Impact of Environmental Protection Regulations on Corporate Performance From Porter Hypothesis Perspective: A Study Based on Publicly Listed Manufacturing Firms Data
Bibliographic record
Abstract
“Porter Hypothesis” believes that environmental protection regulations contribute to cleaner production and green technology innovation which benefit to enhance manufacturing firm performance . We take China’s new “Environmental Protection Regulations (2015), as a quasi-natural experiment, using A-share listed companies in Shenzhen and Shanghai in 2012–2017 as a research sample. Using the propensity score matching and double difference (PSM-DID) method, we empirically test the impact of environmental regulations on the financial performance of these companies. The results show that the new Environmental Protection Law has significantly improved corporate profits of large enterprises large firms. Different from the innovation mechanism emphasized in the literature based on the Porter hypothesis, we find that “Compliance cost heterogeneity” caused by the scale difference of firms better explains the impact of environmental regulations on the profit margin of listed manufacturing firms. Overall, this study contributes novel insights about the economic consequences of environmental regulation and establishes an initial foundation for investigating environmental regulation from the perspective of compliance cost heterogeneity.
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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 | 0.004 | 0.010 |
| 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 | 0.001 | 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".