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Record W2588434581 · doi:10.5539/jas.v9n3p30

Evidence for Mangrove Restoration in the Mahakam Delta, Indonesia, Based on Households’ Willingness to Pay

2017· article· en· W2588434581 on OpenAlexvenueno aff
Heru Susilo, Yoshifumi Takahashi, Mitsuyasu Yabe

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMangroveLivelihoodContingent valuationSustainabilityWillingness to payRestoration ecologyEcosystem servicesBusinessGeographyEnvironmental resource managementNatural resource economicsEcosystemEnvironmental scienceFisheryEcologyEconomicsAgriculture

Abstract

fetched live from OpenAlex

Mangroves provide multiple benefits for local communities’ livelihoods. However, in the Mahakam delta mangroves have declined considerably. This study examines the factors affecting households’ willingness to pay (WTP) for mangrove restoration in three villages in the Mahakam delta and determines whether a mangrove restoration project would be viable and should be implemented in the study area or not. The contingent valuation method was applied through a double-bound dichotomous choice format to estimate the WTP for mangrove restoration. The results showed that over 80% of perception of respondents considered the benefits of mangroves were essential that associated with their livelihoods. Local residents tended to be willing to pay more for mangrove restoration when they acknowledged the benefits of mangroves and when they felt that the sustainability of mangrove ecosystems was their responsibility. The benefits transfer method was also used to estimate the costs and benefits of an ongoing mangrove restoration project in the study area. The benefits provided by mangroves, as estimated based on households’ WTP, clearly outweighed the costs for the mangrove restoration project. We conclude that mangrove restoration should be implemented in the study area by increasing local communities’ awareness and responsibility to protect and manage the mangrove a sustainable.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.189
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.036
GPT teacher head0.274
Teacher spread0.239 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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