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.
This study investigates the distinct impacts of electricity and petroleum consumption on economic growth in Eastern Africa. Using a Panel Autoregressive Distributed Lag Model and data for a period spanning 2000 to 2021, the study examines both the short-run and long-run effects of these energy sources on Gross Domestic Product. The findings reveal that petroleum consumption has a statistically significant and positive impact on GDP in both the short run and long run. In contrast, while electricity consumption shows a positive but statistically insignificant effect on GDP in the short run, it exhibits a negative and statistically significant impact in the long run. These results suggest that policymakers in Eastern Africa should prioritize sustainable petroleum management to maximize its economic benefits while mitigating potential environmental risks. While the negative coefficient of electricity implies a corrective response of the variables to long-run equilibrium in the face of short-term shocks. As a result, it is recommended that economic shocks caused by energy consumption be considered in terms of their relationship to economic growth, whether positive or negative in the long or short term, as decision makers need to address their impact and limit such shocks on economic growth.
The aggregate upsurge in carbon dioxide emissions (CO2) witnessed through environmental degradation and global climate change is a call for great concern. This, therefore, calls for the enactment, utilization and implementation of provisions and policies geared towards curbing this global economic bad without impeding global economic growth rates. This study ascertains the extent to which renewable energy consumption (REC), economic growth (GDP), population growth (POP), globalization (GLO), and financial development (FD) affect carbon dioxide emissions (CO2) in selected G7 economies (France, Germany, Canada, Italy, and the United Kingdom) from 1990–2020. The Dynamic Fixed Effect Autoregressive Distributive Lag (DFE-ARDL) and the Pooled Mean Group ARDL (PMG-ARDL) methods were employed for analysis. The empirical findings for DFE-ARDL showed that REC, GDP, and POP have an adverse association with CO2 in the long-term. However, in the short-term, REC and FD improve the environment, while GDP and POP drive CO2. It is observed that the result for REC in the short and long-run is consistent. The PMG-ARDL results revealed that REC and GLO negatively affect CO2 in the long-run, and in the short-run, GDP spurs CO2, while FD reduces it. The result summary of both methods employed demonstrates that REC, GLO, and FD benefit the environment. At the same time, GDP and POP harm the environment in the short-run but reduce CO2 in the long-run. Conclusively, the research recommends increasing the utilization of renewable energy and policies that enable economic growth and CO2 to move in the opposite direction.
