Amid China's ageing and digital shift, digital exclusion among older adults poses an urgent challenge. To unpack this phenomenon, this study disentangles age, period, and cohort effects on digital exclusion among middle-aged and older Chinese adults. Using three nationally representative surveys (CHARLS 2011-2020, CFPS 2010-2022, and CGSS 2010-2021), we fitted hierarchical age-period-cohort (HAPC) models weighted by cross-sectional survey weights and stabilized inverse probability weights for item response. We further assessed heterogeneity by urban-rural residence, region, multimorbidity, and cognitive risk, and evaluated robustness with APC bounding analyses. Across datasets, digital exclusion increased with age and displayed mild non-linearity, with a small midlife easing followed by a sharper rise at older ages. Period effects declined over the 2010s and early 2020s, although the pace of improvement differed across survey windows. Cohort deviations were present but less consistent than age and period patterns, with an additional excess risk concentrated among cohorts born in the 1950s. Rural and western residents, as well as adults with multimorbidity or cognitive risk, remained consistently more excluded. Over the study period, the urban-rural divide showed no evidence of narrowing, whereas the cognitive-risk gap widened. These findings highlight digital inclusion as a vital pathway for older adults to remain integral participants in an evolving digital society.

翻译：在中国老龄化与数字化转型并行的背景下，老年人群面临的数字排斥已成为紧迫挑战。为解析这一现象，本研究旨在厘清年龄、时期和队列效应对中国中老年人数字排斥的影响。基于三项具有全国代表性的调查数据（CHARLS 2011-2020、CFPS 2010-2022、CGSS 2010-2021），我们采用加权分层年龄-时期-队列（HAPC）模型进行分析，权重包括横截面调查权重以及针对项目回答的稳定逆概率权重。我们进一步通过城乡居住地、区域、共病状况和认知风险评估异质性，并运用APC边界分析检验稳健性。跨数据集结果显示，数字排斥随年龄增长而加剧，并呈现轻微非线性特征：中年时期略有缓解，随后在老年阶段急剧上升。时期效应在2010年代至2020年代初期呈下降趋势，但不同调查窗口的改善速度存在差异。队列偏差确实存在，但其一致性低于年龄与时期模式，其中1950年代出生队列表现出额外的超额风险。农村及西部地区居民、患有共病或存在认知风险的成年人持续面临更严重的数字排斥。在研究期间，城乡数字鸿沟未见缩小迹象，而认知风险相关的差距反而扩大。这些发现表明，促进数字包容是确保老年人在持续演进的数字社会中保持实质性参与的关键路径。