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

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, reduc...

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Detalles Bibliográficos
Autores principales: Liang, Yi, Han, Aixi, Chai, Li, Zhi, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579113/
https://www.ncbi.nlm.nih.gov/pubmed/33050091
http://dx.doi.org/10.3390/ijerph17197349
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author Liang, Yi
Han, Aixi
Chai, Li
Zhi, Hong
author_facet Liang, Yi
Han, Aixi
Chai, Li
Zhi, Hong
author_sort Liang, Yi
collection PubMed
description 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.
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spelling pubmed-75791132020-10-29 Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet Liang, Yi Han, Aixi Chai, Li Zhi, Hong Int J Environ Res Public Health Article 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. MDPI 2020-10-08 2020-10 /pmc/articles/PMC7579113/ /pubmed/33050091 http://dx.doi.org/10.3390/ijerph17197349 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, Yi
Han, Aixi
Chai, Li
Zhi, Hong
Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title_full Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title_fullStr Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title_full_unstemmed Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title_short Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
title_sort using the machine learning method to study the environmental footprints embodied in chinese diet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579113/
https://www.ncbi.nlm.nih.gov/pubmed/33050091
http://dx.doi.org/10.3390/ijerph17197349
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