The Evolution of Corporate Shadow Banking Behavior Under Climate Risk: Insights from Resilience and Capital Structure
Bibliographic record
Abstract
In the context of green transformation, climate change and its economic implications are attracting increasing attention. Based on the Trade-off Theory framework, this study examines how climate risk affects firms’ shadow banking activities in emerging markets. This study focuses on emerging market economies, using a panel dataset of Chinese A-share non-financial listed firms from 2007 to 2023 to systematically examine the relationship between climate risk and shadow banking activities, that is, financing conducted outside the formal banking system. The empirical findings reveal that climate risk significantly dampens the shadow banking activities of non-financial firms. Further mechanism analysis suggests that this effect operates through two key channels: the weakening of corporate resilience and adjustments in capital structure decisions. Moreover, the analysis uncovers heterogeneous impacts of climate risk on shadow banking, depending on the quality of information disclosure, industry characteristics, and the degree of financing constraints. This research provides new insights into the evolution of corporate financial behavior under climate risk and offers empirical evidence to support firms in optimizing their financial strategies and enhancing their financial risk management capabilities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".