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Record W2077959193 · doi:10.1142/s1464333209003427

COMPARATIVE ANALYSIS OF SEA LEGAL REQUIREMENTS AND INSTITUTIONAL STRUCTURE IN CHINA (MAINLAND), CANADA AND THE UK (ENGLAND)

2009· article· en· W2077959193 on OpenAlexaboutno aff
Kai-Yi Zhou, William R. Sheate

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsMainland ChinaChinaStrategic environmental assessmentGovernment (linguistics)Sustainable developmentMainlandPolitical scienceCorporate governanceEnvironmental planningHarmonious SocietyEnvironmental resource managementEnvironmental protectionBusinessEnvironmental impact assessmentPublic administrationGeographyLawEnvironmental science

Abstract

fetched live from OpenAlex

After the Law of the People's Republic of China on Environmental Impact Assessment (the EIA Law) came into effect in China (mainland) in September 2003, and notably in 2006, the Chinese government released a series of laws and regulations to strengthen strategic environmental assessment (SEA) application in China. SEA is acknowledged by the Chinese central government as a tool to help achieve sustainable development, and it is employed as an instrument to emphasise environmental protection and "scientific outlook of development" during the course of the current rapid industrial and urban development, to build a "harmonious society", and ultimately to achieve sustainable development. This paper compares the Chinese (mainland) SEA system, its legal requirements, institutional structure and procedural framework with the UK and the Canadian systems, to provide a comprehensive overview of the current status of the institutional and governance arrangements of the Chinese SEA system. The conclusions point to some possible explanations for less than optimum implementation of SEA in China, and suggestions for ways to improve the Chinese SEA system in the future.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.277
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2009
Admission routes1
Has abstractyes

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