Wood Waste Management from the Furniture Industry: The Environmental Performances of Recycling, Energy Recovery, and Landfill Treatments
Bibliographic record
Abstract
Proper management of wood waste (WW) from the furniture industry has become an important issue. Life-cycle assessment (LCA) is a tool that is widely used for identifying environmental gains in WW management strategies. Thus, the aim of this research was to perform a comparative LCA, analyzing the environmental aspects and impacts of different WW management scenarios generated in the furniture industry in the state of Espirito Santo, Brazil. To conduct the study, five scenarios were designed: medium-density fiberboard (MDF) production (Scenario 1), medium-density particleboard (MDP) production (Scenario 2), solid ceramic brick production (Scenario 3), heat production in the ceramics industry (Scenario 4), and landfill disposal (Scenario 5). The results showed that compared to Scenarios 3 and 4, Scenarios 1 and 2 are potentially more favorable for disposing of WW. Scenario 1 achieved more environmental benefits in all of the impact categories evaluated. Notably, 1 m3 of MDF stores 1080 kg CO2 eq/m3, which results in a net impact of −849 kg CO2 eq/m3 of MDF. Scenario 5 is the least favorable practice. This research designs scenarios that contribute to reductions in the demand for virgin sources and increases in environmental gains.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".