Status of Life Cycle Assessment (LCA) in Africa
Bibliographic record
Abstract
Life cycle assessment (LCA) has received attention as a tool to evaluate the environmental impacts of products and services. In the last 20 years, research on the topic has increased, and now more than 25,000 articles are related to LCA in scientific journals databases such as the Scopus database; however, the concept is relatively new in Africa, where the number of networks has been highlighted to be very low when compared to the other regions. This paper focuses on a review of life cycle assessments conducted in Africa over the last 20 years. It aims at highlighting the current research gap for African LCA. A total of 199 papers were found for the whole continent; this number is lower than that for both Japan and Germany (more than 400 articles each) and nearly equal to developing countries such as Thailand. Agriculture is the sector which received the most attention, representing 53 articles, followed by electricity and energy (60 articles for the two sectors). South Africa (43), Egypt (23), and Tunisia (19) were the countries where most of the research was conducted. Even if the number of articles related to LCA have increased in recent years, many steps still remain. For example, establishing a specific life cycle inventory (LCI) database for African countries or a targeted ideal life cycle impact assessment (LCIA) method. Several African key sectors could also be assessed further.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".