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Climate Change in FAO Potential Impacts of Sea-Level Rise on Populations and Agriculture
The Vulnerability Index


IN ORDER to assess the combined effect of insularity and population, we have used below a very simple "vulnerability index" (or VI), defined as the product of the Insularity Index and population density:

Vulnerability Index = Insularity Index x population density
The purpose, again, is not to develop a new indicator, but simply to allow a global and statistical discussion of some of the population and sea-level rise issues under consideration. There are other indices (see, for instance the good discussion of the Global Vulnerability Assessment [GVA] in WHO 1996, chapter 7). Most indices suffer from one or more of the following problems: they assume linear responses and a somehow uniform distribution of the target population. They are also difficult to use for projections as they relate only to some of the relevant factors. CZMS (for instance, CZMS 1992) defined the Common Methodology for carrying out vulnerability assessments in different countries. Most of the criticisms of the Common Methodology stress difficulties related to the lack of even elementary data, the use of monetary value only to evaluate losses (which is of little relevance in subsistence economies), a lack of attention paid to the resilience and adaptation of certain systems to sea-level rise, and the assumption of linear responses (CZMS, 1992, App. C) [9].

Table 6. Some typical values of Insularity Index, Vulnerability Index (VI) and protection costs.
CountryInsularity Index Vulnerability IndexProtection cost (% GDP)
Libya0.0010 0.00300.08
Zaire0.000016 0.0310.12
United States0.0022 0.0630.02
Sweden0.0078 0.170.14
Nicaragua0.0076 0.240.35
Belgium0.0021 0.700.01
Italy0.026 4.90.04
Netherlands0.013 5.00.03
Greece0.10 8.30.10
United Kingdom0.051 120.02
Jamaica0.028 210.19
Mauritius0.096 520.15
Anguilla0.67 7710
Gaza strip0.11 230NA
Singapore0.31 17000.05
Tokelau10.1 150011
Maldives2.1 180034
Monaco2.2 360000.13
Data from Factbook (1997), UN 1996a UN 1997, median population scenario, as well as CZMS (1990) for the protection costs

Like the Insularity Index, the VI varies over several orders of magnitude. Typical values are given and compared below (Table 7).

The VI shows a good qualitative agreement with other indicators (see Table 6 above and Figure 9 below). Due to its close link with population density, we suggest that it can be used to evaluate some of the changes in vulnerability that may take place in the future. Table 7 and Figure 7 both indicate a relative "flattening" of the distribution between now and 2050, linked with the relative decrease of population growth rates in a number of the countries and territories that are the most vulnerable by current standards.

Table 7. Empirical statistical distribution of vulnerability indices with current and 2050 population densities
 Vulnerability index
Percentile point19952050
0.10.02290.0304
0.20.08020.113
0.30.1930.202
0.40.3790.355
0.51.560.677
0.64.231.105
0.719.84.06
0.852.07.96
0.917552.6
Based on data in Factbook, 1997, and UN, 1997, medium population scenario

Figure 8. Frequency distribution of national vulnerability indices for the countries and territories of the world in 1995 and 2050
graph 8 Based on data in Factbook, 1997, and UN, 1997, medium population scenario.

For instance, while 60% of countries are currently below the VI 4 level, that figure may increase to 70% in 2050. For VI 52 (which is a high vulnerability level occurring in islands: see Table 6), the increase is from 80% to 90%. This is also visible in Figure 8, which shows a relative decrease at the extremely high and low Vulnerability Index values, with a corresponding increase at median VI values between 1 and 100.

If expressed in terms of population, it appears that more people will be living in countries exposed to the "average" vulnerability conditions, while little change will probably occur at the extremes of the vulnerability spectrum. The average vulnerability index undergoes little change between now and 2050 (654 and 662, respectively), but the skew drops markedly from 1.327 to 1.067, indicating a relative decrease of extremely high values, as already noted in Table 7.

Figure 9 shows an interesting relation between the much more complex assessment of CZMS (1990) and the Vulnerability Index, from which the upper limit of the cost associated with different vulnerabilities can be derived. Incidentally, CZMS also assumes a population inertia (protection rather than other options) that may not occur. In practice, retreat, accommodation and protection will coexist.

Figure 9. Cost of protection, in % of GNP* versus Vulnerability Index for 182 countries and territories
graph 9
* as given by CZMS, 1990

Finally, turning to agriculture, a plot of the distribution of arable land as a percentage of total land reveals a Vulnerability Index that is rather symmetric on the logarithmic scale (which is to say, roughly log-normal - Figure 10). Countries where high vulnerabilities are associated with high fractions of arable land are indicated in the graph.

It is clear that the percentage of arable land is rather independent from the Vulnerability Index as defined here, except probably at high percentages, which will tend to be associated with high population densities.

Figure 10. Arable land (in percent of total land) and vulnerability index
graph 10

The most agriculturally oriented economies, as expressed by the percentage of arable land, are generally at moderate vulnerability levels, probably because the most vulnerable economies - those of the small islands - also rely on the ocean for their income and subsistence.


Notes
9. An ideal "Vulnerability Index" should be additive. For instance Tonga has 171 named islands, 36 of which are permanently inhabited, and Fiji has 320 (CGER, 1996), and it would be logical to expect that the total vulnerability should be the sum of the island values. The concept of vulnerability is a very complex one. For a more systematic approach to climatic risk and vulnerability, see e.g. Gommes (1998) or Downing (1991, 1992).


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