Climate change is already creating a host of problems for rural communities especially those dependent on climate sensitive livelihoods such as agriculture, herding, fishing, forest produce gathering, to name a few. For planners, development practitioners and researchers the challenge is to understand how a community may be vulnerable to climate change and why. With this knowledge and information, communities can be mobilised to undertake measures that help them adapt to climate change, reduce its impacts and avoid development patterns/ mal-adaptation that may make communities more vulnerable at a later date.
Toward this end, WOTR has developed a new tool called “CoDriVE – PD” which stands for Community Driven Vulnerability Evaluation – Programme Designer, based on over two decades of developmental experience in India.
CoDriVE-PD is a recombinant tool developed by converging key aspects of three well known international research methodologies and is built on the “5 Livelihood Capitals Framework”. It adopts a systems thinking approach which uncovers interrelationships and interdependencies between them, and generates a quantitative vulnerability code that grades their vulnerability to climate impacts; enabling both communities, planners and practitioners to prioritise and plan for adaptive measures and interventions.
CoDriVE-PD can be used in a wide range of social, economic and agro-ecological contexts in developing countries.
In order to support easy and large-scale application of CoDriVE-PD, a web-based software program is being developed to process and analyse key data of a community with a view to generating a vulnerability profile and suggest situation-specific adaptive actions that may be undertaken. This is a work in progress and as WOTR (and others) apply CoDriVE-PD across geographies and communities, these case studies (and refinements to the tool) will be shared and widely disseminated.
We welcome reader and user feedback and suggestions – it would make the CoDriVE-Programme Designer more accurate, robust and better able to capture local particularities.