Setup And Analyze On The Optimization Model Of China’s Low-Carbon Economy

Since the Industrial Revolution, the consumption of energy like carbon and oil result for the huge development of world economic. However, carbon emissions produced by human economic activity increasing rapidly, and the greenhouse effect leading to the global warm becomes a significant issue around the world in decade. As the biggest country of primal energy consumption, carbon energy producer and carbon emissions,Chinese government had promised lower the carbon emission reduction to 40% compared to the year 2005 in the year2020 at Copenhagen. What’s more, in China 12 th 5-year-plan,the energy intensity and the carbon intensity must have reduced by 16% and 17%respectively comparing to the year of 2010. China is now facing the challenge of stable the local economic, enhance the quality of people’s life and restrict the total amount of carbon emission. In the paper, on the basis of theory of low-carbon economy, proposes the dynamic relationship among the economic growth, energy consumption and the carbon emission firstly. And then, based on the structure of intermediate consumption coefficient matrix, the energy consumption coefficient matrix and the carbon emission coefficient matrix in I-O table, the paper explores the reallocation the production by sectors. We pose a constrained optimization problem, taking into account the reduction of both energy and carbon emissions. Under different scenario like maximize total output, minimize carbon emission etc; the optimal industrial structure can be got from it. And it may provide some evidence for the governor about how to transfer from tradition economy to low-carbon economy and how to update the national industrial structure.Firstly, this paper analyzes the toatal amount of China low-carbon economy. In detail,check the linear granger causality between the variables by Toda Yamamoto method; using the bootstrap method to reconstruct the confidence interval of the coefficients. And then get rid of the linear structure between the variables; make use of nonparametric BDS test to look for the nonlinear dependency among the system. Diks and Panchenco method is used to check the nonlinear causality relationship between the variables. Based on the results,we can get the causality that in short-run, GDP and energy has two-way causality; and GDP causes carbon emissions, vice versa. Energy also can cause the CO2 emissions in one way. Furthermore, we use VAR and MS-VAR model to find out the exact relationship among the three variables. It indicates that the contribution of GDP toward energy is big,and vice versa. The impulse of energy to either GDP or CO2 is positive, the results from GDP impulse toward energy and CO2 is almost the same. Sum up, the energy conservationpolicy can reduce the level of carbon emission but it also hurt the national economy;moreover, the effect won’t last so long. So the only way to develop the low-carbon economy is that keep the growth rate of economy as well as accelerate economic restructuring. In the end, we forecast the three variables by econometric model, and find out the optimal solution.Secondly, we analyze the structure of China low-carbon economy. Patricularly, we impose the data of energy consumption and carbon emissions of each sector s into ordinary I-O table, and do the price deflation based on the price of 2010 to form a comparable carbon emission input-output table from 2005-2012. By using this, we analyze internal mechanism among the economic, energy and carbon emissions. From the result of 2012,the influence of 2nddepartment is very huge and most of those sectors are both heavy-polluting and high-output industries. The influence of 3rd sectors except communications and transportation industry has less power than the 2nd, but they also have lower constrains on national economy. From the results from 2005-2012, our total energy consumption is increasing during these years. The sort of carbon energy consumption is still at the top, and the structure of energy usage remains the same. However, from the aspect of energy intensity, the second industry’s decrease and the service industry keep the same level. It shows that our energy technology is on progress, so as the usage of the energy. From the coefficient of energy whatever the direct coefficient and total coefficient,the top ten sectors are all the second. From the aspect of CO2 emissions, the carbon intensity shows the decreasing trend. Finally, from the point of influence coefficient, most of sectors tend to decrease, neither carbon nor the industry influence, but the relative change is not that big. The influence of the second departments are far important than others, and most of them are resource intensity sector. So, the conclusion is that from2005-2012 the second department is the pillar industry in China, but the curtain from 3rd department to the whole national economy is raising gradually. The industry that has low carbon influence ought to develop priory. The industry which has both high carbon influence and high industrial influence should also develop by update their technology to eliminate backward production capacity and to cut the unit carbon emissions. Those who only have high carbon influence should be moved out from the list.Thirdly, based on the forecasting results and the national industial structure from the former section above, we pose an optimization problem in order to(a) utilize the output of the nation and(b) minimize the total amount of carbon emissions by putting the restriction of the growth rate of the economy, the total amount of energy usage and the total amountof carbon emissions. The optimal solution is our best industry structure of 2017. The energy intensity can cut by 26.04% and the carbon intensity can cut by 48.95% comparing to the data of 2012. And furthermore, we impose the carbon mitigation policy onto constrains, using the same procedure, to find out the potential of carbon reduction under the identical economic system. Our goal can be met to cut extra approx 22% carbon emission at the lower level of GDP growth. Moreover, we use other linear programming-DEA to analyze the environment performance index of whole sectors, and decompose the results by Malmquist productivity index. Our results indicate that most sectors are not DEA efficient during the year of 2005-2012, because of the energy input is not reasonable. Besides, the second departments are the most influence part with respect to our environment; the reason of low score of EPI in service department is the inefficient of resource allocation. The policy maker can make their own choice considering the relationship between the GDP and the amount of carbon emissions; they can also improve the sectors environmental efficient by the slack variables from DEA modelFinally, we sum up the results above, suggest some policy advices and propose some prospect to the further research.