Air pollution rebound and different recovery modes during the period of easing COVID-19 restrictions

Abstract

Although COVID-19 lockdown policies have improved air quality in numerous countries, there is a lack of empirical evidence on the extent to which recovery has resulted in air pollution rebound, and the differences and similarities among regions' recovery modes during the period of easing COVID-19 restrictions. Here, we used daily air quality data and the recovery index constructed by a city-pair inflow index for 119 cities in China to quantify the impact of recovery on air pollution from March 2 to October 30, 2020. Findings show that recovery has significantly increased air pollution. When the recovery level increased by 10 %, the concentration of PM_2.5, SO₂, and NO₂ respectively deteriorated by 1.10, 0.33, 1.25 μg/m³, and the average growth rates of three air pollutants were about 3 %-6 %. Moreover, we used the counterfactual framework and time series clustering with wavelet transform to cluster the rebound trajectory of air pollution for 17 provinces into five recovery modes. Results show that COVID-19 has further intensified regional differentiations in economic development ability and green recovery trend. Three northwestern provinces dependent on their resource endowments belong to energy-intensive recovery mode, which have experienced a sharp rebound of air pollution for two months, thereby making green recovery more challenging to achieve. Three regions with a diversified industrial structure are in industrial-restructuring recovery mode, which has effectively returned to a normal level through adjusting industrial structure and technological innovation. Owing to local policies and the outbreak of COVID-19 in other countries, six provinces in policy-oriented and international trade-oriented recovery modes have not fully recovered to the level without COVID-19 until October 2020. The result highlights the importance of diversifying industrial structure, technological innovation, policy flexibility and industrial upgrading for different recovery modes to achieve long-term green recovery in the future.

Keywords: Air pollution rebound; Counterfactual analysis; Recovery modes; The period of easing COVID-19 restrictions; Time series clustering; Wavelet transform.

Graphical abstract

Fig. 1

Daily recovery index constructed for 119 cities in China. Panel (a) shows the process of constructing the recovery index Recovery_{i, t}. The variables τ_{i, j, t2020} and τ_{i, j, t2019} are respectively the city-pair inflow indices of city i on date t in 2020 and 2019. See Section 2.2.1 for more details. Panel (b) is the line graph for the daily average recovery index of 119 cities in China from March 2 to October 30, 2020.

Fig. 2

Geographical distribution of five recovery modes. 17 provinces are categorized into five modes: energy-intensive recovery mode (red), industrial-restructuring recovery mode (orange), policy-oriented recovery mode (purple), international trade-oriented recovery mode (blue), and less-impacting recovery mode (green). The important coal base, Jing-Jin-Ji region, and three important provinces in the Yangtze River Delta are pointed out in the figure.

Fig. 3

Five recovery modes on the basis of “virtual differences” for regional air pollution in China. The weekly “virtual difference” shown in this figure is called “delta”. Owing to different economic structures, regions' major air pollutants could be heterogeneous. To make the “virtual difference” of each region comparable, this paper uses AQI as the comprehensive measure of actual air quality to calculate “delta”. The dotted line in this figure is the baseline level on the same day in 2019. The dashed line in this figure is the baseline level in January 2020 before COVID-19. Bootstrapped 95 % confidence intervals are shown in shading areas.