Spatial Econometrics Of Total Factor Carbon Productivity Among Provinces In China

On the basic of theories economic growth, new economic geography theory and energy economics theory, this paper estimate the carbon productivity of 31 provinces in China from 2005-2010, and analysis their productivity differences and the causes of these differences. Firstly, values for all carbon emissions 31 provinces and thus carbon productivity provinces were estimated; then this paper use exploratory spatial data analysis to describe the analysis of Chinese provincial spatial distribution of carbon productivity and the evolution of the spatial distribution pattern for visual instructions, and then inspect the spatial autocorrelation and spatial dependence of carbon productivity among provinces. Finally, least squares estimation spatial error model analysis and geographically weighted regression method are applied to analysis openness, public investment, industrial structure, energy prices and population on the impact of carbon productivity.Specifically, firstly,this paper outline the significance and background of the research, and then proposes its research framework based on a literature review of domestic and international research. As the world’s largest country in carbon emissions in current, it is crucial for China government to achieve emission reduction commitments while maintaining economic growth. The GDP and carbon emission reductions to be considered as an indicator of economic development could achieve a low-carbon development under the premise to ensure that China’s economic growth, hence Carbon productivity as measured by unit of carbon consumption brought about by economic growth indicators,research on carbon productivity is both significant and valuable.It is found through literature review that domestic and international research about carbon productivity is still lacking. Especially rare on carbon productivity of provinces. As a result, this paper analysis productivity of carbon in China’s provinces based on Exploratory spatial analysis and spatial analysis of spillovers.Based on the primary energy data, the paper estimate provinces’ carbon emissions by using carbon emissions estimation formula, and select double logarithmic C-D model based on constant returns to scale, GDP in the region as the desirability of output, provinces carbon emissions, capital stock and labor as input variables to calculate the distribution of China’s 31 provinces carbon productivity on 2005-2010. Exploratory Spatial Data Analysis is used to show that except for the downward trend of 2009-2010 national carbon provincial total factor, the 2005-2010 total factor productivity in all regions of the overall carbon upward, Chinese provincial total factor carbon productivity has an obvious spatial distribution pattern. The more obvious one is mostly developed provinces are in high level, and those in growth provinces also faster development in economy, especially for the Eastern seaboard with the continuous development, the Eastern seaboard increasingly prominent location advantages and forming a ring of coastal high-level gathering of spatial patterns. By using the spatial autocorrelation and spatial dependence, the paper gives that National carbon provincial total factor productivity has a significant spatial agglomeration.Having known that the significant spatial agglomeration by the spatial autocorrelation and spatial dependence, then we make a Speculated:the existence of spillovers lead to spatial correlation, thus spatial regression analysis and geographically weighted regression model to be used to make an empirical analysis between carbon productivity and its factors affecting. Establishing the least squares estimation and spatial error model, choosing Gross regional production, secondary industry and tertiary industry output, energy prices, fiscal spending, the total population, foreign investment and other indicators to make a detailed analysis of the spatial spillover effect of total factor carbon productivity. The result shows that among the indicators, energy prices is the most one to effect the total factor carbon productivity. In order to explore the Spatial spillover effect of total factor carbon productivity, the paper make a spatial dependence tests based on the distance 2,3,4,5,6 etc.5 nearest neighbor adjacency matrix, it shows that the best Spatial weights matrix was based on the 5 nearest neighbor adjacency matrix, energy prices is still the most one on the effect of total factor carbon productivity, and the extent of their influence factors are same as classical least squares (OLS) model. This means that the total factor productivity of carbon not only affect by the interaction of an area surrounding neighborhood carbon total factor productivity, but also by error impact of inter-regional differences and thus will have space for non-stationary. In order to handle the problem, the paper estimate each parameter in the space model by geographically weighted regression estimation. Finally a conclusion is made. Provincial energy prices, foreign investment and population exist spatial differences in total factor carbon productivity impact, each of them have a positive correlation with total factor carbon productivity,whereas both the public investment and value of secondary industry and tertiary industry output have a negative correlation.The ending part of this paper draws a summarize of the findings, give policy advices and directions, and discuss the limitations of this paper and the directions for further research.