Abstract :
With the slowing down of domestic economy growth and the deteriorating of international economic environment, stimulating domestic demand becomes the major way of the economic growth of our country, and the maximum potential of stimulating domestic demand lies in the rural area. The question of how to analyze the requirement of electric power in rural areas precisely was meaningful to the adjustment of industrial structure, the increase of farmers´ income. This paper is intended to make clear the definition and classification of rural area electric power consumption structure firstly, and then analyzing the main factors that influence on electric power demand in rural area, the annual data were taken from China Statistical Yearbook during the period 1980-2010, used variables are electricity consumed in rural areas, value-added of the primary industry, per capita net income of rural households, irrigated area and agricultural machinery total power in the analysis, next applying cointegration analysis, vector error correction model and Granger-causality test. The result shows that there exists a longrun stable relation between these variables, inefficient electric power use for agricultural production and electricity consumption of farming are more than rural residents. In the end, summarized the main conclusions and put forward proposals for development of electric power.
Keywords :
agricultural machinery; demand side management; domestic appliances; economic indicators; error correction; farming; international trade; irrigation; power consumption; power markets; Granger causality test; VEC model; agricultural machinery; agricultural production; cointegration analysis; domestic demand stimulation; domestic economy growth; electricity consumption; farming; industrial structure; influence factor; international economic environment; irrigated area; rural area electric power consumption structure; rural electric power demand; rural household; vector error correction model; Electric Power Consumption Structure; Influence Factors; Vector Error Correction Model;