key: cord-0900734-ltwamee0 authors: Wang, Rong; Xiong, Yuankang; Xing, Xiaofan; Yang, Ruipu; Li, Jiarong; Wang, Yijing; Cao, Junji; Balkanski, Yves; Peñuelas, Josep; Ciais, Philippe; Hauglustaine, Didier; Sardans, Jordi; Chen, Jianmin; Ma, Jianmin; Xu, Tang; Kan, Haidong; Zhang, Yan; Oda, Tomohiro; Morawska, Lidia; Zhang, Renhe; Tao, Shu title: Daily CO2 emission reduction indicates the control of activities to contain COVID-19 in China date: 2020-11-04 journal: Innovation (N Y) DOI: 10.1016/j.xinn.2020.100062 sha: 51628c235bda310ab89d07eb9cdec54eadd24db5 doc_id: 900734 cord_uid: ltwamee0 Lockdown measures are essential to containing the spread of coronavirus disease 2019 (COVID-19), but they will slow down economic growth by reducing industrial and commercial activities. However, the benefits of activity control from containing the pandemic has not been examined and assessed. Here we use daily carbon dioxide (CO2) emission reduction in China estimated from statistical data of energy consumption and satellite data of nitrogen dioxide (NO2) measured by the Ozone Monitoring Instrument (OMI) as an indicator for reduced activities consecutive to a lockdown. We perform a correlation analysis to show that a 1% day-1 decrease in the rate of COVID-19 cases is associated with a reduction in daily CO2 emissions of 0.22±0.02% using statistical data of energy consumption relative to emissions without COVID-19, or 0.20±0.02% using satellite data of atmospheric column NO2. We estimate that swift action in China is effective in limiting the number of COVID-19 cases <100,000 with a reduction in CO2 emissions of up to 23% by the end of February 2020, whereas a one-week delay would have required greater containment and a doubling of the emission reduction to meet the same goal. By analyzing the costs of health care and fatalities, we find that the benefits on public-health due to reduced activities in China are ten-fold larger than the loss of gross domestic product. Our findings suggest an unprecedentedly high cost of maintaining activities and CO2 emissions during the COVID-19 pandemic and stress substantial benefits of containment in public health by taking early actions to reduce activities during the outbreak of COVID-19. in Beijing, 19% in Shanghai, 61% in Guangzhou, and 47% nationally during the first 30 days 142 immediately following the 2020 Wuhan travel ban relative to the period of 2020 before the 143 ban ( Figure S3 ). Therefore, we define a concentration confinement factor (CCF) as: 144 CCF hj = C hj,2020 C hj,2016-2019 • C h0,2016-2019 C h0,2020 ( where h is a grid point or a province and j is a day. C hj,2020 and C hj,2016- maxima. This method allows to estimate NO 2 concentration changes due to COVID-19 after 151 correcting for the impact of meteorology, 13 temporal trends 19 and inter-annual variabilities. 18 The NO 2 column concentration fell sharply after the Wuhan travel ban, to reach a maximum 153 reduction during the first 30 days of 80% in Wuhan, 54% in Beijing, 60% in Shanghai, 70% 154 in Guangzhou, and 50% nationally (Figure 2a ). This reduction is close to a recent estimate of 155 48% over China using NO 2 tropospheric vertical column density retrieved from both the OMI 156 and the Tropospheric Monitoring Instrument (TROPOMI), of which the latter offers a higher 157 spatial resolution (0.05º×0.05º) but for a shorter period (2019-present) . 18, 19 The stronger on 19 January, which was only one day before COVID-19 was announced to be a Class B 247 disease and three days before the Wuhan travel ban. 1 Our results confirm that these early actions taken by the Chinese government have averted >100,000 COVID-19 cases relative to Economic losses and gains from reducing activities during the COVID-19 pandemic 265 The social costs of CO 2 emissions are conventionally considered to stem from the economic 266 damage caused by release of CO 2 into the atmosphere and the subsequent climate change. 34 We defined the public-health costs of CO 2 emissions during the COVID-19 pandemic as a 268 sum of the health-care cost for cured cases and the mortality cost for fatal cases if activities 269 and CO 2 emissions are maintained, which will cause an increase in the daily rate of COVID-270 19 cases following the relationship shown in Figure 4 . It should be noted that these public-271 health costs only exist during the epidemic and disappear when the pandemic is terminated. We estimated the costs of health care and fatalities using a Monte Carlo approach (Materials indicating a decline in the efficacy of the containment. We compared the public-health costs 283 associated with a quintile of actual CO 2 emission reduction to the direct reduction in gross 284 domestic product (GDP) (Figure 7c ). The public-health costs associated with the first quintile 285 of CO 2 emission reduction ($3.6-4.0 trillion) are nearly 8-fold larger than the direct loss of 286 GDP in the first quarter of 2020 relative to 2016-2019 ($450 billion). The public-health costs 287 associated with the fourth quintile of CO 2 emission reduction ($50-60 billion) are less than 288 one-fifth of the GDP loss. These results confirm that interventions, using CO 2 emissions as an 289 indicator, could have generated greater benefits than the economic loss in the short term. 7 Policy implications 291 We perform a correlation analysis to confirm that the daily rate of new COVID-19 cases is 292 lower when more activities and CO 2 emissions are contained in China as measured using both 293 statistical data of energy consumption and satellite retrievals of NO 2 column concentration. These relationships lead to substantial public-health costs of maintaining activities and CO 2 295 emissions in the pandemic of COVID-19; avoiding these costs by the control policies in China creates substantial benefits 10-fold larger than the loss of GDP in the first quarter of The authors declare no competing interests. Daily confirmed local COVID-19 cases in 30 provinces 332 We constructed a data set of daily local cases of COVID The procedures for compiling energy data are provided in the Supplementary Methods. 368 Based on energy consumption in the fifteen sectors (one sector is the remaining sectors other 369 than the fourteen sectors in Table S2 ) and cement production over 2016-2020, monthly 370 bottom-up CO 2 emission (E mt ) was estimated as: where m is a month, t is a year, h is a province, s is a sector, q is a type of fuel (1 to 3 for coal, 372 oil and gas, respectively), J mths is the energy consumed or cement produced, f mthsq is the where i is a grid, j is a day, t is a year, C ijt original is NO 2 column concentration retrieved from the 395 satellite, and ϕ i is the coefficient in the regression of the log 10 -transformed NO 2 column 396 concentration against the year t. 397 Second, for each grid, the detrended log 10 -transformed NO 2 column concentration was 398 regressed against the detrended meteorological variables (M j,itk , k=1 to 9 for temperature, 399 pressure, relative humidity, wind speed, zonal wind, meridional wind, atmospheric 400 precipitable water content, boundary layer height, and ozone column concentration). Correlation coefficients between log 10 C ijt detrended and daily meteorological variables are 402 mapped in Figure S2 . Given the correlation between log 10 C ijt detrended and daily meteorological 403 variables, we adopted a partial-least-square regression using a lag model as: 45 where i is a grid, j is a day, t is a year, k denotes one meteorological variable, τ is the lag day 405 in the effect of meteorology on concentration (e.g. τ=3 denotes the meteorological variables 406 on three days before day i), c τ,ik is a regression coefficient, and b i is a constant. Third, log 10 -transformed NO 2 concentration corrected for the impact of meteorology (C ijt ) can 408 be derived as: where M k is the average meteorology over January-May in 2020. The NO 2 column 410 concentration is log 10 -transformed to consider a first-order change in atmospheric physical rather than a specific day. We estimated daily CO 2 emissions on a day in 2020 based on the 420 change in concentration, measured as the concentration confinement factor (CCF) (Eq. 2). We where j is a day, h is a province and m is a month. E jh and E 0jh are the daily CO 2 emission To simulate the daily evolution of new COVID-19 cases by province, we developed a 444 regression model between the percentage of CO 2 emission reduction to emissions in January- May and the daily rate of new cases as: where j is a day, h is a province, V j,h is the daily rate of local new cases, Λ j,h is total CO 2 447 emission reduction as a percentage of CO 2 emissions in January-May (see Eq. 11), S h is the 448 slope of the regression, and B h is the intercept of the regression. Provincial S h and B h are listed in Table S3 . Λ j,h was calculated as: where d is a day, and 152 denotes the last day (d) for 31 May 2020 (d=1 for 1 January 2020). ΔCUE jh is total CO 2 emission reduction due to COVID-19. CUE 0h is total CO 2 emissions in 452 January-May without COVID-19. ΔE dh is change in daily CO 2 emissions due to COVID-19. E dh and E 0dh are daily CO 2 emissions with or without COVID-19, respectively (Eqs. 8,9). For each province, the number of daily new COVID-19 cases (N) was predicted as: where j is a day. To initialize the simulation, we obtained the initial number of daily cases 456 (N 0,h ) as that from the fourth day after the first infection was reported in a province, which 457 was estimated as the average number of daily cases during the three adjacent days with 458 reported cases. In Figures 5-6 , we simulated the spread of COVID-19 under a varying starting day and 460 percentage of emission reduction by modifying Eq. 10 into: where n denotes the change of the start day of interventions (n is positive to denote a delay . We calculated the public-health costs in January-May 2020 for each province as: where h is a province, d is a day, η is an age group, 152 denotes 31 May 2020, N hd is the 473 number of daily new cases, cc h or fc h is the fraction of cured or fatal cases during COVID-19 474 in each province, 47 σ η or δ η is the fraction of age group η in the confirmed or fatal cases, 48 Φ cc,η is the unit cost in the course of infection for a cured case, 35 and Φ fc,η is the unit cost in China are considered except for Tibet, Hong Kong, Macau and Taiwan due to a lack of data. The colors of the lines evolve from red to blue as the distance from Wuhan City increases. Our estimate is compared to two previous bottom-up estimates, which are indicated by The State Council Information Office of the People's Republic of China 2. World Health Organization (WHO) the sum for all provinces is indicated by a red dot. (c-h) Prediction of the 695 number of COVID-19 cases in January-February 2020 under a given starting day and the 696 percentage of CO 2 emission reduction as an indicator of reduced human activity in Hubei and China (g, h). The actual starting day and percentage of CO 2 698 emission reduction are indicated by red pentagrams 700 (a) Using an estimate of CO 2 emissions as an indicator for human activity in 2020 with a 701 bottom-up (red) or top-down (blue) method, the marginal costs of health care for cured cases 702 (dotted line) and of mortality for fatal cases (dashed line) in January-May are estimated when 703 one additional tonne of CO 2 is emitted to show the impact of maintaining activity on a given 704 day between 20 January and 29 February in each province China are calculated as an average weighted by provincial CO 2 emissions Methods). (b) The total costs of health care and mortality in a set of artificial scenarios, 707 where actual daily CO 2 emission reduction is multiplied by a constant value Comparison of the public-health costs associated with a quintile of the actual 710 daily CO 2 emission reduction to the direct loss of gross domestic product The bar for "100%-120%" indicates an artificial scenario with 20% more CO 2 712 emissions reduced than the actual scenario. To assess the uncertainty in our estimate Monte Carlo simulations, of which the median is indicated as a central estimate (line 714 or bar) and the 95% confidence interval is indicated as the uncertainty (shaded area or error 715