Skip to main content

Advertisement

SpringerLink
Log in

Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis

  • Published:
Computational Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. For instance, Cincotti et al. (2008) discuss heterogeneity in financial economics and Yu and Choi (2015) measure environmental performance under regional heterogeneity in China. Various applications may find useful the proposed tackling of heterogeneity in natural disasters and inclusive wealth (Rajapaksa et al. 2017; Sato et al. 2018) or in mineral resources (Tamaki et al. 2018).

  2. Various applications may find useful the proposed tackling of heterogeneity like among others in natural disasters and inclusive wealth (Rajapaksa et al. 2017; Sato et al. 2018) or in mineral resources (Tamaki et al. 2018).

  3. The asymptotic behavior of the estimator is provided in Theorem 2.2 of Kumbhakar et al. (2007) under certain general regularity conditions. The initial conditions needed in optimizations, are chosen using the procedure described in section 3.1 of Kumbhakar et al. (2007) in connection to Gozalo and Linton (2000). To implement the computation, we used the FilterSD software in fortran77.

  4. We discard data sets in which our optimizations did not converge in 1000 steps. This happened in about 25 data sets.

  5. The countries considered are the ones with full record on the variables of interest: Argentina, Australia, Austria, Belgium, Bulgaria, Brazil, Canada, China, Colombia, Czech Republic, Germany, Denmark, Ecuador, Spain, Estonia, Finland, France, United Kingdom, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Japan, South Korea, Lithuania, Latvia, Mexico, Netherlands, Norway, Panama, Poland, Portugal, Romania, Russian Federation, Singapore, Slovak Republic, Slovenia, Sweden, Thailand, Turkey, United States of America, South Africa.

  6. Data were obtained from: http://data.worldbank.org/.

  7. For the recent status on ratifications see http://unfccc.int/paris_agreement/items/9444.php.

  8. Annex I and Annex B are developed signatory nations of Kyoto Protocol subject to GHG emissions’ limits and obliged to reduction targets. While Annex I countries are classified for reduction in the United Nations Framework Convention on Climate Change (UNFCCC), Annex B countries are identified under the more recent Kyoto Protocol with reduction targets officially stated.

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Bojnec, S., & Latruffe, L. (2007). Measures of farm business efficiency. Industrial Management and Data Systems, 108(2), 258–270.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Cincotti, S., Gardini, L., & Lux, T. (2008). New advances in financial economics: Heterogeneity and simulation. Computational Economics, 32(1–2), 1–2.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Färe, R., Grosskipf, S., & Hernandez-Sancho, F. (2004). Environmental performance: An index number approach. Resource and Energy Economics, 26, 343–352.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., & Pasurka, C. (2006). Social responsibility: U.S. power plants 1985–1998. Journal of Productivity Analysis, 26, 259–267.

    Article  Google Scholar 

  • Fell, H., & Linn, J. (2013). Renewable electricity policies, heterogeneity, and cost effectiveness. Journal of Environmental Economics and Management, 66, 688–707.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Koop, G., & Poirier, D. J. (2004). Bayesian variants of some classical semiparametric regression techniques. Journal of Econometrics, 123(2), 259–282.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Langniss, O. (1996). Instruments to foster renewable energy investments in Europe a survey under the financial point of view. Renewable Energy, 9, 1112–1115.

    Article  Google Scholar 

  • Lee, C. W., & Zhong, J. (2015). Financing and risk management of renewable energy projects with a hybrid bond. Renewable Energy, 75, 779–787.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mignon, I., & Bergek, A. (2016). Investments in renewable electricity production: The importance of policy revisited. Renewable Energy, 88, 307–316.

    Article  Google Scholar 

  • Nesta, L., Vona, F., & Nicolli, F. (2014). Environmental policies, competition and innovation in renewable energy. Journal of Environmental Economics and Management, 67, 396–411.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Okazaki, S. (2006). What do we know about mobile internet adopters? A cluster analysis. Information and Management, 43(2), 127–141.

    Article  Google Scholar 

  • Osei-Bryson, K., & Innis, T. M. (2007). A hybrid clustering algorithm. Computers & Operations Research, 34(11), 3255–3269.

    Article  Google Scholar 

  • Pittman, R. W. (1983). Multilateral productivity comparisons with undesirable outputs. Economics Journal, 93, 883–891.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Santín, D., Delgado, F. J., & Valiño, A. (2004). The measurement of technical efficiency: A neural network approach. Applied Economics, 36(6), 627–635.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132, 400–410.

    Article  Google Scholar 

  • Seirford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.

    Article  Google Scholar 

  • Sexton, T. R., Sleeper, S., & Taggart, R. E., Jr. (1994). Improving pupil transportation in North Carolina. Interfaces, 24, 87–103.

    Article  Google Scholar 

  • Simar, L., & Wilson, P. (2000). A general methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27(6), 779–802.

    Article  Google Scholar 

  • Tamaki, T., Shin, K. J., Nakamura, H., & Managi, S. (2018). Shadow prices and production inefficiency of mineral resources. Economic Analysis & Policy, 57, 111–121.

    Article  Google Scholar 

  • Wang, S. (2003). Adaptive non-parametric efficiency frontier analysis: A neural network-based model. Computers & Operations Research, 30(2), 279–295.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yu, Y., & Choi, Y. (2015). Measuring environmental performance under regional heterogeneity in China: A meta-frontier efficiency analysis. Computational Economics, 46(3), 375–388.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zucker, L. G. (1987). Institutional theories of organization. Annual Review of Sociology, 13, 443–464.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Laboratory of Operations Research, Department of Economics, University of Thessaly, Volos, Greece

    George Halkos

  2. Lancaster University Management School, Lancaster, UK

    Mike G. Tsionas

Authors
  1. George Halkos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mike G. Tsionas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Halkos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Halkos, G., Tsionas, M.G. Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis. Comput Econ 54, 1005–1025 (2019). https://doi.org/10.1007/s10614-018-9861-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-018-9861-2

Keywords

JEL Classification

Search

Navigation