In the past decades, food consumption in China has undergone a rapid increase and a significant structure transition, as a result of population growth and economic development. The food system is increasingly threatening the environment by depleting water resources, deteriorating water bodies, aggravating climate change, degrading ecosystems, etc. It is significant to understand how food consumption affected the environment and how its impacts were driven in the historical period. This study reveals the environmental impacts of China’s food system from 1961 to 2017 from a consumption perspective by assessing water, carbon, and ecological footprints. The logarithmic mean Divisia index method was used to examine the drivers of the growing environmental footprints. The assessment results show that all three environmental footprints have had a drastic increase of more than two times during the studied period, which indicates the high environmental pressure posed by food consumption. We also found that, before the 1980s, the main driving forces of the increasing footprints were population and per capita energy intake. From 1984, the diet pattern started to take a positive effect and then became the dominant driver of the growing environmental footprints after the end of the 1990s.

Fossil energy production not only aggravates water depletion but also severely contaminates water resources. This study employed a mixed-unit input–output model to give a life cycle assessment of national average water degradation in production of common types of fossil fuels in China. The results show that the amount of grey water generated is much more than that of consumptive and withdrawn water in all cases. Although there is a high discharge amount of chemical oxygen demand (COD) in fossil fuel production, the pollutants of petroleum (PE) and volatile phenols (VP) require more dilution water than COD. PE is the greatest contributor to water degradation caused by primary fossil fuels, while VP pollution is prominent in production of upgraded fossil fuels. Basically, the main causes of water degradation, PE and VP discharge, occurs at coal mines, oil fields, refinery plants, and coking factories, rather than in the upstream sectors. A scenario analysis showed that water pollution can be significantly reduced if VP discharge in the coking process is controlled to be at the standard concentration. PE requires a standard withalower discharge concentration in order to further mitigate water pollution in production of fossil fuels. The coal production industry has a much lower pollutant removal rate but spends more on wastewater treatment, up to 12% of its profit. The other fossil fuel industries have high removal rates of PE and VP (97%–99%) and thus demand technological renovation to further remove those pollutants at a low concentration.

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.