Drivers of Energy Efficiency in West African Countries
Bibliographic record
Abstract
The ideas behind the energy intensity turn out to be fundamental to the transition to a low-carbon society. This transition requires efforts both in low-emission technology choices and in moving away from path dependency. This entails significant costs in the short and long run for African countries. This paper analyses drivers of energy intensity in 13 West African countries focusing on education and investment over the period 1990 to 2015. Panel data technics are suitable for the analysis. After testing for stationarity and cointegration, we use the Fully Modified Ordinary Least Square method (FMOLS) with cross-sectional independence and the Common Correlated Effect Pooled Mean Group approach (CCEPMG) for cross-sectional dependence. Our main findings show robust evidence that education, energy price and income above a certain threshold play an important role in improving energy intensity in the long run. By controlling for cross-sectional dependence, we find that investment and the urbanization rate become positive and statistically significant. Our empirical findings show that increasing the education level is important to improve energy use, calling for policy action encompassing both sectoral and global measures that is crucial to achieve energy efficiency.
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 | 0.003 |
| 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".