Bibliographic record
Abstract
Two central topics in recent rounds of international trade negotiations have been environmental concerns, and services trade. While each is undoubtedly important, they are unrelated. In this paper I show that the services-environment link is small, for two reasons. First, services account for only a small fraction of overall pollution. For none of five major air pollutants does the service sector account for even four percent of total emissions; for three of the five services account for less than one percent. Second, those service industries that do pollute are the least likely to be traded internationally. Those services for which the U.S. collects and publishes international trade data - presumably those services that are traded internationally - are less polluting than services for which trade data do not exist - presumably because the services are not traded. Even if we limit attention to the services that are traded across borders, the service industries most intensively traded are the ones that pollute the least. The bottom line is simple. International services trade bears little relation to the environment, because services in general contribute relatively little to overall pollution, and those industries that are traded internationally are among the least polluting.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.108 | 0.014 |
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".