Spatiotemporal assessment of land use carbon emission in the Guangdong-Hong Kong-Macao Greater Bay Area:A hybrid static-dynamic model integrated with PLUS

Abstract

Land use change critically shapes spatiotemporal carbon emission patterns in urban agglomerations, yet refined mechanistic analyses and multi-scenario simulations remain limited. Focusing on China's Guangdong-Hong Kong-Macao Greater Bay Area (GBA), this study combines PLUS land use modelling and scenario simulations with hybrid “static-dynamic” carbon models to dissect land‑carbon interactions across historical (2000−2020) and future scenarios (NDS, CRS, CES). Key findings are as follows: (1) A 93.46 % expansion of construction land drove a 218.50 % carbon surge during 2000–2020. Core cities exhibited divergent trends: Guangzhou's “decelerating growth,” Shenzhen's “peak-decline” transition, and Hong Kong's “U-shaped” fluctuations highlighted urban expansion versus low-carbon governance conflicts. (2) By 2030, carbon emissions rise by 3.7 % (NDS) and 0.9 % (CRS), while CES achieves a 1.7 % reduction through ecological space optimization, proving optimal for balancing development and decarbonization. (3) High-emission hotspots historically diffused from urban centers to peripheries, a trend suppressed under CES and CRS. This study establishes a “land use analysis-carbon emission assessment-multi scenario decision making” framework, revealing the long-term, multi-scenario evolution patterns and influencing mechanisms of land-carbon interactions in the highly heterogeneous urban agglomeration of GBA. The findings provide decision-making support and methodological references for low-carbon land use planning.