Deliverable 1.3: First version of the priority list of archetypes
Bibliographic record
Abstract
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".