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Your Money or Your Life: Green Growth Policies and Welfare in 2050

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Abstract

This paper proposes a simple index of economic progress that weighs in the monetary cost induced by climate change mitigation policies as well as the health benefits arising from the reduction in local air pollution. The shadow price of pollution is calculated indirectly through its impact on life expectancy. Taking into account the health benefits of mitigation policies significantly reduces their monetary cost in China and India, as well as in countries with large fossil-based energy-producing sectors (Australia, Canada and the United States).

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Notes

  1. The extended MERGE model includes a social global welfare function which allows to determine the path of GHG local air pollution emission reductions that will maximise welfare, However, the impacts of mitigation policies in terms of GDP costs and health benefits (i.e. reductions in crude premature deaths) used for illustrative purposes in this paper are taken from a scenario where the path of emission reductions is set exogenously and the model solved to produce the least-cost policy mix to meet such path.

  2. Furthermore, if one is prepared to assume that each year of life has the same value (Aldy and Viscusi 2007), it is possible to derive from the VSL the value of a statistical “life-year” for a given discount rate and average life expectancy of the population. This amounts to expressing the VSL as the present discounted value of stream of values of statistical life-year over the remaining life. For example, using a 3 % discount rate and assuming an average life expectancy over the population of 30 years (corresponding to an average age of around 50), then a VSL of USD 5 million as calculated earlier would correspond to a value of a life-year of USD 255,000. However, assuming a constant value for each year of life is not uncontroversial (Lindhjem and Navrud 2011).

  3. As explained by Murphy and Topel (2006), the parameter \({\upalpha }\) is linked to the elasticity of instantaneous utility \({\upvarepsilon }=\hbox {u(c)}/[\hbox {u}^{\prime }(\hbox {c)c}]\), which determines the value of a life-year as well as the VSL. BPS choose the same value for \({\upvarepsilon }\) as Murphy–Topel (2006), namely \({\upvarepsilon } = 0.346\). In their setting, this corresponds to a VSL comprised between USD 1.5 and 2 million for developed countries, and a minimal consumption \(\hbox {c}_{0}\) equal to USD 353.

  4. According to official statistics, life expectancy in South Africa has actually fallen to closer to 50 years since the mid-1990s.

  5. Not surprisingly, the difference is much smaller in percentage terms. While the richer-country citizen would be willing to accept an income reduction of 1.3 % to raise life expectancy by one per cent, the corresponding figure for the poorer-country citizen would be 1.1 %.

  6. It is also reminded in Bollen et al. (2009) that reduced air pollution may result in higher temperature increases in the short run since several of the air pollutants have a cooling effect.

  7. Even though the differences from the baseline scenario in 2050 vary across regions, similar reductions would be observed in China, India and the United States (at around 40 %), while Europe would see smaller declines (closer to 25 %). Also, the time profile of the reductions would differ across regions, with a more rapid decline observed earlier on in advanced economies but a flattening out after 2030, whereas premature deaths would still be falling (relative to baseline) in 2050 in developing countries. As mentioned earlier, this does not take into account the (hard-to-quantify) broader effects of GHG emission reductions through avoided climate damage.

  8. Premature mortality is defined as the number of deaths to persons aged 0–74 divided by the population age 0–74. It represents roughly 10 % of total mortality in developed economies and is two or three times larger in China and India.

  9. Admittedly, this choice is not consistent with the theoretical section where an exponential form is being used as it displays a simple closed-form relationship between mortality rates and life expectancy. Here the objective is not simplicity but the derivation of a realistic rule to transform crude deaths rates into life expectancy measures.

  10. This follows from the shape of the survival function \(\hbox {S(t)} = \hbox {exp}(-(\hbox {t}/{\uplambda })^{\mathrm{k}})\). Actually, one has \(\hbox {T} = {\uplambda }\,\hbox {Gamma}(1+1/\hbox {k})\).

  11. The fact that the gains in life expectancy in the United States are closer to those of China and India than those of Europe and Japan may come as a surprise. To some extent, this reflects cross-country differences GHG emission cuts in the global 50 % reduction scenario: they are stronger in the United States than in the EU or Japan and hence the reduction in NOx and \(\hbox {SO}_{2}\) are also more substantial along with avoided premature deaths, as mentioned earlier (see footnote 7).

  12. In the 50 % global emission cuts scenario, China is expected to reduce GHG emissions by twice as much as India but the monetary value of an extended year of life expectancy is also nearly twice the value in India. The higher benefit for the United States and Japan reflect essentially the higher value of of an extended year of life expectancy, but also in the case of Japan, the substantially smaller cuts in emission.

  13. The comparison is made on the basis growth rates since the full-income measure is not defined in level terms.

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  1. OECD Economics Department and Statistics Directorate, Paris, France

    Alain de Serres & Fabrice Murtin

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  1. Alain de Serres
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Correspondence to Alain de Serres.

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The authors are both from the OECD Economics Department. They would like to thank Romina Boarini, Nils-Axel Braathen, Rob Dellink, Romain Duval, Jorgen Elmeskov, Marco Mira D’Ercole, Stéphanie Jamet, Nick Johnstone, Giuseppe Nicoletti and Jean-Luc Schneider for their comments and suggestions and Irene Sinha for editorial assistance.

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de Serres, A., Murtin, F. Your Money or Your Life: Green Growth Policies and Welfare in 2050. Environ Resource Econ 63, 571–590 (2016). https://doi.org/10.1007/s10640-014-9849-x

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