Decarbonizing urban residential communities with green hydrogen systems
Bibliographic record
Abstract
Community green hydrogen systems, typically consisting of rooftop photovoltaic panels paired with hybrid hydrogen-battery storage, offer urban environments with improved access to clean, on-site energy. However, economically viable pathways for deploying hydrogen storage within urban communities remain unclear. Here we develop a bottom-up energy model linking climate, human behavior and community characteristics to assess the impacts of pathways for deploying community green hydrogen systems in North America from 2030 to 2050. We show that for the same community conditions, the cost difference between the best and worst pathways can be as high as 60%. In particular, the household centralized option emerges as the preferred pathway for most communities. Furthermore, enhancing energy storage demands within these deployment pathways can reduce system design costs up to fourfold. To achieve cost-effective urban decarbonization, the study underscores the critical role of selecting the right deployment pathway and prioritizing the integration of increased energy storage in pathway designs. Distributed green hydrogen systems represent an emerging technology to help decarbonize cities, but the optimal path for expanding them in urban residential communities remains unclear. This study developed a bottom-up energy model to explore the impacts and implications of pathways for deploying green hydrogen energy systems for urban communities in North America.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".