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

摘要/Abstract

摘要：

食物消费作为温室气体排放的重要部分，研究其碳排放的城乡差异与驱动因素对推进“双碳”目标实现区域协同绿色发展具有重要意义。通过混合生命周期评价法测算2014—2023年中国西部地区城乡居民食物消费碳排放量，结合泰尔指数、K-means聚类和地理探测器模型揭示其区域差异性与驱动因素。结果表明：（1） 2014—2023年中国西部地区居民人均食物消费碳排放总量呈增长趋势，由1.51×10⁴ kg·人^-1上涨至2.05×10⁴ kg·人^-1。相较于城镇，青海省农村地区人均食物消费碳排放量增速最为明显，年均增长率为6.1%。（2）区域差异主要源于省内差异，“省域内部消费不同步”成为空间分异的矛盾焦点。多数地区处于食物消费结构过渡期，而内蒙古自治区、重庆市的食物消费碳排放量则主要受本地产业结构的影响。（3）多因子交互作用以双因子增强为主，且城乡异质性突出。城镇表现为人口规模与消费水平协同驱动，农村则呈现交通设施与其他因素交互主导。

关键词: 居民食物消费, 区域差异, 驱动因素, 城乡发展, 中国西部地区

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

付伟, 王煜承, 崔龙芳, 陈建成. 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[J]. 干旱区地理, 2026, 49(5): 1026-1038.

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.

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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%）