Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, September 1–3, 2023, Chongqing, China

Research Article

Global Green GDP Forecasting Model Based on BP Neural Network

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  • @INPROCEEDINGS{10.4108/eai.1-9-2023.2338703,
        author={Wei  Zhang and Yimou  Wang and Xinyu  Li and Jiajun  Lei},
        title={Global Green GDP Forecasting Model Based on BP Neural Network},
        proceedings={Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, September 1--3, 2023, Chongqing, China},
        publisher={EAI},
        proceedings_a={ICPDI},
        year={2023},
        month={11},
        keywords={combination weight leslie model neural network climate change},
        doi={10.4108/eai.1-9-2023.2338703}
    }
    
  • Wei Zhang
    Yimou Wang
    Xinyu Li
    Jiajun Lei
    Year: 2023
    Global Green GDP Forecasting Model Based on BP Neural Network
    ICPDI
    EAI
    DOI: 10.4108/eai.1-9-2023.2338703
Wei Zhang1,*, Yimou Wang1, Xinyu Li1, Jiajun Lei1
  • 1: Chengdu University of Technology
*Contact email: 2725326923@qq.com

Abstract

Multilateral developments would present a challenge to the global harmonization of green GDP as the fundamental criterion of economic health. Green GDP, on the other hand, considered environmental costs and sustainability and contributed to effective global climate crisis. mitigation in contrast to GDP, which was currently used to gauge national economic performance.We built a model of expected global climate mitigation based on BP neural networks. Secondly, to create climate evaluation indicators based on CO2 emissions and temperature variations, seven variables from four typical nations were integrated with GDP. The GGDP data for the USA was added to the model for validation utilizing the test set to examine the generalization capability of the BP neural network. It was discovered that the changing trend was essentially consistent, illustrating that the model had stability.