Technology cooperation for sustainable energy: a review of pathways
Bibliographic record
Abstract
Policy makers, practitioners and academics agree that addressing climate change requires global efforts and that one pillar of any effective approach must include actions undertaken by developing countries. A key mechanism in which developing countries can be engaged is through collaboration to elicit more use of sustainable energy technologies in a region, organization, or community's energy portfolio. Approaches have tended to emphasize the role of the public and/or private sector. An alternative view has also emerged, termed as community approach, noting the absence of civil society actors in the predominant pathways that often characterize technology cooperation in developing countries. Local engagement is important, as it is often these players [whether local governments, community groups, and/or organizations (e.g. hospitals, schools)] who ultimately reap the benefits and/or bear the costs of these technologies. Recent emphasis centres on an enabling environment, recognizing the importance of creating markets for technology diffusion. Tackling the uptake of sustainable energy in a systematic way, emphasis is placed on policies, actors, and institutions. Building on these foundational frameworks, this paper scrutinizes actors and their relationships with a finer grain to better understand sustainable energy technology uptake. The notion is that sustainable energy technology use will increase with the active engagement of local players (earlier on, in a more meaningful way) in the technology cooperation process. This article is categorized under: Energy Policy and Planning > Climate and Environment Energy and Development > Economics and Policy
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".