Incremental Advances to Address Challenges in Long-Term Strategic Energy Planning in LMICs
Bibliographic record
Abstract
The climate crisis requires developing countries to urgently decarbonize energy systems and mobilize finance while balancing competing priorities such as economic growth, energy security, environmental sustainability, and social development. However, many developing countries face challenges in developing long-term energy planning strategies, including limited capacity and reliance on external consultants. To address these issues, 21 international organizations and research institutions have developed five strategic principles for energy planning: national ownership, coherence and inclusivity, human capacity development, analysis robustness, and transparency and accessibility of data and tools. This paper discusses joint efforts to promote and apply these principles using science-based evidence and analytical modelling tools, such as the Open Source Energy Modelling System (OSeMOSYS) and the power system IRENA FlexTool. These tools are part of a suite of emerging modelling tools to support climate-compatible development policies and have been included in accessible teaching material, online courses, summer schools, and capacity development 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".