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Record W4385421409 · doi:10.18280/ijsdp.180730

Life Cycle Assessment of Wood Chips from Residual Biomass: A Case Study

2023· article· en· W4385421409 on OpenAlexvenueno aff
Roberta Rincione, Sonia Longo, Maurizio Cellura, Francesco Guarino, Alberto Brunetti

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsLife-cycle assessmentResidualBiomass (ecology)Environmental sciencePulp and paper industryAgricultural engineeringWaste managementEngineeringMathematicsAgronomyProduction (economics)BiologyEconomicsAlgorithm

Abstract

fetched live from OpenAlex

The paper describes the results of a Life Cycle Assessment study of wood chips obtained from a residual forest biomass, to be used for energy purpose. The analysis is referred to 1 kg of wood chips as functional unit. The system boundaries include the collection of the residual biomass, the chipping process of biomass, the collection and transport of wood chips to the energy plant. The results show that the supply chain examined, with reference to the functional unit, causes an impact of 0.027 kg CO2eq and a consumption of 0.406 MJ of primary energy. A dominance analysis was developed to identify the most impactful stages of the production chain: the steps that contribute most to the impacts within the supply chain are the indirect drag and the chipping process, responsible for the 42% of the overall greenhouse gas emissions and primary energy consumption. Lastly, a sensitivity analysis was carried out, to assess the variation of the impacts related to the distance between the wood chips production site and the place of use. The sensitivity analysis shows that is important to have short supply chains within the proposed domain in order to lower the energy and environmental impacts.

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.

How this classification was reachedexpand

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.304
Teacher spread0.284 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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