Is There a Relationship between Increased Land-Use Intensity and the Rise in PM2.5 Pollution Levels in the Yangtze River Economic Belt, China (2000–2021)?

Abstract

With the rapid urbanization and industrialization of the past few decades, environmental issues have become increasingly prominent, and air pollution in the form of PM2.5 has become a major concern in China. The Chinese government has recognized the severity of these environmental challenges and has placed great emphasis on protecting the environment while promoting economic growth. This study utilizes remote sensing technology to analyze the spatio-temporal evolution characteristics of the decoupling between land-use intensity and PM2.5 in the Yangtze River Economic Belt (YREB) using 2000–2021 series of PM2.5 and land-use-change data, with the Mean Center Change analysis and the Tapio Decoupling Model. This study attempts to analyze the causes of change from the perspectives of economic development stage and policy execution. The study found that the average PM2.5 level decreased by 11.77 μg/m3 during this period, with high levels concentrated in urban areas and low levels found in less developed regions. The central trajectory of the land-use intensity shifted southeast, indicating a consistent trend in urbanization and industrialization in that direction. Meanwhile, the average land-use intensity value increased by 0.19, with the highest values concentrated in urban areas. A total of 71.12% of the regions in the YREB exhibited strong negative decoupling or negative decoupling between land-use intensity and PM2.5 levels, suggesting rapid urbanization and industrialization with a decrease in PM2.5 levels. These findings provide insight into the dynamic relationship between economic development, urbanization, and PM2.5 in China’s Yangtze River Economic Belt.

Spatial–Temporal Evolution, Impact Mechanisms, and Reclamation Potential of Rural Human Settlements in China

“…The mean center change is a spatial analysis technique used to investigate changes in the central location of a set of geographic features over time [102]. The method involves calculating the mean center of a set of features at two or more points in time, and then comparing the resulting mean centers to determine how they have shifted.…”