Environmental regulations, R&D intensity, and enterprise profit rate: Understanding firm performance in heavy pollution industries
Bibliographic record
Abstract
With the worldwide green revolution, especially “pollution prevention and control” as one major strategy, environmental issues have received more and more attention. Environmental regulations, as an institutional norm, directly or indirectly regulate corporate behavior. Therefore, it is significant to examine the relationship between corporate performance and environmental regulations. In this paper, we conduct an empirical study on the relationships among environmental regulations, R&D intensity, and enterprise profit rate. Our data are from the 395 A-share companies in heavy pollution industries listed on the Shanghai and Shenzhen Stock Exchanges in China from 2008 to 2017. Our methodologies include Ordinary Least Squares mixed regression, quantile regression, and Generalized Method of Moments estimation. Our main research findings include the following. First, environmental regulation and R&D intensity both have a positive impact on corporate profit rate at the 1% significance level. Second, there are “threshold” effects on the promotion of corporate profit rate by environmental regulations and R&D intensity. Third, the product of environmental regulation and R&D intensity has a positive impact on corporate profit margin at the 1% significance level. Fourth, the impacts of environmental regulations and R&D intensity on corporate profit rate vary at different quantiles. Finally, R&D intensity is a partial mediation variable in the relationship between environmental regulations and enterprise profit rate. In terms of policy insights, our results suggest that the government formulate appropriate environmental regulations and enhance the support for enterprise R&D to stimulate technological innovation in the heavy pollution industries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".