How Green Economy Contributes in Decreasing the Environment Pollution and Misuse of the Limited Resources?
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
Green economy has invested in the sustainable development of the society across the globe. Therefore, the study has focused on differential ways that green economy provided for the reduction of misusing limited resources along with the reduction of environmental pollution. Since, the study has been conducted on the global issue, the nature of the analysis would be qualitative. The data has been collected from the previous studies on green economy. The results have shown the different factors that affect the society, which included wastes, toxic gases, and the hazardous solvents ecologically as well as economically. The implementation of green chemistry was the solution provided to eliminate poverty and pollution from the society. In the years 1990 and 2010, the emissions of non-methane compounds were increased by 71% and decreased by 4%. Whereas, the emissions of nitrogen oxides were increased by 62% and decreased by 3%. Moreover, intelligent usage of limited resources have provided better ways to increase economic growth and reduce toxins from the atmosphere. Adoption of green economy in the countries can be useful on the economic and social grounds as they helped in decreasing the environment pollution and along with the misuse of limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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