Calculation Of Carbon Emissions And Research On Reduction Path In China

Global climate change has been one of the most serious environmental problems sofar as well as of the most complex challenges in the21st Century. Internationalnegotiations concerning the reduction of climate warming are not only related to humanliving environment, but also directly affect modernization and sustainable developmentof developing countries. At present, according to the international scientific field, one ofthe fundamental measures to solve the climatic problem is to reduce emissions ofgreenhouse gases. As to carbon emissions China is a major country in the world. China’semission reduction has become a hot issue which academic circles at home and abroad,the environmental community and governments are all concerned about. Research onChinese emission reduction is very important, not only conducive to the sustainabledevelopment of China, but also helps to mitigate the global climate warming.Therefore, based on literature reviews and an overview of the basic theory, thispaper uses statistical analysis and econometric analysis method to study China’s carbonemissions in depth. The first part is the introduction, covering the significance of thetopic, research content, research methods and innovative points. The second part tells therelative theories, indicating the relevant concepts on carbon emissions, proposescalculation method on carbon emissions suitable for the situation of our country, andsummarizes the basic theories of carbon emissions. The third part is the analysis of thestatus of carbon emissions, including the overall situation and carbon source distributionof carbon emission. It also analyses the impact of China’s carbon emissions on theeconomy, social and environmental causes by VAR model. The fourth part includes thefactors affecting carbon emissions and the empirical analysis. On the basis of theory,LMDI decomposition model is used to analyze carbon emissions, and give a furtheranalysis on different distributions of carbon emissions through Quantile regression forpanel data model. The fifth part covers reduction path selection and simulation analysis,constructing China Energy and environmental CGE model, introducing energy module,adding two variables (the amount of carbon emission and the intensity of carbonemission. Based on the2011social accounting matrix data, the paper will analyze China’s four major emission reduction paths. The sixth part covers conclusions andrecommendations, involving the summary of the paper and reduction proposals.The main conclusions are as follows:According to overall evaluation results of Carbon emissions, the overall level ofcarbon emissions in China is in a worrying level, with high level of total number ofemissions and low carbon emission efficiency as well as poor situation of carbon sinksand medium level in terms of carbon emissions on average person. From the distributionof carbon emissions, in the region, China’s severe emission areas contributed50%of thetotal carbon emissions, which are the most important regional carbon emissions in China.Through analysis of the causes of difference of regional carbon emissions generated byTheil index, results show that the differences within the Theil index population regionwith population priority contributes about80%to the overall differences, while takingGDP as the Theil index weight indicates that in recent years the whole difference ismainly due to regional differences. In terms of industrial structure, China carbonemissions have more than90%from the second industry, of which80%comes fromindustrial sectors of high energy-consuming industries. In the energy structure, among80%of China’s carbon emissions from coal consumption, about20%comes from the oilconsumption, while only3.46%of the carbon emissions from natural gas. Grey relationalanalysis shows highest correlation between China’s carbon emissions and coal as well asbetween carbon emissions and natural gas.The decomposition results show that in terms of carbon emissions GDP per capitaand population has a positive effect on carbon emissions, that energy consumptionintensity has obvious inhibitory effect on carbon emissions, that carbon emissionsdecomposition per capita and carbon emissions have similar results, that in the angle ofcarbon emission intensity, the increase in it is attributed to the pulling effect, andconsistency between carbon emissions intensity change and the change of energyconsumption intensity, which is consistent with the actual situation.By an empirical analysis of panel data, the effect of China’s carbon emissions andcarbon emissions intensity are further validated. The regression results show that there isno inverted U shape relationship between China’s level of economic development andcarbon emissions, but linear increase feature, with effects of elasticity of greater than1. Improvement of the efficiency of energy use can effectively inhibit the growth of carbonemissions, with the influence coefficient of0.8, the influence coefficient of populationsize of1.2344. It is considered that population size is important factor driving the growthof carbon emissions and effect of industrial proportion is0.7392, making the third carbonemission factors following economic development level and population size. From thecity level, the influence coefficient is0.2123, confirmed as increase in carbon emissionsand the city population proportion. Coefficient of opening to the outside world issignificantly negative, reflecting China’s export trade to curb carbon emissions. Fromforest carbon sink, the influence coefficient is-0.1113, confirming the role of carbonsequestration of forest carbon dioxide and the small effect of forest carbon sink in ourcountry. Interaction analysis shows that a city of China effects of carbon emissions has apositive effect on the level of economic development. Quantile regression results showthat each in the vast majority of quantile factors affect parameter estimates significant at5%level of significance. Based on the robustness of estimating results, we think thattechnology level, economic opening degree and development level of the city are majorfactors to cause the current carbon emissions. GDP per capita is a major factor to causethe increase in carbon emissions, and the effect of high levels of carbon emissions in theregion is more obvious. Other factors on the positive effect of carbon emissions fromhigh to low are the size of the population, industrial structure, energy structure, theconsumption level of residents. They give different effects when carbon emissions are atdifferent quantiles.Simulation analysis on the emission reduction path shows good effect on adjustingthe industrial structure, increasing the reduction proportion of the tertiary industry,promoting the rapid and stable economic development and at the same time also beingable to achieve energy-saving emission reduction. Adjusting energy structures andreducing the proportion of coal consumption is more beneficial to emissions reduction.But it also pays a painful economic cost. Reducing the natural gas effect on the economicimpact is small, but the emission reduction effect is not good. As for technologicalprogress, improving energy efficiency can not only promote the economic developmentbut can play a good reduction effect. Improve coal efficiency has the greatest impact onemissions and improving oil efficiency has the greatest impact on economic variables. As for a carbon tax, levying tax from the price of fossil energy resource is one of the mosteffective ways to reduce emissions. As the tax rate gradually increases, emissionreduction effect is better, but it will also bring some serious economic and socialproblems, therefore it is necessary to find the optimal tax rate to balance the relationshipbetween economic and social development and energy-saving emission reduction.The innovation of this paper lies in:First, the calculation of carbon emissions is still a blank in our country. Based onanalyzing China’s carbon sources and sinks of carbon emissions, this paper proposes thecalculation method for the actual situation for our country, improving the measurementaccuracy of carbon emission index；Second, Kaya equation is improved creatively, introducing the city level, energystructure and industrial structure into the Kaya identities, laying the theoreticalfoundation of factors influencing the carbon dioxide emission, taking advantage ofdecomposition analysis to indicate their contribution to carbon emissions;Third, Theil index, the equity index measuring regions is applied for the first time tothe analysis of the regional difference in carbon emissions, more conducive to findreasons for the existence of the regional differences of carbon emissions;Fourth, for the first time the quantile regression model for panel data with the robustestimation results is used to analyze China’s carbon emissions, more clearly reflecting theeffect of different site factors at each quantile, and introducing forest carbon sink into themodel to study China’s forest carbon sink capacity.Fifth, This paper constructs China energy environmental CGE model, introducingthe efficiency factor and the ad valorem tax rate, applying CGE model to analysis on theimpact of different emission reduction ways on economic development and the reductionso that analysis results are more practical and reliable.