Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet

Yi Liang, Aixi Han, Li Chai and Hong Zhi
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Yi Liang: College of Science, China Agricultural University, Beijing 100083, China
Aixi Han: International College Beijing, China Agricultural University, Beijing 100083, China
Li Chai: International College Beijing, China Agricultural University, Beijing 100083, China
Hong Zhi: International College Beijing, China Agricultural University, Beijing 100083, China

IJERPH, 2020, vol. 17, issue 19, 1-17

Abstract: The food system profoundly affects the sustainable development of the environment and resources. Numerous studies have shown that the food consumption patterns of Chinese residents will bring certain pressure to the environment. Food consumption patterns have individual differences. Therefore, reducing the pressure of food consumption patterns on the environment requires the precise positioning of people with high consumption tendencies. Based on the related concepts of the machine learning method, this paper designs an identification method of the population with a high environmental footprint by using a decision tree as the core and realizes the automatic identification of a large number of users. By using the microdata provided by CHNS(the China Health and Nutrition Survey), we study the relationship between residents’ dietary intake and environmental resource consumption. First, we find that the impact of residents’ food system on the environment shows a certain logistic normal distribution trend. Then, through the decision tree algorithm, we find that four demographic characteristics of gender, income level, education level, and region have the greatest impact on residents’ environmental footprint, where the consumption trends of different characteristics are also significantly different. At the same time, we also use the decision tree to identify the population characteristics with high consumption tendency. This method can effectively improve the identification coverage and accuracy rate and promotes the improvement of residents’ food consumption patterns.

Keywords: CHNS; water footprint; carbon footprint; ecological footprint; diet; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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