Building energy renovation mitigates carbon emissions but often increases material demand and financial costs. This work addresses this problem by investigating the carbon, material, and economic footprints of various renovation scenarios in the Dutch residential sector from 2015 to 2050. Results show that, compared to the baseline, façade refurbishment could lower cumulative lifecycle emissions by up to 0.3%, while raising material use by 21–25% and costs by 2–6%. Sensitivity analysis indicates that refurbishing the heating system offers greater potential for reducing carbon emissions. Rebuilding could cut emissions by up to 17% under an ambitious energy transition, though this would triple material use and construction costs. Circularity strategies could offset up to 89% of the material footprint and reduce carbon emissions by up to 23%. Nonetheless, considerable cost increases from renovations remain inevitable, even with advanced material circulation systems, suggesting circular renovation strategies with enhanced incentives as concerted action.

Parallel imaging is a widely-used acceleration technique for magnetic resonance imaging (MRI). Conventional linear reconstruction approaches in parallel imaging suffer from noise amplification. Recently, a non-linear method that utilizes subject- specific convolutional neural networks for k-space reconstruction, Robust Artificial-neural-networks for k-space Interpolation (RAKI) was proposed and shown to improve noise resilience over linear methods. However, the linear convolutions still provide a sufficient baseline image quality and interpretability. In this paper, we sought to utilize a residual network architecture to combine the benefits of both the linear and non-linear RAKI reconstructions. This hybrid method, called residual RAKI (rRAKI) offers significant improvement in image quality compared to linear method, and improves upon RAKI in highly- accelerated simultaneous multi-slice imaging. Furthermore, it establishes an interpretable view for the use of CNNs in parallel imaging, as the CNN component in the residual network removes the noise amplification arising from the linear part.