Bibliographic record
Abstract
It is a matter of apprehension that wealthy countries are contributing huge to global emission and more responsible for producing greenhouse gases in the atmosphere. There is direct intervention of economic activities like industrialization, urbanization and infrastructure development over carbon footprint across the world and economically developed nations tend to have higher footprints as compared to less developed countries. India, the second largest populous nation is facing dual challenges, one hand, the need to fulfill the energy requirement for development and on the other hand, the global climate challenge. The country is third largest emitter of CO2 and fifth largest economy by nominal GDP in the world. But its per capita emission is much lower than that of developed countries and even it is below the average per capita emission of many developing countries. The present paper is an attempt to map the carbon footprints across the country in respect to the economic status. The paper reveals that there exist huge disparities of economic resources and developmental activities, and based on these the carbon footprint is also varied widely. The footprint is significantly high in developed and urbanized states across the country. Economically developed western and southern part emits more than the underdeveloped region of Bihar, Uttar Pradesh, Jharkhand and Odisha. A correlation has been drawn to map the carbon footprint per capita in respect to poverty ratio and a negative relationship exists among most of the districts, as higher carbon footprint has been reported in the lower poverty stricken districts. The overall low per-capita carbon footprint of the country is due to its huge population is living with nominal amount of energy. The fossil fuel is the major source of energy in the country and for equitable economic development, the huge energy is needed. Hence, to meet the global climate challenge, the country can accomplish the energy requirement by reducing emission of conventional sources and look forward for renewable energy resources, so that the development should not be hindered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".