Testing the Environmental Kuznets Curve Hypothesis with Bird Populations as Habitat‐Specific Environmental Indicators: Evidence from Canada
Bibliographic record
Abstract
The traditional environmental Kuznets curve (EKC) hypothesis postulates that environmental degradation follows an inverted U-shaped relationship with gross domestic product (GDP) per capita. We tested the EKC hypothesis with bird populations in 5 different habitats as environmental quality indicators. Because birds are considered environmental goods, for them the EKC hypothesis would instead be associated with a U-shaped relationship between bird populations and GDP per capita. In keeping with the literature, we included other variables in the analysis-namely, human population density and time index variables (the latter variable captured the impact of persistent and exogenous climate and/or policy changes on bird populations over time). Using data from 9 Canadian provinces gathered over 37 years, we used a generalized least-squares regression for each bird habitat type, which accounted for the panel structure of the data, the cross-sectional dependence across provinces in the residuals, heteroskedasticity, and fixed- or random-effect specifications of the models. We found evidence that supports the EKC hypothesis for 3 of the 5 bird population habitat types. In addition, the relationship between human population density and the different bird populations varied, which emphasizes the complex nature of the impact that human populations have on the environment. The relationship between the time-index variable and the different bird populations also varied, which indicates there are other persistent and significant influences on bird populations over time. Overall our EKC results were consistent with those found for threatened bird species, indicating that economic prosperity does indeed act to benefit some bird populations.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".