We develop energy roadmaps to significantly slow global warming and nearly eliminate air-pollution mortality in 139 countries. These plans call for electrifying all energy sectors (transportation, heating/cooling, industry, agriculture/forestry/ fishing) and providing the electricity with 100% wind, water, and solar (WWS) power. Fully implementing the roadmaps by 2050 avoids 1.5 C global warming and millions of deaths from air pollution annually; creates 24.3 million net new long-term, full-time jobs; reduces energy costs to society; reduces power requirements 42.5%; reduces power disruption; and increases worldwide access to energy.

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This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a single-step featurization method applied to freely available 10m/px Sentinel-2 surface reflectance satellite imagery. These visual features are combined with ten survey questions in a proxy means test (PMT) to estimate whether a household is below the poverty line. We show that the inclusion of visual features reduces the mean error in poverty rate estimates from 4.09% to 3.88% over a nationally representative outof-sample test set. In addition to including satellite imagery features in proxy means tests, we propose an approach for selecting a subset of survey questions that are complementary to the visual features extracted from satellite imagery. Specifically, we design a survey variable selection approach guided by the full survey and image features and use the approach to determine the most relevant set of small survey questions to include in a PMT. We validate the choice of small survey questions in a downstream task of predicting the poverty rate using the small set of questions. This approach results in the best performance -errors in poverty rate decrease from 4.09% to 3.71%. We show that extracted visual features encode geographic and urbanization differences between regions.