Green Securitization, A Legal Structure Currently Unfit for Ecological Transition
Bibliographic record
Abstract
Ever since the 2007-2008 financial crisis, the European banking sector and its regulating authorities have been seeking to revive securitization markets in Europe. The most recent attempt in this direction is the inscription of green securitization in the European sustainable finance agenda. Building on Katharina Pistor's work on the legal coding of capital as well as Eve Chiapello's work on financialization as a socio-technical process, our proposed contribution focuses on the legal structuring of green securitization. Based on the legal documentation and sustainability frameworks of four recent green securitization deals tied to energy-efficiency home improvements, it critically examines the new kind of financial circuits that is being constructed to channel funds towards projects deemed to create positive impacts on the environment. Our analysis shows that green securitization's current legal structure fails to properly incorporate sustainability considerations, whether in its contractual terms, financial metrics, or parties involved.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".