Fine me if you can: Fixed asset intensity and enforcement of environmental regulations in China
Bibliographic record
Abstract
Abstract Why do some firms face more environmental regulatory actions than others? We present a theory focusing on firm‐fixed asset intensity. High fixed asset intensity makes a firm less mobile. A less mobile firm cannot present a credible exit threat, making it more susceptible to stringent enforcement. Analysis of key‐monitored firms in Jiangsu province, China of 2012–2014 shows that higher fixed asset intensity is associated with more pollution levies and a higher chance of receiving a punitive action. This result holds in a battery of robustness checks and an instrumental variable analysis. Furthermore, our 2018 online survey of Chinese firm managers shows that those from high fixed asset intensity firms indeed consider their firms less mobile and they pay more environment‐related operating costs. Finally, data from 2004 Chinese Firm‐Level Industrial Survey demonstrate that fixed asset intensity is positively associated with pollution levies in a national sample of 201,926 manufacturing firms.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".