Financial feasibility assessment of adopting active controlled atmosphere containers for exporting highly perishable fruits during COVID-19: case of Taiwanese atemoya
Bibliographic record
Abstract
Purpose This study deals with attenuating the risk of relying on a single export market, which was heightened by the outbreak of the COVID-19 pandemic. It focuses on Taiwanese atemoya (a fruit with short storage life) and the adoption of active controlled atmosphere (CA) containers, a new technology which lengthens storage time for other export markets. This study looks at the financial feasibility of the technology's first ever use in atemoya exports. Design/methodology/approach Apart from the standard financial assessment tools—like net present value (NPV), internal rate of return (IRR), benefit-cost ratio (BCR) and payback period (PBP)—this study calibrated five different scenarios based on data gathered from relevant market agents including suppliers, exporters, customs brokers and technology developer. Findings Due to the high profit margin and low investment cost, the use of active CA containers for long-haul exports of this highly perishable fruit is found both technically and financially feasible, despite the generally higher operational cost during the pandemic. Research limitations/implications This study looked at three specific export markets: Malaysia, Dubai and Canada. Results here may lack generalizability in other markets, although it is believed that slight deviations would not invalidate the conclusions of this research because short, medium and long distances were all covered therein. Originality/value This paper studies the first time that active CA is used for export of atemoyas to expand existing markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".