Vulnerability Assessment for the State of West Bengal and Madhya Pradesh

  • Vulnerability Assessment for the State of West Bengal and Madhya Pradesh
  • Vulnerability Assessment for the State of West Bengal and Madhya Pradesh

Client: German Development Corporation (GIZ)

Details:

The GIZ project "Climate Change Adaptation in Rural Areas of India" (CCA RAI) with the aim to contribute to improving the livelihoods and adaptive capacities of vulnerable rural communities in India aligns itself to the Government of India’s National Action Plan on Climate Change.

The project focuses on integrating the issue of climate change adaptation in various sector policy decisions that reduce risk and enhance the adaptive capacity of the most vulnerable sectors and groups. It will also develop concrete pilot experiences on adaptation measures together with the Indian state development programmes and supports the up-scaling of successful technical and financial adaptation approaches. . In order to achieve this the project has five components; Vulnerability and risk assessment, Development of technical adaptation options, Climate proofing of rural development programmes, Development of adaptation oriented financial instruments and Information and knowledge management to support mainstreaming national discussions on climate change adaptation. During the initial phase the project is focusing on the vulnerability and risk assessment of the project states viz. Madhya Pradesh, Rajasthan, Tamil Nadu and West Bengal.

The study focused on generating district level Composite Vulnerability Index (CVI) for two States. The composite indices would facilitate the identification of districts, which are vulnerable to climate change and need special attention towards adaptation. Vulnerability to climate change in Madhya Pradesh has been derived using integrated vulnerability assessment approach. Accordingly socio-economic, environmental, agriculture, water resource and forest indicators of vulnerability are employed and classified into adaptive capacity, sensitivity, and exposure. To analyze the data, multivariate statistical method of Principal Component Analysis (PCA) is performed to obtain the component scores. Furthermore, cluster analysis was performed on the indices to group the districts according to their degree of vulnerability using Ward Method of Agglomeration. The districts are grouped into low, moderate, high and very high categories of vulnerability. The outputs are shown spatially using maps.

Previous     Next

Website Designed by Raghwendra Web Solutions