Trade Openness, Foreign Direct Investment and Environmental Sustainability Nexus in Nigeria
Bibliographic record
Abstract
Abstract Subject and purpose of work Although foreign direct investment has the potential to promote sustainable economic growth, research shows a troubling pattern: some countries that attract these investments become “pollution havens” for developed nations. On the other hand, various researchers are of the notion that FDI has the potential to promote sustainability if there are stringent environmental regulations. This has led to a serious debate between the “Pollution Haven” and “Porter” hypotheses. Accordingly, the purpose of this study is to determine which of these hypotheses holds, by examining the impact of trade openness and foreign direct investment on Nigeria’s environmental sustainability. Materials and methods The variables of interest are total greenhouse gas emissions, foreign direct investment (FDI), trade openness, access to electricity, access to clean fuels and technology, and urban population. The Dynamic Ordinary Least Squares (DOLS) estimation technique was deployed in this study. Results The study’s findings indicate that foreign direct investment (FDI) has a statistically significant negative long-run effect on Nigeria’s overall greenhouse gas (GHG) emissions. This robust result, with a coefficient of −0.10478 and a probability of 0.0012, lends strong support to the Porter Hypothesis. While trade openness also exhibits a negative long-run association with GHG emissions, its effect was not found to be statistically significant, showing a coefficient of −0.00166 and a probability of 0.4122. Conclusions As a result, the report suggests that the Nigerian government supports 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 operating 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".