The Impact of Foreign Direct Investment on CO2 Emission
Bibliographic record
Abstract
In the literature, the impact of Foreign Direct Investment (FDI) on carbon dioxide (CO2) emissions is explained by two different hypotheses: Pollution Halo and Pollution Haven Hypothesis. While Pollution Halo hypothesis states that FDI provides advanced technology to countries and accordingly decreases CO2 emissions, Pollution Haven Hypothesis indicates that there is a positive relationship between FDI and CO2. In this regard, in this study, the impact of FDI on CO2 emissions in the selected 10 of G-20 countries in the period of 1970-2010 is investigated by using panel data analysis. The empirical findings show that panels have cross-section dependence and these two panels are stationary in different levels. Moreover, the existence of long term relationship between panels is found by using Durbin Hausmann panel cointegration test. The results of the study also show that while Pollution Halo Hypothesis is valid for USA, France and Argentina, Pollution Haven Hypothesis is valid for UK, Canada, Australia, South Africa, Italy, Mexico and Saudi Arabia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".