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Record W2415055059

Plenary lecture 1: utilization of non-fossil fuel energy options to mitigate climate change and environmental impact

2009· article· en· W2415055059 on OpenAlexaff
Marc A. Rosen

Bibliographic record

VenueInternational Conference on Energy & Environment · 2009
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsRenewable energyFossil fuelEnvironmental scienceEnvironmental impact of the energy industryEnergy carrierGreenhouse gasClimate change mitigationEnergy developmentEfficient energy useEnergy engineeringWaste managementEngineeringEnergy policyEcology
DOInot available

Abstract

fetched live from OpenAlex

Non-fossil fuel energy options can help humanity combat climate change and provide the opportunity for sustainable energy solutions. Non-fossil fuel energy options are diverse, ranging from renewables like solar, wind, geothermal, hydropower, biomass, ocean, tidal and wave energy, through to nuclear energy. The latter may not be a renewable resource, but it avoids greenhouse gas emissions and thus contributes to efforts to avoid climate change. Renewable energy resources are normally free of greenhouse gas emissions, although some like biomass can lead to such emissions if not managed carefully. Non-fossil fuel energy options are not sufficient for avoiding climate change, in that they are not necessarily readily utilizable in their natural forms. Hydrogen energy systems are needed to facilitate the use of non-fossil fuels by allowing them to be converted to two main classes of energy carriers: hydrogen and select hydrogen-derived fuels and electricity. The former allow humanity to meet most of its chemical energy needs, while the latter can satisfy most non-chemical energy demands. High efficiency is also needed to allow the greatest benefits to be attained from all energy options, including nonfossil fuel ones, in terms of climate change and other factors. Efficiency improvements efforts have many dimensions, including energy conservation, improved energy management, fuel substitution, better matching of energy carriers and energy demands, and more efficiency utilization of both energy quantity and quality. The latter two concepts are best considered via the use of exergy analysis, a thermodynamic tool based primarily on the second law of thermodynamics. A case study is considered involving the production of hydrogen from non-fossil energy sources via thermochemical water decomposition. This process is mainly driven by thermal energy, and is anticipated to be usable for large-scale hydrogen production. In thermochemical hydrogen production, a series of complex chemical and other processes occur, with the net result being the splitting of water into hydrogen and oxygen. Most preliminary designs of thermochemical hydrogen production processes are based on nuclear energy and solar energy, thus providing different types of non-fossil fuel options for combating climate change.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.352
Threshold uncertainty score1.000

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)0.0010.000

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 head0.033
GPT teacher head0.264
Teacher spread0.230 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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