Is Digital Reading Resilient to Air Pollution?
利用数字阅读平台数据和秦岭-淮河供暖政策边界回归断点设计，发现空气污染通过认知负荷促使读者采取降级、求熟、碎片化、慢读等适应行为，这些行为完全中介了污染对阅读时长和消费的负面影响。
Air pollution imposes significant cognitive strain and economic costs, yet its effects on digitally mediated, cognitively demanding activities remain underexplored. We examine its impact on digital reading, a context where key digital affordances—ubiquitous access, low switching costs, and personalization—fundamentally distinguish it from print reading. These affordances can buffer immediate disruptions from pollution but also reshape user responses to cognitive fatigue in ways unique to digital environments. Drawing on Cognitive Load Theory, we argue that air pollution depletes cognitive resources and prompts users to adjust their engagement strategies. Using individual-level behavioral data from a major digital reading platform and a classic spatial regression discontinuity design exploiting the Qingling Mountains–Huai River heating policy boundary, we identify four adaptive behaviors facilitated by digital affordances: genre downgrading, familiarity seeking, increased fragmentation, and slower reading pace. Mediation analysis shows that these adaptations fully mediate the effect of pollution-induced cognitive strain on engagement. This suggests that, while they offset the direct effect of cognitive strain, they reduce the utility of reading and thereby lead to lower overall engagement. Through these adaptive behaviors, a one-unit increase in the Air Quality Index reduces reading time by 0.7% and spending by 1.2%. This study integrates perspectives from digital affordances, air pollution, cognitive effects, and reading to uncover affordance-enabled mechanisms through which environmental stressors reshape digital behavior. The results offer design implications for preserving user engagement and policy insights for broadening the assessment of pollution’s societal costs.