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Record W1998839984 · doi:10.5539/enrr.v4n1p39

Importance of National Policy and Local Interpretation in Designing Payment for Forest Environmental Services Scheme for the Ta Leng River Basin in Northeast Vietnam

2014· article· en· W1998839984 on OpenAlexvenueno aff
Bac Viet Dam, Delia Catacutan, Hoang Minh Ha

Bibliographic record

VenueEnvironment and Natural Resources Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersInternational Fund for Agricultural Development
KeywordsPaymentEcosystem servicesService (business)Structural basinEnvironmental resource managementEnvironmental planningScheme (mathematics)GeographyEnvironmental scienceComputer scienceBusinessEcologyGeologyEcosystem

Abstract

fetched live from OpenAlex

The Leng River basin in BacKan province, northwest Vietnam hosts critical natural resources where lessons learnt from the pilot project of payment for forest environmental services (PFES) in Lam Dong and Son La provinces can be applied. PFES is broadly defined as an economic instrument that facilitates payments of forest environmental service flows to forest dwellers. The passage of a national PFES Decree in Vietnam where the K-factor framework was used to determine the payment level of environmental services created both opportunities and challenges in the design and implementation of PFES schemes.This paper presents how the national PFES policy was adapted, and how lessons in the pilot provinces were considered in developing a local PFES scheme. Important considerations and criteria for determining K-factors to standardize payments for similar environmental services, as well as the proposed PFES scheme for the Leng River Basin are discussed. The paper concludes that national policy framework and local interpretation of K-factors are crucially important in designing a PFES scheme that meets the realistic and pro-poor elements of PES. Finally, a PFES scheme must have detailed implementing guidelines that are developed with local stakeholders.

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)0.001
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 score0.096
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.260
Teacher spread0.243 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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