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Record W4414685136 · doi:10.3390/fib13100134

Life Cycle Carbon Footprint Assessment of a Typical Bamboo-Based Fiber Composite Material

2025· article· en· W4414685136 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFibers · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of British Columbia
FundersDivision of Materials ResearchJiangxi Provincial Department of Science and TechnologyNational Natural Science Foundation of China
KeywordsCarbon footprintCarbonizationLife-cycle assessmentBambooComposite numberFiberCarbon fibersProcess (computing)

Abstract

fetched live from OpenAlex

To quantitatively assess the environmental impact of producing a typical bamboo-based fiber composite material—bamboo scrimber (BS)—and to explore pathways for low-carbon optimization, this study adopts the Life Cycle Assessment (LCA) method with a focus on carbon footprint analysis. Using the actual production process of an enterprise as a case study, field data were collected and analyzed for bamboo scrimber with a nominal thickness of 1.5 cm. The results show that the carbon footprint of 1 m2 of this product is 3.11 kg CO2-eq, with the manufacturing stage contributing the highest emissions at 1.45 kg CO2-eq. The primary source of carbon emissions is steam consumption, mainly occurring during the carbonization and drying of bamboo bundles. Therefore, optimizing these stages is crucial for reducing the overall carbon footprint of the product. This study provides a scientific basis for the sustainable development of bamboo-based fiber composite materials and offers practical recommendations for improving their environmental performance in production.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.240
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it