Review of the ADB Clean Energy Program
Bibliographic record
Abstract
The review of the Clean Energy Program of the Asian Development Bank (ADB) looks back into a decade's worth of ADB projects that supported renewable energy generation, energy efficiency, and cleaner fuel.It examines ADB's achievements against the targets set under the results framework of its 2009 Energy Policy.The targets include minimum annual investments in clean energy projects, capacity installed using renewable energy, electricity saved, carbon dioxide emissions reduced, and number of households provided with new and improved electricity connections.It also explores what ADB needs to do to stay relevant amid the evolving operational context to support the ADB Strategy 2030, and the global commitments of the Sustainable Development Goals and the Paris Agreement.The ADB Clean Energy Program seeks to increase efficiency in energy, transport, and urban development; help countries adopt renewable energy sources; and improve access to energy particularly for poor and remote regions.Between 2008 and 2018, ADB invested $2 billion annually on average, which met the minimum annual target set for 2013 onward.A total of $22.12 billion was invested in clean energy for 2008-2018, of which 58.7% was in renewable energy, 38.0% in energy efficiency, and 3.3% in cleaner fuel.For the same period, over 22 million households were provided with new and improved electricity connections, and a cumulative reduction of 144.25 million tons of carbon dioxide equivalent was registered from the clean energy projects.The targets under the 2009 Energy Policy Results Framework were mostly achieved.While ADB invested considerably in renewable energy and energy access projects, investments for energy efficiency need to be further expanded which will in turn, sustain the outcomes of renewable energy and energy access projects.Aside from the electricity sector, more efforts in efficient and cleaner heating, cooling, and cooking will help better living conditions and increase the region's mileage in providing modern energy access and reducing carbon emissions.This review of the Clean Energy Program is a good reference on the performance of ADB energy projects and shows that the ADB energy sector can effectively contribute to providing affordable, reliable, sustainable, and modern energy for all; combating climate change; pursuing a sustainable low-carbon future; and achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".