Data Hub for Life Cycle Assessment of Climate Change Solutions—Hydrogen Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Life cycle assessment, which evaluates the complete life cycle of a product, is considered the standard methodological framework to evaluate the environmental performance of climate change solutions. However, significant challenges exist related to datasets used to quantify these environmental indicators. Although extensive research and commercial data on climate change technologies, pathways, and facilities exist, they are not readily available to practitioners of life cycle assessment in the right format and structure using an open platform. In this study, we propose a new open data hub platform for life cycle assessment, considering a hierarchical data flow starting with raw data collected on climate change technologies at laboratory, pilot, demonstration, or commercial scales to provide the information required for policy and decision-making. This platform makes data accessible at multiple levels for practitioners of life cycle assessment, while making data interoperable across platforms. The proposed data hub platform and workflow are explained through the polymer electrolyte membrane electrolysis hydrogen production as a case study. The climate change environment impact of 1.17 ± 0.03 kg CO2 eq./kg H2 was calculated for the case study. The current data hub platform is limited to evaluating environmental impacts; however, future additions of economic and social aspects are envisaged.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it