The Influence and Prediction of Carbon Emission Trading Policy on the Asset Efficiency of Enterprises in Power-related industries——Based on the RBF Neural Network Prediction Model

In achieving the grand goal of carbon neutrality and carbon emission peak in China, carbon emission trading policy is one of the essential means to deal with climate change and promote the transformation of the low-carbon economy. It is also closely related to the efficiency of enterprise assets. In-depth analysis and prediction of the impact of carbon emissions trading policies on enterprise asset efficiency is of great significance for promoting enterprises' sustainable development and green economic transformation. This study is based on the relevant data of China's electric power related industry enterprises to conduct the above research. The results show that, on the one hand, carbon emissions trading policy has a substantial positive impact on improving the asset efficiency of enterprises in power-related industries. On the other hand, the trained RBF neural network model shows commendable prediction ability. These conclusions are of great significance to improving the asset efficiency of power related industry enterprises and realizing national low-carbon sustainable development goals under the background of double carbon.