THE IMPACT OF CLIMATE CHANGE ON THE RISK-TAKING LEVEL OF CHINESE COMMERCIAL BANKS: EMPIRICAL EVIDENCE FROM CHINESE LOCAL COMMERCIAL BANKS
Bibliographic record
Abstract
China’s risk disasters caused by climate change have increased the risk-taking level of China’s commercial banks, posing a threat to the stable development of financial markets. Based on the data from 152 commercial banks in China from 2011 to 2021, this paper uses the fixed effect model to analyze the effect and mechanism of China’s climate change on the risk-taking level of Chinese commercial banks. The main conclusions are as follows. (1) Climate change has significantly improved the risk-taking level of Chinese commercial banks. The results remain significant under the robustness and endogenous tests, such as dealing with endogenous problems, changing variables and adjusting sample intervals. (2) The results of heterogeneity analysis show that under different regions and different types of conditions, the impact of climate change on the risk-taking level of China’s commercial banks is heterogeneous, and the impact is stronger in urban commercial banks and eastern China. (3) The direct economic losses caused by natural disasters caused by climate change affect the risk-taking level of Chinese commercial banks; however, the adjustment of ex ante disaster insurance can weaken the impact of climate change on the risk-taking level of Chinese commercial banks. This paper studies the impact of climate change on the risk-taking level of Chinese commercial banks from the perspective of bank risk-taking level, which is of great significance to enhance the risk prevention awareness of Chinese commercial banks in response to climate change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".