An advanced generative framework for low-carbon urban morphology optimization

Abstract

Abstract Optimizing urban morphology is pivotal for lowering energy consumption and carbon emissions while supporting sustainable development. However, traditional methods often rely on inefficient post-evaluation, hindering direct, goal-oriented design feedback. This study thus presents the PC-GAN model, an automated block design tool that integrates Pix2pix with a two-step GAN mechanism inspired by CycleGAN, generating high-resolution 3D urban forms under specific climatic and energy-reduction targets. Focusing on Guangzhou’s six main LCZ types (LCZ1, LCZ2, LCZ4, LCZ5, LCZ6, LCZ8) and iterating 10% stepwise reductions in baseline energy use, the model illuminates distinctive low-carbon optimization pathways across various urban morphologies. In doing so, PC-GAN offers rapid generation of diverse design alternatives while fulfilling low-energy requirements. This new framework not only improves planning efficiency and flexibility but also directs future research toward more adaptive, data-driven methodologies for low-carbon urban morphology optimization.