Based on the data from the 2012–2018 China Family Panel Survey, this study examines the impact of household wealth on individuals’ mental health using a two-way fixed effects model. The findings indicate that household wealth exerts a significant positive effect on individuals’ mental health. Furthermore, this study shows that the impact of household wealth on individuals’ mental health is nonlinear but inverted U-shaped. Considering the possible endogeneity problem, this study further examines the effect of household wealth on residents’ mental health using two-stage least squares, and the conclusions remain robust. The results of the heterogeneity analysis indicate that household wealth has a greater impact on the mental health of residents in the low-education group and western region. Furthermore, the results of the mechanisms reveal that household wealth affects mental health by influencing insurance investment and individuals’ labor supply. Moreover, this study finds that household wealth affects individuals’ mental health not only in the short term but also in the medium and long terms. This study provides policy implications for the government toward improving individuals’ mental health.

How artificial intelligence (AI) reshapes the internal structure of firm-level skill demand remains largely uncharted. Using approximately 67 million online job postings from two major Chinese recruitment platforms (2019–2024), we construct firm-by-year potential AI exposure via semantic matching between AI patent texts and detailed occupation task descriptions, decompose exposure into displacement and augmentation components based on task routineness, and measure four skill-category demand shares and their within-category importance from job-description text, with identification from within-firm variation under firm and city-by-year fixed effects. Displacement and augmentation exposure exhibit opposing relationships with skill demand: displacement is negatively associated with the routine cognitive share, while augmentation is positively associated with the nonroutine analytical share. Both forms of exposure are associated with a de-coring pattern, a shallower and more dispersed skill portfolio with within-category importance diverging from share movements, concentrated among low entry-threshold, small firms. Reskilling policy should therefore emphasize portfolio breadth and portable competency frameworks rather than deeper single-track specialization, particularly for workers in small, lower-threshold firms.

In the digital era, cloud computing stands as a pivotal tool in the transformative journey of enterprises, with China’s tech landscape serving as a prime exemplar. However, when enterprises embrace cloud computing, they face complex hurdles, mainly technical ones. To understand how the complexity of cloud migration affects their digital transformation, our research meticulously constructed business process models for business deployment in both non-cloud and cloud contexts, spanning the IaaS, PaaS, and SaaS levels. By harnessing China’s public cloud market data, we constructed the Cloud Migration Complexity Index, providing a tangible metric to gauge the intricacies of cloud migration and their implications on digital transformation. The findings illustrate that a decrease in cloud migration complexity significantly accelerates digital transformation, with the reduction in SaaS complexity having the most profound impact. Analyzing businesses of varying scales, the diminishing complexity of SaaS predominantly boosts digital transformation for non-SME enterprises, while reduced PaaS complexity is most beneficial for SMEs. This study advocates for the government to expand the PaaS market and suggest that cloud providers develop more PaaS-based products to optimize cloud migration both technically and economically.