Advancing green finance: a review of sustainable development
Bibliographic record
Abstract
Abstract This study comprehensively reviews the relationship between green finance and sustainable development, specifically focusing on combatting climate change and achieving carbon neutrality. Utilizing a narrative review methodology, the study examines a range of scholarly articles and publications to identify key themes, findings, and future directions in green finance. The review emphasizes the crucial role of substantial investments in green and low-carbon initiatives to address climate change effectively and promote sustainable economic growth. It highlights the necessity of robust regulatory frameworks that facilitate the availability of green finance and the integration of carbon–neutral practices. Additionally, the paper explores the potential of impact investing, wherein investors accept lower financial returns in exchange for non-financial benefits in green finance. It underscores the influential role of institutional ownership in guiding companies toward enhanced environmental and social performance. Moreover, integrating environmental, social, and governance (ESG) factors in investment decisions is critical for sustainable finance. Addressing the intersection of climate change and risk management, the review highlights the implications of environmental risks on financial decision-making. Effective communication strategies can raise public awareness and support for climate policies. The study concludes by calling for collaboration, further research, and policy measures to advance green finance and foster sustainable economic growth. It recommends aligning financial incentives with sustainable outcomes, fostering transparency, and incorporating social equity in green finance initiatives to contribute towards achieving sustainable development goals and promoting a greener future.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".