Title: Spatiotemporal distribution, trend forecasting, and key factors analysis of CO₂ concentration in Shanghai, China

Authors: Xulong Wu; Jinye Zhang; Ziyue Hu; Ruibei Liu; Hui Lv

Addresses: School of Science, National '111 Research Center' Microelectronics and Integrated Circuits, Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, No. 28, Nanli Road Hongshan District Wuhan, China ' School of Science, National '111 Research Center' Microelectronics and Integrated Circuits, Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, No. 28, Nanli Road Hongshan District Wuhan, China ' School of Science, National '111 Research Center' Microelectronics and Integrated Circuits, Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, No. 28, Nanli Road Hongshan District Wuhan, China ' School of Software Engineering, Hubei Open University, 368 Minzu Avenue, Hongshan District, Wuhan, China ' School of Science, National '111 Research Center' Microelectronics and Integrated Circuits, Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, No. 28, Nanli Road Hongshan District, Wuhan, China

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.

Keywords: CO₂; Shanghai; GOSAT; SARIMA; multiple linear regression; MLR.

DOI: 10.1504/IJGW.2024.139265

International Journal of Global Warming, 2024 Vol.33 No.3, pp.299 - 313

Received: 01 Dec 2023
Accepted: 11 Mar 2024
Published online: 28 Jun 2024 *

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