Sustainability and Performance of Natural Adhesives in Humid Tropical Climates: A Systematic Review and Meta-Analysis with Case Evidence from Nigeria
Bibliographic record
Abstract
Gum arabic, cassava starch, chitosan, lignin are natural adhesives that use renewable sources, and are gaining momentum as substitutes to synthetic adhesives in response to environmental concerns. Yet, to be effective in humid tropical climates, which are characterised by high relative humidity (>80%), high temperatures (25-35 C) and moisture, their effectiveness needs to be systematically tested. This is a review of evidence on performance measurements (bond strength, durability) and sustainability (environmental impact, economic viability), including meta-analysis and a case study of Nigeria. Following PRISMA 2020, searched PubMed, Scopus, Web of Science, Google Scholar, and African Journals Online between January 1990 and August 2025. Eligibility Studies on natural adhesives in humid/tropical conditions that have quantitative results. Records were screened by two reviewers (kappa=0.87); quality determined with Newcastle-Ottawa Scale and Cochrane RoB 2. Random-effects models in R (metafor package) were employed in the meta-analysis of shear strength with subgroup analyses performed according to adhesive type and GRADE certainty. Out of 1,456 records, 78 studies have been included (45 old, 33 new). A meta-analysis (n=22 studies, 612 samples) provided a result as to dry shear strength of 3.58 MPa (95% CI: 2.45-4.71; I 2=73, p<0.001) and wet shear of 1.78 MPa (95% CI: 1.05-2.51; I 2=77, p<0.001). Gum arabic was tough (wet: 1.62 MPa), cassava starch greater dry strength (4.25 MPa). Sustainability: 35-65% lower CO2 emissions. Nigerian cases: gum arabic in particleboards resisted 90% RH. Natural adhesives would work reasonably well in moist tropics with modifications, and would have sustainability advantages. Policy suggestions: support local manufacture in Nigeria to adapt to climate.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".