A Grey Neural Network and Input-Output Combined Forecasting Model and Its Application in Primary Energy Related CO2 Emissions Estimation by Sector in China

AbstractIn this paper, a Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO) was built to forecast by sector primary energy related CO2 emissions. Applied the GNF-IO model, the coal, crude oil and natural gas consumption volume and the related CO2 emissions volume by China's 42 sectors in 2010 were estimated. The total energy-related CO2 emissions volume in China in 2010 was forecasted as 7508.56 million tons, in which 80.2 percent was from coal consumption, 17.6 percent was from oil consumption. By sector CO2 emissions results showed that the energy efficiency work of coal can be focused on electricity, heat production and supply, ferrous metal smelting and rolling processing industry, petroleum processing, coking and nuclear fuel processing industry. The energy efficiency work of crude oil can be emphasized on petroleum processing, coking and nuclear fuel processing industry, raw chemical materials and chemical products