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There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. However, it is a largely complex and versatile methodology which hinders its broader uptake by researchers as well as improved results. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment. Hence, this work contributes to better understanding and further improving the research on transition towards decarbonised society.

Planning the required energy infrastructure for the energy transition is a crucial task for various neighbourhood concepts, such as positive energy districts. However, energy planning often comes with the challenges of data shortages and a lack of comparability among solutions for different districts. This work aims to enable this comparability by introducing an approach for categorising districts according to parameters that are relevant for the planning of neighbourhood energy infrastructures. Four parameters (climate, floor space index, heating demand and share of residential buildings) and their respective ranges (bands) were derived from the literature. Additionally, this work visualised the combination of all parameter bands across Europe to conveniently showcase districts that are comparable according to the selected parameters. This approach and its visualisation could be used in urban planning to share knowledge from existing energy district projects with those planned in comparable districts.

The aim of this paper is to consider one of the most traffic-loaded regions of Astana city (Kazakhstan) and to determine the concentration of carbon-monoxide (CO) in the air during the peak hours. CFD analysis based on the SolidWorks-EFD platform was used to simulate the dispersion of contaminants given the estimated emission rates and weather conditions at the crossroad of Bogenbay Batyr and Zhenis Avenues in Astana. Turbulence prediction was based on k-ε model with wall functions. The governing equations were discretized using the finite volume method and a 2nd order spatial scheme. The mesh verification was based on 1% convergence criterion for a 50% of mesh density increment; air pressure near the wall of a selected building was chosen as the parameter to control the convergence. Numerical results are presented for prevailing conditions during all 4 seasons of the year, demonstrating that the highest levels of CO are recorded in summer and reach the values up to 11.2 ppm which are still lower than the maximum level admitted for humans. Nevertheless, obtained results show that Astana is gradually becoming a city that is likely to reach the critical levels of pollutants in the nearest future if control measures are not taken with enough anticipation. As for a future work, it is proposed to perform in-situ validation of specific scenarios to check and support the results obtained with CFD and to develop then specific policies for tackling the problem before it becomes evident.