The standard measure of the monetary value to an individual of a small change in the risk of mortality is described as the value per statistical life (VSL) (e.g. Jones-Lee, 1974; Vis- cusi, 1978; Hammitt, 2000; Alberini, 2005).
The VSL is defined as the maximum rate at
which an individual would pay to reduce his or her chance of dying by a small amount in
a specified time period (e.g. the current year).
This value should not be interpreted as the
value of a life; this value reflects the trade-off
that populations make between empirically measured mortality risks and money. It is important to note that the VSL is not a constant but may vary with a number of factors including wealth and income, age, anticipated health status and total mortality risk. Since the Assessment spans regions in which income varies considerably the Assess- ment models the impact of income on VSL.
The rate at which VSL varies with income is typically described by the income elasticity,
defined as the proportional change in VSL
that corresponds to a proportional change in income. Adjustment for income is important for estimating VSL in lower-income popula- tions for which direct estimates do not exist. Additionally, the application of different VSLs
to populations with different incomes reflects
differences in willingness and ability to pay for mortality risk reductions that have been documented empirically. This approach does not imply that lives are worth more or less as a function of income levels. Rather, income constrains an individual’s ability to pay for risk reductions.
In this Assessment, the VSL approach is ap- plied in two ways; both approaches rely on the US EPA’s preferred VSL (which is equal to US$9 500 000 for the model year of the analysis: 2030) and are based on both a meta- analysis and a Science Advisory Board review.
The first approach adjusts the US EPA VSL
to each country based on the relationship
between country-specific income per capita
and income per capita in the USA, using an elasticity of 0.40 between income and VSL
Figure 4.8. Change in annual PM2.5 cardiopulmonary and lung cancer and O3 respiratory mortality (lives per
126
(US EPA, 1999). This elasticity reflects the
relationship between percentage changes in two variables. So in the current context, an elasticity of 0.40 implies that a 10 per cent increase in per capita income corresponds to a 4 per cent increase in the willingness to pay (WTP) to avoid mortality risks. The second approach applies the US EPA VSL uniformly across all countries, regardless of individual nations’ income levels.
Although both PM2.5 and O3 impact the in- cidence rates of a wide range of morbidity states, the most economically important of which is chronic illness, this Assessment does not measure the impact of air pollution on morbidity due to a lack of necessary data in many countries.
Table 4.4 shows the results from the mortality valuation exercises conducted in this Assess- ment (based on the values provided in Figures
4.7 and 4.8) for the comparison of mortality incidence in 2030 and 2005.
Table 4.4 indicates that the value of mortality reductions between 2030 and 2005 is consid- erable in magnitude; the value of aggregate avoided global mortality is US$1.28 trillion using GISS concentration changes. The value of avoided mortality is considerably larger when the ECHAM model is used; the value increases to just over US$2.7 trillion. Table 4.4 also shows the regional split of these mortality impacts. Much of the difference be- tween the two models centres on the change
in East Asia, Southeast Asia and the Pacific,
North America and Europe, and South, West
and Central Asia. Specifically, the (absolute)
value of avoided mortalities increases in East
Asia, Southeast Asia and the Pacific by nearly
US$1.5 trillion when the ECHAM model is used relative to the GISS model. In South, West and Central Asia, mortality damages are
Table 4.4. Valuation of Premature Deaths: 2030 versus 2005 according to emissions and concentrations resulting from the reference scenario. All values expressed in 2006 US$ billions. 95 per cent confidence intervals in parenthesis derived from confidence intervals reported for mortality CRF. Values reflect change in mortality damage; negative values imply improvement in welfare. The first column (left) indicates the geographical region (either global or each of five regions), and the second and third columns display the results when using country-specific VSLs for the mortality calculations based on the GISS (2nd column) and ECHAM (3rd column) modeled concentration changes. The fourth and fifth columns break down the results by exposure to PM2.5 and
O3 individually.
Spatial Unit Gross mortality value
(2006 US$ billions)
Gross mortality value (GISS) (2006 US$ billions)
GISS ECHAM PM2.5-related
deaths O3-related deaths Global -1 280 (-399 , -2 360) -2 710 (-797, -5 200) -1 680 (-537, -2 990) 400 (139, 623) Africa 73 (25, 124) -25 (-8, 50) 66 (23, 112) 7 (2, 11) East Asia,
Southeast Asia and the Pacific -237 (-76, -462) -1 720 (-550, 3 090) -564 (-186, -986) 327 (111, 523)
Latin America and
the Caribbean -3 (-1, -6) -38 (-13, -67) -11 (-4, -18) 7 (2, 12)
North America and
Europe -2 040 (-669, -3 560) -2 960 (-964 -5 210) -1 850 (-608, -3 240) -189 (61, -320)
South, West and
Central Asia 931 (322, 1 540) 2 030 (742, 3 220) 683 (238, 1 140) 248 (84, 397)
127 US$1 trillion larger when using the ECHAM
model than when GISS is employed. In North America and Europe, the reduction in mor- tality damages is nearly US$1 trillion larger with ECHAM than when GISS is employed. In Africa the GISS model predicts an increase in mortality damages, while the ECHAM model indicates that mortality damages will be smaller in 2030 than in 2005. A much smaller difference in mortality damages is seen between the two models for Latin Ameri- ca and the Caribbean.
Table 4.4 also expresses the country-specific
VSL results according to whether the predicted deaths are due to PM2.5 or O3 exposure. These results are based on the GISS model. The greatest share of mortality damage is due to ex- posures to PM2.5. Globally, the reduction in pre- mature deaths associated with PM2.5 exposure is valued at US$1.7 trillion. In contrast, O3 expo- sure deaths increase and are valued at US$400 billion. In East Asia, South East Asia and the
Pacific and in Latin America and the Caribbean
this pattern of reduced PM2.5-exposure deaths and increasing O3-exposure deaths holds. And in each of these two regions the (absolute) value of PM2.5-exposure deaths is in excess of that associated with O3. In North America and Eu- rope, both PM2.5- and O3-exposure deaths de- crease relative to 2005 for the reference scenario. In Africa and South, West and Central Asia, premature deaths associated with both pollution species increase.