Structural Mutation Bayesian Analysis Of Relationship Between China's GDP And Carbon Emissions

Due to human activities, mainly carbon dioxide emissions of greenhouse gas accumulation, human society is facing the most serious climate change. The major source of greenhouse gases is to achieve sustained and rapid economic development, population growth, industrialization and urbanization and increasing energy consumption. Energy has been related to the national economy of a country's big problem, generally speaking, a country's industrialization into the heavy industrialization phase, energy consumption will increase significantly. The faster growth of heavy industry in the entire industrial sector in the higher proportion of the entire industrial sector energy consumption rate will be faster, energy consumption will be more. Since 1999, China's energy intensity and flexibility have emerged in an upward trend. In 2002, China's energy consumption elasticity than 1.0,2003 years, energy consumption is more flexible during this period reached the highest point of 1.66. Increased energy consumption will further increase pressure on China's carbon dioxide emissions. China's becomes the world's largest country carbon dioxide emissions. In 2010 government work report, Premier Wen Jiabao proposed that work should focus on improving the quality of economic growth and efficiency, the development and the results obtained with the livelihood of the people up, simply can not afford the cost of sacrificing the environment to for growth. Therefore, the energy consumption of economic development and the relationship between this problem is essential, for our case study of low-carbon economy has important practical significance.China is currently undergoing an economic transformation, international economic situation, external shocks, energy conservation and other economic policies, the introduction of low-carbon, resulting in structural changes in the timing of economic variables, and thus reflected in the relationship between GDP and carbon emissions on the changes. Because Bayesian inference takes into account a priori information, and therefore appear more frequently in the time series structure of the mutation point recognition. In this paper, the Gibbs sampling Bayesian method of point mutations in the low carbon issues in China.First, the paper identified the use of Bayesian methods of point mutations to study the carbon dioxide emissions of China's GDP and point mutations in the sequence of the problem. Calculated by using WinBUGS software programming, analysis shows that China's GDP in the 1971-2008 annual time series of point mutations produced in 1978 and 1989, the two point mutations into the economic development of the three phases is consistent with China's national conditions; point mutations of carbon dioxide produced in 1999, which China entered the second period of heavy industry, power surge, so that the significant increase in carbon dioxide emissions mutation is consistent with national conditions.Second, this paper, carbon emissions as the main object of study, so the sequence of mutations in time for the selected group in 1999, the original sequence is divided into two groups sequence intervals, were established with China's GDP and carbon dioxide emissions lagged variables of the regression equation. Studies have shown that in the time series from 1999 to 2008, carbon emissions on a carbon emissions by the lagged impact of smaller, while the elasticity of GDP with the current increase.Finally, since the founding of China's development path of heavy industrialization, and heavy industry depends on energy production and consumption issues specific descriptions. Combined with China's national conditions of China's future low-carbon economic development, the paper puts forward constructive suggestions.