Welcome to Francis Academic Press

Academic Journal of Environment & Earth Science, 2023, 5(8); doi: 10.25236/AJEE.2023.050809.

Analysis of spatio-temporal pattern of land-use carbon emission in Chang-Zhu-Tan metropolitan area at the grid scale


Gao Ziheng, He Dong, Jing Jie, Chen Yu, Chen Huiling

Corresponding Author:
Chen Huiling

School of Public Administration and Human Geography, Hunan University of Technology and Business, Changsha, 410006, China


Economic activity patterns and land use patterns greatly affect the carbon emission patterns of a certain region. This article is based on 30 meter precision land use data and energy consumption data of the Changzhutan urban agglomeration, using a grid with a side length of 5 kilometers as the research unit. We inferred the spatiotemporal pattern and changes in carbon emissions in the region from 2000 to 2020. We also explored the evolution process of carbon emission grids, carbon source grids, and carbon sink grids at different intensity levels in the region over the past 20 years. This article uses Geoda software to conduct a comprehensive and local Moran's analysis of regional carbon emissions, to explore the spatial autocorrelation between grids. Research has shown that: the total carbon emissions of the Chang-Zhu-Tan urban agglomeration have gone through three stages: first rapid growth, then slow growth, and finally rapid decline. The carbon emissions in 2010 and 2015 were much higher than those in 2000, 2005, and 2020.In 2010 and 2015, a large number of extremely high intensity grids with annual carbon emissions exceeding 500000 tons appeared in the three urban areas of Changsha, Zhuzhou, and Xiangtan. Subsequently, by 2020, the total carbon emissions had fallen back to close to 2005; The central urban area of the three cities of Changsha, Zhuzhou, and Xiangtan is the region with the largest changes in carbon emission patterns within the study area. It is also a high-value core area for carbon emissions and carbon sources. The high carbon emission grid has become increasingly concentrated in the area. The area along National Highway 106 in Zhuzhou is another area with high carbon emissions and carbon source values. The connection between this area and the core area of the Chang-Zhu-Tan metropolitan area is relatively weak due to the influence of terrain and distance. The mountainous and hilly areas are the main functional carriers of carbon sequestration in the entire research area. Several large mountain ranges within the territory are gathering areas for high-value carbon sequestration grids. The carbon emission grid exhibits low to low aggregation in this area. Regarding the task of achieving dual carbon, this article believes that: We must take into account the transformation of economic models within the built-up area, the rational planning and control of regional land use types, and the protection of existing ecological green spaces, and these three directions must be jointly promoted and implemented.


Land-use carbon emission; Spatio-temporal pattern; Grid Analysis; Chang-Zhu-Tan metropolitan area

Cite This Paper

Gao Ziheng, He Dong, Jing Jie, Chen Yu, Chen Huiling. Analysis of spatio-temporal pattern of land-use carbon emission in Chang-Zhu-Tan metropolitan area at the grid scale. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 8: 80-91. https://doi.org/10.25236/AJEE.2023.050809.


[1] The word of the day. Peak carbon dioxide emissions. Baijiahao China Daily Network. 2021-01-04

[2] Hong Kai, Zhu Ziyu. Temporal and spatial characteristics and influential factors of agricultural land use carbon emissions in Pearl River Delta: Based on the data from 1996 to 2014 [J]. Journal of Hunan Agricultural University (Social Sciences Edition), 2017, 18 (01): 70-76. DOI: 10.13331/j.cnki. jhau (ss). 2017.01.011.

[3] Ma Hailiang, Ding Yuanqing, Pang Qinghua. The transformation of lake water utilization and its carbon emission impact in Wuhan [J]. Resources and Environment of the Yangtze River Basin, 2020, 29 (02): 369-375.

[4] Xiao Hongwei, Yi Danhui, Zhang Yaxiong. Research on the spatial measurement of regional carbon emissions in China [J]. Economy and Management, 2013, 27(12):10.

[5] Zhang Leqin, Chen Xiaoping, Wang Wenqin, etc. Measurement and trend prediction of carbon emission effects of construction land changes in Anhui Province in the past 15 years - based on the STIRPAT model [J]. Journal of Environmental Science, 2013, 33(3):950-958.

[6] Yuan Xiaoling, Xi Jihong, Li Chaopeng, etc. Research on Peak Forecast and Emission Reduction Potential of China's Industrial Sector [J]. Statistics and Information Forum, 2020, 35(9):11.

[7] Hou Bo, Yue Wenze, Wang Tengfei.Spatiotemporal Heterogeneity Pattern and Influencing Factors of Carbon Emission in Metropolitan Areas of China: A Case of Shanghai [J]. Economic geography, 2020, 40 (09): 82-90.doi:10.15957/j.cnki.jjdl.2020.09.009.

[8] Yuan J, Xu Y, Hu Z, et al. Peak Energy Consumption and CO2 Emissions in China[J]. Energy Policy, 2014, 68: 508‐523.

[9] Zhang X, Karplus V J, Qi T, et al. Carbon Emissions in China: How Far can New Efforts Bend the Curve? [J]. Energy Economics, 2016, 54: 388‐395.

[10] Li Ying, Huang Xianjin, Zhen Feng. Analysis of Carbon Emission Effects of Different Land Use Patterns in Jiangsu Province [J]. Journal of Agricultural Engineering, 2008, 24 (S2): 102-107.

[11] Fang Jingyun, GuoZhaodi, Park Shilong, et al. Estimation of terrestrial vegetation carbon sink in China from 1981 to 2000 [J]. Chinese Science (Part D: Earth Science), 2007 (06): 804-812.

[12] DuanXiaonan, Wang Xiaoke, Lu Fei, et al. Carbon sequestration and its potential by wetland ecosystems in China [J]. Journal of Ecology, 2008 (02): 463-469.

[13] Lai Li, Huang Xianjin, Liu Weiliang, et al. Adjustment for regional ecological footprint based on input-output technique: a case study of Jiangsu Province in 2002 [J]. Journal of Ecology, 2006 (04): 1285-1292.

[14] Yang, Jie and Huang, Xin: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.