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Abrupt permafrost thaw accelerates the decomposition of soil organic carbon and might double the warming caused by the carbon release. However, the influence of thaw slump evolution on carbon dioxide (CO2) emission rates and its drives remains unclear, which induces large uncertainties in the prediction of permafrost carbon‐climate feedback. Here we collected soil samples in the thaw slump landscapes that happened 1–23 years ago on the northern Qinghai‐Tibet Plateau (QTP) and measured the CO2 release rates using a 189‐day aerobic laboratory incubation in the dark. The incubation results showed that thaw slump occurred 23 years ago reduced soil CO2–C release by 57 ± 19% compared with the undisturbed area. The relative contribution of O‐alkyl C and microbial abundance decreases with the thaw slump initiation time lengthens. We illustrate that soil carbon quality and microbial communities uniquely explained 41% and 13% of the variation in CO2–C release, respectively. We preliminary estimate that the carbon release for thaw slump landscapes on the QTP may be overestimated by approximately 50% if the declining soil CO2–C release is without consideration. Our study highlights the CO2–C release would decrease with the stability of thaw slumps on the warming and wetting QTP, which may weaken the mountain permafrost carbon‐climate feedback. Plain Language Summary: Rapid warming of permafrost regions leads to the widespread development of thaw slumps and accelerates the decomposition and release of soil organic carbon. Yet, how the evolution of thaw slump affects CO2 release remains unclear. We found that CO2 release gradually decreased with the thaw sequence. Labile carbon (O‐alkyl C) proportion and microbial abundance declined along the thaw sequence, while resistant carbon (alkyl C) proportion increased. Soil carbon quality and microbial communities are important factors controlling CO2 release. Applying the results to the entire Tibetan Plateau, we find that permafrost carbon emissions would be overestimated by approximately 50% if the declining soil CO2 release of stabilized thaw slump is without consideration. Our findings suggest that stable thaw slump can weaken CO2 release. Therefore, the assessment of mountain permafrost carbon feedback needs to consider the effect of thaw slump evolution. Key Points: Soil CO2 release was reduced by 57% in areas that collapsed for 23 years compared to the control areasThaw slump leads to a large decrease in O‐alkyl C and soil microbial abundanceSoil carbon quality and microbial communities exert the controls of 41% and 13% over CO2 release, respectively [ABSTRACT FROM AUTHOR]

As an essential part of modern intelligent decision-making science, multi-attribute decision-making problems can effectively select and rank all candidate schemes under multiple indicators. Because of the complexity of the real environment and the uncertainty of the decision-making problem, interval numbers are often used to represent the evaluation information of the object. The existing methods of the multi-attribute decision-making problems rarely use the object set but give the decision results by selection or ranking, which often have strong subjectivity. We propose a ranking method from an acceleration viewpoint based on the three-way decision model to solve the interval-valued multi-attribute decision-making problem. A distance measure of two objects is a measure that describes the relationship between objects. Therefore, the fuzzy dominance distance is introduced to express order relations among objects. First, we present a method to compare any two interval numbers, which converts interval numbers into connection numbers according to the characteristics of interval numbers in multi-attribute decision-making problems. Second, the three-way decision theory is introduced to divide the object set into high, medium, and low dominance regions for the speed and rationality of decision-making. Finally, the multi-attribute decision-making problems can be simplified into the problem of selection in three regions by ranking the objects of the selected region. Unlike traditional methods, the experiments demonstrate that our proposed method has the lowest cost. Our method is shown to be efficient and can obtain comparable results. [ABSTRACT FROM AUTHOR]