A climate resilient city, perforce, has an efficient and robust energy infrastructure that can harvest local energy resources and match energy sources and sinks that vary over space and time. This paper explores the use of an urban building energy model (UBEM) to examine the potential for creating a near-zero carbon neighbourhood in Dublin (Ireland) that is characterised by diverse land-uses and old and new building stock. UBEMs are a relatively new tool that allows the simulation of building energy demand across an urbanised landscape and can account for building layout, including the effects of overshadowing and the potential for facade retrofits and energy generation. In this research, a novel geographic database of buildings is created using archetypes, and the associated information on dimensions, fabric and energy systems is integrated into the Urban Modelling Interface (UMI). The model is used to simulate current and future energy demand based on climate change projections and to test scenarios that apply retrofits to the existing stock and that link proximate land-uses and land-covers. The latter allows a significant decoupling of the neighbourhood from an offsite electricity generation station with a high carbon output. The findings of this paper demonstrate that treating neighbourhoods as single energy entities rather than collections of individual sectors allows the development of bespoke carbon reducing scenarios that are geographically situated. The work shows the value of a neighbourhood-based approach to energy management using UBEMs.

An increasingly important aspect of analyzing the challenges facing cities today is the integration of nature. Nature-based solutions have the potential to successfully cope with the adverse effects of extensive urbanization and climatic change. On the other hand, the incorporation of smartness in cities is a critical issue. This paper aims to analyze the steps towards integrating nature-based solutions and smart city aspects to develop a web-based data platform that focuses on tackling and investigating the role of nature-based solutions in city health and well-being and returns a digital twin of the natural and built environment, including health-related key performance indicators. Seven pilot cities are used as a basis for the analysis. The architecture of a smart green city data platform is described. The interaction with the citizens is ensured through apps and games. The paper lays the foundation for a future “phygital” NBS world.

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work.

The EU’s Green Deal has a goal of a climate-neutral Europe by 2050. Achieving this goal will require a comprehensive set of actions across all economic sectors, especially the building sector, which currently accounts for 40% of the energy consumed.  Residential energy use is a significant contributor, much of it due to the poorly insulated building stock. Making a ‘just transition’ to more energy-efficient cities requires a spatial approach that can address the correspondence of poor housing and people and the potential for energy innovation at a neighbourhood-scale. In this study, a geographic database of building archetypes is developed for use by the Urban Modelling Interface (Umi) to perform simulations of urban energy use intensity and test the efficacy of energy policies. Umi is applied to a neighbourhood of residential buildings in Dublin (Ireland), many of which perform poorly. Simulated annual energy use intensity is evaluated favourably using energy performance certificate data. Umi is used subsequently to design and test the efficacy of district-level energy policies; the results indicate that the most cost-effective mix of envelope retrofit and onsite energy production to achieve the Green Deal’s target of 60% reduction in greenhouse gas emissions by 2030 and 100% by 2050. The methodology shown here employs data and software that is publicly available for many EU countries.