基于MGWR模型的中国人均碳足迹时空演变及影响因素研究

doi:10.12118/j.issn.1000-6060.2025.490

Abstract

Abstract:

To quantitatively assess the per capita carbon footprint and its driving factors in China, this study provides a scientific basis for promoting coordinated regional carbon emission governance from both emission reduction and carbon sequestration perspectives. Using the net primary productivity model, the per capita carbon footprint in China and its provincial-level regions from 2009 to 2022 was estimated. The spatiotemporal patterns of the per capita carbon footprint were explored via exploratory spatial data analysis. Furthermore, from the multidimensional perspective of the economy-energy-ecology nexus, the spatial heterogeneity of factors influencing the per capita carbon footprint was analyzed using the multiscale geographically weighted regression model. The results show that: (1) From 2009 to 2022, the per capita carbon footprint in China and its provincial-level regions exhibited a significant upward trend, with the annual growth rate peaking at 10.56% in 2011. The spatial distribution of the per capita carbon footprint presented a pattern of being higher in the north and lower in the south. Hotspots and sub-hotspots were highly concentrated in Inner Mongolia and its neighboring provinces, whereas sub-coldspots were scattered across southern China. (2) During the study period, the spatial center of gravity of the per capita carbon footprint shifted northwestward, while the main axis of spatial agglomeration showed a southwest-northeast trend. (3) In terms of influencing factors, economic development level serves as a core driving force, exerting a “bidirectional” positive-and-negative effect on per capita carbon footprint in conjunction with the proportion of cultivated land use. Population density, energy intensity, and energy flow act as positive driving factors, while urbanization rate and the proportion of newly increased forestland serve as negative inhibiting factors. The results provide theoretical support for formulation of precision-oriented and differentiated regional policies aimed at achieving carbon peaking and carbon neutrality.

Key words: per capita carbon footprint, spatial heterogeneity, influencing factors, multiscale geographically weighted regression (MGWR) model

ZHAO Jing, WANG Yongyu, SHI Xiaoying. Spatiotemporal evolution and influencing factors of per capita carbon footprint in China based on the MGWR model[J].Arid Land Geography, 2026, 49(8): 1601-1613.

Figures/Tables 11

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References 34

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能源类型	平均低位发热/10^-6 TJ·t^-1	单位热值含碳量/t C·TJ^-1	碳氧化率/%
原煤	20.91	26.37	94
焦炭	28.44	29.42	93
原油	41.82	20.08	98
汽油	43.07	18.90	98
煤油	43.07	19.60	98
柴油	42.65	20.20	98
燃料油	41.82	21.10	98
天然气	38.93	15.32	99
电力	1 kWh电力=0.1721 kg原煤，按此比例关系将电力足迹转化为原煤足迹进行计算

年份	全局Moran’s I	Z值	P值
2009	0.339	3.136	0.002
2012	0.341	3.230	0.001
2015	0.279	2.753	0.006
2018	0.272	2.692	0.007
2022	0.279	3.030	0.002

解释变量	计算过程
经济发展水平	人均GDP（标准化处理）
人口密度	地区总人口数/地区土地总面积（标准化处理）
城镇化率	地区城镇常住人口数占地区常住人口总量比重/%
能源强度	能源消耗总量/地区生产总值（标准化处理）
能源流转率	煤炭油品净输入量占煤炭油品终端消费比重/%
新增林地占比	每年新增林地面积占林地总面积比重/%
耕地利用占比	耕地面积占土地利用总面积比重/%

模型	残差平方	AICc	R²	调整后R²
OLS	19.776	99.635	0.341	0.131
GWR	19.133	99.616	0.362	0.110
MGWR	8.915	96.212	0.703	0.499

Spatiotemporal evolution and influencing factors of per capita carbon footprint in China based on the MGWR model

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