Deliverable 1.3: First version of the priority list of archetypes
Notice bibliographique
Résumé
Deliverable D1.3 reports the results of the activities performed by the NHyRA Consortium in Task 1.3. In recent years, the research community has been investigating to find an answer to the potential indirect impact of H2 on climate change. However, validated data are needed to provide a definitive answer on this topic. As shown in deliverable D1.1, many archetypes of technologies and plants are present in the H2 supply chains. Furthermore, several plants’ configurations and technologies can be implemented in each archetype, increasing the complexity and the number of potential emission sources that should be investigated. As indicated in the project proposal, choices must be made as to which archetypes and sources of H2 emissions should be focused on since resources are usually limited. Therefore, a priority ranking of the archetypes identified in deliverable D1.1 to be investigated is suggested. Some attempts to cover the gaps have been already present in the literature. For example, Copper et al. (2022) conducted a literature review and reported preliminary emission ranges for hydrogen, indicating that green hydrogen may, in certain instances, exhibit higher emissions than blue hydrogen. However, the Authors acknowledged that these findings are subject to a high degree of uncertainty and limited reliability. Consequently, further research is required to enhance the robustness of these estimates. Several approaches are available to provide such a priority ranking. In this work the Analytic Hierarchy Procedure (AHP) method, a ranking prioritization methodology, was adopted, developed and implemented. Six criteria have been identified for the purpose as defined in chapter 4.1: i) existence and/or uncertainty of data about H2 emission in the state of the art; ii) total amount of H2 emission; iii) market penetration (present and future expected potential); iv) archetype to be tested: availability, operational history, and variety of scenarios; v) availability of measurement instruments and methods; vi) cost and time for testing. Then, a weight for each criterion was proposed according to the experts’ inputs and by organizing dedicated face-to-face discussions among the partners of the NHyRA consortium. Following the discussion on the preliminary results of the criteria weighting, it was decided to limit the pairwise comparison of the archetypes to three criteria (existence and/or uncertainty of data about H2 emission in the state of the art, total amount of H2 emission, market penetration (present and future expected potential)), while it was suggested to use the remaining three for the reality check, i.e., to evaluate the feasibility of the testing activities. As a result of this activity some preliminary recommendations can be given. Different opinions appear during the discussion making challenging to reach a consensus without any further data elaboration and aggregation. Based on the aggregation, the total amount of hydrogen emission into the atmosphere is considered the most relevant criteria for prioritization. Otherwise, the remaining two criteria received a similar score. Focusing to the archetypes indicated in the deliverable D1.1, preliminary recommendations can be given. First, archetypes transporting and storing fluids different from H2 in the supply chain can be neglected in the present prioritization since no direct H2 emissions are expected. Second, also low TRL technologies should be excluded since it is foreseen that further technological advancements are expected before these technologies enter the market. Third, few data are available for some archetypes (industrial end-users and electricity generation). Therefore, it was preferred to postpone the prioritization of these two categories when more information on all the archetypes will be available.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».