Back to Search
Start Over
Prediction of surface temperature and CO 2 emission of leading emitters using grey model EGM (1,1, α, θ).
- Source :
-
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Mar; Vol. 30 (14), pp. 39708-39723. Date of Electronic Publication: 2023 Jan 04. - Publication Year :
- 2023
-
Abstract
- The current study projects the increase in surface temperature and CO <subscript>2</subscript> emissions using the EGM (1,1, α, θ) grey model for the six most significant CO <subscript>2</subscript> contributing countries, namely China, the USA, India, Russia, Japan, and Germany. The study uses time series data for surface temperature (in degree celsius) from 2010 to 2020, and CO <subscript>2</subscript> emission (metric tons per capita) data from 2009 to 2019. The empirical results show a downward trend in CO <subscript>2</subscript> emissions from Japan, Germany, the USA, and Russia by 2028. However, in the same time period, CO <subscript>2</subscript> emissions are expected to increase for India and remain nearly constant for China. This study indicates an increase in surface temperature at a significant rate in all the 6 countries: by 6.70 °C for China, 7.52 °C for Germany, 2.95 °C for India, 2.66 °C for Japan, 3.61 °C for Russia, and 13.48 °C for the USA by the end of 2028. The study compares the EGM (1,1, α, θ) grey model with the general EGM (1,1) grey model and finds that the EGM (1,1, α, θ) model performs better in both in-sample and out-of-sample forecasting. The paper also puts forward policy suggestions to mitigate, manage, and reduce increases in surface temperature as well as CO <subscript>2</subscript> emissions.<br /> (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
- Subjects :
- Temperature
China
India
Japan
Carbon Dioxide analysis
Economic Development
Subjects
Details
- Language :
- English
- ISSN :
- 1614-7499
- Volume :
- 30
- Issue :
- 14
- Database :
- MEDLINE
- Journal :
- Environmental science and pollution research international
- Publication Type :
- Academic Journal
- Accession number :
- 36598724
- Full Text :
- https://doi.org/10.1007/s11356-022-24954-x