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Record W2343447295 · doi:10.5539/mas.v10n7p22

Economic Valuation of Oil Palm Plantation Investment in Swamp Area of Tapin Regency, South Kalimantan, Indonesia

2016· article· en· W2343447295 on OpenAlexvenueno aff
Hamdani Hamdani

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSwampValuation (finance)Total economic valueAgricultural economicsNet present valueInternal rate of returnBenefit–cost ratioInvestment (military)Contingent valuationPresent valuePalm oilAgricultural scienceForestryBusinessNatural resource economicsEnvironmental scienceEconomicsEcosystem servicesEcosystemGeographyWillingness to payFinanceProduction (economics)Ecology

Abstract

fetched live from OpenAlex

This study describes the companies’ feasibility determination on oil palm plantation investment. We also compare it with the oil palm plantation in swamp area, by considering economic, social and environmental aspects. We used total valuation method to valuing the environmental value of swamp area. Otherwise, investment analyzed by the model of benefit cost ratio (BCR), net present value (NPV), and internal rate of return (IRR). The study showed that the financial valuation of investment feasibility that is made and proposed by the plantation companies to banks (creditors), did not account the land value (economic, social and environmental values) of swamp ecosystems. The assessment on swampland value obtained Rp232, 570,833,400 or US$24,481 million per year for 10,000 ha area – US$2,448 per ha each year. Although the total value of economic environment is included in the valuation element of oil palm plantation investment in swampland, it still result insufficient value. However, by sensitivity analysis scenario of swampland plantation would become sufficient if the Crude Palm Oil (CPO) price is above US$1300 per ton. It also has a second scenario if the environmental cost can be pressed optimally into Rp9, 025,541 or US$950 per ha per year.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.331

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 head0.020
GPT teacher head0.231
Teacher spread0.211 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

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