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.

翻译：低收入国家（LICs）各行业的气候韧性存在显著差异，其中农业对气候变化最为脆弱。现有研究通常聚焦于单个国家，对跨国适应性与脆弱性的宏观模式提供有限见解。本文通过引入一个利用元分析与跨国面板数据技术的行业气候韧性跨国比较分析框架，以弥补这些不足。该研究识别了低收入国家间的共同脆弱性与适应策略，从而支持更有效的政策设计。此外，本文开发了一种新颖的局域化气候-农业制图技术，将稀疏的农业数据与高分辨率卫星影像相结合，生成气候胁迫下农业生产力的精细尺度地图。采用克里金法等空间插值技术以填补数据空白，从而提供关于区域农业生产力和韧性的详细洞见。研究结果为决策者提供了工具，以优先安排气候适应工作并在区域与国家层面优化资源配置。