Regional Vulnerability

The previous sections documented the wide range of uncertainty in the po- tential direction and magnitude of climate change impact. While many new studies have been conducted, most have focused on specific climate scenar- ios associated with 2 x C O z GChl sceilarios or arbitrary changes in climatic conditions t o provide evidence of the general sensitivity of agriculture and crop production to cliinate change. The wide range of estimates limits the ability t o extend, interpolate, or estra.polate from the specific climate sce- narios used in these studies to ''inore" or "less" climate change or t o draw implications for impacts beyond the sites where studies were conducted.

Given these uncertaiilties in both nlagilitude and direction of impact, a key issue is vulnerability to possible cliinate change. Vulnerability is used here t o mean tlle potential for negative consequences that are difficult t o ameliorate through adaptive measures given the range of possible climate changes that might reasoilably occur. Thus, defining an area or population as vulnerable is not a prediction of negative consequences of climate change; it is an indication that across the range of possible climate changes there are some climatic outcomes that would lead t o relatively more serious consequences for the region than for other regions.

Vulnerability has been used rather loosely in many discussions. Before discussing some of the research that has examined potential vulnerability, I introduce a more formal definition. For the sake of simplicity, consider that climate can be described as a single variable, C. We are uncertain about what value C will take a t some future point, but we can describe the probability, p, that C will take on a specific value by the probability

density functioll f ( C ) . Further, consider t h a t we are able t o describe the sensitivity of agriculture, -4, t o changes in climate by tlie function g(C).

We can then defil~e the expected loss function, L ( C ) as f ( C )

x

g(C). A population, region, or crop is relatively more vulnerable under this definition if the area under L(C) where damages occur is larger than for a comparison

Two, purely illustrative, nunlerical exainples are plotted in Figure 1. For these examples I have choseli t o represent f ( C ) as a gamma distribution. In Panel A damages are represented by a quadratic function; in panel B dam- ages are represented by a logarithinic function. These choices illustrate just two of t h e ways t h a t our expectations about the degree of future climate change and our understanding of tlie sensitivity of an agricultural system t o climate change may interact. I11 these numerical illustrations, the system characterized by quadratic losses (Panel A) is more vulnerable t o loss than the system described by logarithnlic losses. Even though the quadratic sen- sitivity t o climate leads t o potentially larger losses a t extreme temperature change, the system is less vulilerable because climate change is not likely t o be t h a t extreine in tliis example. In fact, the small region of beneficial warming (negative damages) in Panel A gives rise t o a substantial possibility of beneficial effects of warnling for the system described in this panel. In Panel B, in contrast, damages initially rise sharply but the rate of increase slows. This characterizatioll of systein sensitivity indicates damages across the entire range of expected temperature change.

Even though damages do not have the potential t o become as severe as in Panel A, the system is more vulnerable t o damage because climate is more likely t o be in the relatively higher damage range of the sensitivity function.

In practice, multiple dimensions of climate affect any agricultural sys- tem. T h e simple characterizatioll in Figure 1 is meant t o make the definition of vulnerability mathenlatically precise even though it is not possible a t this time t o formally estimate the multidimensional, joint distribution of impor- tant climate variables. Nor do we precisely know the damage function t h a t

Temperature Distributin

Temperature

Temperature

1.2

-0 8

Tenperature

Figure 1. Defining vulnerability.

[~errperature Distribution

1

Temperature

peq

Tenperature

I

Expected Loss

I

Tenperature

will become wetter, the region is not vulnerable. Another region in a humid agro-climatic zone may be vulnerable if substantial warming and drying are likely for the area.

Up t o this point, I have not been explicit with regard to what I propose to measure as a damage. The existing literature suggests several different possible measures and therefore several different dimensions of vulnerability.

Many studies focus on crop yields. Evidence suggests that yields of crops grown where temperature could easily exceed threshold values during critical crop growth periods are more vulnerable to warming (e.g., rice sterility:

Matthews et al., 1994a, 1994b).

Farmer or farm sector vulnerability may be measured in terms of im- pact on profitability or viability of the farming system. Farmers with limited

financial resources and farming systems with few adaptive technological op- portunities available t o limit or reverse adverse climate change may suffer significant disruption and financial loss for relatively small changes in crop yields and productivity, or these farms may be located in areas more likely t o suffer yield losses. For example, Parry et al. (1988a, 198813) focused on semi-arid and cool temperate and cold agricultural areas as those t h a t might be more clearly affected by climate change and climate variability.

Regional economic vulnerability reflects the sensitivity of the regional or national economy t o farm sector and climate change impacts. A regional economy t h a t offers only limited employment alternatives for workers dislo- cated by the changing profitability of farming and other climatically sensitive sectors may be relatively more vulilerable than those t h a t are economically diverse. For example, Roseilberg (1993) examined the Great Plains area of t h e USA because of its heavy dependence on agriculture. Increasing aridity is expected in this region under clinlate change, and thus it was considered t o be potentially more economically vulnerable than other regions in the USA.

Hunger vulnerability has been used t o mean the "aggregate measure of the factors t h a t influence exposure t o hunger and predisposition t o its con- sequences" involving "interactions of climate change, resource constraints, population growth, and economic development" (Downing, 1992; Bohl et al., 1994). Downing (1992) concluded that the semi-extensive farming zone, on t h e margin of more intensive land uses, appears t o be particularly sensitive t o small changes in climate. Socioeconomic groups in such areas, already vulnerable in terms of self-sufficiency and food security, could be further marginalized. In all likelihood we should not look only a t agriculturally de- pendent people. We must coilsider the ineans people have within society and t h e family t o obtain food and how their allocation will change if production potential changes. A poor urban household may suffer due t o production losses elsewhere in the region while the rural farmer may continue t o eat. Or, women and children of rural peasant farms may go hungry, while "excess"

production from the region is sold. Assessing who has the means and rights t o food during shortfalls is thus likely t o be more critical in a climate vul- nerability study than assessing how production may change. For hunger and famine in general, the relative importance of acquiring (versus producing ) food has been denlollstrated by Sen (1981, 1993).

Given the diverse currently existing conditions, the geographical varia- tion likely t o exist in ally climate cllange scenario, and the wide uncertainty

that must be associated with local prediction of future climates, some vul- nerable agricultural areas and populations are likely in nearly every region, even if the expected value for the region is a net benefit. This makes vulner- ability a relative concept - while there may be a few areas where even the most extreme climate change we can imagine would not generate losses, in general, the problem is t o consider whether a particular region or population is relatively more vulnerable than others.

While perhaps most difficult t o evaluate, vulnerability in terms of hunger and malnutrition ought t o be the first concern. If so, we can almost certainly eliminate the richer countries of the world as vulnerable. For poorer regions, it is the poorest members of these areas or those that could be made poor by climate change that are most a t risk. The wide uncertainty with regard t o local and regional climate change means it is difficult t o rule out negative possibilities for any area. Thus, without even considering specific climate scenarios we can assert that, of the world's populations, those who are cur- rently poor, malnourished, and dependent on local production for food are the most vulnerable in terms of hunger and malnutrition t o climate change.

Similarly, severe economic vulnerability is most likely where a large share of the population depends on agriculture, leaving few alternative employment opportunities. Again, we need not a.ssess climate scenarios or projected yield changes t o establish where these vulnerable populations live. Given these considerations, Table 6 presents some of the critical dimensions of areas of the world that might be used t o assess vulnerability. While the table is too aggregated t o identify specifica.lly vulnerable populations, it is indicative of where many of these people a,re liliely t o be. Because of the wide range of uncertainty in precipitation, the only climatic dimension likely t o enter sig- nificantly in a n assessment of vulnerability is temperature. Cool regions are more likely t o be limited by low temperatures, and thus warming may prove beneficial ^- these a.reas may still suffer if precipitation changes are adverse.

However, further wa,rming is unlikely t o benefit already warm regions. Thus, global warming appears somewhat stacked against the already warm areas.

Coincidentally (or not), these regions tend to also be home t o some of the world's poorest.

The focus on hunger and malnutrition as a first priority does not mean that other types of vulnerability are unimportant. Regional economic devel- opment, land degradation, or increased environmental stress resulting from agricultural production under a changed climate are important concerns as well.

Table 6. Basic regional agricultural indicators and vulnerability. Sub-Saharan Middle East/ South Southeast East Former Latin USA, Africa North Africa Asia Asia Asia Oceania USSR Europe America Canada Agric. land (%)a 41 2 7 5 5 3 6 5 1 57 27 47 3 6 3 7 Cropland (%) 7 7 44 13 11 6 10 29 7 13 Irrigated (%) 5 2 1 3 1 21 11 4 9 13 10 8 Land area (lo6 ha) 2390 1167 478 615 993 845 2327 473 2052 1839 Climateb (1) Population (lo6) 566 Agric. pop. (%) 62 Pop./ha cropland 3.6 Agric. prod. (106t) Cereals 5 7 Roots aitd tubers 111 Pulses 5.7 Sugar cane aitd beet 60 Meat 6.7 1991 GNP/cap.' 350 1940 320 930 590 13780 2700 15300 3390 32100 Annual growth -1.2 -2.4 3.1 3.9 7.1 1.5 N.A. 3.2 -0.3 1.7 Agric. (% of GDP)' >30% 10-19% >30% 30 to >30% 20~29% <6% 10-29% <6% 10-19% <6% "Agricultural land includes grazing land and cropland, reported as a percentage of total land area. Cropland is reported as a percentage of agricultural land. Irrigated area is reported as a percentage of cropland. *Climate: (1) tropical; arid, humid. (2) subtropical, tropical; arid. (3) tropical, subtropical; humid, arid. (4) tropical; humid. (5) subtropical, temperate oceanic, continental; humid. (6) tropical, temperate, oceanic subtropical; arid, humid. (7) polar, continental, temperate oceanic; humid, arid. (8) temperate oceanic, some subtropical; humid, arid. (9) tropical, subtropical; mostly humid (10) continental, subtropical, polar, temp. oceanic; humid, arid. 'Gross national product (GNP) is in 1991 US dollars; annual growth (percent per annum) is for the period 1980-1991. Note: East Asia GNP excludes Japan. Also, regional GNP data generally include only those countries for which data are given in Table 1 in World Development Indicators. Countries with a population of more than 4 million for which GNP data are not available include Vietnam, Democratic People's Republic of Korea, Afghanistan, Cuba, Iraq, Myanmar, Cambodia, Zaire, Somalia, Libya, and Angola; land areas are in hectares, production is in tonnes. Sources: Computed from FA0 Statistics Division, 1992; GNP per capita, GNP growth rates, and agriculture as a share of the economy are from World Bank; World Development Indicators 1993 and temperature and climate classes from Rotter et al., 1995. a

Im Dokument Climate Change: Integrating Science, Economics, and Policy (Seite 72-78)