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Record W4241687068 · doi:10.24124/2018/58978

Greenhouse gas lifecycle assessment of biochar and biocoal application in British Columbia

2018· dissertation· en· W4241687068 on OpenAlexafffundabout
Geoff de Ruiter

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of VictoriaUniversity of Northern British Columbia
FundersUniversity of British ColumbiaPacific Institute for Climate SolutionsUniversity of Northern British Columbia
KeywordsBiocharGreenhouse gasCarbon sequestrationEnvironmental scienceBiomass (ecology)Waste managementPelletsFossil fuelRaw materialPyrolysisCarbon fibersCoalCharcoalEnvironmental engineeringPulp and paper industryEngineeringCarbon dioxideChemistryAgronomyMaterials science

Abstract

fetched live from OpenAlex

Biochar, a form of black carbon produced from pyrolyzed biomass, has been touted as a product that may suppress agricultural soil emissions while also sequestering carbon. BC Biocarbon LTD, a recently established company in McBride, BC, has developed a method of producing a new product called biocoal. This biocoal is produced from a combination of crushed biochar and an organic-based binder also made from the original biomass feedstock. As their biocoal contains similar properties to fossil coal or petroleum coke, its use to reduce emissions as an energy fuel or sequestration method may be favourable to biochar’s use as a soil additive. Additionally, this biocoal may present a method of long-term carbon sequestration if buried. This dissertation assessed the greenhouse gas emissions from the production of biocoal from BC Biocarbon’s system and compared the results to wood pellet production and delivery (Project 1), coal and petroleum coke displacement (Project 2), landfilling for carbon sequestration, while also assessing biochar’s potential soil greenhouse gas reductions with added carbon sequestration (Project 3), and a regional and province-wide assessment for reducing emissions in BC using available sawmill and roadside slash residues (Project 4). Project 1 showed that when comparing biocoal made from sawmill residues to locally produced wood pellets, transportation emissions may be decreased 64% due to biocoal’s higher heating value. When comparing emissions produced for biocoal or wood pellets at gate, biocoal may show a 42% reduction in emissions or up to a 51% increase in emissions, however this is largely dependent on the data-sourced scenarios and their underlying assumptions of emissions allocation. Project 2 showed that displacing petroleum coke in cement kilns offered the largest reduction potential compared to coal applications such as electricity generation, or lead smelting. Project 3 showed that under 3 average conditions, sequestering biocoal offered greater emission reduction potential than soil applied biochar. Finally, Project 4 showed that an estimated GHG emission reduction or carbon sequestration of 28,000,00 Mg CO2e/year from current available residues, and 20,006,000 Mg CO2e/year in 10 years’ time, BC has the potential to reduce its current emissions by around 46%, and 33% in 10 years.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.252
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2018
Admission routes3
Has abstractyes

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