The change in diet structure is one of the critical features of social transformation, and diet structure is directly related to human health. In China, with rapid economic development, changes in the diet structure of the population have begun and are proceeding at a fairly rapid rate. In order to reveal how the Chinese diet is approaching or deviating from the nutritional goal, a novel index, NDBI (National Dietary Balance Index), is developed in this study to investigate the Chinese diet from 1961 to 2017 at a national level. The results show that the Chinese diet has transitioned from the under-intake stage to the over-intake stage. Before the 1980s, Chinese people ate all foods inadequately except staple foods; after the 1980s, the issue of under-intake began to fade, and the intake of meats even became excessive. The intake of staple foods is always excessive during this period. Currently, the Chinese diet is still unhealthy because of the inadequate intake of dairy products and the excessive intake of staple foods and meats. By evaluating diet structure on a national level, this study can help people to better understand how the Chinese diet deviated from the nutritional goal and provides information for policymakers intervening in China’s food consumption.

Nutritional intake has important impacts on human health. A sufficient supply of nutrients is required to ensure high-level nutrition in a population. Assessment of nutrient supply adequacy can help to develop evidence-based policies and thereby promote public health. This study estimates the supply adequacy of nutrients in China’s food system from 1965 to 2018 at the national level, aiming to reveal whether the supply of nutrients meets the demand. The results show that the nutrient supply in China’s food system has experienced a sharp increase in the past five decades, and the deficiency in nutrient supply has been greatly mitigated. Although most nutrients such as potassium are already sufficiently supplied in China’s current food system, some nutrients, especially calcium and zinc, still need a further enlarged supply to improve the nutrition condition of the Chinese population. Besides encouraging a healthy diet, supply-side regulation, e.g., fortification and enrichment, is also needed to improve nutrient availability. This study helps people better understand the development and current situation of nutrient adequacy in China’s food supply, thereby providing information and implications for policymakers.

Apparel manufacturing involves high water consumption and heavy water pollution in its supply chain, e.g., planting cotton, producing chemical fibers, and dyeing. This study employs a multi-regional input–output (MRIO) model to (1) assess the life cycle of blue and grey water (chemical oxygen demand (COD) specific) of China’s apparel manufacturing; (2) reveal the hidden linkage among sectors and regions in the whole supply chain; and (3) identify the key regions and upstream sectors with the most water consumption and heaviest water pollution. We found that the agricultural sector (i.e., planting fiber crops) is responsible for primary water consumption and water pollution. In addition, different provinces assume different production roles. Guangdong is a major output province in apparel manufacturing. However, its economic output is contributed to by other regions, such as blue water from Xinjiang and Jiangsu and grey water from Hebei and Shandong. Our research reveals the significance of taking an inter-regional perspective on water resource issues throughout the supply chain in apparel manufacturing. The sustainable development of China’s apparel manufacturing relies on improving water-use efficiency and reasonable industrial layout. The results are of significance and informative for policymakers to build a water-sustainable apparel industry.

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.