Vulnerability Assessment of Northern Ghana to Climate Variability

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For vulnerability analysis, we first normalised the various indicators by carrying standardisation procedure to ensure uniformity and comparability among the indicators (Vincent 2004). We employed a ranking system to assign coefficients to the indicators. Two criteria were employed here. Lowest value or highest value indicators that contributed most to vulnerability were assigned the highest co-efficient value of 1.0 (on a scale from 0.0 to 1.0). For example, in highest value case- suppose the poverty rate for three regions A, B and C were 75%, 65% and 44.5% respectively. Here, region A with the highest poverty rate was hypothetically more vulnerable than the other two regions. We assigned this region vulnerability coefficient 1.00, which represents the highest vulnerability coefficient (on a scale from 0.00 to 1.00). The vulnerability coefficients for the other two regions were calculated by dividing their poverty rate values by that of region A. So the vulnerability coefficient for Region B was 65/75=0.867and for region C was 44.5/75= 0.593. For lowest value, with average monthly income for regions A, B and C are $200, $150 and $174.5 respectively, the most vulnerable (at least hypothetically) was the region with the lowest figure (income) but not region with the highest figure (as was the case with poverty rate). Here, we assigned the highest vulnerability coefficient of 1.00 to the region with the lowest value in this case is Region B. The vulnerability coefficients for

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