Assessing the Carbon Emission Driven by the Consumption of Carbohydrate-Rich Foods: The Case of China
Keyword
sustainable dietsnutritional adequacy
environmental impact
K-means clustering
linear programming
Date
2019-03-28
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Yang, X.; Zhang, Z.; Chen, H.; Zhao, R.; Xu, Z.; Xie, A.; Chen, Q. Assessing the Carbon Emission Driven by the Consumption of Carbohydrate-Rich Foods: The Case of China. Sustainability 2019, 11, 1875. https://doi.org/10.3390/su11071875Abstract
Background: Carbohydrate-rich (CR) foods are essential parts of the Chinese diet. However, CR foods are often given less attention than animal-based foods. The objectives of this study were to analyze the carbon emissions caused by CR foods and to generate sustainable diets with low climate impact and adequate nutrients. Methods: Twelve common CR food consumption records from 4857 individuals were analyzed using K-means clustering algorithms. Furthermore, linear programming was used to generate optimized diets. Results: Total carbon emissions by CR foods was 683.38g CO2eq per day per capita, accounting for an annual total of 341.9Mt CO2eq. All individuals were ultimately divided into eight clusters, and none of the popular clusters were low carbon or nutrient sufficient. Optimized diets could reduce about 40% of carbon emissions compared to the average current diet. However, significant structural differences exist between the current diet and optimized diets. Conclusions: To reduce carbon emissions from the food chain, CR foods should be a research focus. Current Chinese diets need a big change to achieve positive environmental and health goals. The reduction of rice and wheat-based foods and an increase of bean foods were the focus of structural dietary change in CR food consumption.Funder
Fujian Provincial Social Science Research Base for Ecological Civilization; Guangdong Planning Projects of Philosophy and Social Science; Natural Science Foundation of Guangdong Province; Fujian Provincial Social Science Research Base for Ecological Civilization; Chinese Ministry of Education; China Postdoctoral Science FoundationGrant Number
KXJD1815A; FJ2018JDZ062; GD16YGL03; 2017A030313395; K81NIF01A; 19YJCZH203; 2016M602476ae974a485f413a2113503eed53cd6c53
https://doi.org/10.3390/su11071875
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