Economic Growth Decoupling Municipal Solid Waste Loads in Terms of Environmental Kuznets Curve: Symptom of the Decoupling in India
Bibliographic record
Abstract
The Environmental Kuznets curve is a hypothetical relationship between various indicators of environmental degradation and income per capita. In the early stages of economic growth degradation and pollution increases, but beyond some level of income per capita it tends to reverse, so that at high economic growth leads to environmental improvement. This implies that the environmental impact indicator is an inverted U-shape with income per capita. With respect of the income, hypothetical turning point would eventually occur with the characteristic of Environmental Kuznets Curve (EKC). . India also serves a kinds of improvement or opportunities that could be pursued in other developing countries. This article focuses mainly on evidence of decoupling between economic growth and municipal solid waste generation in developing countries such as India, in which relevant data are more readily available. India is an interesting subject of study because of its large territory, rapid economic growth and differentiated regions. In the first phase, this article analyzes the course of GDP per capita with municipal solid waste generation from 1947 to onwards to 2004. In this article in the second phase, regression analysis among the municipal solid waste management factors is conducted on state wide data set in order to find the key stage for efficient environmental improvement.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".