Empty promises: Some requirements for a successful implementation of decarbonisation strategies in developing countries
Bibliographic record
Abstract
Decarbonisation strategies are crucial for mitigating the adverse effects of climate change and achieving sustainable development. However, the successful implementation of these strategies in developing countries remains a significant challenge due to resource constraints, competing development priorities, and institutional barriers. This paper provides a comprehensive overview of decarbonisation efforts and impacts through an extensive review of existing research, reports, and case studies. The research includes a detailed examination of decarbonisation initiatives, complemented by case studies from seven industrialised (USA, EU27, Germany, Italy, France, Finland, and Australia) and six developing countries (China, Brazil, South Africa, India, Mexico, and Kenya). These case studies showcase practical efforts and illustrate current trends in decarbonisation. The findings underscore the importance of political will, financial resources, technological capacity, and social acceptance as critical requirements for the successful implementation of decarbonisation strategies in developing countries. The paper emphasises the need for international cooperation, capacity-building, and aligning decarbonisation goals with broader socio-economic objectives to ensure these strategies contribute meaningfully to sustainable development.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".