中国西部地区城乡居民食物消费碳排放差异与驱动因素分析

doi:10.12118/j.issn.1000-6060.2025.431

Abstract

Abstract:

Food consumption is an important part of greenhouse gas emissions. Studying its urban-rural differences in carbon emissions and driving factors is critical to promoting regional coordinated green development to achieve the “dual carbon” goals. Food consumption carbon emissions among urban and rural residents in the western region of China from 2014 to 2023 were calculated using the hybrid life cycle assessment method. The regional differences and driving factors were revealed by combining the Theil index, K-means clustering, and the geographical detector model. The results revealed that (1) From 2014 to 2023, the total per capita food consumption carbon emissions in the western region exhibited an increasing trend, rising from 1.51×10⁴ kg·person^-1 to 2.05×10⁴ kg·person^-1. Compared with that of urban areas, the per capita food consumption carbon emissions in rural areas of Qinghai Province increased most significantly, with an average annual growth rate of 6.1%. (2) The regional differences mainly stem from intraprovincial disparities, with “asynchronous consumption within provinces” becoming the focal point of spatial differentiation. Most regions are in a transitional period of food consumption structure, while the food consumption carbon emissions in Inner Mongolia Autonomous Region and Chongqing Municipality are mainly influenced by the local industrial structure. (3) The interaction of multiple factors is mainly characterized by the enhancement of two-factor interactions, and the urban-rural heterogeneity is prominent. In urban areas, it is driven by the synergy of population size and consumption level, whereas in rural areas, it is dominated by the interaction of transportation facilities and other factors.

Key words: residents’ food consumption, regional differences, driving factors, urban and rural development, western region of China

FU Wei, WANG Yucheng, CUI Longfang, CHEN Jiancheng. Carbon emission differences and driving factors of food consumption among urban and rural residents in the western region of China[J].Arid Land Geography, 2026, 49(5): 1026-1038.

Figures/Tables 12

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食物种类	植物性食物						动物性食物
食物种类	粮食	植物油	蔬菜	瓜果	食糖		猪肉	牛肉	羊肉	禽肉	蛋类	水产品	奶类
综合碳折算系数	0.3268	1.1588	0.0274	0.0498	0.3965		1.1892	1.3657	1.3657	1.0062	0.9026	0.7315	0.4256

食物种类	植物性食物						动物性食物
食物种类	粮食	植物油	蔬菜	瓜果	食糖		猪肉	牛肉	羊肉	禽肉	蛋类	水产品	奶类
烹饪加工系数	0.1090	0.6540	0.0109	0.0000	0.0000		0.1588	0.1772	0.1772	0.1363	0.1363	0.0818	0.0164

区域	省（自治区、直辖市）
西北地区	内蒙古自治区、陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区
西南地区	广西壮族自治区、重庆市、四川省、贵州省、云南省

年份	总体差异	组内差异	组间差异	组内
年份	总体差异	组内差异	组间差异	西北地区	西南地区
2014	0.0270	0.0141（52.21%）	0.0129（47.79%）	0.0260（39.80%）	0.0057（12.41%）
2015	0.0203	0.0119（58.69%）	0.0084（41.31%）	0.0223（43.84%）	0.0050（14.85%）
2016	0.0157	0.0102（64.79%）	0.0055（35.21%）	0.0184（45.39%）	0.0050（19.40%）
2017	0.0145	0.0114（78.32%）	0.0032（21.68%）	0.0236（60.63%）	0.0041（17.69%）
2018	0.0145	0.0111（76.34%）	0.0034（23.66%）	0.0233（60.47%）	0.0037（15.87%）
2019	0.0165	0.0158（95.74%）	0.0007（4.26%）	0.0263（56.48%）	0.0100（39.25%）
2020	0.0157	0.0155（98.85%）	0.0002（1.15%）	0.0241（53.17%）	0.0110（45.68%）
2021	0.0155	0.0147（94.76%）	0.0008（5.24%）	0.0210（47.96%）	0.0113（46.81%）
2022	0.0166	0.0145（87.00%）	0.0022（13.00%）	0.0191（42.17%）	0.0118（44.83%）
2023	0.0171	0.0149（86.79%）	0.0023（13.21%）	0.0194（41.88%）	0.0122（44.91%）

年份	泰尔指数	组内差异	组间差异	组内
年份	泰尔指数	组内差异	组间差异	西北地区	西南地区
2014	0.0261	0.0136（51.96%）	0.0126（48.04%）	0.0290（37.20%）	0.0058（14.76%）
2015	0.0197	0.0115（58.52%）	0.0082（41.48%）	0.0244（41.63%）	0.0050（16.89%）
2016	0.0153	0.0099（64.57%）	0.0054（35.43%）	0.0195（43.00%）	0.0050（21.57%）
2017	0.0138	0.0107（77.58%）	0.0031（22.42%）	0.0241（58.27%）	0.0040（19.31%）
2018	0.0137	0.0103（75.36%）	0.0034（24.64%）	0.0236（57.96%）	0.0036（17.40%）
2019	0.0160	0.0153（95.66%）	0.0007（4.34%）	0.0268（56.21%）	0.0095（39.45%）
2020	0.0155	0.0153（98.84%）	0.0002（1.16%）	0.0247（53.87%）	0.0105（44.97%）
2021	0.0152	0.0144（94.70%）	0.0008（5.30%）	0.0215（47.57%）	0.0108（47.13%）
2022	0.0162	0.0141（86.84%）	0.0021（13.16%）	0.0197（40.79%）	0.0112（46.06%）
2023	0.0168	0.0145（86.68%）	0.0022（13.32%）	0.0202（40.60%）	0.0116（46.07%）

Carbon emission differences and driving factors of food consumption among urban and rural residents in the western region of China

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区域	省（自治区、直辖市）
高碳-高收-高城地区	内蒙古自治区、重庆市
中碳-中收-中城地区	陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区、广西壮族自治区、四川省、贵州省、云南省

类型	变量名称
经济结构	城镇居民人均可支配收入（U₁）/元·人^-1
	城镇居民消费价格指数（U₂）
	城镇居民商品零售价格指数（U₃）
	农村居民人均可支配收入（R₁）/元·人^-1
	农村居民消费价格指数（R₂）
	农村居民商品零售价格指数（R₃）
人口结构	城镇人口规模（U₄）/人
人口结构	农村人口规模（R₄）/人
消费结构	城镇居民人均植物性食物消费量（U₅）/kg·人^-1
	城镇居民人均动物性食物消费量（U₆）/kg·人^-1
	城镇居民恩格尔系数（U₇）/%
	农村居民人均植物性食物消费量（R₅）/kg·人^-1
	农村居民人均动物性食物消费量（R₆）/kg·人^-1
	农村居民恩格尔系数（R₇）/%
	人均家庭电冰箱拥有量（X₁）/台
交通结构	旅客周转量（X₂）/人·km
交通结构	货物周转量（X₃）/t·km

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[2]	ZHU Yiting, LIANG Guixian, ZHOU Chunshan, XIN Long, LI Jinxue. Spatiotemporal evolution characteristics and driving factors of new quality productive forces development of cultural tourism in Xinjiang [J]. Arid Land Geography, 2026, 49(1): 128-139.
[3]	LIU Quanyu, LIU Xinlu, LI Guizhen, LI Congjuan. Fractal characteristics and driving factors of soil particle size in the surface layer of Xinjiang [J]. Arid Land Geography, 2026, 49(1): 56-68.
[4]	XU Jing, YANG Bin. Coupling coordination of new urbanization and ecological security and spatial-temporal non-stationarity of its driving factors in Gansu Province [J]. Arid Land Geography, 2025, 48(8): 1432-1444.
[5]	WANG Xin, ZHENG Fang, HE Lingyao, HE Haoliang, HOU Ying. Spatiotemporal differentiation pattern and driving factors of common prosperity level in Ningxia county areas [J]. Arid Land Geography, 2025, 48(7): 1293-1303.
[6]	WANG Peipei, WANG Jiao, CAI Yongmei. Research on the spatial differentiation pattern and driving factors of Chinese ski resorts from a multi-scale perspective [J]. Arid Land Geography, 2025, 48(6): 1080-1088.
[7]	MA Xiaomin, ZHANG Zhibin, GUO Qianqian, ZHAO Xuewei, ZHANG Ning. Spatial and temporal evolution and driving factors of population in Lanzhou City from 2000 to 2020 [J]. Arid Land Geography, 2025, 48(1): 168-178.
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[9]	LI Zhi, ZHU Chenggang, WANG Jiayou, LIU Yongchang, WANG Chuan, ZHANG Xueqi, HAN Shiru, FANG Gonghuan. Estimation of evaporation loss from typical lakes in the Kumukuli Basin, East Kunlun Mountains [J]. Arid Land Geography, 2024, 47(8): 1263-1276.
[10]	XIA Wenhao, HUO Yu, LU Yuan, WANG Chaoyi. Spatialtemporal differences and spatial spillover effects of agricultural carbon emissions in Xinjiang [J]. Arid Land Geography, 2024, 47(6): 1084-1096.
[11]	WANG Jian, GUAN Yao, HE Xinghong, DAI Yunhao, CHEN Yiwei, WANG Yuqiang, LI Huiwen, FAN Debao. Dynamic monitoring and evaluation of ecological environment quality in Tumshuk City from 2000 to 2021 [J]. Arid Land Geography, 2024, 47(3): 465-473.
[12]	ZHOU Cheng, ZHAO Yaling, REN Minmin, JIN Yiting, LYU Sisi. Spatiotemporal differentiation, center of gravity evolution and driving factors of national wetland parks in the Yellow River Basin [J]. Arid Land Geography, 2024, 47(3): 506-514.
[13]	WANG Huixian, YANG Bei, YANG Ningjun, LI Luyao. Tourism economic differences and key driving factors in the Yellow River Basin [J]. Arid Land Geography, 2024, 47(3): 515-527.
[14]	REN Zijian, WANG Jianglin, XU Henian, QIN Chun. Evolution and driving factors of megadrought and pluvial events in the Qilian Mountains during the past 500 years [J]. Arid Land Geography, 2024, 47(2): 214-227.
[15]	JI Zenan, FU Wenting, WANG Pengcheng, LIAO Sheng, PAN Zheng. Coupling and coordination of digital economy and green development of agriculture in arid zones: A case of Xinjiang [J]. Arid Land Geography, 2024, 47(11): 1981-1990.