Is East Asia Industrializing Too Quickly? Environmental Regulation in Its Special Economic Zones
Bibliographic record
Abstract
East Asia is undergoing its own Industrial Revolution. Special economic zones (SEZs) are playing a key role in its economic transformation. However, industrialization has brought great environmental concern. Over recent decades, China, the Philippines, South Korea, and other newly industrializing economies in East Asia have designated special areas for foreign investment and export production to which have been conceded favourable investment and trade conditions, and often exemption from certain kinds of regulation. "Race to the bottom" and related theories of the effects of inter-jurisdictional competition for investment predict that environmental regulation would be compromised in SEZs. Contrary to such hypotheses, there is some evidence that environmental regulation in East Asia's industrializing zones is stricter than in other parts of their economies, and that foreign investors are sometimes more strictly regulated than local businesses. The experience of East Asia's SEZs - particularly in China - suggests we need to re-think how we conceptualise the relationships between environmental law and foreign investment in the context of rapidly industrializing developing countries. This experience also reveals persistent weaknesses in the legal systems of East Asia and the fragility of the rule of (environmental) law. To address this, further reform to the environmental regulation of SEZs should be grounded in more wide-ranging and basic improvements to administrative regimes, policy instruments and access to justice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".