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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".