Green Light or Green Burden: ESG’s Dual Effect on Financing Constraints in China’s Heavily Polluting Industries
Bibliographic record
Abstract
Using a firm-level panel of China’s heavily polluting industries from 2014 to 2023, this paper employs two-way fixed-effects regressions and a battery of robustness checks to examine how ESG performance affects corporate financing constraints and the channels through which effects operate. We uncover a paradox: overall ESG performance is associated with reduced financing constraint, whereas the environmental subcomponent alone significantly aggravates firms’ financing difficulties. Moderating analyses show that stricter regional environmental regulations and higher persistence in firms’ innovation outputs weaken the easing effect of aggregate ESG performance and may even fully offset it under certain conditions. Mechanism tests reveal that ESG mitigates constraints mainly by enhancing corporate reputation and curbing green agency costs. Heterogeneity analyses further indicate that the environmental-induced tightening effect is more pronounced in state-owned enterprises, firms in eastern provinces, and those located in regions with lower levels of new-quality productivity. These findings point to a trade-off between the short-term compliance costs of environmental investment and the longer-run signaling and informational benefits of ESG disclosure. Policy implications include the need for targeted green-finance support, improved ESG transparency and verification, and measures to accelerate innovation pathways that shorten the payback period for environmental investments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".