Environmental regulations and green innovation: The role of trade and technology transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a theoretical model that predicts an inverted U-shaped relationship between green innovation and environmental regulation under free trade. Our theory also determines the conditions under which international technology transfers increase green innovation. The predictions are tested empirically by estimating a fixed effects Poisson model with data for a panel of OECD and BRICS countries over the period 1990 to 2019. The predicted inverted U-shaped relationship is confirmed by the empirical results when using the Environmental Policy Stringency Index (EPS) and its components as proxies for environmental regulations. The empirical results also show that technology transfers increase green innovation at any given level of environmental regulation. Moreover, we allow for heterogeneous effects for OECD and non-OECD countries and find that while implementing stricter environmental regulations in non-OECD countries increases green innovation, the reverse is likely to hold for most OECD countries. When distinguishing by type of regulation, our findings show that market-based regulations are more effective in non-OECD countries for fostering green innovation, while non-market-based regulations are more effective in OECD countries. The main policy implication is that the type of environmental policies through which countries aim at achieving zero-net emissions have different implications depending on their stage of development in the presence of international trade.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it