Operating Efficiency Analysis of Energy Industries in Taiwan

Abstract:

The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.

• Periodical: Advanced Materials Research (Volumes 869-870)
• Pages: 612-620
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.869-870.612
• Online since: December 2013
• Authors: Nattanin Ueasin, Anupong Wongchai*
• Keywords: Data Envelopment Analysis (DEA), Energy Industries, Operating Efficiency, Super-Efficiency, Tobit Regression Model

References

[1] F. Chen, S. M. Lu, K. T. Tseng, S. C. Lee and E. Wang. Assessment of Renewable Energy Reserves in Taiwan. Renewable and Sustainable Energy Reviews Vol. 14 (2010), pp.2511-2528.

DOI: 10.1016/j.rser.2010.06.021

[2] F. Chen, S. M. Lu, E. Wang and K. T. Tseng. Renewable Energy in Taiwan: Its Developing Status and Strategy. Renewable and Sustainable Energy Reviews Vol. 14 (2010), p.2029-(2038).

DOI: 10.1016/j.rser.2010.03.009

[3] A. Charnes, W. W. Cooper and E. Rhodes. Measuring the Efficiency of Decision-Making Units. European Journal of Operational Research Vol. 2 (1978), pp.429-444.

DOI: 10.1016/0377-2217(78)90138-8

[4] P. Andersen and N. C. Petersen. A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science Vol. 39 (1993), pp.1261-1264.

DOI: 10.1287/mnsc.39.10.1261

[5] R. Perrigot and C. P. Barros. Technical Efficiency of French Retailers. Journal of Retailing and Consumer Services Vol. 15 (2008), pp.296-305.

DOI: 10.1016/j.jretconser.2007.06.003

[6] T. A. Nahra, D. Mendez and J. A. Alexander. Employing Super-Efficiency Analysis as an Alternative to DEA. European Journal of Operational Research Vol. 196 (2009), pp.1097-1106.

DOI: 10.1016/j.ejor.2008.04.022

[7] X. M. Duan and B. Y. Li. Analysis of China's Interprovincial Energy Efficiency. IEEE Conference on Information, Innovation, and Industrial Engineering Vol. 4 (2010), pp.517-520.

[8] A. Wongchai, C. F. Tai and K. C. Peng. An Application of Super-Efficiency and Tobit Method for Financial Efficiency Analysis of Food Industrial Companies in Taiwan. The 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS) (2011).

DOI: 10.1109/icemms.2011.6015809

[9] P. Zhou, B. W. Angand K.L. Poh. Measuring Environmental Performance under Different Environmental DEA Technologies. Energy Economics Vol. 30 (2008), pp.1-14.

DOI: 10.1016/j.eneco.2006.05.001

[10] T. Sueyoshi and M. Goto. Efficiency-based Rank Assessment for Electric Power Industry: A Combined use of Data Envelopment Analysis (DEA) and DEA-discriminant Analysis (DA). Energy Economics Vol. 4 (2012), pp.634-644.

DOI: 10.1016/j.eneco.2011.04.001

[11] F. H. Sancho, M. M. Senante and R. S. Garrido. Energy Efficiency in Spanish Wastewater Treatment Plants: A Non-Radial DEA Approach. Science of The Total Environment Vol. 8 (2011), pp.2693-2699.

DOI: 10.1016/j.scitotenv.2011.04.018

[12] H. S. Lee, C. W. Chuand J. Zhu. Super-Efficiency DEA in the Presence of Infeasibility. European Journal of Operational Research Vol. 212 (2011), pp.141-147.

DOI: 10.1016/j.ejor.2011.01.022

[13] A. Charnes, W. W. Cooper and E. Rhodes. Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through. Management Science Vol. 27 (1981), pp.668-697.

DOI: 10.1287/mnsc.27.6.668

[14] J. Tobin. Estimation of Relationships for Limited Dependent Variables. Econometrica Vol. 26 (1958), pp.24-36.

DOI: 10.2307/1907382

[15] G. S. Maddala and K. Lahiri, Introduction to Econometrics, John Wiley and Sons Incorporation4th Edition (2009).

[16] P. Thollander, M. Danestig and P. Rohdin. Energy Policies for Increased Industrial Energy Efficiency: Evaluation of a Local Energy Program for Manufacturing SMEs. Energy PolicyVol. 35 (2007), pp.5774-5783.

DOI: 10.1016/j.enpol.2007.06.013

[17] K. A. Kim, C.P. Kitsabunnaratand J.R. Nofsinger. Large Shareholders, Board Independence, and Minority Shareholder Rights: Evidence from Europe. Journal of Corporate Finance Vol. 13 (2007), pp.859-880.

DOI: 10.1016/j.jcorpfin.2007.09.001