Green Business Models and the Green Finance Landscape
Bibliographic record
Abstract
The present report includes two parts. The first part is a literature review that covers specificdimensions of knowledge about Green Business Models (GBMs) in respect of conceptualdefinitions and the assessment of GBMs. The review categorises information into areas thatare deemed of interest for any practitioner wishing to support the development and growth ofgreen business models. The second part provides an overview of the ‘green financelandscape’ and classifies green finance from a structural and from a quantitative perspectivewithin the overall financial market. It further provides an overview of relevant stakeholders inthis landscape, and analyses their potential role for financing and developing green businessmodels. Both parts aim at providing background knowledge necessary to find a commonunderstanding across work processes and project partners of the Green-Win project,facilitating the further work process within the project, in particular the identification andevaluation of concrete GBMs.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".