A Robust Alternative to Environmental Performance Index

Abstract

A composite index called the Environmental Performance Index (EPI) was obtained to evaluate countries’ environmental performance. This index was calculated for 180 countries concerning 24 environmental indicators. However, it is well known that there are huge differences between countries regarding environmental factors besides social, economic, and cultural factors. This case aggravates the doubt that the data set has outliers. Therefore, the index values should be obtained such that they are insensitive to outliers. This study aims to generate a composite index, which is a robust alternative to EPI. For this aim, we use the Robust Principal Component Analysis (ROBPCA) and the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), which is a multi-criteria decision-making method.

Keywords

• Environmental performance index (EPI)
• robust EPI
• ROBPCA
• TOPSIS
• composite index

References

1. E. P. Index, Environmental Performance Index, Yale University and Columbia University: NewHaven, CT, USA.
2. P. A. Stanwick, S. D. Stanwick, The Relationship between Corporate Social Performance, and Organizational Size, Financial Performance, and Environmental Performance: An Empirical Examination, Journal of Business Ethics 17(2) (1998) 195–204.
3. D. M. Patten, The Relation between Environmental Performance and Environmental Disclosure: A Research Note, Accounting, Organizations and Society 27(8) (2002) 763–773.
4. S. A. Al-Tuwaijri, T. E. Christensen, K. E. Hughes, The Relations among Environmental Disclosure, Environmental Performance, and Economic Performance: A Simultaneous Equations Approach, Accounting, Organizations and Society 29(5-6) (2004) 447–471.
5. P. M. Clarkson, Y. Li, G. D. Richardson, F. P. Vasvari, Revisiting the Relation between Environmental Performance and Environmental Disclosure: An Empirical Analysis, Accounting, Organizations and Society 33(4-5) (2008) 303–327.
6. J. Wen, Y. Hao, G.-F. Feng, C.-P. Chang, Does Government Ideology Influence Environmentalperformance? Evidence Based on a New Dataset, Economic Systems 40(2) (2016) 232–246.
7. G. Halkos, A. Zisiadou, Relating Environmental Performance with Socioeconomic and Culturalfactors, Environmental Economics and Policy Studies 20(1) (2018) 69–88.
8. R. F ̈are, S. Grosskopf, F. Hernandez-Sancho, Environmental Performance: An Index Number Approach, Resource and Energy Economics 26(4) (2004) 343–352.

9. P. Zhou, B. Ang, K. Poh, Slacks-based Efficiency Measures for Modeling Environmental Performance, Ecological Economics 60(1) (2006) 111–118.
10. P. Zhou, K. L. Poh, B. W. Ang, A Non-Radial DEA Approach to Measuring Environmental Performance, European Journal of Operational Research 178(1) (2007) 1–9.
11. M. Kortelainen, Dynamic Environmental Performance Analysis: A Malmquist Index Approach, Ecological Economics 64(4) (2008) 701–715.
12. P. Zhou, B. W. Ang, K. L. Poh, Measuring Environmental Performance under Different Environmental DEA Technologies, Energy Economics 30(1) (2008) 1–14.
13. W. Liu, J. Tian, L. Chen, W. Lu, Y. Gao, Environmental Performance Analysis of Eco-Industrial Parks in China: A Data Envelopment Analysis Approach, Journal of Industrial Ecology 19(6) (2015) 1070–1081.
14. J. R. C.-E. Commission, et al., Handbook on Constructing Composite Indicators: Methodology and User Quide, OECD Publishing, 2008.
15. R. Ram, Composite Indices of Physical Quality of Life, Basic Needs Fulfilment, and Income: A ‘Principal Component’ Representation, Journal of Development Economics 11(2) (1982) 227–247.
16. D. J. Slottje, Measuring the Quality of Life Across Countries, The Review of Economics and Statistics (1991) 684–693.
17. B. Biswas, F. Caliendo, A Multivariate Analysis of the Human Development Index, Economics Research Institute Study Paper 11 (2002) 1.
18. D. Lai, Principal Component Analysis on Human Development Indicators of China, Social Indicatorsresearch 61(3) (2003) 319–330.
19. K. M. Wong, Well-being and Economic Development: A Principal Components Analysis, International Journal of Happiness and Development 1(2) (2013) 131–141.
20. H. Bulut, Y. Öner, The Evaluation of Socioeconomic Development of Development Agency Regions in Turkey using Classical and Robust Principal Component Analyses, Journal of Applied Statistics 44(16) (2017) 2936–2948.
21. S. Alpaykut, A Study for Analysing Well-Being for Provinces in Turkey by Using Principal Component Analysis and TOPSIS, Journal of Suleyman Demirel University Institute of Social Sciences 29(4) (2017) 367–395.
22. M. Hubert, P. J. Rousseeuw, K. Vanden Branden, ROBPCA: A New Approach to robust principal component analysis, Technometrics 47(1) (2005) 64–79.
23. V. Todorov, P. Filzmoser, An Object-Oriented Framework for Robust Multivariate Analysis, Journal of Statistical Software 32(1) (2009) 1–47.
24. C.-L. Hwang, K. Yoon, Methods for Multiple Attribute Decision Making, in: Multiple Attribute Decision Making, Springer (1981) 58–191.
25. B. A. C. Martin, MCDM: Multi-Criteria Decision Making Methods for Crisp Data, r package version 1.2 (2016) .URL https://CRAN.R-project.org/package=MCDM
26. Y. University, Environmental Performance Index (2018) .URL:https://epi.yale.edu