Many emerging economy countries face major challenges in accessing high-resolution, locally relevant information on renewable energy resources. While global atlases exist, they often lack the spatial detail, methodological transparency, and local ownership required for effective policy and planning. This paper presents a standardized, open-source framework, developed under the World Meteorological Organization, that enables national institutions to generate their own high-resolution renewable energy atlases for solar, wind, and hydropower through a structured Co-design, Co-development, and Co-production (CO-CO-CO) process. The framework integrates ERA5 reanalysis, satellite products, and national observational networks using a modified optimal interpolation scheme and AI-based downscaling (Multilayer Perceptron), producing maps at resolutions down to 90 m. It was implemented with four pilot countries: Chile and Iran (solar), Costa Rica (wind), and Malawi (hydropower), demonstrating accuracy improvements up to 20–35% in selected pilots on standard error metrics, and delivering enhanced spatial detail and bias correction in the others. Together, these results enable resource assessments that reflect local climatology and stakeholder priorities. All workflows, Jupyter notebooks, and documentation are delivered as open-source tools, ensuring reproducibility and long-term national capacity. By bridging the gap between global data and local decision-making, this framework supports evidence-based energy planning, enhances institutional autonomy, and provides a scalable, replicable pathway for countries worldwide to develop independent renewable energy assessment capabilities while integrating climate change considerations into national energy strategies.
Petitta et al. (Wed,) studied this question.