Evaluating the spatiotemporal variations in atmospheric CO2 concentrations in China and identifying factors contributing to its increase

Abstract

Understanding the patterns and trends of atmospheric carbon dioxide (CO₂) is essential for comprehending the global carbon cycle and making accurate future climate predictions. CO₂ levels are influenced by complex and often interrelated factors, requiring innovative approaches that can tie place-specific factors with CO₂ concentrations. This study utilized the Orbiting Carbon Observatory-2 (OCO-2) data to explore the changes of CO₂ concentrations in China over the past decade. Additionally, climate parameters, vegetation cover, and anthropogenic activities were combined to explain temporal and spatial changes in CO₂ concentrations, using Geodetector and Multiscale Geographically Weighted Regression (MGWR) model. The results revealed a consistent increase (2.54 ppm/yr) and significant spatial agglomeration (High-High cluster in the east, Low-Low cluster in the west) of CO₂ concentrations in China. The spatial location (q = 0.68) emerged as the primary determinant of CO₂ levels, with population variable (q = 0.55) representing the secondary influencing factor. The interactions among natural elements and anthropogenic activities had substantially elevated CO₂ levels. Compared to the Geographically Weighted Regression (GWR), and Ordinary Least Squares (OLS) models, the MGWR model demonstrated superior capability in revealing the varying spatial scales of influence among different variables, making it more suitable for investigating the impacts of multiple factors on atmospheric CO₂ concentrations. The MGWR revealed significant variations in the optimal bandwidths among different explanatory variables, with temperature, precipitation, and LAI operating at much smaller scales. The findings are expected to provide valuable insights into regional processes influencing CO₂ concentrations and the development of targeted interventions.