The Study Of The Land Resource Growthdrage On China's Regional Economic Growth

The economic growth is at the core of the economy and is very important for China. However, nowadays the process of economic development highlights the increasingly serious contradiction between resources and economic growth, the supply of natural resources will be limited, the limited resources and unlimited human needs contradicts, expressed as the universal economics phenomenon:scarcity of the resources. Both the distribution of the resources and the economic growth are regionally unbalanced. Therefore, to explore the relationship between economic growth and carrying capacity of natural resources, to explore what part the land resource play in the unbalanced economic growth to consider how to better use natural resources to meet the needs of economic growth has a great theoretical and practical significance.After the review of research at home and abroad in an integrated system, relied on translog production function, this article establishes the constraint model of economic growth on land resources. It is estimated that the drag coefficient of economic growth in the constraints under natural condition, comprehensively evaluate the effects of resistance. Finally, empirical research is done on economic growth in China, in the course of the study, panel data on unilateral root test is conducted by a effective amendment, while estimated coefficient confidence interval were properly adjusted due to actual situation to the correct estimatation of landl resource constraints drag coefficient in China's economic growth, and make a number of policy recommendations based on calculated results of the economic growth of China.The innovation is that this paper studies the growthdrag in the regional economy growth from two dimensions of space and time. This paper analyses not only the 31 provinces'situation but also the situation from 1997 to 2008. It shows the value of the growthdrag is high and different among different areas. Because of data limitations, it's hard to get the high accuracy. This will be improved in the future study.