Why and how the regulation of emerging technologies occurs is not clear in the literature. In this study, we adapt the multiple streams framework – often used for explaining agenda‐setting and policy adoption – to examine the phenomenon. We hypothesize how technological change affects policy‐making and identify conditions under which the streams can be (de‐)coupled. We trace the formulation of the General Data Protection Regulation to show that the regulation occupied the legislative agenda when a policy window was exploited through policy entrepreneurship to frame technological change as a problem for data privacy and legislative harmonization within the European Union. Although constituencies interested in promoting internet technologies made every effort to stall the regulation, various actors, activities, and events helped the streams remain coupled, eventually leading to its adoption. We conclude that the alignment of problem, policy, politics, and technology – through policy entrepreneurship – influences the timing and design of technology regulation.

Background: Many low- and middle-income countries are experiencing high and increasing exposure to ambient fine particulate air pollution (PM2.5). The effect of PM2.5 on infant and child mortality is usually modelled using concentration response curves extrapolated from studies conducted in settings with low ambient air pollution, which may not capture its full effect. Methods: We pool data on more than half a million births from 69 nationally representative Demographic and Health Surveys that were conducted in 43 low- and middle-income countries between 1998 and 2014, and we calculate early-life exposure (exposure in utero and post partum) to ambient PM2.5 using high-resolution calibrated satellite data matched to the child’s place of residence. We estimate the association between the log of early-life PM2.5 exposure, both overall and separated by type, and the odds of neonatal and infant mortality, adjusting for child-level, parent-level and household-level characteristics. Results: We find little evidence that early-life exposure to overall PM2.5 is associated with higher odds of mortality relative to low exposure to PM2.5. However, about half of PM2.5 is naturally occurring dust and sea-salt whereas half is from other sources, comprising mainly carbon-based compounds, which are mostly due to human activity. We find a very strong association between exposure to carbonaceous PM2.5 and infant mortality, particularly neonatal mortality, i.e. mortality in the first 28 days after birth. We estimate that, at the mean level of exposure in the sample to carbonaceous PM2.5—10.9 µg/m3—the odds of neonatal mortality are over 50% higher than in the absence of pollution. Conclusion: Our results suggest that the current World Health Organization guideline of limiting the overall ambient PM2.5 level to less than 10 µg/m³ should be augmented with a lower limit for harmful carbonaceous PM2.5.

Although cities have risen to prominence as climate actors, emissions’ data scarcity has been the primary challenge to evaluating their performance. Here we develop a scalable, replicable machine learning approach for evaluating the mitigation performance for nearly all local administrative areas in Europe from 2001-2018. By combining publicly available, spatially explicit environmental and socio-economic data with self-reported emissions data from European cities, we predict annual carbon dioxide emissions to explore trends in city-scale mitigation performance. We find that European cities participating in transnational climate initiatives have likely decreased emissions since 2001, with slightly more than half likely to have achieved their 2020 emissions reduction target. Cities who report emissions data are more likely to have achieved greater reductions than those who fail to report any data. Despite its limitations, our model provides a replicable, scalable starting point for understanding city-level climate emissions mitigation performance.