In the current tide of technological revolution and industrial transformation, digitalization and greening have surfaced as two prominent trends, serving as the inherent requisites and impelling forces of new productive forces. Augmenting carbon emission efficiency is a crucial pathway for attaining green development. Digital inclusive finance, as an innovative financial paradigm, is a significant determinant influencing carbon emission efficiency and the progression of green development. By harnessing panel data from 108 cities within the Yangtze River Economic Belt spanning from 2011 to 2021, this study deploys the super-efficiency SBM model to gauge carbon emission efficiency. Additionally, it employs fixed effects and mediating mechanism models to empirically scrutinize the impact of digital inclusive finance on carbon emission efficiency within urban agglomerations in the Yangtze River Economic Belt. The study further probes its spatial effects through a spatial Durbin model. The research findings disclose that digital inclusive finance can substantially augment carbon emission efficiency in regional cities along the Yangtze River Economic Belt, manifesting notable regional correlations. This enhancement is accomplished by propelling industrial structure upgrades and augmenting scientific and technological capabilities. To enhance carbon emission efficiency in the Yangtze River Economic Belt, efforts should be centered around advancing digital inclusive finance, expediting industrial structural transformation, and fortifying scientific and technological development.

Digital engagement has become an important factor influencing mental health, yet the complex effects of different usage patterns still require deeper clarification. This study distinguishes digital engagement into instrumental-oriented digital engagement (IE) and affect-oriented digital engagement (AE), and utilizes panel data on the working-age population from the China Family Panel Studies (CFPS) from 2016 to 2022 to examine their associations with depression and their underlying competitive resource mechanisms. The results indicate that IE is associated with higher depression levels, whereas AE is associated with lower depression levels. Mechanism analysis reveals that both types of digital engagement contribute to enhancing psychological mastery resources, but also consume physiological recovery resources such as sleep and exercise. Among them, the physiological loss associated with IE is more likely to offset its psychological gains, whereas AE is more likely to perform an emotional recovery function under conventional intensity levels. Further analysis shows that the association between IE and depression is stronger among populations with high social role stress, and the association between AE and depression exhibits nonlinear characteristics. The study suggests that different digital engagement patterns exhibit asymmetric and competitive resource characteristics. Public health interventions should not simply emphasize reducing digital use, but rather guide individuals to maintain a dynamic balance between instrumental digital loads and moderate emotional recovery.