La institucionalización del medio ambiente en la agenda internacional y el nacimiento de mercado de bienes y servicios ambientales: el caso de algunas economías de Asia Pacífico
Bibliographic record
Abstract
Environmental goods and services (EGS) are a thriving market that emerged in the 1990s as a result of international agreements to curb environmental degradation and climate change. What started as a struggle between environmental advocates and trade advocates paved the way for negotiation between two closely interconnected international regimes: climate change and trade. The current international debate on the EGS market focuses, on the one hand, on pointing to the poor role that this activity has played in curbing the deterioration of the environment. On the other hand, in the important economic success that the commerce of this type of products is having. Today, the economic spill of the EGS trade is just over two trillion dollars and is expected to increase. Australia, Canada, China, Taiwan, Hong Kong, Japan, South Korea, New Zealand, Singapore and the United States are among the 17 most prosperous economies of this type of market. The supremacy in the international competitiveness of the Asia Pacific Rim over this niche provides elements to expect a relative decrease in environmental impact, because this region emits 63% of all greenhouse gases (GHS) that are generated on the planet. This paper analyzes the importance of the BSA market in a world whose main challenge should be to solve environmental deterioration. However, quantitative data to measure environmental deterioration and economic prosperity show that, at least, in the short term, the environment has not benefited.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".