Hydrogen energy systems for decarbonizing smart cities and industrial applications: A review
Bibliographic record
Abstract
Hydrogen is increasingly recognized as a key energy vector for achieving deep decarbonization across urban and industrial sectors. Supporting global efforts to reduce greenhouse gas (GHG) emissions and achieve the Sustainable Development Goals (SDGs), it is essential to understand the multi-sectoral role of the hydrogen value chain, spanning production, storage, and end-use applications, with particular emphasis on smart city systems and industrial processes. Green hydrogen production technologies, including alkaline water electrolysis (AWE), proton exchange membrane (PEM) electrolysis, anion exchange membrane (AEM) electrolysis, and solid oxide electrolysis cells (SOECs), are evaluated in terms of efficiency, scalability, and integration potential. Storage pathways are examined across physical storage (compressed gas, cryo-compressed, and liquid hydrogen), material-based storage (solid-state absorption in metal hydrides and chemical carriers such as LOHCs and ammonia), and geological storage (salt caverns, depleted gas reservoirs, and deep saline aquifers), highlighting their suitability for urban and industrial contexts. In the smart city domain, hydrogen is analyzed as an enabler of zero-emission transportation, low-carbon residential and commercial heating, and renewable-integrated smart grids with long-duration storage capabilities. System-level studies demonstrate that coordinated integration of these applications can deliver higher overall energy efficiency, deeper reductions in life-cycle GHG emissions, and improved resilience of urban energy systems compared with sector-specific approaches. Policy frameworks, safety standards, and digitalization strategies are reviewed to illustrate how hydrogen infrastructure can be embedded into interconnected urban energy systems. Furthermore, industrial applications focus on hydrogen’s potential to decarbonize energy-intensive processes and enable sector coupling between electricity, heat, and manufacturing. The environmental implications of hydrogen deployment are also considered, including resource efficiency, life-cycle emissions, and ecosystem impacts. In contrast to reviews addressing isolated aspects of hydrogen technologies, this study synthesizes technological, infrastructural, and policy dimensions, integrating insights from over 400 studies to highlight the multifaceted role of hydrogen in sustainable urban development and industrial decarbonization, and the added benefits achievable through coordinated, cross-sector deployment strategies.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".