Time to say goodbye? The impact of environmental regulation on foreign divestment
Bibliographic record
Abstract
Abstract We look at divestments by foreign firms—a topic that has received comparatively little attention in the literature—and investigate how changes in the regulatory environment in the host country may impact on such divestment decisions. We use the implementation of China's two control zones (TCZ) policy as a “quasi‐natural experiment,” using detailed firm‐level combined with city‐level data for the empirical analysis. Our results show that the implementation of the TCZ policy has led to higher probabilities of divestments by foreign firms in cities and industries targeted by the TCZ policy. The mechanism behind this seems to be a TCZ‐induced increase in discharge fees and efforts to reduce SO2 emissions. Allowing for heterogeneity of effects, we find that the effect is particularly strong for firms from source countries with less stringent environmental regulation and those using less advanced technology. We also show that firms using intermediates from polluting industries also experience a higher probability of divestment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".