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Enregistrement W3029636709 · doi:10.1186/s40068-020-00169-2

Waste heat: the dominating root cause of current global warming

2020· article· en· W3029636709 sur OpenAlexaff
Qinghan Bian

Notice bibliographique

RevueENVIRONMENTAL SYSTEMS RESEARCH · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésGlobal warmingEnvironmental scienceContext (archaeology)Climate changeClimatologyWaste heatGreenhouse gasClimate modelEffects of global warmingAtmospheric sciencesNatural resource economicsHeat exchangerGeographyGeologyEngineeringEconomics

Résumé

récupéré en direct d'OpenAlex

Abstract Background Pursuing GHG reductions by means of all resources and efforts has turned out no result to stop or even slow the global warming: the globe still gets warmer and warmer, especially in the recent years, at record-breaking rate almost each single year. Additionally, no definitive relationship has been found between the warming and the atmospheric GHG concentration. The link between them even in IPCC’s report lacks support and is unconvincing. All these imply that something else is responsible for the warming. On the other hand, huge amount of residual heat or waste heat from human activities has been poured into the climate system but has not been considered seriously in the context of global warming or climate change. Results This article features deploying the basic principles of thermodynamics and applying a new model, Equivalent Climate Change Model, to analyse the currently available data on world energy consumption between 1965 and 2017, and to study the relation between the global warming and the waste heat entered the climate system. The results show that the temperature changes in air, oceans and land are definitively correlated to the respective heat allocated from the waste heat stream based on their specific heat capacities, with high certainty and reliability. The observed anomalies in air fall within a range of simulations at an equivalent climate change surface air boundary layer depth between 50 and 100 m (60 ~ 100 m in recent decades due to more establishments of high-rising heat discharging sources); the anomalies in oceans fall within a range of simulations at an equivalent climate change waters surface boundary layer depth between 0.10 and 0.20 m (0.125 ~ 0.20 m in recent decades); and the anomalies in land fall within a range of simulations at an equivalent climate change land surface boundary layer depth between 0.05 and 0.10 m (0.06 ~ 0.10 m in recent decades). The simulation results at the air layer depth of 70 m are almost the same as NASA’s Lowess smoothing trend. Forecast of future global warming based on this model under the scenario of business as usual indicates that the possible air temperature risings will be in the range of 0.68 ~ 1.13 °C in 2030 and 0.73 ~ 1.22 °C in 2040; the possible sea temperature risings will be in the range of 0.61 ~ 0.98 °C in 2030, 0.66 ~ 1.05 °C in 2040; and the possible land temperature risings will be in the range of 1.02 ~ 1.71 °C in 2030, 1.10 ~ 1.84 °C in 2040. However, if the energy conversion efficiency increased by 10% by 2030 and another 10% by 2040, then the possible air temperature risings would be in the range of 0.54 ~ 0.90 °C in 2030 and 0.44 ~ 0.73 °C in 2040; the possible sea temperature risings would be in the range of 0.49 ~ 0.78 °C in 2030, and 0.40 ~ 0.64 °C in 2040; and the possible land temperature risings would be in the range of 0.81 ~ 1.36 °C in 2030 and 0.66 ~ 1.11 °C in 2040. The observed global average air temperature changes and the Lowess Smoothing values in 2018 and 2019 fall within the range set by the air layer depth between 60 and 100 m, are consistent with the forecast under the scenario of business as usual, further confirms the reliability of this approach. Conclusions Greenhouse gases are not the culprit of the current global warming, instead, huge amount of residual heat or waste heat discharged into the environment from human activities has dominated the warming (beside of solar irradiance and volcano eruptions). Pursuing GHG reductions is bound to be ineffective in preventing the globe from further warming but increases unnecessary burdens. Switching to 100% of surface renewable energies is the ideal solution to completely solve further warming problem. However, geotherm does cause global warming although it is a type of renewable energy. Increasing energy’s conversion efficiency can effectively help slow down the warming, it requires vast investment and will embrace breakthroughs in technologies. Changing human’s behavior individually and socially and retrofitting can decrease the energy consumption and the amount of heat entering the environment and thus help mitigate climate change and its impact in the most cost-effective way. Unlike the General Circulation Models that can only simulate the past air temperature changes with greater uncertainty, the Equivalent Climate Change Model can not only trace the past temperature changes in air, oceans and land, but also can predict the future changes in them, respectively, with high certainty and reliability.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,049
Tête enseignante GPT0,313
Écart entre enseignants0,264 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations53
Publié2020
Routes d'admission1
Résumé présentoui

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