The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China.

In recent years, carbon emissions have become a hot spot issue, and countries have made efforts to control the increasing rate of CO₂ concentration. Prior studies have mainly focused on the national total carbon emissions, but per capita carbon emissions are still poorly known. Here, we used multiple economic development indices to investigate the dynamics of per capita carbon emissions. Additionally, we used the Mann–Kendall test to assess the directions and magnitudes of trends and to investigate abrupt changes in per capita carbon emissions. Our results showed the highest positive growth rate of 0.439 mts/yr in Oman, and the highest negative growth rate of −0.462 mts/yr in the United Arab Emirates. Hurst Index analysis showed that about 86% of countries will keep the current trends of carbon emissions if current mitigation measures remain unchanged. Furthermore, we analyzed the shift in the center of gravity for per capita carbon emissions and used the contribution decomposition method to identify the drivers for the shift, which changed direction in 2004. The main driver behind the westward shift in the gravity center before 2004 was the fact that carbon emissions grew more strongly in the west than in the east before 2004, while the driver for behind the eastward shift in the gravity center after 2004 was a combination of emission reductions in the west and emission increases in the east. Our results highlighted the importance of understanding that the per capita CO₂ emissions are clearly defined within the context of global carbon neutrality, which can help policymakers set more reasonable targets with which to better achieve carbon neutrality goals.

The dielectric relaxation frequency ${\mathrm{\omega }}_0$ is investigated for a ${\mathrm{Rb}}_{0.30}$ ${\mathrm{MoO}}_3$ thin sample from ∼84 K up to the vicinity of the Peierls transition by using the combined ac-dc method. ${\mathrm{\omega }}_0$ follows an Arrhenius behavior below ∼150 K, similar to that found in other materials. A maximum is observed in 150–160 K. An empirical formula associated with a distribution of dielectric-relaxation times was used to fit the experimental data of the combined ac-dc method.