Impact Of Climate On Chinese Energy Consumption

This thesis summarizes a series of estimating indexes, computing technologies and modes which have been formed in studying the impact of climatic change on energy consumption in the recent 50years. To counter the problems that still exist in this research field, it brings the crop output-separating theory and method in the study of the influence of climate change on agriculture to this field. It also goes further into the relation and its changes of energy consumption to economy and climate with the method of statistical analysis. According to the change characteristics of energy consumption, we can divide it into social economies energy consumption and climate energy consumption in the way of multinomial. It draws the following conclusions by analyzing the relation and its changes of climate energy consumption and climate factors, social factors. (1)Generally speaking, the Shannxi area and Beijing-Tianjin area have the highest correlation coefficient of climate energy consumption and degree day, while the next rank includes Yangtze River delta and the Zhujiang Delta which have higher coefficient between consumption and cooling degree day. The factors influencing the correlation coefficient of climate energy consumption and degree day mainly consist of degree day, economic development level, industrial structure, disaster area and output of energy, among them the output of energy and the per capita gross national product are the most outstanding factors. (2)South China and the Chiangnan area have the highest the correlation coefficient of climate energy consumption and flood area, while North China and the Northeast area have the highest coefficient of climate energy consumption and drought area. In China, the drought energy consumption is the primary part in disaster energy consumption. The major influence factors of coefficient are disaster area, energy output, agriculture and industry product proportion in the gross national product, degree day. (3)During 1984-1998, Shandong province, the Yangtze River delta and the Zhejiang area had obvious rise in the relevance between warming degree day and climate energy consumption, and northeast's Inner Mongolia area also had an obvious escalation trend, while Chiangnan area and Xinjiang region had a remarkable drop. As to cooling degree day, Beijing and Tianjin area, Yangtze River delta and Zhujiang Delta have the obvious rise, the mid-west province has the remarkable drop and northeast the northeast area has the trend of escalation. (4)During 1984-1998, the relevance of flood area and the climate energy consumption in coastal area, Inner Mongolia and Shanxi province declined obviously, but in Guangxi and Hubei and Hunan Provinces it increased. Overall speaking, the relevance of droughts area and climate energy consumption had a more obvious ascendant trend than that of flood area, the changes mainly embodyed in Chiangnan, South China and the coastal area, secondly Yunnan and Guangxi also have the obvious change (5)Per capita gross national product, GDP, tertiary industry product have more influence on the change of the relevance between climate energy consumption and degree day, which means the relation between climate energy consumption and climate degree day is in connection with social economic development. And the cooling energy consumption is closer to economy and stress more in the meantime economical development situation. (6)Usually China spent energy in warming in advance of in cooling which generally start when reaching certain level in economic development. At present, the energy consumed by warming is steadier and less likely to be influenced by recent economic development than that by cooling. In the present stage the economic development in most Chinese provinces have already reached or exceeded the warmingenergy consumption level but still can not meet the need of cooling. (7)The correlation coefficient of drought area and climate energy consumption increases with the growth of agriculture product proportion in GDP and per capital of GDP. As to flood...