Exploring the Nexus among Environmental Pollution, Economic Growth Energy Use and Foreign Direct Investment in Sub Sahara Africa
Bibliographic record
Abstract
This research seeks to enhance the current literature by exploring the nexus among environmental contamination, economic growth, energy use and foreign direct investment in 6 Selected Sub-Saharan-African-nations for a time of 34 years (1980-2014). By applying, panel unit root (CADF and CIPS, Cross-sectional independence test), panel cointegration (Pedroni and Kao cointegration test, Panel PP, Panel ADF), Hausman poolability test and an auto-regressive distributed lag procedure in view of the pooled mean group estimation (ARDL/PMG), experimental findings discloses that alluding to the related probability values, the null hypothesis of cross-sectional independence for all variables is rejected because they are not stationary at levels but rather stationary at their first difference. The variables are altogether integrated at the same order I(1). Findings revealed that there is a confirmation of a bi-directional causality between energy use and CO2 in the short-run as well as one-way causality running from energy use to CO2 in the long run. There is additionally a significant positive outcome and uni-directional causality from CO2 to foreign direct investment in the long-run yet no causal relationship in the short-run. An increase in energy use by 1% causes an increase in CO2 by 49%. An increase in economic growth by 1% causes an increment in CO2 by 16% and an increase in economic growth squared by 1% diminish CO2 by 46%. The positive and negative impact of economic growth and its square approve the EKC theory. To guarantee sustainable economic development Goal, more strict laws like sequestration ought to be worked out, use of sustainable power source ought to be stressed. GDP ought to be multiplied to diminish CO2 by the utilization of eco-technology for instance carbon capturing, to save lives and also to maintain a green environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".