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AbstractUtilizing data that encompass municipalities and regions within China at the prefectural level and beyond, spanning the period from 2012 to 2021, this study employed the spatial Durbin model to assess the spatial spillover impact of the digital economy in propelling low-carbon advancement within regional physical industries. This investigation elucidates the spatial spillover mechanism that underlies the low-carbon evolution of regional industries catalyzed by the digital economy and offers nuanced insights. The findings delineate the following observations: (1) The digital economy propels the low-carbon progression of indigenous physical industries and stimulates the low-carbon development of proximate regions' physical industries through discernible spatial spillover effects. (2) The spatial spillover ramifications of the digital economy manifest a substantive correlation with urban attributes, including geographical positioning, population size, and market integration levels. Notably, municipalities situated in the southeast coastal region, those characterized by larger population sizes, and those exhibiting heightened market integration levels show greater spatial spillover effects attributable to the digital economy. (3) The safeguarding of intangible asset equity property, a pivotal institutional underpinning for fostering digital economic development, amplifies the spatial spillover effect of the digital economy in propelling low-carbon development within regional industries. (4) As geographical and spatial distances expand, the spatial spillover effect of the digital economy attenuates, indicating a diminishing influence with increasing spatial separation.

Since China's launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.