居民食物消费变化引致的环境足迹测算

首先对比分析了2014～2020年城乡居民食物消费结构的变化特征,其次测算了人口规模、城镇化率、人均食物消费量和食物消费结构变化对土地-水-碳足迹变化的影响程度,最后设计了食物现状消费结构S0、发展消费结构S1和更趋近膳食指南的最优消费结构S2三种情景,测算了2025年和2030年3种情景下居民食物消费引致的环境足迹.结果表明:人均食物消费量和消费结构逐渐成为影响环境足迹的主要因素,2018～2020年城镇(农村)居民人均食物消费量和消费结构对环境足迹的平均贡献率分别为51.1%(51.6%)和-17.4%(-13.1%); S1和S2下居民食物消费引致的环境足迹均小于在S0下的值.在S2下环境足迹的减少更明显.在2025年城镇(农村)居民在S2下食物消费引致的土地、水、碳足迹比在S0下减少10.5%(11.5%)、19.6%(17.2%)、12.6%(13.7%);在2030年将减少11.6%(11.9%)、21.0%(16.8%)、13.6%(14.7%).基于分析结果,提出了如何减少食物消费引致的环境足迹的建议.

Measuring the environmental footprints caused by the changes of residents' food consumption

This paper first compared and analyzed the changing characteristics of urban and rural residents' food consumption from 2014 to 2020, then calculated the contributions of population size, urbanization rate, per capita food consumption volume, and food consumption structure to the environmental footprints. Finally, we designed three scenarios: the current food consumption structure S0, the developing consumption structure S1, and the optimal consumption structure S2, closer to the dietary guideline in 2022 for Chinese residents, to measure the environmental footprints led by residents' food consumption in 2025 and 2030. The results show that the per capita food consumption volume and dietary structure have gradually become the main factors affecting the environmental footprints. The average contribution of per capita food consumption volume and consumption structure of urban (rural) residents to the environmental footprints in 2018~2020 was 51.1% (51.6%) and -17.4% (-13.1%) in China. The environmental footprints caused by the changes in residents' food consumption in S1and S2 would be lower than those in S0, and the differences would be more evident in S2. Compared with the environmental footprints in S0, the land, water, and carbon footprints caused by urban (rural) residents' food consumption in S2 would drop 10.5% (11.5%), 19.6% (17.2%), 12.6% (13.7%) respectively in 2025, those would reduce 11.6% (11.9%), 21.0% (16.8%), 13.6% (14.7%) in 2030 respectively. Based on the analysis of the results, we suggested practical ways to reduce the environmental footprints caused by food consumption.