Climate Change Mitigation Perspectives from Sub-Saharan Africa: The Technical Pathways to Deep Decarbonization at the City Level
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".