Analysing the Impact of Anthropogenic Factors on the Environment in India
Bibliographic record
Abstract
Using carbon dioxide as a surrogate measure of various environmental impacts, this paper analyses the effects of anthropogenic factors on CO2 emissions in India. The paper uses the STIRPAT model with data for 1960-2007. The results show that urbanisation has the largest potential negative effects on the environment, followed by population, service sector, industrial sector and GDP per capita. While analysing the potential effects of various anthropogenic factors on the environment, accounted for by average annual growth rates, population emerges as the single largest factor contributing towards emissions, followed by urbanisation (the degree of contribution to change in CO2 emissions by these factors are 33.8% and 29.7% respectively). Hence, there is a need for serious consideration of policy changes with regard to demographic and urban planning in India in order to reduce the effects of these factors on the environment. For instance, India could adopt a two-child policy like the one-child policy in China to control population growth. Unplanned and haphazard urbanisation can lead to inefficient use of energy resources that may hinder the efforts to reduce carbon dioxide emissions in India. Hence sustainable urban planning across Indian states is very much essential for better management of environmental resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".