Soil pollution and site remediation policies in China: A review
Bibliographic record
Abstract
It was not until the 1980s that China’s policy makers became aware of the detrimental impacts on urban health from soil pollution as a result of industrial waste emissions. For the past three decades, the Chinese government has strived to prevent and control industrial pollution. Setting appropriate environmental policies is the key to mitigating the legacy of industrial waste emissions accumulated for three decades. In this paper, we review the development process by outlining the evolution of the policies and the resulting legal infrastructure in terms of acts, regulations, ordinances, and standards. Deficiencies of the existing policies are identified. In the early stages, environmental policies were fragmented, consisting of single-purpose laws that are narrowly focused. With time, these policies gradually evolved to become better integrated and comprehensive management plans. However, the laws emphasize contaminated site restoration instead of preventing soil pollution. The legal framework shows that the policies that are in place often lack clear mandates because the authorizations are piggybacked on environmental acts and regulations that do not directly address issues of soil pollution. Furthermore, implementation plans are impractical due to outdated soil quality standards, unclear soil cleanup goals, unenforceable liability and supervision mechanisms, limited funding, lack of transparency and public outreach, and the unreliable financial and technical capabilities of the remediation industries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".