Climate Change Mitigation Perspectives from Sub-Saharan Africa: The Technical Pathways to Deep Decarbonization at the City Level
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The complex and multidimensional effect of climate change, coupled with low socioeconomic development, in Sub-Saharan Africa (SSA) makes the region vulnerable to the changing climate and threatens its inhabitants’ survival, livelihood, and health. Subnational actions have been widely acclaimed as effective in combatting climate change. Local governments in SSA have been developing and implementing climate action plans to reduce greenhouse gas (GHG) emissions. In this article, we qualitatively assessed climate change mitigation technical pathways at the city level by studying four major African megacities’ climate plans and actions. The cities studied are Accra, Ghana; Addis Ababa, Ethiopia; Lagos, Nigeria; and Nairobi, Kenya. This study provides insight into the novel and innovative policy design and instrumentation options to sustainably address climate change mitigation in SSA. With the past literature focusing on climate adaptation for the Global South, this study shows leading context-specific efforts in climate change mitigation that simultaneously address local sustainable development needs. Our assessment identified the prioritized technical pathways for climate change mitigation in the selected cities, as well as innovative techniques and areas for improvement. Given that it also identifies emerging best practices, this study’s findings can be helpful to local governments and practitioners pursuing local deep decarbonization and international organizations supporting these programs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it