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Research On Dynamic Relationship Between Industrial Carbon Emissions And Its Influencing Factors In China

Posted on:2014-06-23Degree:DoctorType:Dissertation
Country:ChinaCandidate:X S RenFull Text:PDF
GTID:1109330452470604Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
During the rapid development stage of industrialization, large amounts of carbonemissions have been caused by excessive consumption of fossil energy, whileeconomic power has been significantly improved in China. In recent years, theshortage of energy, climate anomalies, and environmental pollution has a seriousimpact on the national scientific development and people’s quality of life. Savingenergy and reducing carbon emissions efficiently is the only way to build a national"resource-saving and environment-friendly society. For the industry with the largestamounts of energy consumption, this paper investigated industrial carbon emissionsand their impact on the dynamic relationship between the factors and the developmentof industrial carbon reduction in-depth, in order to provide data supports and policysuggestions for our country.Base on the environmental Kuznets curve, decoupling,basic theories of economic convergence, combined with the status quo of industrialcarbon emissions at home and abroad, this paper did innovative research on the studyof the dynamic relationship between China’s industrial carbon emissions and itsinfluencing factors.(1)Analysis on China’s industrial carbon emissions influence factors STIRPATmodel, in order to overcome the heteroscedasticity caused by the shorter time seriesand the multicollinearity caused by the shorter time series, partial least squaresmethod is used to calculate the elasticity coefficient of the influence factors ofindustrial carbon emissions, and using decoupling model is to estimate the decouplingstate of industrial carbon emissions and industrial output with the time series data. Onthis basis, adopting the gray forecasting GM (1,1) model is to predict the future valueof industrial carbon emissions, the size of the population, affluence and technical levelin2011-2020, prediction accuracy is relatively high.(2) A panel of China’s30provincial industrial carbon emissions and thecorresponding per capita industrial output data for the study sample, the paperanalyzes the provincial industrial carbon emissions and the regional distribution andspatial clustering effect of provincial industrial carbon intensity by using ArcGIS andGeoDa Software, on this basis, the existence of the Kuznets curve were constructedtaking the provincial industrial carbon emissions and the provincial industrial carbonintensity for the study, existence of Kuznets Curve for the provincial industrial carbon emissions is to discussed by the individual effects with variable coefficients panelregression model. Through the inflexion recognition, the paper carries differenceanalysis of the provincial carbon emissions and the process of industrialization.(3) Panel vector autoregression (PVAR) is used to analyze the dynamicrelationship among the industrial carbon emissions influence factors in provinces ofChina. Panel GMM estimates the coefficient of population size, industrial output percapita and industrial technical level to the provincial industrial carbon emissions, andthen by using the panel variance decomposition, the paper analyszes the contributionrate of the population size, industrial output per capita, technical level to theprovincial industrial carbon emissions changes. Finally, through Panel pulse responsefunction the effect of three factors on changes in industrial carbon emissions impact isanalyzed.
Keywords/Search Tags:Industrial carbon emissions, STIRPAT model, Partial Least Squares, Decoupling, EKC curve, Panel regression, Panel vector autoregression
PDF Full Text Request
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