Life cycle assessment of renewable energy technologies in Northern Africa: A critical review
Bibliographic record
Abstract
The need to transition from fossil energy sources, a major contributor to greenhouse gases has become more critical than ever in the face of rising climate threats. Consequently, there has been a wider acceptance and deployment of renewable energy sources. Life Cycle Assessment (LCA), on the other hand, is a standardized tool that has been deployed to comprehend the environmental effects of these alternative energy systems. Most studies conducted in LCA on renewable energy are mostly featured in regions like Europe, Asia, North and South America. While leaving a substantial gap in the volume of work conducted so far in Africa, especially concerning North African countries, a region that shares the largest energy-related CO₂ emissions in the continent. Thus, an in-depth review article is required to discuss the state-of-art on life cycle assessment of renewable energy technologies in North Africa, highlighting the region’s peculiarities, outlook, and future prospects. Aspects including the study’s overview, goal, scope, kind of renewable energy sources, functional unit, system boundary, and impact categories are included in this review. Results from this review reveal that studies on LCA in this area of work are still at their early stages, accounting for only 2% of the total LCA research in the continent, with solar and bioenergy constituting most of the case studies with 27% and 33% of the total research outlook. In terms of GWP contribution, bioenergy and wind energy recorded the most and least impact in the region, respectively. Findings from this review can help policymakers and researchers have a broader understanding of the environmental contributions of various renewable energy deployed in the region while seeking to improve and regularize the LCA methodology as a standard tool for evaluation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".