Climate change and poverty: building resilience of rural mountain communities in South Sikkim, Eastern Himalaya, India

Abstract

The rural mountain communities have long faced challenges from a range of social, economic, political and environmental factors and the threat from these factors has only intensified due to the current climate change. This study was conducted in South Sikkim, a mountain region located in the Indian Eastern Himalaya, to get a deeper insight of the multitude of barriers and stresses that a poor rural mountain community experiences. The purpose of the study was to get community’s perception on the kind of interventions that they consider important to lift them out of poverty and enhance their resilience to manage climate risk. The analysis is based on focus group discussions and household survey, using a multidimensional poverty assessment tool. The study highlights that the vulnerability of the study region to climate change is not concentrated to physical or geographical factors alone, but mostly to the socio-economic factors like lack of access to education, health care, limited livelihood opportunities, limited resources, etc. People consider that these non-climatic factors act as barriers for them to overcome poverty, contribute to their weak resilience, and make it extremely difficult for them to manage the risk posed by climate change. The study therefore suggests that it is of utmost importance that the interventions are planned in ways that address the multidimensional poverty in the region which in turn will enhance community’s inherent capacity to adapt to current as well as future climate risk.

Appendix

AHP was used to quantify the multi-stakeholders response: The process consists of four steps: pair-wise comparison, making comparison matrix, developing the priority vector, and estimating the consistency ratio (Saaty 2003; Teknomo 2006). Pair- wise comparisons were drawn from the stakeholders consultation where all the pairs of subcomponents of a theme were compared on a nien point scale represented in five broad categories of responses (Fig. 5). This classification of the scale is designed to minimize the error and inconsistency in human responses.

Fig. 5

The scale used for pairwise comparison

The pair-wise comparison was used to prepare comparison matrix, the example depicted in Fig. 5 shows that the availability and storage of domestic water is strongly more important than the water quality in one of the sample GPWs. The comparison matrix based on the response is shown in Fig. 6 and is used to determine the principle eigen value. In Fig. 6, the diagonal (green) depicts the pair of the same subcomponent and hence is equal to 1. The cell above the diagonal (black) in the matrix contains the data from the FGD on the pair-wise comparison. The cells below the diagonal (red) in the matrix represent the reciprocals of the values in the cells above the diagonal for each of the pairs of subcomponents. The rule followed to fill the cells above the diagonal is that if the value A is preferred over B on the nine point scale (Fig. 5) then the actual point on the scale is entered in the cell. whereas if B is preferred over A then the reciprocal of the point on the scale is inserted in the cell. For instance, information from Fig. 5 is used to fill the cell representing the availability and storage, and quality in Fig. 6 (first row and second column).

Fig. 6

Comparison matrix (also called as reciprocal matrix)

The Eigen vector is developed by summation of the columns in the reciprocal matrix and then dividing each element (value in the cell) of the column by the sum of the column, this process is known as standardization. The sum of these values is always equal to 1(shown in Fig. 7).The normalized Eigen value for domestic water theme for Ramaram GPW is expressed in Table 4 and the average of thematic index score of Pamphok and Ramaram for all the ten themes in Table 1 (relative weights).

Table 4 Normalized Eigen value for the Ramaram GPW

Fig. 7

Standardization through development of the eigen vector

The last step of the process is checking the consistency in response of the respondent through the consistency ratio (CR). CR is the ratio of consistency index (CI) and the random consistency index (RI). To arrive at CI first the principal eigen value is calculated, through summation of products between the sum of columns of the reciprocal matrix (Fig. 6) and each element of eigen vector (Fig. 7). Then CI is calculated by using the principal eigen value (λ _max) and the number of subcomponent in a theme (n) (Eq. 4) (Saaty 2003; Teknomo 2006)

$$ {\text{Consistency}}\,{\text{Index}} = \frac{{\lambda_{\hbox{max} } - n}}{n - 1} $$

(4)

The standardized RI is based on repeated experimental studies and is established as there is a specific random consistency index score for number of subcomponents. If CR (CI/RI) is within 10% then the relative weights are acceptable because conceptually in AHP 10% inconsistency in human response is accepted. The relative weights of the subcomponents are integrated in the MPAT as explained in Eq. 2. The process has been exemplified in Table 5.

Table 5 Illustrative example of the analytical hierarchy process (AHP)