The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network

In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

• Liu Zhiyuan
• Sun Zongdi

• Carbon trading price
• carbon trading volume
• BP neural network model
• Shanghai City.

[1] Ye Hongyu, Xu Xiaodong. Correlation Analysis of Industrial Structure Adjustment and Carbon Dioxide Emissions in Shanghai (J). J. University of Shanghai for Science and Technology, 2014, Vol.36, No.6: 614-618.
[2] Zhao Min, Zhang Weiguo, Yu Lizhong. Carbon Emissions from Energy Consumption in Shanghai City (J). Research of Environmental Sciences, 2009, Vol.22, No.8: 984-989.
[3] Liang Chaohui. The historical characteristics and Long-term Trend of Carbon Emissions in Shanghai (J). Shanghai Economic Research, 2009(7):78-87.
[4] Ma Shujiao. The research of carbon market price forecasting and risk management (D). Wuyi University, 2015, 06.
[5] Ji Qingmei. Research on the carbon emission price mechnism based on BP neural network (D). Shaanxi Normal University, 2015,06.
[6] Zhou Fen. Study on the characteristics and prediction of carbon emissions in Shanghai (D). Shanghai Normal University, 2015, 10.
[7] Zhuang Dedong. A Study on the EU carbon market dependence structure and risk spillover effect on price fluctuations of carbon emissions (D). Guangzhou: South China University of Technology, 2014, 06.
[8] Wang Tingting, Zhang Yali, Wang Miaohan. A research on the risk measurement of China’s carbon financial market (J). Financial Forum, 2016, 249(9): 57-67.
[9] Si Shoukui. Mathematical modeling algorithms and procedures (M). Naval Aeronautical Engineering Institute, 2007,9.
[10] Ji Qingmei. The research on the mechanism of carbon emission price in EU based on BP neural network (D), 2015, 06.