Impact of Heterogeneous Environmental Regulation on Carbon Emissions: Firm-Level Evidence from China's Manufacturing Industry
Bibliographic record
Abstract
Government implementation of environmental regulatory measures is typically an efficacious means of mitigating carbon emissions from firms, and the diversification of regulatory forms provides producers with diversified choice space so that enterprises can choose ecologically favorable production methods according to their own circumstances. Based on the unbalanced panel data of 2140 listed manufacturing companies in China from 2011 to 2019, this study uses a fixed-effects model to investigate the impact and mechanisms of command-and-control, market-based, and public participation environmental regulations on corporate carbon emissions. The study finds that all three types of environmental regulations significantly reduce corporate carbon emissions, and the results are robust. Mechanism tests suggest that environmental regulations can reduce corporate carbon emissions by increasing research and development (R&D) investment. Heterogeneity analysis indicates that the carbon reduction effect of environmental regulations is more pronounced in samples of enterprises in the eastern region, heavily-polluting industries, and highly marketized industries. Further analysis reveals that digital transformation negatively moderates the impact of command-and-control and public participation environmental regulations on corporate carbon emissions. The aforementioned findings offer policymakers significant empirical evidence on effectively promoting the development of low-carbon initiatives and enhancing coordination in China’ s government regulations for environmental governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".