Bibliographic record
Abstract
The sixth edition of the Global Green Finance Index (GGFI 6) was published on 27 October 2020. GGFI 6 provides evaluations of the depth and quality of the green finance offerings of 74 major financial centres around the world. The GGFI serves as a valuable reference into the development of green finance for policy and investment decision-makers. The GGFI is updated and published every March and September. 120 financial centres were researched for GGFI 6 of which 74 now feature in the index. The GGFI is compiled using 135 instrumental factors. These quantitative measures are provided by third parties including the World Bank, the Economist Intelligence Unit, the OECD and the United Nations. The instrumental factors are combined with financial centre assessments provided by respondents to the GGFI online questionnaire. GGFI 6 uses 4,219 assessments from 617 respondents. The Results Of GGFI 6 include: Amsterdam retained its leading position in the depth index. Zurich has risen to second place. In the quality index, these positions are reversed, with Zurich leading and Amsterdam second. San Francisco was the leading centre outside Western Europe, ranking seventh for depth and equal 10th for quality. New entrant, Helsinki, ranked 19th equal for depth and 12th for quality. On depth, San Francisco and Oslo moved into the top 10, while Montreal and Hamburg fell back. On quality, Oslo rose nine places to fourth, Munich and San Francisco moved into the top 10, and Hamburg and Vienna fell back. Narrow margins separate centres at top of the tables. Among the top 10 centres the spread of ratings is 31 out of 1,000 for depth (44 in GGFI 5) and 43 for quality (44 in GGFI 5). Western Europe Western Europe continues to improve its ratings across depth and quality, with all centres receiving improved ratings for both depth and quality, although a number of centres fell in the rankings as other centres improved faster. Oslo moved into the top 10 for depth and quality. Stockholm, Vienna, Brussels, Hamburg, Frankfurt, Madrid, Rome, Milan, Jersey, Isle Of Man, and Malta fell in the rankings for both depth and quality. North America USA centres have gained ground on Canadian centres in GGFI 6. San Francisco replaced Montreal as the top ranking centre for depth and continued to lead the region for quality, now featuring in the top 10 globally in both rankings. Vancouver also moved up in both measures, and now takes second position in both measures in the region. Montreal, Toronto, and Calgary fell in the rankings for depth. Toronto also fell in the quality rankings
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.034 |
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".