Bibliometric Analysis of Green Finance and Climate Change in Post-Paris Agreement Era
Bibliographic record
Abstract
Climate change is undeniably one of the long-term challenges confronting humanity across the globe. Various nations have taken initiatives that help reduce greenhouse gas emissions to the environment as well as accelerate financial flows to clean and sustainable projects. The paper provides an overview of green finance after the Paris Agreement by adopting a bibliometric analysis of the selected literature. The study reviewed the literature from the Web of Science database between 2015 and 2022. Data cleaning, formatting, and analysis was performed using VOSviewer and R-studio. Our study indicates increased scholarly interest on the issue of green financing. Most scientific research has been published in climate policy and sustainability journals but lacks mainstream interest in economic and finance journals. Based on our results, it is recommended that further studies on green financing be carried out from the economic and financial perspective using quantitative approaches to supplement the existing literature and provide a wider view to policy makers and regulators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.033 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".