Effective application of feedforward neural networks in environmental economics: based on the comparison between traditional methods and neural network methods

BingXue Han; Yuan Wang; Ze Yang; JingXin Huang; NingJing Zhang; ZiYan Zhang

doi:10.1117/12.2637137

15 June 2022 Effective application of feedforward neural networks in environmental economics: based on the comparison between traditional methods and neural network methods

BingXue Han, Yuan Wang, Ze Yang, JingXin Huang, NingJing Zhang, ZiYan Zhang

Author Affiliations +

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122850U (2022) https://doi.org/10.1117/12.2637137
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

The neural network has been fully applied in various disciplines, but it has not been applied in the econometric forecasting method of environmental economics. This paper identifies the research topic as the relationship between corporate environmental protection policy regulation and corporate performance. To analyze the application advantages of neural network forecasting methods in environmental economics, we compare traditional methods with neural network forecasting methods. We use linear regression and MLP feed-forward neural network algorithm to predict and fit relevant data of Chinese listed companies. By comparing the implementation process and the results, we concluded that the neural network algorithm has higher prediction accuracy than the traditional linear regression algorithm to analyze the actual situation.

Citation Download Citation

BingXue Han, Yuan Wang, Ze Yang, JingXin Huang, NingJing Zhang, and ZiYan Zhang "Effective application of feedforward neural networks in environmental economics: based on the comparison between traditional methods and neural network methods", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122850U (15 June 2022); https://doi.org/10.1117/12.2637137

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