Power Shift: Emerging Prospects for Easing Electricity Poverty in Myanmar With Distributed Low-Carbon Generation
Bibliographic record
Abstract
Myanmar is among the least electrified countries in the world. Prospects are examined to assess the opportunity for a paradigm shift to deliver rapid relief from electricity poverty and change the trajectory of national development. Expansion of electricity supply is currently planned around the model of large power plants and a national grid. Experience elsewhere suggests it will take several decades for this model to supply electricity to most of the population and come at considerable cost to environmental quality, particularly rivers. However, there are signs that a distributed generation model could be widely developed over several years. Already, local markets are supplying domestic electricity generation systems. In 2012 development partners identified opportunities for interventions to reduce electricity poverty. Several successful commercial models for supplying affordable electricity to poor people in neighbouring countries could be adapted to Myanmar. Furthermore, market and technological trends in distributed power generation are coherent with national policy goals of securing energy independence and increasing use of renewable sources. Given the scale of unmet demand and clean energy resources, particularly solar and biomass, an opportunity is open for alleviating electricity poverty in years, rather than decades. If a hybrid centralized-distributed power system emerges over the next few years Myanmar may be better placed to resist and adapt to climate change and global shifts in energy markets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".