Using the Machine Learning Method to Study the Environmental Footprints Embodied in Chinese Diet
Bibliographic Data
| ID | 15467699 |
|---|---|
| Authors | Yi Liang (0000-0003-3760-4433, China Agricultural University), Aixi Han (0000-0002-9312-7072, China Agricultural University), Li Chai (0009-0007-5342-5140, China Agricultural University, corresponding author), Hong Zhi (China Agricultural University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 19 |
| Pages | 7349-7349 |
| Publication date | 2020-10-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17197349 |
| PMID | 33050091 |
| OpenAlex | W3092304686 |
| Language | EN |
| Citations received | 2 |
| References cited | 39 |
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
China · Consumption (sociology · Decision tree · Ecological footprint · Environmental health · Geography · Identification (biology · Machine learning · Microdata (statistics · Population · Sustainable development · Agriculture Sustainability and Environmental Impact · Computer Science · Energy, Environment, Economic Growth · Environmental Impact and Sustainability · Medicine
A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries
Cohort Profile
Climate Change and Food Systems
Integrating Ecological, Carbon and Water footprint into a “Footprint Family” of indicators
Induction of decision trees
The water footprint of humanity
The New Cooperative Medical Scheme in rural China
Reducing food’s environmental impacts through producers and consumers
An extended environmental input–output lifecycle assessment model to study the urban food–energy–water nexus
Drivers of the Growing Water, Carbon and Ecological Footprints of the Chinese Diet from 1961 to 2017
Water Footprint of Food Consumption by Chinese Residents
Food security and sustainable use of natural resources
Meeting future food demand with current agricultural resources
Green Returns to Education
On the estimation of potential food waste reduction to support sustainable production and consumption policies
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,4 |
| Citation span | 2021 - 2022 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |