Carbon Decoupling Characteristics, Driving Factors Analysis And Trend Forecast In Beijing Area

Increased carbon dioxide emissions have caused global warming.It play a profound impact on human survival and development. At the beginning of2013, due to the discharge of pollutants can not be dissipated, raging haze weather is a wake-up call to China’s environmental problems once again, and the situation is more serious in Beijing. In this background, to study the relationship between carbon emissions and economic development of Beijing and the reason of the real drivers of carbon emissions in Beijing will help to measure the key indicators of energy carbon emissions in Beijing area. Then it can help to have a definite object in view proposed emission reduction policies, and provide a theoretical basis for the construction of the "world city".In this paper, first of all, Tapio and IGT decoupling model was used to study the decoupling characteristics between economic development and carbon emissions in Beijing area. The study found that:except for1993,1997and2011, other years in the Beijing area economic development and energy decoupling relationship between carbon emissions are relative decoupling; the optimization of energy consumption structure, increasing the use of clean energy, strengthen energy-saving emission reduction efforts and industry based industries in the third structural forms are the main cause of most of the relative decoupling state in Beijing area. Tapio decoupling elastic equations and IGT decoupled state equations in the study have advantages and disadvantages, Tapio decoupling elasticity equations have higher accuracy, while IGT equation can calculate the critical value of decoupling.Furthermore, in order to study the effects of decoupling characteristics in Beijing area.We use the LMDI decomposition method to conduct driving factor decomposition for energy carbon emissions in Beijing. They are energy structure, industrial structure, per capita output, per capita income, production of energy intensity, life energy intensity, total population, transportation energy intensity, the average length of transport routes of transportation,traffic scale, ten factors at all. On this basis, we use STIRPAT model to analyze the impact of these factors. Results showed that:the energy intensity of Beijing area is the largest energy carbon negative driving factors, energy structure and industrial structure have made great contribution to emission reduction, and the scale of economic development and the population size is a major factor in stimulating the growth of carbon emissions in Beijing area, the transportation industry to the energy of carbon emissions in Beijing area can not be ignored. The most important factor which affects the elastic size on carbon emission in Beijing city is urbanization rate. Population, city rate, per capita GDP, the proportion of coal consumption and the Beijing vehicle has a promoting effect on carbon emissions; energy consumption intensity and the third industry proportion of GDP has inhibitory effect on carbon emissions.Finally, with the help of the comparison between the GM-PLS combination forecast model and GM (1,1) model, we find that the prediction accuracy of GM-PLS model is higher than that of GM(1,1) single prediction accuracy. Then we use the GM-PLS combination forecasting model to predict the carbon discharge strength from2012-2015in Beijing area. From the prediction results, the energy-saving emission reduction target in Beijing is very good.