This study employs panel data and a dynamic Almost Ideal Demand System (AIDS) model to investigate the habit formation effect of food consumption among Chinese rural residents and its consequential impact on nutritional intake. The dataset, spanning from 2012 to 2018, encompasses nine provinces in China and involves 5390 rural households. The findings reveal that, excluding beef, mutton, and poultry, there are significant habit formation effect on the consumption of food categories, notably grains, vegetables, and edible oils. Lower-income and younger demographics demonstrate a more pronounced reliance on established dietary habits. Influenced by the habit formation effect, there is a substantial reduction in the income elasticity differences across various food types. Overlooking the habit formation effect in food consumption would lead to an underestimation of the income elasticity of energy, fat, and carbohydrates. This suggests that, over the long term, food consumption habit formation is a pivotal factor in enabling the enhancement of residents’ dietary structures, amplifying the incremental energy intake associated with income increases, and accelerating the transition towards nutritional surplus. The conclusions drawn from this study offer valuable insights for ensuring food security and nutritional balance. Policy-makers of food and nutrition strategies should duly consider the habit formation effect on residents’ food consumption, and seek to optimize dietary patterns and promote nutritional transformation by food consumption habit intervention.

China is undergoing a rapid dietary transition as well as a changing income distribution. In this paper, we examine the impacts of income heterogeneity on the prediction of food consumption using a dataset that covered 22,210 urban households in China’s 6 provinces. The two-stage Exact Affine Stone Index Implicit Marshallian Demand System (EASI demand system) model, which deals with the problem of censoring and endogeneity, is applied to estimate demand elasticity across income strata. Additionally, a dynamic simulation method considering income heterogeneity is conducted to predict future food consumption trends. The results reveal that income elasticity follows a decreasing trend with income growth. Furthermore, the results show that the consumption of major food items in the 15th period will increase by 7.9% to 42.0% over the base period. The growth potential of low-income groups is significantly higher than that of middle- and high-income groups. However, the prediction results may be overestimated if the differences in consumer behavior across income groups and the dynamic simulation procedure are not taken into account. Our study indicates that the consumption features of different income groups need to be included in food consumption forecasts. Moreover, the government should formulate food policies for different income groups to promote a sustainable food system transformation.