Balancing Solar Energy, Thermal Comfort, and Emissions: A Data-Driven Urban Morphology Optimization Approach

Chenhang Bian, Panpan Hu, Chun Yin Li, Chi Chung Lee () and Xi Chen ()
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Chenhang Bian: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Panpan Hu: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Chun Yin Li: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Chi Chung Lee: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Xi Chen: Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: Urban morphology critically shapes environmental performance, yet few studies integrate multiple sustainability targets within a unified modeling framework for its design optimization. This study proposes a data-driven, multi-scale approach that combines parametric simulation, artificial neural network-based multi-task learning (MTL), SHAP interpretability, and NSGA-II optimization to assess and optimize urban form across 18 districts in Hong Kong. Four key sustainability targets—photovoltaic generation (PVG), accumulated urban heat island intensity (AUHII), indoor overheating degree (IOD), and carbon emission intensity (CEI)—were jointly predicted using an artificial neural network-based MTL model. The prediction results outperform single-task models, achieving R 2 values of 0.710 (PVG), 0.559 (AUHII), 0.819 (IOD), and 0.405 (CEI), respectively. SHAP analysis identifies building height, density, and orientation as the most important design factors, revealing trade-offs between solar access, thermal stress, and emissions. Urban form design strategies are informed by the multi-objective optimization, with the optimal solution featuring a building height of 72.11 m, building centroid distance of 109.92 m, and east-facing orientation (183°). The optimal configuration yields the highest PVG (55.26 kWh/m 2 ), lowest CEI (359.76 kg/m 2 /y), and relatively acceptable AUHII (294.13 °C·y) and IOD (92.74 °C·h). This study offers a balanced path toward carbon reduction, thermal resilience, and renewable energy utilization in compact cities for either new town planning or existing district renovation.

Keywords: urban morphology factors; thermal and energy environment; multi-task learning model; SHAP method; nonlinear relationship (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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