Abstract

Since reform and opening up, China's economy has developed rapidly and the income level of its population has been rising. The composition of food consumption has grown steadily with rising income levels, reflecting the increasing importance of meat in the daily consumption of the population. Beef is in high demand because of its high protein, high energy and low fat content, which fits many residents' modern concept of a healthy diet. Beef husbandry occupies an important position in Inner Mongolia. If beef prices change dramatically, it will not only affect local beef consumption and People's Daily life, but also further affect the dynamics of beef prices in China. In this regard, it is necessary to study the impact of hulun Buir beef price fluctuation on consumer purchasing behavior. With the increase of income level, hulun Buir's food consumption has also grown steadily. Beef meets consumers' demand for a healthy diet, high in protein, calories and fat, and the demand for beef is increasing among consumers. Urbanization provides a great opportunity for the development of beef consumption market, especially with the gradual strengthening of consumers' purchasing level, which generates a high demand for beef. Beef consumption in Hulunbuir is higher than the national average, but not enough to stimulate the industry. Therefore, it is necessary to systematically study the beef consumption of Hulun Buir population with appropriate economic theories. Research on beef consumption provides practical guidance for producers and traders, and has theoretical implications for the government in terms of industrial development, industrial regulation and sustainable consumption. The research methods of this paper mainly include theoretical analysis and empirical analysis. This document is applicable to hulunbuir residents. Through questionnaire survey, we find out and understand consumers' consumption demands and consumption characteristics related to beef, and analyze and summarize the impact of beef price increase on consumers' purchase through gender, age, education background, race, monthly family income and family population.