23rd International Sustainable Development Research Society Conference June 14-16, 2017 Bogotá, Colombia
HIP — a Happier Index for the Planet? Julia Bondarchik1 (Lappeenranta University of Technology), Matylda Jabłońska-Sabuka2 (Lappeenranta University of Technology), Lassi Linnanen3 (Lappeenranta University of Technology), Tuomo Kauranne4 (Lappeenranta University of Technology)
Introduction
Results
This work is based on the Happy Planet Index (HPI), which is an example of the global measures to assess sustainable wellbeing. HPI uses global data on experienced wellbeing, life expectancy, and ecological footprint to generate an index revealing which countries are most efficient at producing long, happy lives for their inhabitants, whilst maintaining the conditions for future generations to do the same.
By utilizing the proposed methodology, the new sustainability ranking was obtained for a list of 140 countries. Degree of correlation between the original HPI ranking and the proposed HIP equals 0.96 (Spearman's correlation coefficient). Table 1. The top–10 and bottom–5 ranked countries calculated using the Happier Index for the Planet in comparison with the original Happy Planet Index 2016 results. r
HPI
1
Costa Rica
Bangladesh
(8)
2
Mexico
Costa Rica
(1)
3
Colombia
Vietnam
(5)
4
Vanuatu
Nicaragua
(7)
5
Vietnam
Colombia
(3)
6
Panama
Vanuatu
(4)
7
Nicaragua
Jamaica
(11)
8
Bangladesh
Sri Lanka
(28)
9
Thailand
Palestine
(22)
Materials and Methods
10
Ecuador
Albania
(13)
In our calculations we use the same indicators as proposed in HPI: life expectancy (inequality adjusted), wellbeing (inequality adjusted) and ecological footprint. The HIP methodology includes the following 5 steps:
136
Mongolia
Cote d’Ivoire
(135)
137
Benin
Nigeria
(95)
138
Togo
Swaziland
(132)
139
Luxembourg
Sierra Leone
(133)
140
Chad
Chad
(140)
We propose an alternative geometrically motivated parameter-free method dubbed a Happier Index for the Planet (HIP). The method is independent of the number of sub-indices to be combined and eliminates mutual correlation between component indices by using Singular Value Decomposition (SVD) analysis. By applying our methodology, we reconstruct the latest Happy Planet Index results (2016) and prove the feasibility of the proposed approach.
1) data normalization; 2) singular value decomposition;
HIP
3) renormalization of the principal components; 4) identification of the positive/negative impact of the principal components; 5) HIP calculation.
As can be seen from the equations below, such an approach results in illuminating the free parameters used in the original Happy Planet Index calculations.
Conclusions Based on the results, we argue that the proposed Happier Index for the Planet calculations are more robust than the ones for Happy Planet Index. It was proved that there is no need to re-tune any constants through hand-picked educated guesses, no matter how many sub-indices are required to be considered. On the other hand, we can claim that by altering the life expectancy and wellbeing measures with the inequality adjustments in the HPI calculations have strengthen the methodological approach from a mathematical point of view.
1Julia
Bondarchik
[email protected] Lappeenranta University of Technology, School of Energy Systems, Department of Sustainability Science, PO Box 20, 53851 Lappeenranta, Finland
3Lassi
2Matylda
4Tuomo
Jabłońska-Sabuka
[email protected] Lappeenranta University of Technology, School of Engineering Science, Department of Mathematics and Physics, PO Box 20, 53851 Lappeenranta, Finland
Linnanen
[email protected] Lappeenranta University of Technology, School of Energy Systems, Department of Sustainability Science, PO Box 20, 53851 Lappeenranta, Finland Kauranne
[email protected] Lappeenranta University of Technology, School of Engineering Science, Department of Mathematics and Physics, PO Box 20, 53851 Lappeenranta, Finland
