Fine me if you can: Fixed asset intensity and enforcement of environmental regulations in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it