Advancing green finance: a review of climate change and decarbonization
Bibliographic record
Abstract
Abstract This paper comprehensively reviews the interconnections between climate change, decarbonization, and green finance. The urgency of addressing climate change and its catastrophic consequences needs to focus on green finance as a vital tool in the global struggle against environmental damage. Green finance involves supplying investments, loans, or capital to support environmentally friendly activities, facilitating the transition to a more sustainable future. This review explores the theoretical frame of reference for green finance, including its impacts on climate change, decarbonization of economies, carbon-stranded assets, risk management, renewable energy, and sustainable economic growth. Additionally, it examines regional focuses in Asia, such as the importance of green finance in China and the beliefs and challenges of green finance in Bangladesh. The review also discusses future directions and recommendations for advancing green finance. The review examines the current research in green finance and how it can address environmental challenges and promote sustainable development. More research needs to be conducted in mainstream economics and finance journals to bridge the knowledge gap and foster broader scholarly engagement in green finance. Researchers, policymakers, investors, and stakeholders will receive help from the study's reliable and robust insights into combating climate change and promoting sustainable development.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".