Revisiting the pollution-haven vs. porter hypotheses: Empirical evidence from Nigeria
Bibliographic record
Abstract
Since the 1970s, the role of trade liberalization and foreign direct investment in promoting environmental sustainability has been a hot topic in academics. While some research supports the Porter hypothesis, others support the pollution-haven hypothesis. Accordingly, this study aims to determine whether the pollution haven hypothesis holds by examining how trade openness and foreign direct investment affect Nigeria’s environmental sustainability for the period of 1981 to 2021. By deploying the dynamic ordinary least square (DOLS) estimation technique, the study outcomes indicate that trade openness and foreign direct investment have a negative and significant long-term effect on Nigeria’s greenhouse gas emissions. Therefore, the results of this study support the Potter hypothesis, which holds that emerging nations become centers of advanced and cleaner technology as a result of trade liberalization and foreign direct investment. As a result, the study suggests that the Nigerian government should support the creation of compressed natural gas (CNG) stations and the switch to CNG-powered vehicles. The Nigerian government can also promote investment in the green energy industry by offering tax holidays and other benefits to companies in this field. Furthermore, there should be a widespread public education campaign on the threat posed by global warming and the necessity of planting trees to mitigate the effects of climate change and discourage tree-cutting.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".