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Since the reform and opening up, especially in the 21st century, the Chinese economy began a new round of rapid growth, led by iron and steel, chemicals, heavy machinery and other heavy industry investment in the field of human scale. This is a significant stage of development characterized by high energy consumption, in 2003 China's share of world GDP growth of 3%, but consumes 40% of the world's coal, and sulfur dioxide emissions are highest in the world list, therefore, China's energy and environmental pressures facing increasing. To alleviate this pressure, the national \ability to achieve energy efficiency goals in the provinces as well as how to decompose and implement, become our main problem at this stage research. This is the technical economics, resource and environmental economics, systems theory and other theories under the guidance of the National Social Science Fund Project (03BJY0088) and Shaanxi Normal University Graduate Innovation Fund under the joint auspices, the use of statistical software (Lotusl-2 -3 and Excle) and geographic information systems (GIS), based on more than 30 provinces in China from 1990 to 2005 the relevant data, through literature review and data collection, and the large number of empirical investigation in the field, based on proposed and validated Wan million yuan output value of energy output and SO 2 emissions environmental learning curve, and based on this analysis of China's \\Through the above-depth analysis, comprehensive study of the important results and come to the following conclusions: (1) Environmental Learning Curve Construction and preliminary validation. Environmental learning curve is the enterprise (or industry) in the production process, with the increase in production or production process is repeated, resulting in unit product (or output) resource consumption (or waste material discharged) volume rendering regular changes. Environmental learning curve is in active learning and passive learning together, and then by the company to the industry and regional level to push the process. By analyzing the Chinese mining industry and China's overall regional perspective, the yuan output value of resource consumption and waste discharge are presented along with the growth of GDP per capita exponential decay, and the correlation coefficients mostly above 0.90. (2) Chinese yuan output value of more than 30 provinces in energy consumption and SO 2 emissions as GDP per capita also showed exponential decay, in line with environmental learning curve logarithmic - linear models and Stanford -B curve type, and in addition to Hainan, Ningxia, Guizhou individual provinces, the correlation coefficients above 0.90. This analysis of the provinces for the energy saving potential and energy saving target areas decomposition provides a theoretical basis. (3) from the time point of view, China's total energy consumption in each province and SO 2 emissions are increasing year by year with economic development; while yuan output value of energy consumption and the yuan output value of SO 2 emissions, along with the level of economic development and technological improvements declining, that our provinces energy efficiency and environmental technologies is gradually increased. (4) from the spatial point of view, some of the western and central regions lagging behind the main producing areas yuan output value of energy consumption and energy yuan output value of SO 2 emissions higher energy saving potential is higher, such as Shanxi, Inner Mongolia , Shaanxi, Xinjiang, Ningxia, Gansu and other provinces: the eastern coastal developed areas yuan output value of energy consumption and the yuan output value of SO 2 emissions low, little energy-saving potential, such as Beijing, Shanghai, Jiangsu, , Zhejiang, Guangdong and other provinces; energy saving potential of the rest of the Central provinces in between. By analyzing the spatial distribution of energy-saving potential, it is for the study of our energy reduction goals can be achieved and how the region decomposition provides a strong basis. (5) Environmental Learning curve model based on China's \volume and energy consumption related, Shandong, Shanxi, Hebei, Liaoning and other large energy consumption and Guangdong, Jiangsu, Zhejiang, Shanghai and other economically strong contribution to saving big contribution rate, while Hainan, Yunnan, Qinghai, Ningxia and other provinces , because of the economic contribution of small and energy is relatively small, so as to arrive China's \
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