How Are Energy-Related R&D Investments Effective on Environment-Related Patents? Empirical Evidence from the USA and Canada
Bibliographic record
Abstract
All related economic actors have been interested in combating climate change as consistent with developing interest in environmental issues. In this context, R&D investment funds can be highly beneficial in developing environmental patents, which may have a key role in solving environmental problems. Accordingly, the study analyzes the marginal effect of sub-types of R&D investments on environment-related patents by focusing on the USA and Canada as the leading R&D investing countries, using data between 1990 and 2021, and adopting a kernel-based regularized least squares (KRLS) model. The results show that on the patents (i) R&D investments in cross-cutting technologies/research, nuclear, and renewable have a stimulating effect in the USA; (ii) R&D investments in renewable support the increase in Canada; (iii) in both USA and Canada, R&D investments in fossil fuels have a decreasing effect, whereas R&D investments in energy efficiency have no significant effect; (iv) Among all, R&D investments in cross cutting technologies/research (renewable) have the highest increasing effect on the patents in USA (Canada); (v) marginal effect of the sub-types of R&D investments on the patents varies across factors, countries, and percentiles; (vi) the KRLS model has a high prediction performance, reaching ~97.1%. Overall, the study emphasizes the average and pointwise marginal effects of R&D investments on the patents, which imply that R&D investments should be re-distributed by considering their effects on the patents so that a successful policy on environmental patents can be designed by benefitting energy-related R&D investments.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".