Spatiotemporal distribution, trend forecasting, and key factors analysis of CO₂ concentration in Shanghai, China
by Xulong Wu; Jinye Zhang; Ziyue Hu; Ruibei Liu; Hui Lv
International Journal of Global Warming (IJGW), Vol. 33, No. 3, 2024

Abstract: We analysed China's spatial and temporal CO₂ concentration patterns in this study. Advanced interpolation techniques including Kriging and PCHIP were employed to process GOSAT CO₂ Level 3 products to improve spatial resolution and bridge temporal gaps. Furthermore, a SARIMA(1,0,2) (1,1,2)12 model was developed to forecast CO₂ concentration of Shanghai for the next year. Utilising a MLR approach (R² = 0.995, DW = 3.356), our findings underscored the dominant roles of GDP (β = 0.814) and population (β = 0.253) in driving CO₂ concentration upward in Shanghai, while NDVI (β = -0.240) emerged as a crucial factor in reducing CO₂ levels.

Online publication date: Fri, 28-Jun-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Global Warming (IJGW):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com