Local content and linkage development in African energy transitions: lessons from oil and gas
Bibliographic record
Abstract
Expectations of development and jobs associated with the shift to Renewable Energy (RE) are significant in lower-income African countries. As a result, local content policies (LCPs) are currently spreading from the petroleum sector into solar and wind. As with oil and gas, the purpose of LCPs in RE is to prevent the formation of enclaves dominated by foreign multinational corporations with limited involvement of domestic firms, few economic linkages to other sectors, and few local jobs. Due to their novelty, the outcomes of such interventions in RE are still uncertain and under-researched. Based on a combination of research undertaken by the authors and reviews of the relevant literature on local content experiences in the petroleum sector and nascent experiences in RE, this paper explores how LCPs can produce the predicted ‘virtuous circles’ from RE investment in lower income countries. Outcomes are likely to differ according to context as well as policy. Therefore, we argue that, while lower-income African countries can benefit from LCPs in RE, experiences from oil and gas suggest that their effectiveness will vary depending on the character of their resource and the associated scale of operations, the pre-existing competencies and maturity of the sector in the country concerned, and the design and enforcement of LCPs, which in turn are affected by the country’s broader political-economy dynamics. A second argument is that countries should weigh the costs of pursuing linkage development, which are often passed on to host-country governments, against what they can realistically achieve.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".