The AIIB and Sustainable Infrastructure: A Hybrid Layered Approach
Bibliographic record
Abstract
Abstract This essay examines the AIIB’s approach to investing in sustainable infrastructure (SI). The main argument is that the AIIB is taking a hybrid layered approach to SI investment. On the one hand, the Bank is following the ‘do no harm’ pathway of the traditional MDBs, of using safeguards to avoid and compensate adverse social and environmental impacts. On the other hand, it is pursuing innovation, and a more transformative agenda, that encourages investment in SI projects that generate broader, positive developmental spillovers. In pursuing its hybrid agenda, the AIIB is developing its own multi‐layered safeguards regime to ensure smooth and strong SI investment, and alignment between the Bank’s overarching strategic policy, its ESF, sector and thematic strategies, and projects. The analysis also details three ways in which the AIIB stands out from other MDBs for how it is ‘trying new things’ with its approach to SI investment: first, is how ‘economic sustainability’ is one of the main considerations for project selection alongside environmental, social and governance sustainability; second, how the Bank has integrated social and indigenous and oversight safeguards into its ‘environmental and social framework’ (ESF); third, its creation of large‐scale public‐private Funds for green finance and climate finance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".