Co-impacts of energy-related climate change mitigation in Africa’s least developed countries: the evidence base and research needs
Bibliographic record
Abstract
This article analyses the debate associated with the co-impacts of climate change mitigation in developing countries, with a particular focus upon Africa’s least developed countries. While these countries’ emissions of greenhouse gases are relatively small (and they do not have emission limitation commitments in the current international regime), inattention to the mitigation agenda would mean that developing countries both miss potential funding opportunities and fail to ‘climate-proof’ their development strategies. A focus, therefore, upon the short-term, local, developmental impacts that serve to change the relative attractiveness of different mitigation options from the perspective of the developing country is in these countries’ current strategic interests. In this article, I examine three energy-related climate change mitigation options: improved cookstoves, carbon-free electricity and improved energy efficiency in industry. Following a conventional ‘climate analysis’ of each, the potential co-benefits and co-costs – drawn from the general literature and then investigated more specifically for the African countries under scrutiny – are identified. This examination reveals that relatively little work focusing explicitly, and simultaneously, upon climate change mitigation and co-impacts has been carried out in Africa’s least developed countries. In conclusion, a call for cross-fertilisation of information between heretofore disparate research communities is made. Additionally, the development of an integrated research agenda is identified as a priority, and the basis of this agenda is articulated.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".