Endogenous Growth and Environmental Kuznets Curve: Lessons from FDI Impact on Economic Growth in Sub-Saharan Africa
Bibliographic record
Abstract
Purpose: This study aims to determine the influence of Foreign Direct Investments (FDI) on economic growth in Sub-Saharan Africa (SSA). It examines the endogenous growth theory and the Environmental Kuznets Curve (EKC) theory, and how they relate to the regional data.Method: Using panel quantile autoregression models, this study explores the relationship between FDI inflows into SSA with energy consumption, carbon emissions, and economic growth. The study is based on data from 1975 to 2018.Result: The study findings conclusively demonstrate that foreign direct investment has a significant impact on the economic growth of the SSA region. Furthermore, the study reveals that energy consumption and carbon emissions in the SSA have consistently increased throughout the study period, with foreign direct investment being identified as the primary driver of this trend. These findings are consistent with the Environmental Kuznets Curve (EKC) hypothesis, as well as the endogenous growth theory, which suggests that FDI operations can have negative consequences on the host environment.Practical Implications for Economic Growth and Development: The study suggests that Sub-Saharan Africa should manage FDI carefully to balance economic growth with environmental sustainability by promoting green investments and creating an investment-friendly environment.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".