Company’s Competitiveness Enhancement for Thai Agribusiness through the Clean Development Mechanism (CDM)
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it