Evaluating the reduction in pollution caused by a sudden change in emissions is complicated by the confounding effect of weather variations. We propose an approach based on machine learning to build counterfactual scenarios that address the effect of weather and apply it to the COVID-19 lockdown of Lombardy, Italy. We show that the lockdown reduced background concentrations of PM2.5 by 3.84 µg m−3 (16%) and NO2 by 10.85 µg m−3 (33%). Improvement in air quality saved at least 11% of the years of life lost and 19% of the premature deaths attributable to COVID-19 in the region during the same period. The analysis highlights the benefits of improving air quality and the need for an integrated policy response addressing the full diversity of emission sources.

The harsh lockdown measures that marked the response to the COVID-19 outbreak in the Italian region of Lombardy provides a unique natural experiment for assessing the sensitivity of local air pollution to emissions. However, evaluating the pollution benefits of the lockdown is complicated by confounding factors such as variations in weather. We use a machine learning algorithm that does not require identifying comparable but unaffected regions while addressing the effect of weather. We show that the lockdown, albeit virtually halting most human activities, reduced background concentrations of PM2.5 by 3.84 µg/m3 (16%) and NO2 by 10.85 µg/m3 (33%). Improved air quality has saved at least 11% of the years of life lost and 19% of the premature deaths attributable to COVID-19 in the region. Although air pollution has significantly decreased, it has often remained above safety thresholds. The analysis highlights the diversity of air pollution sources and the need for an expansive policy response.

It is well established that temperature variability affects a range of outcomes relevant to human welfare, including health, emotion and mood, and productivity across a number of economic sectors. However, a critical and still unresolved empirical question is whether temperature variation has a long-lasting effect on economic productivity and, therefore, whether damages compound over time in response to long-lived changes in temperature expected with climate change. Several studies have identified a relationship between temperature and gross domestic product (GDP), but empirical evidence as to the persistence of these effects is still weak. This paper presents a novel approach to isolate the persistent component of temperature effects on output using lower frequency temperature variation. The effects are heterogeneous across countries but collectively, using three different GDP datasets, we find evidence of persistent effects, implying temperature affects the determinants of economic growth, not just economic productivity. This, in turn, means that the aggregate effects of climate change on GDP may be far larger and far more uncertain than currently represented in integrated assessment models used to calculate the social cost of carbon.