The original version of this article was revised: Table 1 columns 10 and 16 Avg. price values were updated. Figure 2b was also updated as some plots in the graphs went missing in the original publication.
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Su, YW. Electricity demand in industrial and service sectors in Taiwan. Energy Efficiency 11, 1541–1557 (2018). https://doi.org/10.1007/s12053-018-9615-y
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DOI: https://doi.org/10.1007/s12053-018-9615-y