Bridging the Macro-Micro Divide through a New Paradigm for Climate Resilience Assessment in Data-Scarce Regions

Efforts to assess climate resilience in low-income countries (LICs) are often hampered by fragmented data systems and analytical silos between national and local scales. This study proposes and operationalizes an integrated empirical framework that bridges macroeconomic econometric modeling and micro-level spatial analysis to measure and visualize climate resilience in data-scarce settings. Using Uganda as a core case study, we estimate sectoral resilience through dynamic panel regression and generate spatial productivity surfaces using kriging interpolation on sparse field and satellite data. We introduce the Resilience Asymmetry Surface (RAS), a diagnostic tool that synthesizes income and climate stress to highlight structural vulnerability and intervention leverage points. The results uncover stark cross-sectoral and spatial heterogeneity in resilience outcomes, demonstrating that reliance on single-scale assessments can misdirect adaptation investments. Our framework enables data-efficient, actionable diagnostics that can inform national strategies and localized interventions alike. This work advances a scalable, policy-relevant methodology for integrated climate resilience planning in LICs.