Abstract

At present, the international market has gradually carried out research on forecasting the trading price of carbon emissions with macroeconomics and energy market as the main influencing factors, supplemented by other factors such as policy factors. In order to speed up the construction of carbon accounting system, enrich the content of China's carbon pricing mechanism and improve the carbon emission trading market, this study constructs an index system from four aspects: macro-economy, energy price, climate environment and international market, and objectively screens out the important influencing factors of carbon trading price through Lasso regression, and uses BP neural network prediction model to predict carbon trading price. [7] We should focus on reducing the proportion of coal consumption by port enterprises and increasing other clean energy consumption. This result can provide basis and reference for the construction of zero-carbon ports and the construction of ocean-port carbon trading information platform in China.

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