Heuristic Urban-scale Life Cycle Assessment of Districts to Determine Their Carbon Footprints

Schildt, Maximilian; Cuypers, Johannes; Malhotra, Avichal; Shamovich, Maxim; Frisch, Jérôme; Treeck, Christoph van

doi:10.26868/25746308.2022.c035

Cited by 2 publications

(4 citation statements)

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“…While Teco employs a dynamic heat load simulation approach (Section 2.1), DisteLCA upscales statistical values of energy use and GWP by m 2 . With previous work indicating that Teco actually overestimates the GWP of operational energy use for certain districts [95], the authors conclude that DisteLCA's statistical approach leads to a significant overestimation of emissions during buildings' operational stage. This may be attributed to the fundamental difference between the dynamic simulation of the predicted energy demand in Teco, and the arithmetic means of actual energy use employed by DisteLCA.…”

Section: Modelling and Simulation Accuracy And Usabilitymentioning

confidence: 60%

“…This section describes Teco [95], a heuristic approach to determine the life cycle inventory and assess the environmental impact of large building stocks implemented in Python and Modelica. It is based on the Tool for Energy Analysis and Simulation for Efficient Retrofit+ (TEASER+) [40,96], an enrichment framework for UBEM, and therefore extends its classes for LCA calculations.…”

Section: Tool 1: Tecomentioning

confidence: 99%

“…Both TEASER+ and Teco are open-source and have been developed at the authors' affiliated Institute of Energy Efficiency and Sustainable Building e3D at RWTH Aachen University. A detailed description of Teco's development process and scientific validation thereof can be found in [95].…”

Section: Tool 1: Tecomentioning

confidence: 99%

“…As a consequence, Teco's approach is seen as more favourable in generating LCA data on a large scale. However, it should be noted that the tool is not yet able to consider interior walls to an appropriate degree (see [95]). Moreover, any heuristic needs to be carefully calibrated in order to attain a satisfying balance between result accuracy and input as well as computational requirements.…”

Section: Modelling and Simulation Accuracy And Usabilitymentioning

confidence: 99%

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On the Potential of District-Scale Life Cycle Assessments of Buildings

et al. 2023

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Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA’s granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach.

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Section: Modelling and Simulation Accuracy And Usabilitymentioning

confidence: 60%