The Impact of Poverty on the Environment: Surprising Findings from the Indian Case
Bibliographic record
Abstract
In the 1980s a powerful school of thought, propagated by the Brundtland Commission Report and seconded by powerful think tanks, developed which asserted that poverty was a major cause for environmental degradation. This implied that significant alleviation of poverty would also substantially reduce environmental degradation. Some reasons were given for this view, prominent among these being the compulsion of poorer households to mine natural capital to meet their needs, sometimes in a dirty manner. In course of time, a less recognized counter-school emerged which pointed out the flaws in the Brundtland hypothesis: the greater gasoline consumption of richer households, their greater possession of consumer durables sourced from natural capital, and the higher power of the rich to mine limited and open access natural capital for commercial gain, among others. The debate needs to be obviously resolved through quantitative studies, hitherto lacking in the Indian case. Using results from NSSO data for 3 recent years and 4 sources of dirty fuel we show that there is a general tendency for the non-poor to consume more of these fuels than the poor. This is a surprising result and it shows that poverty alleviation, though desirable, is probably not even a partial cure for environmental degradation. Some explanations for this result, based on the relative magnitudes of clean fuel consumption by the non-poor and poor, are provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".