The Associative Network Structure Characteristics and Link Prediction of Industrial Carbon Emission in China
Description
By using Minimum Flow Analysis, this study combines the Economic Input-Output Life Cycle Assessment and qualitative input-output approach method together to generate a cross-sector carbon emission network. From three aspects, including the overall network characteristics, node centrality, and block model, the associative network structure characteristic of China’s industrial carbon emission is analyzed with the social network analysis method. Based on the modeling idea of dynamic linking, the structural characteristic of the associative network of China’s industrial emission in 2017 has been predicted in this study. China’s input-output table is compiled every five years. The latest available data is from 2012. Therefore, the Chinese input-output tables of the four years of 1997, 2002, 2007, and 2012 are selected as the basic data. The input and output tables of the four years are all from the National Economic Accounting Department of the National Bureau of Statistics of China. The number of industries in 2002, 2007 and 2012 is 124, 122, 135, and 139 respectively. In addition, with reference to the carbon emission calculation methods and parameters published by the IPCC (2006) combined with the relevant parameters published by the Chinese government, this study estimated the direct carbon emissions volume of each industry. In order to ensure the accuracy of the estimation results, this study takes into account 16 kinds of fossil energy sources continuously reported in the statistical yearbook, including raw coal, clean coal, other coal washing, coke, other coking products, coke oven gas, other gas, crude oil, gasoline, kerosene, diesel, fuel oil, other petroleum products, liquefied petroleum gas, refinery dry gas and natural gas. The data of the total carbon emissions (million tons) of energy consumption in industry, the terminal fossil energy consumption in industry (million tons or tens of cubic meters, excluding the consumption of raw materials for product manufacturing) are from the corresponding yearly China Energy Statistical Yearbook; The data of the average low calorific value of the energy source (kJ/kg or kJ/m3) is from the China Energy Statistical Yearbook (2014). The carbon content of the energy fuel (kg/106 kJ), is from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The data of energy carbon oxidation rate is from the Guidelines for the Compilation of China’s Provincial Greenhouse Gas Inventories. In addition, for the consistency of the input-output table and the industry classification in the China Energy Statistics Yearbook, referring to the China National Economic Industry Classification Standard (GB/T 4754-2011), this study divided the industries in each year to be analyzed into 30 sub-sectors.