A Comparative Study on Environment Credit Risk Management of Commercial Banks in the Asia‐Pacific Region
Bibliographic record
Abstract
Abstract Environmental credit risk management (ECRM) is significant in the reduction of environmental risks for banks, the expansion of economic instruments for governmental environmental management and the promotion of green growth in the Asia‐Pacific region. In this paper, we reconstructed an evaluation criterion with 32 indicators for ECRM performance of banks, and selected 120 sample banks from 12 countries in this region for a comparative study. We conducted a gap analysis among banks with systematic ECRM and those with preliminary ECRM, suggesting that five indicators need to be improved by the former and 12 indicators caused gaps of the latter. We found banks in different countries with different ECRM performance levels: the Canadian, US and Japanese banks performed the best; the banks from Australia, Republic of Korea, China and Thailand had modest performance and the banks from the other five countries had a low level of performance. The influential factors of policy, voluntary code and green income incentive for banks' ECRM performance are discussed with the results of a correspondence analysis graph and policy practice in several representative countries. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".