Reconciling the Environmental Kuznets Curve with the Free Rider Problem
Bibliographic record
Abstract
I would like to first give a very special thanks to my advisor, Christopher Timmins, Ph.D. His keen insight and passion concerning issues within environmental economics served to motivate and inspire my research. I am also indebted to Kent Kimbrough, Ph.D. and the other members of his honors seminar course for their unyielding patience in helping me cope with the seemingly insurmountable obstacles that arose while formulating this study. I would finally like to express my gratitude for the unwavering moral support I received from fellow economics student Evan Beard, as well as my other friends and family members, throughout the development of my thesis. The current paper studies the Environmental Kuznets Curve (EKC) hypothesis, which claims a parabolic relation exists between per capita GDP and environmental degradation. This would suggest a developing nation could expect to increase pollution significantly during the beginnings of industrialization and then, as the country began switching to a service-oriented economy, could expect pollution levels to begin to eventually drop with increasing per capita income. There has been much debate over said issue and the main goal of this paper is to study how the environmental free rider problem may play a role in plaguing the validity of the EKC model. Environmental free riding would allow nations to externalize some of the costs of their pollution such that it may never become economical to lower pollution levels despite rising income. My research focuses on an empirical study of carbon dioxide and sulfur dioxide emissions and ultimately supports the hypothesis that the effects of the free rider problem can be expected to spuriously affect the validity of the EKC model for certain pollutants. 3
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".