During COVID-19, the draconian countermeasures adopted to mitigate the spreading of Sars-CoV-2, including the forced closure of schools, public facilities and workplaces, drastically reduced the vehicle traffic and the industrial activities, providing an unprecedented setup for testing sustainability policies.
Capitalizing on powerful tools such as partial correlation, Granger causality and Bayesian state-space models, we propose a complex-causal analysis to investigate the relationship between 16 environmental conditions and human activities variables.
We statistically proved that, concomitantly with the reduction in both the mobility and the energy demand, the NO2 average concentration significantly decreases during the lockdown in 2020 when compared against the same period in 2019. On the one hand, causal analysis points out the influence of the human activities on the NO2 concentration as well as the possible influence of meteorological conditions, such as precipitation and wind speed, that could cause variation on air pollution. On the other hand, even though the results of the Bayesian state-space model seem to reveal a clear causal impact of the lockdown on the NO2 concentration, given the current data availability the strength of evidence for the Bayesian causal analysis might be debatable, to say the least.
We argue that, despite the relaxation of a broad spectrum of human activities during the lockdown, the backbone of human activity – including, for instance, the supply chain of essential goods and commodities – has never really stopped. This fact proves the efficiency of the region in providing indispensable services during emergencies but, at the same time, it suggests that a lockdown could be not enough in changing pollutants concentrations and, consequently, in being regarded as a strategy for pollution control and climate change mitigation.
Therefore, policy strategies more effective and economically sustainable than lockdowns must be considered for pollution control and climate change mitigation. Our analysis, framed within a systemic view, and strongly based on causality, makes our results grounded on statistical physics and applied math, demonstrating the relevance of integrative complex-data analysis for sustainability investigations.