Addressing Challenges in Long-Term Strategic Energy Planning in LMICs: Learning Pathways in an Energy Planning Ecosystem
Bibliographic record
Abstract
This paper presents an innovative approach to address global energy planning challenges in low- and middle-income countries (LMICs). It proposes an integrated framework consisting of an international enabling environment, a delivery ecosystem, and a community of practice. The framework aims to enhance national agency and coordination, overcome limitations of outsourcing, and improve the accessibility and usability of consultant outputs. Five strategic principles for energy planning in LMICs are introduced, prioritizing national ownership, coherence, inclusivity, human capacity development, analysis robustness, transparency, and accessibility. Two key knowledge products, the Open-Source Energy Modelling System (OSeMOSYS) and the power system Flexibility Tool (IRENA FlexTool), are highlighted as examples from the delivery ecosystem. To ensure sustainability, a community of practice called OpTIMUS is introduced. Preliminary outcomes from the deployment of these pathways show promise and further investigation is needed to assess long-term impacts, scalability, replication, and deployment costs. (Full Abstract available in the manuscript)
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.010 |
| 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".