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Record W2075335211 · doi:10.3390/f5092163

Legal Harvesting, Sustainable Sourcing and Cascaded Use of Wood for Bioenergy: Their Coverage through Existing Certification Frameworks for Sustainable Forest Management

2014· article· en· W2075335211 on OpenAlexaboutno aff
Richard Sikkema, Martin Junginger, J. van Dam, Gerben Stegeman, D. Durrant, André Faaij

Bibliographic record

VenueForests · 2014
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersScience Foundation Ireland
KeywordsCertified woodBusinessCertificationSustainable forest managementLegislationSustainabilityForest managementEnvironmental resource managementEnvironmental protectionEnvironmental planningForestryEnvironmental scienceAgroforestry

Abstract

fetched live from OpenAlex

The first objective of this paper was to provide an inventory of developments of certification schemes for sustainable biomass production, following recent EU legislation (both formalized and under development). One main pillar is the EU Timber Regulation for legal harvesting; a second one is the EU’s 2010 recommendations for sustainable woody biomass sourcing for energy; the third one is the EU Waste Directive. The second objective was to benchmark the coverage of this (draft) legislation, when wood product certificates for sustainable forest management (SFM) are used as proof of the related legislative requirements. We studied North America, as it is a major biomass supplier to the EU-28. Together with existing forest legislation in the US and Canada, SFM certificates are actively used to cover the EU’s (draft) legislation. However, North American forests are only partially certified with fibers coming from certified forests; these are referred to as forest management (FM) fibers. Other certified fibers should come from complementary risk assessments downstream in the supply chain (risk based fibers). Our benchmark concludes that: (a) FM fiber certification by the Forest Stewardship Council (FSC) and the Program for the Endorsement of Forest Certification (PEFC) international standards show the highest level of coverage with EU’s (draft) legislation; (b) There is insufficient coverage for risk based fibers by FSC Controlled Wood (FSC-CW), PEFC Due Diligence (PEFC-DD), or SFI-fiber sourcing (SFI-FS). Other weaknesses identified for elaboration are: (c) Alignment in definitions are needed, such as for primary forest, high carbon stock, and wood waste (cascading); (d) Imperfect mass balance (fiber check downstream) needs to be solved, as non-certified fiber flows are inadequately monitored; (e) Add-on of a GHG calculation tool is needed, as GHG life cycle reporting is not covered by any of the SFM frameworks.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 designNot applicable
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

Citations46
Published2014
Admission routes1
Has abstractyes

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