Analysis of the Factors Affecting the Choice of Whether to Internalize or Outsource the Task of Greenhouse Gas Inventory Calculations: The Cases of Indonesia, Vietnam, and Thailand
Bibliographic record
Abstract
Developing countries need to build long-term institutional capabilities for a national greenhouse gas (GHG) inventory under the transparency framework of the Paris Agreement. By selecting three Southeast Asian countries as the cases, Indonesia, Vietnam, and Thailand, the present study comparatively examined their institutional designs for producing the GHG inventories. They are common in terms that their national focal points make the overall coordination and other relevant line ministries provide activity data. A major difference exists regarding who is tasked to perform calculations of GHG inventories. By using the framework of Hood concerning the choice of whether to work through specific performance contracts or through direct employment, this study discussed that the variations between the countries may be associated with their differences in the following two factors: One is the number of potential service providers, as expressed by the number of GHG inventory experts as registered in the roster of the United Nations, and the other is the level of uncertainty about how the task is to be done, as measured by a share of the agriculture, forestry and other land use sector in the national GHG inventory. The development of the endogenous research base can contribute to the long-term improvement in GHG inventories. The finding has implications for assistance in building the transparency-related capacity. Development cooperation with developing countries may extend to identifying the categories that are crucial for their current GHG inventories and collaborating relevant research activities with national experts, including young researchers.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".