Bibliographic record
Abstract
This thesis comprises four empirical essays on environmental and development economics. In the first chapter, we examine to what extent individual and contextual level factors influence individuals to contribute financially to prevent environmental pollution. We find that rich people, individuals with higher education, as well as those who possess post-materialist values are more likely to be concerned about environmental pollution. We also observe the country in which individuals live matter in their willingness to contribute. More precisely, we find democracy and government stability reduce individuals’ intention to donate to prevent environmental damage mainly in developed countries. The second chapter deals with the relation between economic growth and environmental degradation by focusing on the issue of whether the inverted U-shaped relation exist. The study discloses no evidence for the U-shaped relation. However, the empirical result points toward a non-linear relationship between environmental degradation and economic growth, that is, emissions tend to rise rapidly in the early stages with economic growth, and then emissions continue to increase but a lower rate in the later stages. The third chapter investigates the long-run as well as the causal relationship between energy consumption and economic growth in a group of Sub-Saharan Africa. The result discovers the existence of a long-run equilibrium relationship between clean energy consumption and economic growth. Furthermore, the short-run and the long-run dynamics indicate unidirectional Granger causality running from clean energy consumption to economic growth without any feedback effects. The last chapter of this thesis concerns with convergence of emissions across Canadian provinces. The study determines convergence clubs better characterizes Canadian’s emissions. In other words, we detect the existence of segmentation in emissions across Canadian provinces.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".