Bibliographic record
Abstract
Purpose This paper analyzes the connection between the sustainability performance of Chinese banks and their financial indicators to explore whether sustainability regulations can be implemented without decreasing the financial performance of the banking sector. Design/methodology/approach The study examined reports and websites of Chinese banks, categorized different corporate sustainability aspects and conducted panel regression and Granger causality to analyze cause and effect variables. Findings The environmental and social performance of Chinese banks increased significantly between 2009 and 2013. Furthermore, a bi-directional causality between financial performance and sustainability performance of Chinese banks has been found. Based on institutional theory, this interaction may be influenced by the Chinese Green Credit Policy. Research limitations/implications The findings suggest that corporate sustainability performance and financial performance are not a trade-off but correlate positively. Further research is needed to analyze the effect of financial regulations, such as the Chinese Green Credit Policy. Practical implications According to the good management theory by Waddock and Graves (1997) that claims a positive impact of corporate social performance on financial performance, Chinese banks can invest in corporate sustainability to increase their financial success and re-invest parts of the additional returns – also called slack resources – in sustainability activities. Social implications Chinese banks are able to influence the economy to become greener and less polluting without sacrificing financial returns. Originality/value This is the first study to explore the sustainability performance of Chinese banks, including their products and services.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".