Decoding spatiotemporal dynamics in atmospheric CO in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis.

Understanding the primary factors influencing atmospheric carbon dioxide (CO) dynamics is essential for addressing global climate change. However, these factors' spatial and temporal impact is seldom considered in the extant literature. This study employs a geographically and temporally weighted regression (GTWR) to examine the magnitude and direction of the effects that human activities, ecological conditions, and meteorological parameters exert on atmospheric CO variations.

Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios.

Global warming is a crucial factor affecting crop production in ecologically vulnerable areas. Warming-induced changes in the yields of different crops could pose significant challenges to food security and sustainability assessment. In this study, the World Food Studies model and a remote sensing product assimilation algorithm were used to develop a spatially explicit crop assimilation model applicable to the Loess Plateau of China.

Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China.

Spatio-temporal integrated assessment of land-use change impacts on carbon storage services is a new and important research field in land science and landscape ecology. The objective of this paper is to use an integrated SD-CLUE-S and InVEST model to simulate and predict land-use changes impacts during 2000-2018 on carbon storage at pixel and regional scales in the Zhangye oasis, Northwest China. The SD-CLUE-S model was used to simulate land-use change, and three land-use scenarios (current trend, moderate protection, and strict protection) were defined in collaboration with oasis socioeconomic development and ecological environment conservation by local government.