A Data Mining Approach to Green Financial Product Innovation and Assessment of Environmental and Economic Benefits
Bibliographic record
Abstract
In the current period, green finance has become an inevitable trend in the development of the financial industry. The study collects the audience demand of green financial products through Octopus collector, uses micro-word cloud analysis system for data de-weighting and Chinese word segmentation, and calculates the keyword weights in the words using TF-IDF algorithm, and realizes the identification of green financial product innovation and economic benefits by combining with multi-dimensional innovation map. Subsequently, the indicators on green financial product innovation and environmental economy from 2007 to 2022 are combed, and a VAR-based econometric model is established to analyze the impact relationship between green financial product innovation and environmental economic benefits. The results show that when the lag period is 10 periods, the contribution of environmental economic benefit itself to the change of environmental economic benefit tends to be 23.79%, while the contribution of green investment products, green bond products and green insurance products to environmental economic benefit tends to be 9.72%, 20.23% and 21.83%, respectively. Green product innovation has a certain influence on the fluctuation of environmental economic benefits, and green bond products and green insurance products have a greater impact on environmental economic benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".