Comparing the per capita incomes of urban and rural households reveals a sizable income disparity in the latter, as well as an uneven distribution of income across China's regions. This article discusses and ranks the drivers that affect rural development. Among the most critical are farmer support factors, transportation infrastructure development, information and communication technology development, and rural entrepreneurship. The decline in the Engel coefficient, which reflects changes in welfare and income elasticity estimates of food demand, and income elasticity itself, indicates that food demand for Chinese farmers has become extremely inelastic. The ranking of China's regions in terms of per capita income growth shows that the intended policy of wealth equalisation of the country's population is being followed.The objective is to modernise agriculture and reduce regional disparities for China to achieve the status of a moderately developed country shortly, which will require state assistance. The emphasis is on farmer welfare, rural areas, and agriculture. In the face of massive population densities, regional disparities, and vast heterogeneous territory, the land consolidation practice is demonstrated in various regions as a platform and lever for rural life revitalisation. Rural tourism development is defined as a means of generating additional jobs and ensuring the long-term viability of villages.The rural elite's decisive role in leading the agricultural sector's restructuring is emphasised. Central and local governments can use the results to strengthen the legislative framework, develop plans and programmes, and identify rural development models.
The paper identifies and prioritizes the drivers of innovation and development of agribusinesses in China. By applying systemic and logical approaches, as well as the decomposition analysis method, the authors classified innovation drivers in the agribusiness sector and projects implementing various digital tools in agriculture. Statistical and other calculations were used to estimate innovation growth rates. The research findings can be applied by: central and local governments to develop programs for recombining innovation opportunities, as well as training programs for farmers.
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