Factibilité De Valorisation Des Pulpes De Café Dans La Transition Energétique
Bibliographic record
Abstract
The ongoing reliance on wood energy is a major obstacle to the energy transition, while also exacerbating deforestation and ecosystem degradation. In this context, the recovery of agricultural residues offers a sustainable alternative that is still largely untapped. Among these residues, coffee pulp, a by-product representing 40 to 50% of the weight of pulped cherries, is an abundant but largely neglected resource. This study therefore presents a review of the literature aimed at assessing the feasibility of its energy recovery. The methodology was based on a systematic literature search in academic and institutional databases, covering the period 2010–2025 and resulting in the selection of 64 relevant documents. The results highlight: (i) a significant but poorly quantified availability of pulp; (ii) current uses limited to artisanal composting, which also generates significant environmental externalities; (iii) three main technological pathways for recovery, such as methanization, pyrolysis, and briquette production, all of which are technically feasible but face financial, institutional, and social obstacles; and (iv) the decisive role of cooperatives, local governance, and community acceptability in the sustainable adoption of these innovations. As a result, energy recovery from coffee pulp represents a strategic opportunity to reduce pressure on forests, diversify energy sources, and strengthen the socio-economic sustainability of Congolese agricultural systems.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".