Bibliographic record
Abstract
Purpose Regulatory and institutional changes, restructuring and/or privatization of the erstwhile vertically integrated electricity networks have been adopted by all Sub‐Saharan African (SSA) countries in their pursuit of rural and urban electrification, poverty reduction and economic growth. But advances with the reforms remain limited and the results are at best debatable. The purpose of this paper is to examine the reasons for the unsuccessful implementation of deregulation in Sub‐Sahara electricity markets. Design/methodology/approach The paper examines the experiences with deregulation of the electricity industries in developed and developing economies and surmises on the factors that have contributed to the success of reforms in some industrialized countries and identifies the factors that have contributed to the failure of reforms in SSA. The “evidence‐based economics” (EBE) methodology is used to analyze the existing models of regulation and their differences particularly as they are practiced in SSA and developed economies. A gap analysis is realized by highlighting the differences between best practices and the existing level of knowledge. Two case studies are analyzed and the collection of information is assessed in a way that is useful for the development and implementation of the most appropriate models of regulation for SSA. Findings The paper finds that the current trend to the regionalization of the electricity markets in SSA and the creation of regional power pools make possible the creation of a genuine regional electricity market which would provide new opportunities for the adoption and adaptation of more advanced models of regulation (2‐G and/or 3‐G) similar to the ones currently employed by some developed economies in Europe and North America. To do so, regulators in SSA need to adopt a more dynamic approach to regulation. Research limitations/implications Given the comparative approach of this paper, it is not possible to prove that SSA countries will succeed in their electricity reforms by adopting the 2‐G and 3‐G regulatory models. Nonetheless, if they do follow the dynamic approach to regulation, as suggested in the paper, their chances to succeed are much better. Practical implications The analysis of this paper has major implications for governments, regulators, shareholders, customers and employees of the electricity industry. A better understanding of the reasons for the failure of previous reforms and the identification of major advantages and disadvantages of the electricity markets in SSA provide new opportunities and challenges. The success of the application of the 3‐G model may increase the competitiveness of the electricity industry and productive capacity of Sub‐Saharan countries. Social implications Electricity is an essential input in any industrial and commercial process. Its availability reduces costs, enhances productivity and creates jobs in other sectors. The social well‐being of Sub‐Saharan countries would increase by adopting the 3‐G model suggested in this paper. Originality/value To the best of the author's knowledge, there are no recent studies dealing with the same issues particularly for Sub‐Sahara Africa. This paper fulfils the gap that exists in the literature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".