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.

Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests are widely distributed in this region, and phenology extraction based on vegetation indices has certain limitations, while SIF-based phenology extraction offers a viable alternative. This study first evaluated phenological results derived from three solar-induced chlorophyll fluorescence (SIF) datasets, six curve-fitting methods, and five phenological extraction thresholds at flux sites to determine the optimal threshold and SIF data for phenological indicator extraction. Secondly, uncertainties in phenological indicators obtained from the six fitting methods were quantified at the regional scale. Finally, based on the optimal phenological results, the spatiotemporal variations in phenology in Southwest China were systematically analyzed. Results show: (1) Optimal thresholds are 20% for the start of growing season (SOS) and 30% for the end of growing season (EOS), with GOSIF best for SOS and EOS, and CSIF for the peak of growing season (POS). (2) Cubic Smoothing Spline (CS) has the lowest uncertainty for SOS, while Savitzky–Golay Filter (SG) has the lowest for EOS and POS. (3) Phenology exhibits significant spatial heterogeneity, with SOS and POS generally showing an advancing trend, and EOS and length of growing season (LOS) showing a delaying (extending) trend. This study provides a reference for phenology extraction in regions with frequent cloud cover and widespread evergreen vegetation, supporting effective assessment of regional ecosystem dynamics and carbon balance.

Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data.

Evapotranspiration (ET) plays a pivotal role in the water and energy cycles, yet the accuracy of ET products and their driving mechanisms varies significantly across complex topographies. This study systematically evaluates five ET products and investigates their driving mechanisms across the Tibetan Plateau (TP), the Yunnan–Guizhou Plateau (YGP), and the lowlands of China. The results show that, while ET products generally achieve higher accuracy in lowlands, the PML_V2 and ETMonitor demonstrate superior performance and sensitivity in the challenging plateau environments. Distinct regional heterogeneity was observed: the TP is dominated by thermal radiation factors, the YGP is constrained by both thermal and moisture conditions, and the lowlands are driven by the synergistic interaction of thermal, moisture, and vegetation factors. Unlike previous studies, which often treat these regions homogeneously, this work highlights the critical necessity of region-specific model selection to mitigate uncertainties arising from terrain changes. These findings advance understanding of ET discrepancies, facilitate the selection of appropriate ET products, promote model optimization in ET retrieval research, and enhance water management and hydrological modeling under varying topographical conditions.