Satellite Observations Reveal Inequalities in the Progress and Effectiveness of Recent Electrification in Sub-Saharan Africa
Bibliographic record
Abstract
Ending energy poverty is a necessary condition for achieving the Sustainable Development Goals (SDGs). Boosting electricity access levels is, however, insufficient if consumption and reliability indicators stagnate. Previous research has shown that satellite-derived data can complement field surveys in tracking energy poverty but with little consideration for the multi-dimensionality of energy access and the role of demographic dynamics. Here, we process 6 years of high-resolution population, nighttime light, and settlement data for sub-Saharan Africa to derive multi-dimensional estimates of electricity access. Our results, validated against a range of sources, confirm a recent surge in electrification such that >115 million people gained access over the 2014–2019 period. Yet, they reveal wide inequalities in the quality of electrification, which cannot be observed in the existing statistics. The pace of electrification must more than triple to fulfill SDG 7.1.1 by 2030. Efforts could fall short if aimed solely at boosting numbers of national electricity connections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".