Environmental Best Practices, It Begins with Us: Business, Local Governments, and International Community Should Work Together
Bibliographic record
Abstract
The paper provides some explanations and best practices for two questions: What factors influence the adoption of environmental policies and regulations as a strategic asset? How do local governments better manage their environmental policies on a global basis? The United Nations has raised international awareness and has made their best effort to implement such plans to resolve climate change and global warming concerns. There are various talks about the connection between business and geopolitics in regard to climate change and environmental responsibility. Through these talks, we find that national culture and political forces can substantially affect all policy functions and there are many ways the national culture and geopolitics can affect the adoption of environmental policies. Interestingly, the environmental policies seem to favor rather big companies and developed countries that have the financial resources to adopt the policies, while small companies and developing countries are seemingly left in the dust. Having a strong understanding and flexible solution to the problem is immensely required. Bringing all stakeholders into the environmental conversation can greatly benefit business, local governments, and international community as well. The findings of the study may help local governments formulate better effective environmental policies complying with the international standards.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.028 | 0.041 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".