CHIASMUS

Inspiration

By 2050, 70% of the global population will live in cities. Cities are responsible for over 60% of energy use and 70% of human-induced greenhouse gas (GHG) emissions, thus greatly contributing to climate change. With increasing urbanization, cities and buildings will need to improve their environmental performance drastically. Policymakers, building stakeholders, citizens, etc., need to proceed with mitigation and adaptation measures and adopt tailored decarbonization strategies consistent with political, climatic and socio-demographic contexts. Long-term solutions for more climate-neutral cities are emerging, featuring technological changes, GHG emission reduction targets, fossil fuel phase-outs and national renovation policies.

However, in terms of urban climate actions, there are still many knowledge gaps to be addressed. For example, there is still little understanding of the effect of mitigation actions on buildings’ indoor environmental quality (IEQ) at the urban level, and the impact of actions by homeowners on the urban climate at a street or district level. In addition, the definition and evaluation of adequate climate strategies at the urban level is still limited by approaches and data (on urban building stock and climate) - that lack proper granularity and spatial differentiation.

Innovation

CHIASMUS aims to define a new hybrid urban building stock performance modelling approach, combining dynamic simulations and data analysis, and featuring nested, locally informed, uncertainty estimation methods to address climate assumptions and building stock parameterizations in relation to different levels of data availability and granularity (e.g. data-rich vs. data-scarce contexts).

The project will focus on the cities of Antwerp and Brussels to evaluate the results of the model for cities with different data granularity and diverse characteristics and demands (e.g. building types, socio-demographic attributes, urban fabric, etc.). To do this, it will leverage LIST’s expertise from other projects related to urban buildings and energy modelling, such as DAEDALUS, SemanticLCA, EPC RECAST and LegoFit.

One of the innovative aspects of the project is the application of a hybrid modelling approach, in which simulation-based (dynamic building performance) and data-driven models will be applied in parallel. In both approaches, the uncertainties associated with climate assumptions and building stock simplifications (relative to energy use and impacts) will be assessed, in order to enhance model reliability across scales.

LIST will assess the impacts of current scenarios, while accounting for future building stock and climates (time horizon: up to 2050) in a prospective life cycle assessment (LCA) framework by considering, for example, the technology advancements in materials and energy mixes resulting from different decarbonization scenarios, as well as the future climate conditions expected within the selected time horizon and in the local urban context (using high spatial resolution downscaling of atmospheric parameters at the urban level).

The materials and energy mixes for future scenarios will be underpinned by Integrated Assessment Models (IAM). The prospective scenarios will be complemented by regional and time variability assessments to perform sensitivity analysis and at the same time, the uncertainty of the prospective LCA results will be quantified. In addition, geographically differentiated and locally informed climate mitigation and adaptation measures will be tested, such as Nature Based Solutions (NBS), urban green infrastructure implementations, architectural upgrading and the adaptation of occupant behaviour.

Finally, for street trees and urban forests, long-term carbon sequestration and urban cooling strategies will be simulated using the NBenefit$ 3.0 tool developed by LIST, which underpins a system dynamics model (SDM) combining ecosystem (dis)services with LCA. The iterative evaluation of integrated strategies will enable the effects of the new hybrid modelling approach to be considered, facilitating its transfer to other, more data-scarce, contexts (e.g. Luxembourg).

Impact

CHIASMUS will make key contributions to advance the state-of-the-art by delivering a new hybrid urban building stock performance model as well as district-to-building level mitigation and adaptation integrated strategies, and the related assessment from a lifecycle perspective. Building on the hybrid modelling, it will be informed by local parameters, such as the urban microclimate, building stock characteristics and behaviour models, and by spatial socio-demographic data, while being aligned with future technologies and expected inventories in a prospective LCA approach.

These models could support, on one side local administrators and urban policymakers in designing climate-resilient cities and increase the climate resilience of existing cities, and on the other side building promoters and construction industries, in order for them to invest in the best technical solutions to improve buildings’ climate resilience.