Ghana’s energy access journey so far: a review of key strategies
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a comprehensive review of the programmes being implemented with a view to ascertaining if they adequately address the energy needs of the poor more holistically and sustainably. Design/methodology/approach The content of this desktop review is based on information collected through a review of available energy policy documents from the Ghana Government and related governmental agencies, such as the Energy Commission and Ghana Statistical Services, international energy-related agencies, such as the International Energy Agency (World Vision, 2013), as well as other related web searches. Additionally, global and Sub-Saharan African energy access documents were reviewed by analysing secondary data from the World Bank and UN policy reports, statistical data, strategies, regulations, protocols and other related documents (World Vision, 2013). Furthermore, some policy documents on energy access and usage were explored mainly from Senegal and Ghana to ascertain governments’ policies, regulations and strategies in the implementation of energy access policies. Findings The paper offers all the various strategies being implemented in an attempt to establish a foothold on the problem of affording the poor with clean and affordable energies. The paper also presents the rich experiences of Senegal in its bid to see expanded access in liquefied petroleum gas usage by residential consumers. Originality/value The paper provides some policy and theoretical implications for improving Ghana’s energy access.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".