A TEST OF THE ENVIRONMENTAL KUZNETS CURVE USING LONG‐TERM WATERSHED INPUTS
Bibliographic record
Abstract
The Environmental Kuznets Curve (EKC) postulates that environmental quality is initially degraded with increasing economic prosperity, until reaching some turning point where environmental quality improves with increases in wealth. Tests using environmental indicators beyond those that affect human health have been less supportive of the EKC idea. We hypothesize that environmental changes impacting human health are more likely to show evidence of an EKC than variables less directly related to human health. Furthermore, the EKC hypothesis assumes that ecological damage is reversible, and we argue that this assumption is not always valid. We test for evidence of an EKC in Dane County, Wisconsin, using non‐point‐source pollution time series data for Lake Mendota throughout the 20th century. We examine metals deposited in lake sediments (cadmium, chromium, copper, and lead), other non‐point‐source pollutants such as sulfur and dissolved reactive phosphorus (DRP), and water clarity (measured by Secchi disk depth). We relate changes in ecological variables to changes in real wealth per capita (RWPC) in Dane County over time. The EKC did not describe the relationship between all ecological and economic indicators tested; however, several variables were related to RWPC. Our strongest results (for Secchi depth, DRP, and copper) show increasing pollution with increasing wealth. Secchi depth and DRP are related to water quality and clarity, which have value to society but less direct, immediate health consequences. P pollution may also be fairly irreversible over short time scales. The best models and plots for cadmium, chromium, and lead suggest improvements in environmental quality with increases in RWPC, although these trends were not statistically significant. Results for sulfur were inconclusive. Overall, wealth did not explain much of the variability in any of the ecological variables examined here, suggesting that consideration of additional factors are necessary to explain their dynamics. Economic prosperity cannot be expected to improve all aspects of the environment, but may be biased toward aspects that are ecologically reversible phenomena or of concern to human health.
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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 | 0.016 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".