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Record W2040747003 · doi:10.5539/jsd.v4n2p80

Company’s Competitiveness Enhancement for Thai Agribusiness through the Clean Development Mechanism (CDM)

2011· article· en· W2040747003 on OpenAlexvenueno aff
Amornwan Resanond, Thanwa Jittsanguan, Damrong Sriphraram

Bibliographic record

VenueJournal of Sustainable Development · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsClean Development MechanismAgribusinessBusinessKyoto ProtocolSustainabilityRevenueGreenhouse gasProductivityEnvironmental economicsNatural resource economicsAgricultural scienceEconomicsAgricultureEnvironmental scienceFinanceEconomic growth

Abstract

fetched live from OpenAlex

Ratification to the Kyoto Protocol allows Thailand to voluntarily participate in the Clean Development Mechanism (CDM). CDM not only promotes environmental integrity but also offers business sustainability, which will be then able to enhance company’s competiveness. Due to these enthusiastic impressions, number of CDM registered projects in Thailand has been increased from 5 to 40 projects between 2005 and 2010, respectively. Several business sectors in Thailand have been moving their position toward to CDM including agribusiness sectors namely sugar, tapioca, rice, agrofuel, livestock and forestry. This study carries out an in-depth analysis on the correlations between CDM mechanism and agribusinesses in Thailand through the competitiveness indicator with a view to affirm that the level of competitiveness in Thailand agribusinesses can be enhanced through the CDM scheme. Productivity improvements in term of technology and project financial before and after the CDM application are served as competiveness indicators. The study concludes that CDM offers opportunity for company to move toward a better technology with a better operation performance and greenhouse gas reduction. The improvement of productivity level is found through an anticipated revenue stream from carbon credits which delivered project’s rate of return well above company’s hurdle rate of approximately 10-16% for 4 types of agribusinesses i.e. palm oil, rice mill, ethanol and tapioca. Despite abovementioned benefits,, CDM still faces a lot of challenges including a requirement on a substantial amount of investment which requires for starting the CDM process, risk of local and international approvals, deliverable risk, and uncertainty on CDM processing time and the future of CDM after the first commitment period, 2012. These challenges, however, can be overcome by well-disciplined preparation and better understanding on CDM process and requirements.

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)0.003
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.226
Teacher spread0.191 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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