Prediction of GGDP Based on SEEA-2012 and Logistic Model

Min Chen, Jie Shen, Yun Wu, Tianhong Zhou

2023

Abstract

The traditional GDP cannot understand the ecological damage and environmental pollution in the process of development, so it cannot really show the real situation of a country’s economy, so in order to measure the true economic health of a country, taking into account environmental factors, the GGDP was proposed. In this paper, SEEA-2012 accounting method is chosen, three indexes which affect GGDP are selected, and the indexes which affect GGDP are queried and calculated by SEEA-2012 accounting method, using the Logistic model to forecast the data of natural capital in 2012-2021, using natural capital consumption data and global temperature data to establish the BP neural network model to forecast the global temperature, compared with the actual global temperature, the change of temperature was slowed down, and the stability of the model is judged to be good by sensitivity analysis after adding GDP factors.

Paper Citation

in Harvard Style

Chen M., Shen J., Wu Y. and Zhou T. (2023). Prediction of GGDP Based on SEEA-2012 and Logistic Model. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 474-482. DOI: 10.5220/0012286200003807

in Bibtex Style

@conference{anit23,
author={Min Chen and Jie Shen and Yun Wu and Tianhong Zhou},
title={Prediction of GGDP Based on SEEA-2012 and Logistic Model},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={474-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012286200003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Prediction of GGDP Based on SEEA-2012 and Logistic Model
SN - 978-989-758-677-4
AU - Chen M.
AU - Shen J.
AU - Wu Y.
AU - Zhou T.
PY - 2023
SP - 474
EP - 482
DO - 10.5220/0012286200003807
PB - SciTePress