The Role of the People's Bank of China and Financial Supervisory Authorities for Greening China's Financial System
Bibliographic record
Abstract
Climate change has become a significant threat to global economies in recent years. The idea that governments, banks, and regulators should work together to combat climate change and promote sustainable financing is gaining traction. The world’s central banks and financial regulators must take action on climate change and support sustainable financing. For example, consider the proliferation of regulatory bodies like the Sustainable Banking Network and central banks. The literature review is descriptive and relies on secondary sources. The report covers the first four quarters of 2021 and summarizes the monetary authority’s policy operations (goals and achievements). This study includes all scheduled banks and non-banking financial institutions in China in 2021, both public and private, due to their roles in green and sustainable finance. We spoke with four seasoned market analysts and four active and retired government officials and policymakers from central banks and financial supervisory authorities. While China’s economic development is undoubtedly threatened by climate change, the country has little choice but to continue to rely on its time-tested approaches to creating riches. The country cannot progress otherwise. Good news: The People’s Bank of China is making eco-friendly banking the norm in China’s financial sector.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".