Research on the Dual Effects of Corporate Physical and Transition Climate Risks on Total Factor Productivity
Bibliographic record
Abstract
This study empirically investigates the impacts of corporate climate risk, physical climate risk, and transition climate risk on firms’ total factor productivity (TFP), along with their underlying mechanisms, by employing a panel multidimensional fixed-effects model. The sample comprises Chinese listed companies spanning the period from 2007 to 2022. The findings reveal a significant positive effect of corporate climate risk and transition climate risk on corporate TFP, whereas physical climate risk exerts a substantial negative impact. Specifically, corporate climate risk and transition climate risk enhance TFP by elevating green technological innovation levels. Conversely, physical climate risk amplifies financing constraints, thereby diminishing TFP. Notably, these effects are accentuated in firms with higher return on assets, superior internal control quality, and greater institutional investor ownership, particularly among heavily polluting enterprises. Furthermore, while the positive effects of corporate climate risk and transition climate risk on TFP persist with a four-year lag, the negative impact of physical climate risk on TFP diminishes and becomes statistically insignificant over the same period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".