Optimizing Urban Land-Use Through Deep Reinforcement Learning: A Case Study in Hangzhou for Reducing Carbon Emissions

Urban land-use optimization plays a vital role in mitigating the escalating carbon emissions of rapidly growing cities. This study employs advanced computational intelligence to address urban carbon reduction through optimized spatial configurations. A Deep Reinforcement Learning (DRL) framework is proposed that integrates Points of Interest (POI), Areas of Interest (AOI), and Transportation System Data (TSD) to generate fine-grained carbon emission maps guiding land-use adjustments. In the case study of Hangzhou, China, results show that a carefully designed reward function enables the DRL agent to selectively optimize land-use structures, prioritizing the centralization of residential, dining, and commercial areas to form high-density, mixed-use urban clusters. This spatial reorganization leads to notable reductions in carbon emissions and improvements in resource-use efficiency. The proposed DRL-based framework provides a scientific basis for policy development toward sustainable land-use and urban density optimization. By merging advanced AI techniques with urban planning, this research contributes to the creation of low-carbon, resilient, and environmentally sustainable cities capable of addressing global climate challenges. The optimized DRL agent achieved carbon emission reductions of up to 15% compared to baseline configurations in the Hangzhou case study. Spatial concentration analysis revealed a 23% increase in residential area clustering and 31% increase in commercial zone centralization over 400 training episodes. The PPO-based model demonstrated superior performance compared to genetic algorithm and linear regression baselines, with lower policy loss (converging to <0.01) and critic loss (converging to <0.005) after early stopping at 400 episodes. However, this study is limited by its deterministic environment model, geographic specificity to Hangzhou, and exclusive focus on carbon reduction without incorporating socioeconomic constraints.