Technological change and international interaction in environmental policies
Bibliographic record
Abstract
This paper considers the impact of differences in endogenous technological change between two countries on global pollution emissions under international strategic interaction in environmental policies. First, we demonstrate that an environmentally lagging country's technology may continue to advance through a learning-by-doing effect until it exceeds the environmental friendliness of a leading country that initially had the cleanest technology (i.e., environmental leapfrogging could occur). Whether a country eventually becomes an environmentally leading country depends on the country size and its awareness of environmental quality. Second, we find that global emissions fluctuate despite the fact that environmental technology advances in both countries. Global emissions eventually become constant because both countries cease to tighten environmental regulations when their technologies are sufficiently clean. The final emissions might be larger than emissions in early stages of adjustment under dirty technologies. If environmental leapfrogging frequently occurs, both countries possess similarly clean technologies, thereby reducing long-term global pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".