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Record W1583662284 · doi:10.18174/121837

Greening Small and Medium-Sized Enterprises: Evaluating Environmental Policy in Vietnam

2006· dissertation· en· W1583662284 on OpenAlexfundno aff
Lê Văn Khoa

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsnot available
FundersDanish International Development AgencyEuropean CommissionKementerian Sumber Asli dan Alam SekitarUnited States Agency for International DevelopmentInternational Development Research CentreStyrelsen för Internationellt UtvecklingssamarbeteMinisterio de Ciencia, Tecnología y Medio AmbienteUnited Nations Development ProgrammeU.S. Department of Transportation
KeywordsRelocationHo chi minhBusinessEnvironmental pollutionVietnameseModernization theoryControl (management)Ecological modernizationEnvironmental planningSmall and medium-sized enterprisesViet namOrder (exchange)Economic growthPoliticsEnvironmental protectionPolitical scienceGeographyEconomicsEconomySocioeconomicsFinanceManagement

Abstract

fetched live from OpenAlex

Small and Medium-sized Enterprises (SMEs) contribute considerably to the economic and social development of bothViet Namand Ho Chi Minh City (HCMC). But at the same time this sector causes severe environmental problems. Up till now, no systematic review and assessment has been carried out on the City's environmental pollution control programmes towards SMEs. Given that, the research applied the concepts of Political Modernization and developed a suitable environmental policy evaluation methodology in order to analyze three cases of prominent pollution control measures: Relocation of polluting enterprises programme, end-of-pipe treatment solutions and the cleaner production approach.Based on those results and experiences drawn from literature, this research aimed to contribute to the improvement of existing environmental policies towards SMEs in HCMC and the development of new feasible, effective and suitable environmental policies for greening the SME sector.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.192
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2006
Admission routes1
Has abstractyes

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