Abstract:This paper calculated the spatial and temporal distribution of carbon storage in Guangdong Province from 1990 to 2020 based on the land use carbon storage database and the Carbon module of the InVEST model. Morans' I index and Getis-Ord Gi* were used to analyze the spatial and temporal distribution characteristics of carbon storage at the grid scale. Then, the LISA agglomeration maps were obtained by calculating Anselin Local Moran's I. Finally, the PLUS model and 14 land use drivers are applied to predict the land use and its carbon storage distribution in 2050. The results showed that land use changes significantly affected the regional carbon storage and the highest carbon density was found in forest land and grassland, 188.44 and 329.34Mg/hm2, respectively.. The spatial pattern of carbon stocks is characterized by "low in the central part, high in the north, and medium in the east and west". The spatial distribution of carbon storage is consistent with the distribution of land use. The areas with significantly high values of carbon storage were located in the northern part of Guangdong Province, where there were few construction land and more concentrated ecological land. The areas with significantly low values were located in the Pearl River Delta region where the intensity of land development and ecological land fragmentation were higher. Under the natural development scenario, the carbon storage of land use in Guangdong Province will be reduced by 43.2721 million Mg by 2050. With the further development of land space, the carbon stock of coastal cities around the Pearl River Estuary will continue to decline. Therefore, increasing carbon storage in vegetation ecosystem is one of the most important ways to achieve carbon neutrality. We should continue to maintain the important position of the ecological protection barrier in the mountainous areas of northern Guangdong, slow down the intensity of urban land development in the Pearl River Delta, improve the ability to conserve land for construction, and form a balanced and coordinated pattern of land use and carbon storage.
林彤, 杨木壮, 吴大放, 刘锋, 杨金海, 王颖佳. 基于InVEST-PLUS模型的碳储量空间关联性及预测——以广东省为例[J]. 中国环境科学, 2022, 42(10): 4827-4839.
LIN Tong, YANG Mu-zhuang, WU Da-fang, LIU Feng, YANG Jin-hai, WANG Ying-jia. Spatial correlation and prediction of land use carbon storage based on the InVEST-PLUS model- A case study in Guangdong Province. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(10): 4827-4839.
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