Foreign direct investment, economic growth and environmental quality in Africa: revisiting the pollution haven and environmental Kuznets curve hypotheses
Bibliographic record
Abstract
Purpose This study examines the environmental effects of foreign direct investment (FDI) inflows and economic growth by revisiting the pollution haven and EKC hypotheses in the context of Africa. Design/methodology/approach The underlying relationships are unravelled with the help of quantile regressions for a panel of 46 African countries over the 1996–2022 period. Findings The results show that FDI inflows significantly increase CO2 emissions, supporting the pollution haven hypothesis (PHH) in Africa. There is also evidence of the N-shaped EKC hypothesis. When analysing different income groups, PHH and EKC remain consistent, except in low-income countries where only PHH is observed. However, the environmental impact of FDI inflows and economic growth decreases at higher quantiles. These findings suggest that policymakers in Africa should strengthen environmental regulations and adopt common environmental standards that encourage green technologies. Originality/value This study fills an empirical research gap by comprehensively examining the relationship between FDI, economic growth, and environmental degradation in African countries. Unlike previous studies focused on the inverted U-shaped EKC, our research reveals the existence of an N-shaped EKC in Africa.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".