III. Bringing inequalities to the forefront of climate assessments

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Key messages

  • Natural and social scientists have adopted an integrated approach to assess the complexity of climate impacts and policy responses in the face of them. They are integrating a suite of models that capture the multiple inter-linkages across and within the environmental, economic and social dimensions of development. They use these models to generate a cascade of scenarios about potential impacts of climate projections, with and without policies, for the world or at lower geographical levels.
  • The focus of this approach needs to be sharpened by moving away from the narrow focus on long-term climate change and mitigation, to a broader analysis that includes adaptation, resilience, climate variability and extreme weather events, and from the standard accounting of the costs and benefits of typically a single policy, to a broader analysis of economy-wide feasibility of policies for climate resilience. Moreover, the analysis of inequalities should be at the forefront of the assessments.
  • Existing modelling frameworks can be deployed to address inequalities through the analysis of impacts of climate hazards and policies on: livelihoods that rely on climate sensitive natural resources; the distribution of income on the basis of ownership and employment of production factors; human capital and access to public services; households whose socio-economic characteristics make them particularly vulnerable; and inequalities and vulnerabilities that stakeholders feel make them less resilient.
  • Countries have much to gain from enhancing their technical capacities to develop and use integrated climate impact assessments for policy decision making. Better use of these methodologies require improvements in: governance to ensure assessments play a critical role in policy making, statistical and modelling capacity to develop assessments, and communication of scenario results to encourage policy dialogue with all stakeholders. Strengthening their statistical and modelling capacity also requires strengthening international collaboration to improve data sources and methodologies.
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