Spatial dynamics of per capita building carbon emissions in the Greater Bay Area: Pathways to net zero carbon by 2060

Abstract

Achieving carbon neutrality by 2060 presents a critical challenge for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a region contributing 12% of China's GDP with diverse urban forms and rapidly evolving energy demands. This study aims to provides a detailed spatial and temporal analysis of per capita building carbon emissions in the GBA from 2030 to 2060 under Business-As-Usual (BAU) and three Carbon Neutrality (CN) scenarios. With a fine spatial resolution of 500m x 500m, the study employs the Long-range Energy Alternatives Planning (LEAP) system for multi-scenario emissions predictions, and applies Random Forest Regression (RFR) and Support Vector Regression (SVR) to model spatial variability and identify key emission drivers. Results reveal significant spatial variability, with per capita emissions under BAU exceeding 420.7 tons/person in 2030 and decreasing only to 271.8 tons/person by 2060. Under CN1 and CN3, which emphasize aggressive decarbonization and carbon capture, the highest per capita emissions reach negative levels by 2060 in the key cities like Guangzhou and Shenzhen. Population density, urban form, and land use are identified as influencing emissions patterns. This study highlights the need for localized strategies that combine both supply- and demand-side measures to achieve deep decarbonization. It encourages urban planners and policymakers to adopt green building practices, energy storage technologies, and strong policy frameworks to create sustainable urban environments. The framework can also be applied to other urban regions and emphasizes the importance of integrating technological innovations with tailored policies to reach a net-zero carbon built environment.