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Record W2154728976 · doi:10.3390/su2061472

A Methodology for Assessing the Sustainability of Hydrogen Production from Solid Fuels

2010· article· en· W2154728976 on OpenAlexafffund
Nirmal V. Gnanapragasam, B. Venkateswara Reddy, Marc A. Rosen

Bibliographic record

VenueSustainability · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSustainabilityBiomass (ecology)Production (economics)Flexibility (engineering)Process (computing)Scale (ratio)CoalEnvironmental economicsHydrogen productionEnvironmental scienceDimension (graph theory)Process engineeringEnvironmental resource managementWaste managementComputer scienceEngineeringHydrogenEcologyMathematicsEconomicsChemistry

Abstract

fetched live from OpenAlex

A methodology for assessing the sustainability of hydrogen production using solid fuels is introduced, in which three sustainability dimensions (ecological, sociological and technological) are considered along with ten indicators for each dimension. Values for each indicator are assigned on a 10-point scale based on a high of 1 and a low of 0, depending on the characteristic of the criteria associated with each element or process, utilizing data reported in the literature. An illustrative example is presented to compare two solid fuels for hydrogen production: coal and biomass. The results suggest that qualitative sustainability indicators can be reasonably defined based on evaluations of system feasibility, and that adequate flexibility and comprehensiveness is provided through the use of ten indicators for each of the dimensions for every process or element involved in hydrogen production using solid fuels. Also, the assessment index values suggest that biomasses have better sustainability than coals, and that it may be advantageous to use coals in combination with biomass to increase their ecological and social sustainability. The sustainability assessment methodology can be made increasingly quantitative, and is likely extendable to other energy systems, but additional research and development is needed to lead to a more fully developed approach.

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)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.347
Teacher spread0.324 · 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.

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

Citations18
Published2010
Admission routes2
Has abstractyes

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