Bibliographic record
Abstract
This paper estimates the relationship between the level of economic growth and the extent of environmental pollution for a wide range of both industrialized and emerging countries. Using data from 28 countries over the period 1975-1998, the paper finds support for an inverted U- shaped economic growth-pollution relationship. Using the aggregate level of CO2 as the measure of pollution and real GDP per capita as the measure of economic growth, the following countries appear to be operating on the rising portion of the inverted U relationship: India, China, Nigeria, and Thailand. On the other hand, the following eight countries appear to lie on the declining portion of the inverted U- relationship: Brazil, South Korea, Spain, United Kingdom, Canada, France, United States, and Japan. Furthermore, ten of the remaining fourteen countries, with per capita GDP below $4, 000 exhibited a positive regression coefficient, although none were statistically significant. The turning point appears to occur at a level of GDP per capita, perhaps as low as $3, 000-4, 000. The paper explores the energy prospects and environmental polices of three of the worlds largest and fastest growing economies, China, India, and Brazil. These three countries are found to play a key role in the empirical findings of this study. The study demonstrates that growth in knowledge and improvements in environmental technology can compensate for an inevitable increase in the use of natural resources in production.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.004 | 0.001 |
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".