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The transport sector offers limited options to reduce greenhouse gas emissions as compared with other sectors, such as power generation and industrial sectors. To understand the potential reduction of energy consumption and associated emissions through fuel substitution or transportation service demand reduction, this study estimates own- and cross-price elasticities of various fuels used for transportation. The analysis shows, like many previous studies, that an increase in fuel prices would not have a large effect on transport sector carbon dioxide emissions, due to limited substitution possibilities among fuels for transportation. The study also finds that price-induced changes that lead to an increase in the rate of adoption of fuel-efficient vehicles would be more effective than a policy to cause fuel substitution.

Cross-Price Elasticity --- Emission Intensity --- Fuel Switching --- Own-Price Elasticity --- Transportation Sector

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A Smart City is the perfect environment to study and exploit the interactions between actors because its architecture already integrates vaious elements to collect data and connect to its citizens. Furthermore, the proliferation of web platforms (e.g., social media and web fora) and the increased affordability of sensors and IoT devices (e.g., smart meters) make data related to a large and diverse set of users accessible, as their activities in the digital world reflect their real-life actions. These new technologies can be of great use for the stakeholders as, on the one hand, they provide them with semantically rich inputs and frequent updates at a relatively cheap cost and, on the other, form a direct channel of communication with the citizens. To fully exploit these new data sources, we need both novel computational methods (e.g., AI, data mining algorithms, knowledge representation) that are suitable for analyzing and understanding the dynamics behind energy consumption and also a deeper understanding of how these methods can be integrated into the existing design and decision processes (e.g., human-in-the-loop processes).Therefore, this Special Issue welcomed original multidisciplinary research works about AI, data science methods, and their integration in existing design/decision-making processes in the domain of energy consumption in Smart Cities.

Research & information: general --- Physics --- building energy flexibility --- HOMER software --- peak clipping --- load shifting --- energy saving --- building performance assessment --- indoor environment quality --- occupants’ satisfaction --- post-occupancy evaluation --- Green Building Index --- tropical climate --- building performance simulation --- CO2 emission --- occupant’s comfort --- window allocation --- climate change --- energy consumption --- building energy load --- thermal load --- future weather --- operative temperature --- cooling load --- daily energy need --- solar gains --- nZEB --- historical buildings --- TRNSYS --- buildings retrofitting --- buildings office --- economic feasibility --- Renewable Energy Systems (RESs) --- Zero Energy District (ZED) --- Digital Twin (DT) --- Building Information Modelling (BIM) --- Geographic Information System (GIS) --- Revit software’s --- asymmetric duty cycle control --- bifilar coil --- pulse duty cycle control --- induction heating --- metal melting --- phase shift control --- pulse density modulation --- series resonant inverter --- variable frequency control --- building operation and maintenance --- extended reality --- virtual reality --- augmented reality --- mixed reality --- immersive technologies --- digital twins --- metaverse --- positive energy district --- district energy infrastructure --- decarbonisation of neighbourhoods --- GIS --- energy transition --- smart city policy --- carbon emission intensity --- digital transformation --- green innovation --- difference-in-differences --- n/a --- occupants' satisfaction --- occupant's comfort --- Revit software's