Carbon footprint of food consumption by rural residents in China: spatial-temporal pattern and driving factors

doi:10.3969/j.issn.1004-1524.20240807

Acta Agriculturae Zhejiangensis ›› 2025, Vol. 37 ›› Issue (9): 1991-2002.DOI: 10.3969/j.issn.1004-1524.20240807

• Agricultural Economy and Development • Previous Articles Next Articles

Carbon footprint of food consumption by rural residents in China: spatial-temporal pattern and driving factors

QI Huibo¹^,²(), JI Peng¹, ZHENG Qiaoer¹, ZHAO Jing¹, LONG Fei²^,^*()

¹ College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
² Zhejiang Rural Revitalization Research Institute, Hangzhou 311300, China

Received:2024-09-13 Online:2025-09-25 Published:2025-10-15
Contact: LONG Fei

Abstract

Abstract:

It is important to understand the evolution of the carbon footprint of rural residents' food consumption to explore the emission reduction path from the rural residents' food consumption side. Based on the basic characteristics of rural residents' food consumption, the direct and indirect carbon footprints of rural residents' food consumption were calculted in 30 provinces (autonomous regions, municipalities) of China excluding Hong Kong, Macao, Taiwan and Xizang from 2001 to 2022. The spatial and temporal patterns and driving factors were analysed by using spatial econometric models. It was found that: 1) Rural residents' food consumption exhibited unbalanced and insufficient development level. The total direct carbon footprint was decreasing in the study period, while the per capita direct carbon footprint was decreasing first and then increasing. 2) There were significant spatial clustering characteristics and spillover effects of rural residents' food consumption carbon footprint in all sample provinces (autonomous regions, municipalities). The spillover effects mainly concentrated in high-high and low-low clusters. 3) The driving factors of the carbon footprint of rural residents' food consumption consisted of population, economy, technology, trade and food consumption structure, and the mechanism on carbon footprint of rural residents' food consumption varied among sample provinces (autonomous regions, municipalities). Based on these findings, there was urgent need to introduce sustainable food consumption policies for rural residents which balanced the nutrition and low carbon goals. Besides, efforts should be made to promote the carbon reduction of rural residents' food consumption in all aspects through price interventions, consumption subsidies, and popularization of science and technology.

Key words: rural residents, food consumption, carbon footprint, spatial-temporal pattern, driving factors

CLC Number:

F323.2

QI Huibo, JI Peng, ZHENG Qiaoer, ZHAO Jing, LONG Fei. Carbon footprint of food consumption by rural residents in China: spatial-temporal pattern and driving factors[J]. Acta Agriculturae Zhejiangensis, 2025, 37(9): 1991-2002.

Figures/Tables 8

References 31

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类别 Category	种类 Type	综合碳折算系数 Comprehensive carbon conversion factor	直接碳折算系数 Direct carbon conversion factor	料肉转化比 Feed-meat conversion ratio	烹饪加工碳折算系数 Carbon conversion factor for cooking processing
植物性食物	粮食Grain	0.326 8	0.326 8	—	0.109 0
Plant-based food	蔬菜Vegetables	0.027 4	0.027 4	—	0.010 9
	瓜果Fruits and melons	0.049 8	0.049 8	—	0
	食糖Sugar	0.396 5	0.396 5	—	0
	植物油Vegetable oil	0.766 6	0.766 6	—	0.654 0
动物性食物	猪肉Pork	0.982 7	0.254 6	2.86	0.158 8
Animal-based food	牛肉Beef	1.120 2	0.254 6	3.40	0.190 8
	羊肉Mutton	1.120 2	0.254 6	3.40	0.190 8
	禽类Poultry	0.840 2	0.254 6	2.30	0.136 3
	蛋类Eggs	0.498 3	0.151 0	2.30	0.054 5
	奶类Milk	0.132 7	0.062 9	1.11	0.016 4
	水产品Aquatic products	0.401 2	0.143 3	1.80	0.081 8

类别 Category	种类 Type	综合碳折算系数 Comprehensive carbon conversion factor	直接碳折算系数 Direct carbon conversion factor	料肉转化比 Feed-meat conversion ratio	烹饪加工碳折算系数 Carbon conversion factor for cooking processing
植物性食物	粮食Grain	0.326 8	0.326 8	—	0.109 0
Plant-based food	蔬菜Vegetables	0.027 4	0.027 4	—	0.010 9
	瓜果Fruits and melons	0.049 8	0.049 8	—	0
	食糖Sugar	0.396 5	0.396 5	—	0
	植物油Vegetable oil	0.766 6	0.766 6	—	0.654 0
动物性食物	猪肉Pork	0.982 7	0.254 6	2.86	0.158 8
Animal-based food	牛肉Beef	1.120 2	0.254 6	3.40	0.190 8
	羊肉Mutton	1.120 2	0.254 6	3.40	0.190 8
	禽类Poultry	0.840 2	0.254 6	2.30	0.136 3
	蛋类Eggs	0.498 3	0.151 0	2.30	0.054 5
	奶类Milk	0.132 7	0.062 9	1.11	0.016 4
	水产品Aquatic products	0.401 2	0.143 3	1.80	0.081 8

类型 Type	推荐摄入量 Recommended intake amount	2001年实际摄入量 Actual intake amount in 2001	2007年实际摄入量 Actual intake amount in 2007	2012年实际摄入量 Actual intake amount in 2012	2016年实际摄入量 Actual intake amount in 2016	2022年实际摄入量 Actual intake amount in 2022
粮食Grain	125~285	653.7	546.6	450.1	430.7	450.1
蔬菜Vegetables	300~500	299.5	271.2	232.1	250.7	286.6
奶类Milk	300~500	2.9	9.6	14.5	18.1	23.0
动物性食物Animal-based food	120~200	72.1	79.2	87.9	122.2	181.4
瓜果Fruits and melons	200~350	52.2	56.1	74.0	100.8	127.9
植物油Vegetable oil	25~30	19.2	16.4	21.4	27.9	29.6

类型 Type	推荐摄入量 Recommended intake amount	2001年实际摄入量 Actual intake amount in 2001	2007年实际摄入量 Actual intake amount in 2007	2012年实际摄入量 Actual intake amount in 2012	2016年实际摄入量 Actual intake amount in 2016	2022年实际摄入量 Actual intake amount in 2022
粮食Grain	125~285	653.7	546.6	450.1	430.7	450.1
蔬菜Vegetables	300~500	299.5	271.2	232.1	250.7	286.6
奶类Milk	300~500	2.9	9.6	14.5	18.1	23.0
动物性食物Animal-based food	120~200	72.1	79.2	87.9	122.2	181.4
瓜果Fruits and melons	200~350	52.2	56.1	74.0	100.8	127.9
植物油Vegetable oil	25~30	19.2	16.4	21.4	27.9	29.6

年份 Year	直接碳足迹Direct carbon footprint			间接碳足迹Indirect carbon footprint			碳足迹Carbon footprint
年份 Year	I	Z值Z value	p值p value	I	Z值Z value	p值p value	I	Z值Z value	p值p value
2001	0.147	5.464	<0.001	0.118	4.981	<0.001	0.120	4.786	<0.001
2002	0.121	4.917	<0.001	0.083	3.764	<0.001	0.076	3.464	<0.001
2003	0.098	3.999	<0.001	0.115	4.872	<0.001	0.076	3.356	<0.001
2004	0.098	4.017	<0.001	0.121	4.970	<0.001	0.075	3.296	<0.001
2005	0.105	4.201	<0.001	0.125	5.071	<0.001	0.051	2.610	0.005
2006	0.122	4.729	<0.001	0.147	5.719	<0.001	0.092	3.889	<0.001
2007	0.116	4.535	<0.001	0.156	5.970	<0.001	0.095	3.985	<0.001
2008	0.091	3.802	<0.001	0.148	5.715	<0.001	0.088	3.777	<0.001
2009	0.071	3.184	0.001	0.132	5.154	<0.001	0.052	2.620	0.004
2010	0.060	2.853	0.002	0.112	4.512	<0.001	0.047	2.474	0.007
2011	0.055	2.764	0.003	0.117	4.601	<0.001	0.050	2.554	0.005
2012	0.021	1.707	0.044	0.119	4.655	<0.001	0.046	2.441	0.007
2013	0.024	1.778	0.038	0.099	4.043	<0.001	0.025	1.804	0.036
2014	0.115	4.514	<0.001	0.046	2.418	0.008	0.055	2.671	0.004
2015	0.092	3.788	<0.001	0.046	2.424	0.008	0.043	2.327	0.010
2016	0.059	2.828	0.002	0.044	2.365	0.009	0.023	1.724	0.042
2017	0.064	2.969	0.001	0.042	2.292	0.011	0.029	1.919	0.028
2018	0.057	2.749	0.003	0.064	2.946	0.002	0.036	2.124	0.017
2019	0.056	2.728	0.003	0.048	2.455	0.007	0.040	2.221	0.013
2020	0.046	2.437	0.007	0.043	2.312	0.010	0.033	2.037	0.021
2021	0.086	3.616	<0.001	0.086	3.617	<0.001	0.078	3.371	<0.001
2022	0.079	3.415	<0.001	0.042	2.631	0.004	0.068	3.140	0.001

Carbon footprint of food consumption by rural residents in China: spatial-temporal pattern and driving factors

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变量 Variable	样本量 Sample size	平均值 Mean	标准差 Standard deviation	最小值 Minimum value	最大值 Maximum value
ln C	660	5.15	0.19	4.73	5.67
ln P₁	660	1.12	0.13	0.70	1.44
ln P₂	660	2.36	0.37	1.11	3.54
ln E₁	660	17.74	1.56	14.50	21.18
ln E₂	660	10.31	0.85	8.01	12.16
ln T₁	660	1.94	0.52	0.54	2.92
ln T₂	660	5.78	0.82	2.59	7.82
ln I_p	660	4.65	0.04	4.56	4.78
ln S₁	660	1.59	0.43	0.20	2.45
ln S₂	660	1.51	0.66	-0.92	2.64

变量 Variable	空间固定效应模型Space fixed model			时间固定效应模型Time fixed model			时空固定效应模型Space-time fixed model
变量 Variable	回归系数 Regression coefficient	p值 p value	Z值 Z value	回归系数 Regression coefficient	p值 p value	Z值 Z value	回归系数 Regression coefficient	p值 p value	Z值 Z value
ln P₁	-0.108 8	0.152	-1.43	-0.198 0^***	0.001	-3.27	-0.112 3	0.127	-1.53
ln P₂	0.058 3^***	0.001	3.25	0.067 5^***	<0.001	3.98	0.056 2^***	0.001	3.22
ln E₁	0.260 3^***	<0.001	3.94	0.225 7^***	<0.001	7.68	0.181 2^***	0.009	2.62
ln E₂	0.051 9^*	0.051	1.95	0.051 9^*	0.051	1.95	0.056 1^**	0.036	2.09
ln T₁	-0.030 6^*	0.054	-1.93	-0.010 3	0.301	-1.04	-0.032 1^**	0.035	-2.11
ln T₂	0.062 6^***	<0.001	3.64	0.059 6^***	<0.001	9.55	0.065 1^***	<0.001	3.82
ln I_p	-0.097 1	0.692	-0.40	-0.188 2	0.517	-0.65	0.002 6	0.991	0.01
ln S₁	-0.028 6	0.225	-1.21	0.091 8^***	<0.001	6.93	-0.029 3	0.209	-1.25
ln S₂	-0.015 0	0.385	-0.87	0.064 3^***	<0.001	5.32	0.009 2	0.616	0.50
Wln P₁	0.021 2	0.827	0.22	0.183 8^*	0.068	1.83	0.028 4	0.807	0.24
Wln P₂	0.042 8	0.250	1.15	0.063 7^*	0.083	1.73	0.011 9	0.772	0.29
Wln E₁	-0.333 3^***	<0.001	-4.79	-0.233 9^***	<0.001	-4.41	-0.500 4^***	<0.001	-4.11
Wln E₂	-0.230 1^***	<0.001	-7.22	-0.230 1^***	<0.001	-7.22	-0.185 6^***	<0.001	-4.25
Wln T₁	0.086 2^***	0.003	2.93	0.051 3^**	0.013	2.49	0.057 8^*	0.070	1.81
Wln T₂	-0.032 9	0.232	-1.19	-0.047 0^***	<0.001	-3.92	0.022 0	0.492	0.69
Wln I_p	-0.107 7	0.672	-0.42	-0.006 3	0.989	-0.01	0.114 0	0.766	0.30
Wln S₁	0.039 8	0.235	1.19	0.180 2^***	<0.001	6.30	0.073 9^*	0.092	1.68
Wln S₂	-0.021 0	0.489	-0.69	0.050 0^*	0.061	1.87	0.049 7	0.192	1.30
Wln C	0.230 3^***	<0.001	4.89	0.145 7^***	0.006	2.74	-0.002 1	0.971	-0.04

变量 Variable	各变量在不同地区的回归系数Regression coefficient in different regions
变量 Variable	东部地区Eastern China	中部地区Central China	西部地区Western China	东北地区Northeast China
ln P₁	-0.191 2^**	-0.284 0^**	0.096 7	-1.589 1^***
ln P₂	0.102 9^***	0.012 5	0.082 8^***	-0.150 6
ln E₁	0.500 3^***	0.686 5^***	0.251 6^***	0.823 8^**
ln E₂	-0.053 5	-0.231 9^***	-0.092 6^***	-0.042 6
ln T₁	-0.060 2^***	0.060 4^**	0.106 7^***	-0.215 7^***
ln T₂	0.024 1^*	-0.029 3	-0.016 2	-0.075 7
ln I_p	-1.179 0^***	-0.559 9	0.394 6	1.983 2^***
ln S₁	-0.136 3^***	0.097 9^**	0.075 8^***	0.242 0^***
ln S₂	-0.105 7^**	-0.122 0^**	0.043 6^***	-0.130 3
Wln P₁	0.166 4	0.324 1	-0.505 4^***	-1.632 4^***
Wln P₂	0.023 1	0.212 3	0.033 3	-0.264 4
Wln E₁	0.101 0	1.633 2^***	-0.378 0^***	0.313 0
Wln E₂	-0.301 7^***	-0.383 4^***	-0.334 8^***	-0.055 5
Wln T₁	0.068 0^**	0.191 4^***	0.252 2^***	-0.374 2^***
Wln T₂	-0.048 8^**	-0.255 7^**	-0.139 9^***	-0.076 9
Wln I_p	0.947 5	-0.084 1	-0.509 5	3.776 9^***
Wln S₁	-0.105 7^***	0.095 7^*	-0.184 7^***	0.204 1^*
Wln S₂	0.166 4	-0.071 9	0.197 5^***	0.215 3
Wln C	0.054 8	-0.461 5^***	-0.607 7^***	-0.443 0^***
样本量Sample size	220	132	242	66
决定系数	0.694 9	0.618 1	0.772 9	0.676 6
Determination coefficient(R²)

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