Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)

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Analysis of Green GDP and Global Temperature Forecast Based on Time Series Model

Authors
Siyi Lyu1, *, Ziqin Zhou2
1Reading Academy, Nanjing University of Information Science and Technology, Jiangsu, 210044, People’s Republic of China
2Changwang School of Honors, Nanjing University of Information Science and Technology, Jiangsu, 210044, People’s Republic of China
*Corresponding author. Email: 1184007046@qq.com
Corresponding Author
Siyi Lyu
Available Online 26 July 2023.
DOI
10.2991/978-94-6463-200-2_116How to use a DOI?
Keywords
GGDP; Global temperature prediction model; Sustainable development
Abstract

GDP is the most well-known and widely-used indicator of a country's economic health. Yet, because the GDP does not include natural resources, it does not take environmental protection and resource utilization into account. Thus, green GDP should be considered a significant measure of a nation's economic health. Using principal component analysis, this research identifies several representative primary factors. Then, the relationship between the primary components, GGDP, and global mean temperature is studied, along with the impact of each factor on the temperature forecast for 2020–2040. A model for predicting the global temperature decline has been established. The results show that the probability of positive correlation between the positive growth rate of GGDP and the 50-year temperature change is 74%, indicating that: (1) GGDP can reflect the rule of temperature change, and its factor change is significantly correlated with temperature; The effect of greenhouse gases on ocean temperature is more significant. In different countries, the main variable as a percentage of GDP varies according to climate.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
26 July 2023
ISBN
10.2991/978-94-6463-200-2_116
ISSN
2589-4919
DOI
10.2991/978-94-6463-200-2_116How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Siyi Lyu
AU  - Ziqin Zhou
PY  - 2023
DA  - 2023/07/26
TI  - Analysis of Green GDP and Global Temperature Forecast Based on Time Series Model
BT  - Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)
PB  - Atlantis Press
SP  - 1098
EP  - 1104
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-200-2_116
DO  - 10.2991/978-94-6463-200-2_116
ID  - Lyu2023
ER  -