The Measurement of Satisfaction Degree with Controllable and Uncontrollable Based on DEA Approach

Wei LU, Shenshen LIN

Abstract


Data envelopment analysis (DEA) has gained great popularity in environmental performance measurement because it can provide a synthetic, standardized environmental performance index when pollutants are suitably incorporated into the traditional DEA framework. This paper applies the DEA approaches to evaluate the CO2 emission performance and measure its satisfaction degree of 40 countries and regions from 2008 to 2009. We use the input variables of capital, energy consumption and population and the output variables of gross domestic product (GDP) and the amount of fossil-fuel CO2 emissions. Past studies about the application of DEA to environmental performance measurement have not considered uncontrollable factors. In this paper, we present the DEA formulas with controllable and uncontrollable factors to measure environment performance and its satisfaction degree. We first define and construct the environmental production technologies with desirable and undesirable outputs. The degree of environment satisfaction performance based on the DEA approach can be computed by solving a series of data envelopment analysis formulas. A case study of 40 countries and regions applying the DEA approach is also presented.

Keywords


DEA; CO2 Emissions; Environment Performance; Controllable; Uncontrollable

References


[1] Ang, B. W., Choi, K. H., (2002). Boundary problem in carbon emission decomposition. Energy Policy, 20(2), 1201-1205.

[2] Ang, B. W., Zhou, P., Tay, L. P., (2011). Potential for reducing global carbon emissions from electricity production- a benchmarking analysis. Energy Policy, 39(3), 2482-2489.

[3] Banker, R. D., Charnes, A., Cooper, W. W., (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.

[4] Charnes, A., Cooper, W. W., Rhodes, E., (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.

[5] Choi, K. H., Ang, B. W., (2001). A time series analysis of energy related carbon emission in Korea. Energy Policy, 29(7), 1155-1161.

[6] Chu, W., Jinlan, N., Limin, D., (2012). Regional allocation of carbon dioxide abatement in China. China Economic Review, 23(7), 552–565.

[7] Cooper, W. W., Seiford, L. M., Tone, K., (2000). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Boston: Kluwer Academic Publisher.

[8] Fare, R., Grosskopf, S., Lovell, C. A. K., Pasurka, C., (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The review of Economics and Statistics, 71(5), 90-98.

[9] Fare, R., Grosskopf, S., Hernandez-Sancho, F., (2004). Environmental performance: an index number approach. Resource and Energy Economic, 26(6), 343-352.

[10] International Energy Agency (IEA). (2011). Key world energy statistics. Paris: OECD, [11] José, .L. Z, Angel, M. P., (2001). Environmental efficiency and regulatory standards: the case of CO2 emissions from OECD industries. Resource and Energy Economics. 23(14), 63–83.

[12] Mielnik, O., Goldemberg, J., (1999). The evolution of the ‘‘carbonization index’’ in developing countries. Energy Policy, 27(9), 307–308.

[13] Scheel, H., (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research. 132(5), 400-410.

[14] Seiford, L. M., Zhu, J., (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research. 142(1), 16-20.

[15] Seiford, L. M., Zhu, J., (2005). A response to comments on modeling undesirable factors in efficiency evaluation. European Journal of Operational Research. 161(4), 579-581.

[16] Stephan, O., Ivan, S., (2006). Design guideline for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting.

[17] Tol, R. S. J., (2005). The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy, 33(6), 2064-2074.

[18] Tol, R. S. J., Pacala, S. W., Socolow, R. H., (2009). Understanding long-term energy use and carbon emissions in the USA. Journal of Policy Modeling, 3(1), 425–445.

[19] Tone, K., (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(7), 498-509.

[20] Wang, Q. W., Zhou, P., Zhou, D. Q., (2010). Research on dynamic carbon dioxide emissions performance, regional disparity and affecting factors in China. China Industrial Economics, 1(1), 45–54.

[21] Wei, Y., Fan, Y., Lu, C., Tsai, H. T., (2004). The assessment of vulnerability to natural disasters in China by the DEA method. Environmental Impact Assessment Review, 24(4), 427-439.

[22] Xiao, D. G., Zhu, L., Fan, Y., (2011). Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy, 39(1), 2352–2360.

[23] Yang, H. L., Pollitt, M., (2009). Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants. European Journal of Operational Research, 197(3), 1095-1105.

[24] Yu, M., (2004). Measuring physical efficiency of domestic airports in Taiwan with undesirable outputs and environmental factors. Journal of Air Transport Management, 10(4), 295-303.

[25] Zaim, O., (2004). Measuring environmental performance of state manufacturing through changes in pollution intensities: a DEA framework. Ecological Economics, 48(1), 37-47.

[26] Zhou, P, Ang, B.W., Poh, K.L. (2007). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(5), 1–14.

[27] Zhou, P., Ang, B. W., Poh, K. L., (2008). Measuring environmental performance under different environment DEA technologies. Energy Economics, 30(1), 1-14.

[28] Zhou, P., Ang, B. W., Wang, H., (2012). Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach. European Journal of Operational Research., 221(3), 625-635.

[29] Zofio, J. L., Prieto, A. M., (2001). Environmental efficiency and regulatory standards: the case of CO2 emissions from OECD industries. Resource and Energy Economics, 23(1), 63–83.




DOI: http://dx.doi.org/10.3968/j.mse.1913035X20130701.2230

Refbacks

  • There are currently no refbacks.


Copyright (c)




Share us to:   


Reminder

  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.


We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; mse@cscanada.net; mse@cscanada.org

 Articles published in Management Science and Engineering are licensed under Creative Commons Attribution 4.0 (CC-BY).

 MANAGEMENT SCIENCE AND ENGINEERING Editorial Office

Address:1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.

Telephone: 1-514-558 6138
Http://www.cscanada.net Http://www.cscanada.org

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures