Exploring scaling differences and spatial heterogeneity in drivers of carbon storage Changes: A comprehensive geographic analysis framework

Abstract

Understanding the spatiotemporal dynamics of carbon storage in regional ecosystem and its underlying mechanisms is essential for environmental science. This study presents an integrated geographical analysis framework to identify and analyze the primary factors influencing carbon storage across various spatial scales. Initially, a global-scale Geographic Detector identified key factors affecting carbon storage, refined later using an Ordinary Least Squares (OLS) model to mitigate multicollinearity and redundancy. Subsequently, a multi-scale geographic weighted regression model (MGWR) explored spatial heterogeneity and nuanced impacts of these factors across various spatial scales. Our findings reveal a consistent downward trend in carbon storage in Fujian Province, China, from 1995 to 2020, accompanied by significant spatial autocorrelation. Elevation, slope, annual precipitation, and distance to the expressway emerged as primary determinants, synergistically enhancing global-scale spatial variance in carbon storage. Slope and distance to the expressway have a broad influence, while the added value of the primary industry and annual precipitation show localized effects. Collectively, these factors positively impact carbon storage, although with considerable variability in spatial orientation and magnitude of influence. This framework highlights the scale-specific and heterogeneous nature of carbon storage drivers, offering fresh insights into the mechanisms governing its spatiotemporal patterns in regional ecosystems.

Keywords

• Carbon storage
• Spatiotemporal evolution
• Influencing factors
• Geographic scales