Environmental Compliance Reactions to R & D Practices and Intellectual Capital: A Worldwide Evidence
Bibliographic record
Abstract
Our ecosystem and mainly our natural resources represent a vital ecological portfolio. Environment is good for business at a time business is not keen to invest in compliance measures. Sustaining the environment is costly and adds additional burden. The burden not only financial, it is mainly technical and managerial. Producing renewable energy and maintaining freshwater resource, reducing electricity production and emissions depend also on R & D practices such as trademark and patent applications. This also needs engineers, technicians, scientists and human capabilities with continuous knowledge and intellectual capital. The main research hypothesis has tested the effect of environmental protection and compliance on the R&D in intellectual capital creation. Environmental reactions are measured by “renewable internal freshwater resources”, “electricity production from renewable sources”, “access to electricity” and “alternative and nuclear energy”, while R & D practices are measured by “trademark applications”, “technicians in R&D” and “patent applications”. Research hypotheses has been tested using panel regression analysis according to GMM technique. Using data of 94 countries during the period from 2001 to 2019, findings show that there are significant effect of “trademark applications” on “renewable internal freshwater resources” and of “technicians in R&D” on each of “electricity production from renewable sources” “access to electricity” and “alternative and nuclear energy”. Besides, “patent applications” seems to have significant effect on “alternative and nuclear energy”. Results have found that countries that have made environmental improvements are those who have invested in intellectual capital and made significant steps in improving their environmental R&D Capabilities.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".