Econometrics Analysis of the Relationship between Climate Change and Economic Growth in Selected West African Countries
Bibliographic record
Abstract
Linkages between Climate Change, Economic Growth and Poverty Reduction have become increasingly popular in local and international communities. This is due to the fact that we are currently facing pressing issues about climate change and poverty reduction effects in our planet. In this paper an empirical testing of the effects of Climate Change, Economic Growth and Poverty Reduction was carried out. Panel estimation methods of fixed effect, random effect, and panel unit root test-fisher type with trend and constant were applied. From the results, shows that economic growth has a negative and highly significant effect on the growth of poverty in the selected West African countries. Using growth rate of economics as dependent variable, the result shows that growth of poverty is highly significant. The population living in rural areas is significant with growth of poverty and highly significant with growth of food security. The policy recommendation is that the government of the west African countries should put in place strategies to reduce poverty, climate change effects on economics growth by following measures; to have strong institution and avoidance of corruption.Such strategies contain to counter climate change effects and increase the resilience of the economy, society and country in general.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".