Foreign direct investment, gross domestic product and carbon dioxide emission in sub-Saharan Africa: A disaggregated analysis
Bibliographic record
Abstract
This paper investigates the heterogeneous effect of sector-level foreign direct investment on carbon dioxide (CO2) emissions in 36 sampled SSA countries from 1990 to 2016. By using the system GMM estimation technique, the study reveals that industry FDI increases CO2 emissions validating the pollution haven hypothesis while Agric FDI and service FDI reduce CO2 emissions. In general, a U shape hypothesis holds for Agric FDI and CO2 emissions, but an inverted U shape for industry FDI and Industry CO2 emissions and a linear and negative relationship between services FDI and services CO2 emissions. Thus, there is a need to evaluate the environmental cost of investment in the industrial sector before granting foreign investors a permit to operate. In addition, there should be specific policies to attract FDI into the agriculture and services sectors to benefit from the positive spillover effect of transfers of cleaner technology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".