An international comparison of the outcomes of environmental regulation
Bibliographic record
Abstract
Whilst there is much discussion about the stringency of environmental regulations and the variability of industrial environmental performance in different countries, there are very few robust evaluations that allow meaningful comparisons to be made. This is partly because data scarcity restricts the ability to make 'like for like' comparisons across countries and over time. This paper combines data on benzene emissions from Pollution Release and Transfer Registers with data on industrial production from oil refineries to generate normalized measures of industrial environmental performance across eight Organisation for Economic Cooperation and Development countries and the EU-15. We find that normalized emissions levels are improving in nearly all countries, and that there is some convergence in emissions performance between countries, but that there are still very significant variations across countries. We find that average emissions levels are lower in Japan and Germany than in the USA and Australia, which in turn are lower than in Canada and the EU-15, but we note that average emissions in the EU-15 are significantly affected by particularly high emissions in the UK. These findings have significant implications for wider debates on the stringency of environmental regulations and the variability of industrial environmental performance in different countries.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".