Green Technology Transfers and Border Tax Adjustments
Bibliographic record
Abstract
We develop a two-country general equilibrium model of foreign assistance tied to environmental clean-up in the presence of transboundary pollution. The recipient country generates pollution as a by-product in the production of a ‘dirty’ good, which it consumes as well as exports to the donor country. In contrast to the literature which typically treats aid as a monetary transfer, we assume that foreign aid consists in a transfer of environmental technology that lowers the cost of public clean-up in the recipient country. We highlight the fact that the marginal propensities to consume the polluting good in the donor and recipient countries are driving the terms of trade effect at work in our model. The environmental and welfare outcomes are influenced by the direct, terms of trade and abatement effects of the transfer. We show that such tied aid may be Pareto improving if the clean-up effect of the foreign aid is strong enough to compensate for the donor’s monetary and terms of trade losses. We finally analyze the effects of the green transfer combined with an appropriate border tax adjustment. Contrary to intuition, we find that green technology transfers and border tax adjustments are not complements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".