| As early as 2009,China announced its carbon emission reduction targets to the world by 2020.2020 is coming,the coming emission reduction pressure poses a great challenge to our country,and the scientific,reasonable and targeted policy recommendations are the key to achieving the goal of emission reduction,only by analyzing the national and provincial carbon emissions deeply can we formulate reasonable policy recommendations.Therefore,from the thinking of the national level to the provincial level,this paper grasps the completion potential of emission reduction targets from the national carbon emissions in the whole,and analyzes the provincial carbon emissions and its influencing factors completely.Firstly,the anticipated accomplishment of China’s emission reduction targets is grasped in the whole.Improving the combination model with the criteria of maximizing precision,a novel grey index combination forecasting model based on IOWGA-Markov is proposed by combining the induced ordered weighted average operator and Markov chain.And the model is used to predict the intensity of carbon emissions and the total amount of carbon emissions in China,the prediction results show that,according to the current trend of carbon emissions,our country has great potential to accomplish the proposed emission reduction targets.The total amount of carbon emissions in China still have not reached its peak,the task of reducing emissions is still severe.Therefore,it is necessary to conduct a detailed study of the carbon emissions in the province.Secondly,the carbon emissions in the province are estimated and its spatial and temporal characteristics are analyzed.The basic situation of carbon emissions in the province and the trend of its time variation are analyzed,social network analysis technology was introduced to analyze its spatial correlation characteristics from the perspective of the network based on the correction of gravitational model.The results show that the carbon emissions are on the rise in the whole,the structure of the spatial correlation network is becoming more and more complicated;different provinces have different roles in the network,the network centers have been changed since 2013.Therefore,it is necessary to consider spatial effects when modeling carbon emissions.Finally,the spatial model of provincial carbon emissions and its influencing factors is established.The Lasso algorithm is used to reduce the dimension of the influencing factors of carbon emissions;the economic distance of the region is depicted based on the economic grey relational degree between regions,and the grey gravitational spatial weight matrix is constructed with the geographical factors when improving the spatial weight matrix;using spatial error model to expand EKC curve theory when the model is set,this paper studies the influencing factors of carbon emissions from the national scope and the eastern,central and western regions respectively by comparing the geographic proximity spatial weight matrix with grey gravitation spatial weight matrix.The results show that the spatial weight matrix constructed in this paper is superior to the geographically adjacent spatial weight matrix in the test of spatial autocorrelation and the fitting of the model,because it shows good adaptability;different regions have different carbon emission Kuznets curves and different influencing factors of carbon emissions;finally,according to the model results of different regions,the corresponding policy recommendations are given. |
