Bridging the macro–micro divide through a new paradigm for climate resilience assessment in data-scarce regions

Climate resilience assessment in low-income countries (LICs) remains constrained by fragmented data systems and weak integration across analytical scales. This study develops and validates a two-tier empirical framework that unifies cross-country econometric modelling with subnational spatial diagnostics to measure and visualise resilience in data-scarce contexts. Using Uganda as a core test case, we estimate sectoral resilience through dynamic System Generalized Method of Moments panel regressions and generate high-resolution productivity maps via kriging of field and satellite indicators. The framework introduces the Resilience Asymmetry Surface, which quantifies how climatic stress interacts with structural capacity to produce divergent resilience outcomes. Results reveal that rainfall variability and infrastructural deficits jointly drive resilience asymmetries, while integration across macro–micro tiers enhances diagnostic accuracy and policy relevance. By combining statistical rigour, spatial precision, and full reproducibility, the approach enables targeted adaptation planning and scalable resilience benchmarking across the global South.