Environmentally Responsible Happy Nation Index: Refinements and 2015 Rankings

Abstract

Due to the inadequacy of GDP and the importance of environmental protection from a global and long-term perspective, Ng (Social Indic Res 85:425–446, 2008) proposes the Environmentally Responsible Happy Nation Index (ERHNI) as an indicator of national success. This paper refines ERHNI through first, better estimates of key variables, second, better accounting for external costs through the inclusion of other major greenhouse gases, third, increasing sample size and fourth, using world average as a representative baseline instead of China. After which, we rank countries according to our revised and improved ERHNI and compare the results against those of Ng’s (2008). It is hoped that this paper will make contributions towards a more complete measurement of national success for future developments in a world threatened by global warming.

Appendices

Appendix 1

See Tables 3, 4 and 5.

Table 3 Data sources for ERHNI calculations

Table 4 Global Warming Potentials.

Table 5 New ERHNI rankings

Interestingly, East Asian countries show progress since Ng’s ranking, with the exception of China, whose rankings fall from 51st to 109th despite its years of steadfast growth (Zhu 2012)

Appendix 2: Environmentally Responsible Happy Nation Index (ERHNI)

Ng (2008) agrees with the view that income-based measurements like the GDP and GNP are insufficient in measuring national success due to its inability to consider for environmental and social issues. As a result, Ng (2008) proposed the Environmentally Responsible Happy Nation Index (ERHNI) and defined it as “the amount of Happy Live Years (HLY) a nation achieves for an average person less the per capita external costs (PCEC) imposed externally on the global community” (Ng 2008). The index aims at valuing national success by encouraging nations to reach high levels of adjusted HLY as a means of maximizing happiness and also aims to reduce PCEC so as to minimize external costs imposed on the rest of the world (Ng 2008).

$$ERHNI = Adjusted\; HLY - PCEC$$

where HLY is the product of an index of happiness and life expectancy. The initial calculations of HLY were normalized from a scale of 0–100 or 0–10 into 0–1. This means that within a range of 0–10, the number 5 would be equivalent to the level of neutrality where an individual is neither happy nor unhappy, or in other words, zero net happiness. The problem with not taking into account the point of neutrality is that a very long but very unhappy life would result in a higher HLY value than that of a moderate length but happy life. An example by Ng (2008) would be that the HLY of an individual with 90 years of unhappy (happiness index = 4) life will score 36 while the HLY of another individual with 50 years of quite happy (happiness index = 7) life will score 35. Such an outcome would belie the intentions of the HLY and produce unreasonable results. Hence, Ng refined the happy life years (HLY) as seen in this equation: $Adjusted \;HLY = \left( {Happiness - 5} \right) \times 0.1$, where 5 (out of 10) represents the point of neutrality and 0.1 converts it into a scale of 0–1. By counting the value over neutrality as being valuable, any value below 5 would be deemed as unhappiness.

To take into account the external costs imposed on others, Ng proposed to subtract, from adjusted or net HLY, the per capita external costs (PCEC = 0.34 × per capita total CO₂ Emissions), which represents the “aggregate costs imposed on the global community by the nation concerned in per capita terms” (Ng 2008).

The ERHNI includes both positive (happiness) and negative (external costs) aspects. It is crucial to be mindful that environmental problems not only affect the internal of a country, but also have an impact on the rest of the world. The ERHNI is able to capture such externalities by taking into account the costs that a country imposed on others.

However, the ERHNI has its own shortcomings. Even Ng (2008) acknowledges that “the existing happiness or life satisfaction measures are not perfectly accurate and the external costs measures are also very rudimentary and incomplete” (Ng 2008). Such imperfections presented an opportunity for this paper to value add to the current ERHNI and provide better data support for the “very rough and incomplete estimates” as mentioned by Ng (2008). As this paper aims to revise ERHNI, we produce a new set of rankings that will be used to compare against the 2008 rankings and revise and apply ERHNI to enhance its usefulness.