Dietary Acculturation Generates Virtual Carbon Flow: The Overlaid Effects Of Geographically Varied Dietary Patterns And Population Migration In Urban And Materials-Flowing China
JOURNAL OF CLEANER PRODUCTION(2020)
摘要
Quantifying the carbon footprint (CF) of food-demand can aid in mitigating the effects of global climate change, especially in the context of urbanisation, which enriches urban diets with animal products. Urbanisation also attracts large numbers of people migrating from rural areas and even across provinces in China. Migrants gradually shift from the dietary patterns of their hometowns, toward those of their destinations, a process known as "dietary acculturation." However, few studies have explored the dietary CF resulting from such migrations. Here, we compared the dietary patterns of rural vs. urban households in 31 Chinese provinces, and quantified the CF of dietary acculturation by overlaying interprovincial migrant populations with geographically varied dietary patterns. Our results show that the mean net dietary CF increased by 21 Mt CO(2)e yr(-1) due to dietary acculturation. The increased intakes of aquatic products and beef jointly contributed 75% of this CF growth, while declined cereal intake offset similar to 44%. The wealthy province of Guangdong, which has the largest immigrant population, accounts for similar to 30% of growth in the dietary CF. Regression analyses revealed that the Engel coefficient had a strong marginal effect on the variations in CF attributable to dietary acculturation. Additionally, the dietary CF of migrant populations was positively correlated with the virtual environmental flows (e.g., carbon, water, and energy) that are embedded in regional commodity trades. This quantification of the CF of dietary acculturation provides novel insights that may be used to further quantify the carbon emissions of food demand. (C) 2020 Elsevier Ltd. All rights reserved.
更多查看译文
关键词
Geographically varied diet, Floating population, Dietary acculturation, Urbanisation, Climate change
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要