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Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis

  • George Halkos
  • Mike G. Tsionas
Article
  • 8 Downloads

Abstract

This paper proposes a novel way of modeling heterogeneity in the context of environmental performance estimation when using Data Envelopment Analysis. In the recent literature estimation of productive efficiency is common and relies on inputs and outputs identifying an environmental production technology in cases of joint production of good and bad outputs. However, heterogeneity is an important issue in this context. Our proposed novel approach relies on identification of different groups using a multivariate mixture-of-normals-distribution. The new techniques are applied to a data set of 44 countries during 1996–2014 concerning the finance of environmental efforts where significant problems of heterogeneity both in cross-sectional as well as in the time dimension are anticipated. For this purpose, apart from the usual variables of the production function, proxies of environmental investments like renewable electricity output and research and development expenditures are used. The sampling properties of the new approach are investigated using a Monte Carlo experiment. The problem of structural breaks over time is also considered with a penalty term in local likelihood estimation.

Keywords

Environmental performance Heterogeneity Production Data Envelopment Analysis Multivariate-mixture-of-normals-distributions 

JEL Classification

Q56 Q42 C13 C14 

Notes

Acknowledgements

We would like to thank the Editor Professor Hans Amman and three anonymous reviewers for the comments provided in relation to an earlier version of our paper. Any remaining errors are solely the authors’ responsibility.

References

  1. Abrell, J., & Rausch, S. (2016). Cross-country electricity trade, renewable energy and European transmission infrastructure policy. Journal of Environmental Economics and Management, 79, 87–113.CrossRefGoogle Scholar
  2. Athanassopoulos, A. D., & Curram, S. (1996). A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units. Journal of Operational Research Society, 47(8), 1000–1017.CrossRefGoogle Scholar
  3. Azadeh, A., Ghaderi, S. F., Tarverdian, S., & Saberi, M. (2007). Forecasting electrical consumption by integration of neural network, time series and ANOVA. Applied Mathematics and Computation, 186(2), 1753–1761.CrossRefGoogle Scholar
  4. Barr, R., Durcholz, M., & Seiford, L. (1994). Peeling the DEA onion: Layering and rank-ordering DMUs using tiered DEA. Southern Methodist University Technical Report, 1994/2000.Google Scholar
  5. Bergek, A., Mignon, I., & Sundberg, G. (2013). Who invest in renewable electricity production? Empirical evidence and suggestions for further research. Energy Policy, 56, 568–581.CrossRefGoogle Scholar
  6. Bojnec, S., & Latruffe, L. (2007). Measures of farm business efficiency. Industrial Management and Data Systems, 108(2), 258–270.CrossRefGoogle Scholar
  7. Çelebi, D., & Bayraktar, D. (2008). An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Systems with Applications, 35(4), 1698–1710.CrossRefGoogle Scholar
  8. Cincotti, S., Gardini, L., & Lux, T. (2008). New advances in financial economics: Heterogeneity and simulation. Computational Economics, 32(1–2), 1–2.CrossRefGoogle Scholar
  9. Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132, 245–259.CrossRefGoogle Scholar
  10. Färe, R., Grosskipf, S., & Hernandez-Sancho, F. (2004). Environmental performance: An index number approach. Resource and Energy Economics, 26, 343–352.CrossRefGoogle Scholar
  11. Färe, 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, 90–98.CrossRefGoogle Scholar
  12. Färe, R., Grosskopf, S., & Pasurka, C. (2006). Social responsibility: U.S. power plants 1985–1998. Journal of Productivity Analysis, 26, 259–267.CrossRefGoogle Scholar
  13. Fell, H., & Linn, J. (2013). Renewable electricity policies, heterogeneity, and cost effectiveness. Journal of Environmental Economics and Management, 66, 688–707.CrossRefGoogle Scholar
  14. Haas, D. A., & Murphy, F. H. (2003). Compensating for non-homogeneity in decision making units in data envelopment analysis. European Journal of Operational Research, 44(3), 530–544.CrossRefGoogle Scholar
  15. Koop, G., & Poirier, D. J. (2004). Bayesian variants of some classical semiparametric regression techniques. Journal of Econometrics, 123(2), 259–282.CrossRefGoogle Scholar
  16. Kumbhakar, S. C., Park, B. U., Simar, L., & Tsionas, E. G. (2007). Nonparametric stochastic frontiers: A local maximum likelihood approach. Journal of Econometrics, 137(1), 1–27.CrossRefGoogle Scholar
  17. Langniss, O. (1996). Instruments to foster renewable energy investments in Europe a survey under the financial point of view. Renewable Energy, 9, 1112–1115.CrossRefGoogle Scholar
  18. Lee, C. W., & Zhong, J. (2015). Financing and risk management of renewable energy projects with a hybrid bond. Renewable Energy, 75, 779–787.CrossRefGoogle Scholar
  19. Martín-Barrera, G., Zamora-Ramírez, C., & González, J. M. (2016). Application of real options valuation for analyzing the impact of public R&D financing on renewable energy projects: A company’s perspective. Renewable and Sustainable Energy Reviews, 63, 292–301.CrossRefGoogle Scholar
  20. Meimand, M., Cavana, R. Y., & Laking, R. (2002). Using DEA and survival analysis for measuring performance of branches in New Zealand’s Accident compensation Corporation. Journal of the Operational Research Society, 53(3), 303–313.CrossRefGoogle Scholar
  21. Mignon, I., & Bergek, A. (2016). Investments in renewable electricity production: The importance of policy revisited. Renewable Energy, 88, 307–316.CrossRefGoogle Scholar
  22. Nesta, L., Vona, F., & Nicolli, F. (2014). Environmental policies, competition and innovation in renewable energy. Journal of Environmental Economics and Management, 67, 396–411.CrossRefGoogle Scholar
  23. Noailly, J., & Smeets, R. (2015). Directing technical change fossil-fuel to renewable energy innovation: An application using firm-level patent data. Journal of Environmental Economics and Management, 72, 15–37.CrossRefGoogle Scholar
  24. Okazaki, S. (2006). What do we know about mobile internet adopters? A cluster analysis. Information and Management, 43(2), 127–141.CrossRefGoogle Scholar
  25. Osei-Bryson, K., & Innis, T. M. (2007). A hybrid clustering algorithm. Computers & Operations Research, 34(11), 3255–3269.CrossRefGoogle Scholar
  26. Pittman, R. W. (1983). Multilateral productivity comparisons with undesirable outputs. Economics Journal, 93, 883–891.CrossRefGoogle Scholar
  27. Rajapaksa, D., Islam, M., & Managi, S. (2017). Natural capital depletion: The impact of natural disasters on inclusive growth. Economics of Disasters and Climate Change, 1(3), 233–244.CrossRefGoogle Scholar
  28. Sakris, J. (2007). Preparing you data for DEA. In: J. Zhu & W. D. Cook (Eds.), Modelling data irregularities and structural complexities in DEA (Chapter 17 pp, pp. 305–320). Springer.Google Scholar
  29. Samoilenko, S., & Osei-Bryson, K. M. (2010). Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206, 479–487.CrossRefGoogle Scholar
  30. Santín, D., Delgado, F. J., & Valiño, A. (2004). The measurement of technical efficiency: A neural network approach. Applied Economics, 36(6), 627–635.CrossRefGoogle Scholar
  31. Sarrico, C. S., & Dyson, R. G. (2000). Performance measurement in UK universities—The institutional perspective. Journal of Operational Research Society, 51(7), 789–800.Google Scholar
  32. Sato, M., Kenta, K., & Managi, S. (2018). Inclusive wealth, total factor productivity, and sustainability: An empirical analysis, environmental economics and policy studies. Environmental Economics and Policy Studies.  https://doi.org/10.1007/s10018-018-0213-1.CrossRefGoogle Scholar
  33. Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132, 400–410.CrossRefGoogle Scholar
  34. Seirford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.CrossRefGoogle Scholar
  35. Sexton, T. R., Sleeper, S., & Taggart, R. E., Jr. (1994). Improving pupil transportation in North Carolina. Interfaces, 24, 87–103.CrossRefGoogle Scholar
  36. Simar, L., & Wilson, P. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779–802.CrossRefGoogle Scholar
  37. Tamaki, T., Shin, K. J., Nakamura, H., & Managi, S. (2018). Shadow prices and production inefficiency of mineral resources. Economic Analysis & Policy, 57, 111–121.CrossRefGoogle Scholar
  38. Wang, S. (2003). Adaptive non-parametric efficiency frontier analysis: A neural network-based model. Computers & Operations Research, 30(2), 279–295.CrossRefGoogle Scholar
  39. Wu, M.-C., Lin, S.-Y., & Lin, C.-H. (2006). An effective application of decision tree to stock trading. Expert Systems with Applications, 31(2), 270–274.CrossRefGoogle Scholar
  40. Yu, Y., & Choi, Y. (2015). Measuring environmental performance under regional heterogeneity in China: A meta-frontier efficiency analysis. Computational Economics, 46(3), 375–388.CrossRefGoogle Scholar
  41. Zhou, P., Delmas, M. A., & Kohli, A. (2017). Constructing meaningful environmental indices: A nonparametric frontier approach. Journal of Environmental Economics and Management.  https://doi.org/10.1016/j.jeem.2017.04.003.CrossRefGoogle Scholar
  42. Zucker, L. G. (1987). Institutional theories of organization. Annual Review of Sociology, 13, 443–464.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Operations Research, Department of EconomicsUniversity of ThessalyVolosGreece
  2. 2.Lancaster University Management SchoolLancasterUK

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