Predicting embodied carbon reduction by evaluating building shape parameters in preliminary design through the Dom-ino system

Adopting carbon-efficient building shapes has significant potential to reduce embodied carbon emissions (ECEs). Early-stage design decisions play a crucial role in this process, yet current methods lack predictive models for ECEs based on building shape parameters. This study addresses this gap by utilizing the Dom-ino system, which includes concrete and steel framing, to develop a method for estimating ECEs. The study analyzes the influence of building shape parameters, such as plan aspect ratio and number of floors, on ECEs and fits equations to the results to establish predictive models. Our findings indicate that these parameters significantly impact ECEs, and the study delves into the mechanisms by which they affect structural components and material usage. The developed method provides architects with a tool to evaluate and optimize building designs for lower carbon impact during the early stages of design. This approach supports a swift and environmentally conscious design process, enhancing the ability to predict and reduce ECEs in architectural projects.