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Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of small-scale urban growth simulation to model urban agglomeration growth to assist in carbon-neutral planning, ignoring the significant characteristics of the process to achieve carbon neutrality: large-scale and long-term. This paper proposes a framework to model large-scale and long-term urban growth, which couples a quantity module and a spatial module to model the quantity and spatial allocation of urban land, respectively. This framework integrates the inertia of historical land-use change, the driving effects of the urbanization law (S-curve), and the traction of the urban agglomeration network to model the long-term quantity change of urban land. Moreover, it couples a partitioned modeling framework, spatially heterogeneous rules derived by geographically weighted regression (GWR), and quantified land-use planning orientations to build a cellular automata (CA) model to accurately allocate the urbanized cells in a large-scale spatial domain. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) as an example, the proposed framework is calibrated by the urban growth from 2000 to 2010 and validated by that from 2010 to 2020. The figure of merit (FoM) of the results simulated by the framework is 0.2926, and the simulated results are also assessed by some evidence, which both confirm the good performance of the framework to model large-scale and long-term urban growth. Coupling with the coefficients proposed by the Intergovernmental Panel on Climate Change (IPCC), this framework is used to project the carbon emissions caused by urban growth in the GHMGBA from 2020 to 2050. The results indicate that Guangzhou, Foshan, Huizhou, and Jiangmen are under great pressure to achieve the carbon-neutral targets in the future, while Hong Kong, Macao, Shenzhen, and Zhuhai are relatively easy to bring up to the standard. This research contributes to the ability of land-use models to simulate large-scale and long-term urban growth to predict carbon emissions and to support the carbon-neutral planning of the GHMGBA. [ABSTRACT FROM AUTHOR]

Previous studies on global carbon emissions from forest loss have been marked by great discrepancies due to uncertainties regarding the lost area and the densities of different carbon pools. In this study, we employed a new global 30 m land cover dynamic dataset (GLC_FCS30D) to improve the assessment of forest loss areas; then, we combined multi-sourced carbon stock products to enhance the information on carbon density. Afterwards, we estimated the global carbon emissions from forest loss over the period of 1985–2020 based on the method recommended by the Intergovernmental Panel on Climate Change Guidelines (IPCC). The results indicate that global forest loss continued to accelerate over the past 35 years, totaling about 582.17 Mha and leading to total committed carbon emissions of 35.22 ± 9.38 PgC. Tropical zones dominated global carbon emissions (~2/3) due to their higher carbon density and greater forest loss. Furthermore, global emissions more than doubled in the period of 2015–2020 (1.77 ± 0.44 PgC/yr) compared to those in 1985–2000 (0.69 ± 0.21 PgC/yr). Notably, the forest loss at high altitudes (i.e., above 1000 m) more than tripled in mountainous regions, resulting in more pronounced carbon emissions in these areas. Therefore, the accelerating trend of global carbon emissions from forest loss indicates that great challenges still remain for achieving the COP 26 Declaration to halt forest loss by 2030. [ABSTRACT FROM AUTHOR]