Urban energy models (UEMs) simulate energy use at the urban scale and are used to inform urban planning, policy development, infrastructure development, and digital twin monitoring and forecasting. Recent technological improvements have spurred interest in large, multi-domain UEMs, which analyse multiple interconnected parts of these energy systems, such as geography, transport, and buildings. Reviews have focussed on single domains or aspects of UEM data. However, multi-domain UEMs require detailed multi-domain data inputs to provide accurate results. This paper provides a comprehensive review of data requirements and a repository of data-specific information for researchers, including data formats, sources, acquisition methods, bridging methods, and challenges. The review was conducted using academic search engines and the authors’ direct research experience. Domains are characterised by Climate, Geographic, Building, Transportation, Demographics, Energy Networks and Consumption, and Distributed Energy Resources. Additionally, challenges common to multiple sectors are identified, and methods for addressing these are proposed. The paper concludes with a series of recommendations drawing from the general and sector-specific challenges. Overall, a large amount of data exists, but their use by urban energy modellers is limited due to lack of coordination and standardisation, and concerns over privacy and commercial interests. Coordinated public effort is required to overcome these limitations and improve the results of UEMs in the future.
Hydrogen energy offers a significant potential for reducing carbon emissions and integrating clean energy across sectors such as heavy-duty vehicles, energy-intensive industries, and building heating. This study analyzes the energy efficiency and emissions of grey and blue hydrogen supply chains, identifying key issues such as high energy consumption and losses in transportation, steam methane reforming, and liquid hydrogen storage. Truck transportation emerges as the highest emitter, with emissions ranging from 0.140 to 0.150 kg CO2e per kg of hydrogen. Using a bi-objective Dijkstra Algorithm, the study identifies the most energy-emissions-efficient pathways and reveals a trade-off between energy efficiency and greenhouse gas emissions. Grey hydrogen shows higher energy efficiency (38.0%) but higher emissions (0.1689 kg CO2e per kg of hydrogen). In contrast, with 60% and 90% carbon capture and storage, blue hydrogen has slightly lower energy efficiencies (37.5% and 36.9%) but reduced emissions (0.1564 and 0.1514 kg CO2e per kg of hydrogen). Liquefied natural gas and hydrogen offer high energy efficiency but increase emissions, while compressed natural gas and hydrogen slightly reduce efficiency but nearly halve emissions. Hence, compressed options are preferable for an energy-emissions-efficient shortest path.
