Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions

TL;DR

This paper presents a framework for cross-country analysis of climate resilience in low-income countries and introduces a novel localized mapping technique combining sparse data with satellite imagery to improve agricultural resilience assessment.

Contribution

It introduces a new framework for cross-country comparison of climate resilience and a localized mapping method integrating sparse data with satellite imagery for detailed agricultural analysis.

Findings

Identifies shared vulnerabilities and adaptation strategies across LICs.

Develops a localized climate-agriculture mapping technique.

Provides policy tools for targeted climate adaptation.

Abstract

Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limited insights into broader cross-country patterns of adaptation and vulnerability. This paper addresses these gaps by introducing a framework for cross-country comparative analysis of sectoral climate resilience using meta-analysis and cross-country panel data techniques. The study identifies shared vulnerabilities and adaptation strategies across LICs, enabling more effective policy design. Additionally, a novel localized climate-agriculture mapping technique is developed, integrating sparse agricultural data with high-resolution satellite imagery to generate fine-grained maps of agricultural productivity under climate stress. Spatial interpolation methods, such as kriging, are used to address data gaps, providing detailed insights into regional agricultural productivity and resilience. The findings offer policymakers tools to prioritize climate adaptation efforts and optimize resource allocation both regionally and nationally.