Re-Calculation of Happy Planet Index Using DEA Models

• Authors: Josef Jablonsky
• Languages of publication: EN

Abstracts

EN

Happy Planet Index (HPI) is an aggregated index that measures the extent to which each nation produces long and happy lives per unit of environmental input. The HPI uses global data on life expectancy, experienced well-being, and ecological footprint to rank countries. The last HPI report was published in 2012 and it contains data for 151 countries from all continents. The aim of the paper is to re-calculate the HPI using DEA models and other multiple criteria decision making techniques and compare the results obtained results. MCDM methods evaluate alternatives (countries) according to the set of criteria with respect to given preferences. Most of them allow ranking of alternatives according to aggregated indices defined by various methods. DEA models compare the countries with the best performers in the data set and measure the efficiency of transformation of multiple inputs into multiple outputs. Even though they are based on different principles than MCDM methods they allow ranking of evaluated units according to their efficiency or super-efficiency scores. The paper analyzes both methodological approaches and compares their results.

Keywords

EN

Analiza wielokryterialna; Jakość życia; Metody statystyczne; Podejmowanie decyzji; Quality of life; Decision making; Statistical methods; Multicriteria analysis

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Multiple Criteria Decision Making
• Year: 2013
• Volume: 8
• Pages: 56-66

Contributors

author

Josef Jablonsky

References

• Abdallah S., Michaelson J., Shah S., Stoll L., Marks N. (2012), The Happy Planet Index: 2012 Report - A Global Index of Sustainable Well-being, Available at http://www.happyplanetindex.org/assets/happy-planet-index- report.pdf.
• Boruckea M., Mooreb D., Cranstonb G., Graeeya K., Ihaa K., Joy L., Lazarusa E., Moralesa J.C., Wackernagela M., Galli A. (2013), Accounting for Demand and Supply of the Biosphere's Regenerative Capacity: The National Footprint Accounts ' Underlying Methodology and Framework, Ecological Indicators, 24, p. 518-533.
• Charnes A., Cooper W.W., Rhodes E. (1978), Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2, p. 429- 444.
• Cooper W.W., SeifordL.M., Tone K. (2000), Data Envelopment Analysis, Kluwer Publ., Boston.
• Despotis D.K. (2005), A Reassessment of Human Development Index via Data Envelopment Analysis, Journal of the Operational Research Society, 56 (8), p. 969-980.
• Jablonsky J., Dlouhy M. (2010), Solving DEA and MCDM Problems in Spreadsheets, in: Proceedings of the 7th International Conference on Efficiency and Responsibility in Education (ERIE 2010), University of Life Sciences, Prague, p. 126-136.
• Mahlberg B., Obersteiner M. (2001), Remeasuring the HD1 by Data Envelopment Analysis, Interim Report IR-01-069, IIASA, Laxenburg.
• Tone K. (2001), A Slack-based Measure of Efficiency in Data Envelopment Analysis, European Journal of Operational Research 130, p. 498-509.
• Tone K. (2002), A Slack-based Measure of Super-efficiency in Data Envelopment Analysis, European Journal of Operational Research 143, p. 32-41.

Identifiers

ISSN

2084-1531