Cross-country comparative analysis of climate resilience and localized mapping in data-sparse regions - Katende, Ronald (Frontiers in Environmental Science, 2025)
IntroductionClimate resilience varies substantially across low-income countries (LICs), with agriculture often being the most vulnerable sector. Agricultural systems in these regions are typically rainfed, labor-intensive, and highly sensitive to climate variability. Yet, many LICs lack the high-resolution data needed to assess resilience at both national and local levels.MethodsThis study proposes a two-part framework to evaluate climate resilience across data sparse settings. First, sector-specific resilience is assessed at the national level using harmonized panel data and dynamic panel GMM regression models, incorporating structural and climate related variables. Second, a localized mapping approach is developed that integrates sparse field data with satellite-derived indicators. Agricultural productivity is interpolated across regions using kriging, a geostatistical technique optimized for sparse datasets. The study introduces the Resilience Asymmetry Surface (RAS) to visualize how resilience jointly depends on income and climate stress.ResultsNational-level analysis shows that service sectors are more resilient to climate variability, while agriculture remains particularly vulnerable without structural support. At the local level, kriging-based interpolation of agricultural yield using sparse ground data and satellite inputs proves robust, with cross-validated RMSE values under 0.6 tons per hectare in Uganda, Kenya, and India. The RAS further highlights that similar climate exposures can yield very different resilience outcomes depending on a country’s economic conditions.DiscussionThis framework enables climate-informed planning even in data-constrained environments by combining cross-country econometric modeling with localized spatial analysis. It supports national strategy development and targeted regional interventions, providing practical tools for policymakers seeking to strengthen resilience in LICs. The approach is scalable, cost-effective, and leverages openly available data, making it accessible for use in similarly under-resourced contexts.