COVID-19 causes record decline in global CO2 emissions
Zhu Liu, Philippe Ciais, Zhu Deng, Ruixue Lei, Steven J. Davis, Sha Feng, Bo Zheng, Duo Cui, Xinyu Dou, Pan He, Biqing Zhu, Chenxi Lu, Piyu Ke, Taochun Sun, Yuan Wang, Xu Yue, Yilong Wang, Yadong Lei, Hao Zhou, Zhaonan Cai, Yuhui Wu, Runtao Guo, Tingxuan Han, Jinjun Xue, Olivier Boucher, Eulalie Boucher, Frederic Chevallier, Yimin Wei, Haiwang Zhong, Chongqing Kang, Ning Zhang, Bin Chen, Fengming Xi, François Marie, Qiang Zhang, Dabo Guan, Peng Gong, Daniel M. Kammen, Kebin He, Hans Joachim Schellnhuber
11 Near-real-time data captured record decline in global CO2 emissions due to COVID-19
Zhu Liu , Philippe Ciais , Zhu Deng , Ruixue Lei , Steven J. Davis , Sha Feng , Bo Zheng , Duo Cui , Xinyu Dou , Pan He , Biqing Zhu , Chenxi Lu , Piyu Ke , Taochun Sun , Yuan Wang , Xu Yue , Yilong Wang , Yadong Lei , Hao Zhou , Zhaonan Cai , Yuhui Wu , Runtao Guo , Tingxuan Han , Jinjun Xue
14, 15, 16 , Olivier Boucher , Eulalie Boucher , Frederic Chevallier , Yimin Wei , Haiwang Zhong , Chongqing Kang , Ning Zhang , Bin Chen , Fengming Xi , François Marie , Qiang Zhang , Dabo Guan , Peng Gong , Daniel M. Kammen , Kebin He , Hans Joachim Schellnhuber * Corresponding authors: [email protected], † Authors contribute equally
The conside rable cessation of human activitie s during the COVID-19 pande mic has affe cted global e ne rgy use and CO e missions. Here we show the unpre ce de nte d de crease in global fossil CO e missions from January to April 2020 was of 7.8% (938 Mt CO with a +6.8% of 2-σ unce rtainty) whe n compare d with the pe riod last ye ar. In addition othe r e me rging e stimate s of COVID impacts base d on monthly e ne rgy supply or e stimate d parame te rs , this study contribute s to anothe r step that constructed the near-re al-time daily CO2 e mission inve ntorie s base d on activity from powe r ge ne ration (for 29 countrie s), industry (for 73 countrie s), road transportation (for 406 citie s ), aviation and maritime transportation and comme rcial and re side ntial se ctors emissions (for 206 countrie s). The e stimates distinguished the de cline of CO2 due to COVID-19 from the daily, we e kly and se asonal variations as we ll as the holiday e ve nts. The COVID-re late d de cre ases in CO2 e missions in road transportation (340.4 Mt CO , -15.5%), powe r (292.5 Mt CO , -6.4% compare d to 2019), industry (136.2 Mt CO , -4.4%), aviation (92.8 Mt CO , -28.9%), re sidential (43.4 Mt CO , -2.7%), and inte rnational shipping (35.9Mt CO , -15%). Regionally, de cre ases in China we re the large st and e arlie st (234.5 Mt CO , -6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO , -12.0%) and the U.S. (162.4 Mt CO , -9.5%). The de cline s of CO2 are consistent with re gional nitroge n oxide s conce ntrations observed by sate llite s and ground-base d ne tworks, but the calculate d signal of e missions de cre ases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. Howe ver, with obse rved fast CO2 re cove ry in China and partial re -ope ning globally, our findings suggest the longe r-te rm e ffe cts on CO2 e missions are unknown and should be care fully monitore d using multiple me asure s. [288 words] Introduction
COVID-19 has caused hundreds of thousands of deaths worldwide since December of 2019, together with large-scale ongoing reductions in human activities and profound effects on different national economies. Industrial production and energy consumption in some countries were reported to decline by up to 30% in just a few weeks as lockdowns were imposed to protect public health. Fossil fuel and cement CO emissions are directly linked to human activities. Initial estimates of emissions changes based on a limited sample of power plants and indirect satellite observations of atmospheric pollutants have suggested that we may be witnessing the largest drop of emissions since the end of the Second World War. However the detailed inventories of energy and fuel use that have historically been used to assess CO emissions are only available with a lag of one or two years that result in challenges to assessing the CO dynamics due to COVID-19. More recently there are emerging estimates that provides pioneering assessment on the impacts, for example, Internationa l Energy Agency uses monthly fossil fuel energy demand to conclude a -5% decline of CO in Jan-April 2020 , and another big step by Le Quéré et al. using parameter measures that compile government policies and activity data to estimate the decrease in CO emissions during forced confinements, suggesting daily global CO2 emissions decreased by –17% by early April 2020 compared with the mean 2019 levels. Those studies provide initial steps to estimate the extent of COVID impacts on CO emission (-4% to -8%) in 2020 and reached profound impacts and attentions from public and academia. However, there still lacking for quantitative assessment to distinguish the decline of CO emissions due to COVID19 from the daily, weekly and seasonal CO emission variations as well as the holiday events, and capture the real time dynamics of CO emissions to precise ly reflect the extent of the CO decline due to COVID-19. To do so requires detailed CO2 emission inventories for near real time for year 2020 to date as well as for previous years as the baseline. More importantly, such inventory is critical to monitor the future CO dynamic s after the forced confinement released gradually since March 2020, in which the emission trends can be various. Here, we conducted a new step that constructed the near-real-time CO2 emission inventories with temporal resolution to daily in period Jan 2019 to May 2020. Such inventorie s captured the daily, weekly and seasonal variations within (in 2020) and without COVID-19 (in 2019), as well as impacts by holidays in major countries such as Spring Festival in China , thus the impacts of COVID-19 can be distinguished. Details of our data sources and analytic methods are provided in the Methods section. In summary, we calculated regional CO emissions between January and May 2020 and compare them to the calculated regional CO emissions in same period in 2019, drawing on hourly datasets of electricity power production and CO emissions in 29 countries (including the substantial variations in carbon intensity associated with electricity production), three different indexes of daily vehicle traffic / mobility in 416 cities worldwide, monthly production data for cement, steel and other energy intensive industrial products in 73 countries, daily maritime and aircraft transportation activity data, as well as proxies for the residential and the commercial building emissions (see Methods for data sources). Together, these data cover almost all fossil and industry sources of global CO emissions, including CO2 emission from cement production that not been considered by IEA assessment . Re sults
The near real time emissions in Jan 1 st st from April 30 th in 2020 when compared with same period in 2019 (Fig 1). There are also remarkable seasonal (-6%, +8%), weekly(-12%, +12%) and daily (-12%, +14%) min-max variations of CO2 emissions showing in whole year 2019 (Fig 1a) and in January to May 2020 (Fig 1b), mainly due to the heating and cooling demands and difference of activities in different seasons (i.e. heating degree days-HDD and cooling degree days-CDD), weekday and weekend, as well as holiday events. To distinguish the COVID-19 impacts from seasonal variations, we calculated the difference of emission in 2020 from same period in 2019 by comparing the daily emissions in same period (Fig1b), the total difference of 938 Mt CO is the largest decline with its decrease rate ever seen on record (Fig 1c), with its half year decrease larger than the total annual decrease (790 Mt CO2) during World War II (Fig 1d). The daily mean emission in Jan-April 2020 (92.0 Mt CO2 per day) is 6% less than daily mean CO2 in same period 2019 (98.0 Mt CO2 per day), the decline is more signific a nt in April (-14.4% in April 2020 compared with 2019), but showing the narrow of decline in May. The cumulated emission in Jan-April 2020 (11127 Mt CO2 for first 121 days in 2020) is 7.8% less than that (12065 Mt CO2 for first 120 days in 2019). The results showing that the decline slows in late April globally, mainly contributed by the recovery of emission in China and the Europe, along with the release of pandemic in these regions (Fig 1e). The dataset also captured the impact of daily emission decline due to holida ys, for example, spring festival in China result in the decline of daily CO2 in China’s power sector in 2019 similar to the level during COVID19 pandemic in 2020, and the data also reflect the impact on emission decline from Qingming Holiday(April 5 th ) and Labor Day Holiday (May 1 st ) (Fig 1f). Figure 1. Daily CO2 e missions from 2019 to April 30 th ’ s powe r se ctor in addition to the impact from COVID-19. It is important to note that first months of 2020 were exceptionally warm across much of the northern hemisphere, meaning that 2020 CO emissions would have been somewhat lowe r than the same period in 2019 even without the disruption in economic activities and energy production caused by COVID-19 and related lockdowns. Thus, we considered the impacts on CO from COVID-19 by removing the difference of daily CO due to temperature variation between 2019 and 2020 in the winter months (January-March), and comparing the CO emission in 2020 with the same period in 2019 . The daily CO2 in 2020 show significant decline when compared with 2019, despite the seasonal variations. Figure 2 shows estimated trends in total CO emissions globally and in several major regions. Globally, we find a global 7.8% decrease of CO emissions during the first four months of 2020 (solid black curve) compared with the same period in 2019 (dashed black curve). In the first quarter of 2020, the most pronounced decline occurred in China, where emissions fell by -9.3%, with substantial but progressively smaller decreases in Europe (EU27 & UK) (-8.4%), the U.S. (-4.7%), Japan (-3.6%), India (-2.5%) and Russia (-2.1%). The large and early drop in Chinese emissions correspond to an early outbreak of COVID-19 and strict lockdown measures, which were relaxed throughout March. However, due to the rapid control of COVID-19 pandemic, the CO emission recovered quickly after March, the 2020-2019 difference in China’s emissions were much less in March (-8.1%) than in February (-14.6%), and China’s CO2 in April 2020 is about 0.8% higher than the CO in April 2019. The data also show that emission declines due to China’s spring festival reached similar level of the daily emission decline (-10% of daily CO2 decline within Spring festival when compared with the average daily CO2 in February 2019). In other countries, decreases in emissions due to COVID-19 weren’t apparent until late February or March, coincident with the onset of lock- downs in different countries, with greater decreases generally observed in March (U.S.:-13.8% , EU27 & UK: -8.1%, India: -16.4%, Brazil:-11.0%, Japan -4.1%) than in February (U.S.: 1.9%, EU27 & UK: -8.4%, India: 6.2%, Brazil: -1.6%, Japan -1.1%), and larger decreases showing in April (U.S.:-25.6%, EU27 & UK: -25.0%, India: -27.9%, Brazil:-26.6 %, Japan -6.7%). So when it comes to the emissions in the first four months of 2020, the largest decrease by -12.0% occurred in Europe (EU27 & UK), with smaller decrease in the U.S. (-9.5%), India (-8.5%), Brazil (-7.0%), China (-6.9%), Japan (-4.3%) and Russia (-3.4%). Figure 2. Daily CO2 e missions in the first quarte r of 2019 (dotte d line ) and 2020 (Solid Line ) for the world, U.S., Italy, China, Brazil, Spain, India, UK, Ge rmany, Japan, Russia and France . Diffe re nt color for countrie s re pre sents diffe re nt contine nts . Figure 3 | (a) Global fossil fue l and ce me nt CO2 e missions (Seven-days running me an) diffe re nce be twe e n 2020 and 2019 for diffe re nt se ctors. (b-e ) Emissions diffe re nce s for diffe re nt se ctors for diffe re nt re gions. The gre e n line s for the transport se ctor are for ground (mainly road) transport e missions and the light blue line is for aviation e missions changes for the e ntire globe . Figure 3 shows the breakdown of emissions decreases by sector. The largest contributions to the global decrease in emissions come from ground transport (340.0 Mt CO , 36% of the total; orange in Fig. 3a) and power (-292.5 Mt CO , 31% of the total first four months decrease; red in Fig. 3a) with decreases from industry sector just slightly less (-136.2 Mt CO , 15% of the total; yellow in Fig. 3a), and relatively small decreases in residential emissions (-43.4 Mt CO , 5% of the total; green in Fig. 3a). The rest of the reduction comes from aviation and ships emissions. Powe r Ge ne ration
Our estimates of power sector emissions rely on near real time hourly or daily electricity data. Thus, we are able to resolve the effects of weather-driven variations of renewable electricity supply (See
Methods for temperature adjustment) as well as the increases in natural gas relative to coal for power generation in 2020 in the U.S. that has been caused by very low oil prices. In the winter months (January-March) of 2020, the temperature variation compared to 2019 leads to -0.8% reduction in the power emissions. Figure 1b shows that in the first four months of 2020, globa l CO emissions from the power sector declined by -6.4% (-292.5 Mt CO ), with a small decline in China (-6.0%, -91.1 Mt CO ) and somewhat larger decreases in the U.S. (-7.7%, -43.8 Mt CO ), India (-9.2%, 39.7 Mt CO ) and the EU-27 & UK (-22.5%, -82.0 Mt CO ). Some of the drop in China’s power sector emissions are due to prevailing warmer winter temperatures, and the near-zero differences in late January and early February between 2020 and 2019 are because this was when the country’s spring festival occurred in 2019 (Fig 1e). The decline of power generation in Ching Ming Festival (April 5 th ) and the Labor Day holiday (May 1 st -5 th ) are also reflected by the data. Meanwhile, power emissions in Russia and Japan were almost stable in the first four months of 2020 (-2.4%, -7.0 Mt CO ; -2.9%, -5.6 Mt CO ) (See SI Table S2). Industry and ce me nt production e missions
Industry emissions from steel, chemicals and other manufactured products from fossil fue l combustion and the cement production process represent on average 29% of the global CO emissions, with a much larger share of national emissions in developing countries (39% in China and 33% in India) . We collected data separately for steel, chemicals (based on 8 chemica l products) and 26 other industrial products, as well as for cement production in China, for a better attribution of industrial CO emissions changes (See SI Table S6). Only emissions from direc t fuel consumption and chemical process emissions by the industry sector were considere d, electricity related emissions for the industry being already counted in the power generation sector. In the first four months of 2020, global industry emissions fell by -4.4% in most countrie s, including China (-3.5%, -43.8 Mt CO ), U.S. (-6.4%, -17.1 Mt CO ), EU27 & UK (-7.3%, -15.1 Mt CO ) and India (-7.9%, -22.1 Mt CO ). In China, emissions from steel production (41.6% of national industrial emissions from fuel combustion) remained essentially the same with slight increases in January and February by 1.5% and 5.0%, respectively, and only decreased in March by 1.7% (the production data suggests no difference in emission in April).This rather surprising result demonstrates the core status of steel industry in China’s industrial structure. Overall, despite the COVID-19 pandemics, emissions from the steel industry thus resulted 1.4% higher in the first four months of 2020. For the cement industry (22.2% of China’s industrial emissions from fue l combustion), estimates based on the official reports from National Bureau of Statistics show a considerable decline of -14.4% in the first four months in 2020, namely -29.5% in January and February combined, -18.3% in March but 3.8% increase in April. The latter recovery substantiate d a concurrent recovery in various industrial activities. Emissions from the production of chemicals in China also decreased by 1.5%, while emissions from other industries had fallen by 4.8% in the same time period. In addition, cement production usually entails the decomposition of c alcium carbonate in making cement clinker, which produces the so-called process emission. The absolute amount of CO abated from such process emission was estimated to be 37.3 Mt in China with the same relative reduction as to the fuel combustion in producing cements. Ground transportation e missions
Emissions from ground transportation (SI Figure S4) were calculated based on TomTom congestion level with daily transportation activity data for 416 global cities in more than 50 countries. Ground transportation (See Methods for data and calculation process) contributes 18% of the world CO emissions and decreased dramatically by -15.5% in the first four months (-340 Mt CO ) thus contributing 36.3% of the decline of the emissions from all sectors (Figure 3). In China, as cities started locking down in the last week of January, the average emissions from transport during that month decreased by -18.5%. In February, ground transport emissions droppe d abruptly by -53.4%, compared to the same month in 2019. However, the reduction has been shrinking in March (-25.9%) and April (-16.3%) respectively. The emissions from ground transport have dramatically decreased in other countries just after lockdown measures since March. Ground transport emissions in U.S. and India dropped by -22.8% and -25.9% in March since the lockdown measures, and continued to decrease by -50.0% and -65.6% in April respectively. Emissions in European countries dropped by -16.6% and -32.1% in March and April, with France, Spain and Italy showing the largest reductions of -15.8%, -14.2% and 13.9% respectively in the first four month in 2020 (SI Figure S4). Aviation and ships e missions
Emissions from global aviation decreased by -28.9% during the first four months (-92.8 Mt CO ), among which those from international aviation decreased by -63.6 Mt CO (SI Figure S5). Domestic aircraft emissions are included in our global estimates, but only international aviation emissions attributed to different countries (See Methods for data and calculation process). The total number of flights and global aviation emissions shows two consecutive decreases, one by the end of January in Asia and another since the middle of March in the rest of the world. Emissions declined sharply after mid-March, coincident with travel bans and lock-down measures. CO emissions from international shipping (see Methods for data and calculation process) decreased by -15.0% (-32.6 Mt CO ) in the first four months. Comme rcial and Re sidential buildings
We used atmospheric ERA5 temperature data to interpret the consumption variability and accounted for the weekly cycle and the occurrence of holidays that differ among countries and from year to year. The emissions from fuel use (oil and gas) in commercial and residential buildings was estimated using population-weighted heating degree days by the ERA515 reanalysis of 2 meters air temperature for 206 countries. We found that the global heating demand in the first four months declined by -2.7% compared to 2019, owing to the abnormal warm northern-hemisphere winter16, resulting in the decreased of emissions. The results indicate that the residential consumption is mostly driven by the temperature, and that there was no significant change during the lockdown period (i.e. no significant change with regards to the year-to-year variability when the contribution of temperature variability was removed). To verify such estimation, we analyzed the natural gas consumption for commercial and residential buildings for 6 countries (France, Italy, Great Britain, Belgium, Netherlands and Spain) of Western Europe for which daily data was available from national operators. The data were converted to CO2 emissions using emission factors that account for gas quality. For these 6 countries, these data converted to CO2 emissions were used after rescaling to annual national residential emissions from EDGAR for the year 2019 to account for additional use of liquid and solid fuels used in the residentia l sector, which is counted in EDGAR but not covered by our natural gas c onsumption data. Nevertheless, for the analyzed countries, the industry data show a decrease of the demand by ≈
20% for a few weeks after the start of the lockdown. Note that the industry consumption is a small fraction of that of the residential sector, so that the lockdown has had an overall small impact on the gas use for these countries. These results support the assumption of the method used for all other countrie s that the gas consumption was little affected by the lockdown and that the gas consumption decrease or increase during the period was mostly the result of variations of temperatures.
Obse rvation and ve rification from air quality data
Our estimates of decreases in fossil and industry CO emissions (See Methods and SI Table S9) are consistent with observed changes in nitrogen dioxide (NO ) emissions, which are also mainly produced by fossil fuel combustion. Tropospheric NO column concentration data from satellites , and surface NO concentrations from air quality stations show a decrease (See Methods , SI Figures S7 and SI Table 2) consistent with the reduction of fossil carbon fuels emissions presented above. Overall, NO decreased over China in the first four months of 2020 is consistent with our calculated NO2 emission declines based on near real time activity and emission data (See Methods and SI Table S9),) . Over the U.K., France, Germany, and Italy, NO decreased by a similar amount than in the U.S. Over India, NO showed a weaker decline, also consistent with satellite data. Overall, NO declines in January and February over China are also the largest declines since the OMI data become available in 2004 (SI Figure S10). The consistent results from both ground based and satellite monitoring systems confirm the significant decline of the NO2 concentrations due to COVID-19(See Methods). Based on the OMI satellite data, Over the U.K., France, Germany, and Italy, NO decreased by a similar amount than in the U.S.. Over India, NO showed a weaker decline, also consistent with satellite data. Discussion
It is still unclear to what extent annual CO emissions will be continue to be affected by the COVID-19 pandemic, which will depend on the efficacy and stringency of public health polic ie s and the recovery of economies and human activities around the world. The IMF predicts that the global annual economic output (GDP) will decrease by -3.0% in 2020, which is worse than the financial crisis in 2008 , and yet this projection was based on the assumption that the COVID- 19 epidemics will fade globally in the second half of this year. Based on near real time activity and emission data, we estimate a decrease of 7.8% of global CO2 emissions in first 4 months of 2020, the largest ratio of decrease ever recorded, larger than during the 2009 economic crisis. However, such reduction so far have very limited impacts on global CO2 concentration, with the parts per million (ppm) molecules of CO2 concentration only decreased by 0.13ppm (the current CO2 concentration is 416ppm) given that 1 part per million of atmospheric CO2 is equivalent to 2.13 Gigatonnes Carbon. More importantly, with observed fast recovery of CO2 in China and the reopening of economy globally, the annual decrease rate of CO2 would be expected to less than 8%, yet the long term trends are still unknown. Nevertheless, given negative impacts on the carbon intensive industry sector such as cement production, we inferred improvements of the ratio of the emission intensity (CO2 emissions per unit of GDP) in China (3.5%), US (4.5%) and Europe (1.8%), although such improvement are the consequence of highest-ever cost payed for the reduction of 1t of CO2 - between 1k and 10k US$ per ton, it still suggesting a unique opportunity for green investments and low carbon developme nt in the years to come, for which global concerted efforts will urgently be needed. The ability to monitor trends in emissions in near real time that we demonstrate here will be invaluable in adaptively managing the transition.5
It is still unclear to what extent annual CO emissions will be continue to be affected by the COVID-19 pandemic, which will depend on the efficacy and stringency of public health polic ie s and the recovery of economies and human activities around the world. The IMF predicts that the global annual economic output (GDP) will decrease by -3.0% in 2020, which is worse than the financial crisis in 2008 , and yet this projection was based on the assumption that the COVID- 19 epidemics will fade globally in the second half of this year. Based on near real time activity and emission data, we estimate a decrease of 7.8% of global CO2 emissions in first 4 months of 2020, the largest ratio of decrease ever recorded, larger than during the 2009 economic crisis. However, such reduction so far have very limited impacts on global CO2 concentration, with the parts per million (ppm) molecules of CO2 concentration only decreased by 0.13ppm (the current CO2 concentration is 416ppm) given that 1 part per million of atmospheric CO2 is equivalent to 2.13 Gigatonnes Carbon. More importantly, with observed fast recovery of CO2 in China and the reopening of economy globally, the annual decrease rate of CO2 would be expected to less than 8%, yet the long term trends are still unknown. Nevertheless, given negative impacts on the carbon intensive industry sector such as cement production, we inferred improvements of the ratio of the emission intensity (CO2 emissions per unit of GDP) in China (3.5%), US (4.5%) and Europe (1.8%), although such improvement are the consequence of highest-ever cost payed for the reduction of 1t of CO2 - between 1k and 10k US$ per ton, it still suggesting a unique opportunity for green investments and low carbon developme nt in the years to come, for which global concerted efforts will urgently be needed. The ability to monitor trends in emissions in near real time that we demonstrate here will be invaluable in adaptively managing the transition.5 Me thods CO emission in baseline year 2019 The CO emissions and sectoral structure in 2018 for countries and regions are extracted from EDGAR V 5.0 , and the emissions are scaled to the year 2019 based on the growth rates from Liu et al. and the Global Carbon Budget 2019 . For countries with no current estimates of emission growth rates in 2019 such as Russia, Japan and Brazil, we assume their growth rates of emissions were 0.5% based on the emission growth rates of rest of world . Given the large uncertainty of CO emission in China , we calculated CO emissions based on the methodology developed previously: Emissions = ∑ ∑ ∑(𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑑𝑎𝑡𝑎 𝑖,𝑗,𝑘 × 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟𝑠 𝑖,𝑗,𝑘 ) (1) i, j, k reflect the regions, sectors and fuel types respectively. In our calculation, i covers countries. j covers four sectors that are power generation, industry, transportation and household consumption, k covers three primary fossil fuel types which are coal, oil and natural gas. Emission factors can be further separated into the net heating values for each fuel “v”, the energy obtained per unit of fuel (TJ per t fuel), the carbon content “c” (t C TJ-1 fuel) and the oxidization rate “o”, which is the fraction (in %) of fuel oxidized during combustion and emitted to the atmosphere. Emission = ∑ ∑ ∑(𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑑𝑎𝑡𝑎 𝑖,𝑗,𝑘 × 𝑣 𝑖,𝑗,𝑘 × 𝑐 𝑖,𝑗,𝑘 × 𝑜 𝑖,𝑗,𝑘 ) (2) For China, the energy consumption of coal, oil and gas in 2000-2017 are based on energy balanc e tables from China Energy Statistical Yearbook . However, due to the two years lag of the publications of China Energy Statistical Yearbook, we project the energy consumption of coal, oil and gas in 2018 and 2019 by multiplying the annual growth rates of coal, oil and gas reported on the Statistical Communiqué . Country-specific emission factors are adopted in the calculation, which are relatively lower to IPCC default emission factors . We assumed that the emission factors and the structure remain unchanged for each country in 2020 when comparing with 2019. Thus, the rate of change of the emission is calculated based solely on the change of the energy consumption data in 2020 compared to the same period of 2019. Based on the assumption of sectoral carbon intensity and energy structure remain unchanged from 2018, the EDGAR sectors were aggregated into several main sectors, including power sector, ground transport sector, industry sector, residential sector, aviation sector and international shipping sector.
Power sector . For China, we used daily thermal generation data in China to calculate the daily emission changes. For India, daily total electricity generation data by production types are updated by Power System Operation Corporation Limited (https://posoco.in/reports/daily-reports/), and we calculated the thermal production by aggregating the electricity production by
Coal , Lignite , and
Gas, Naphtha & Diesel Slovenia, Spain, Sweden) and United Kingdom are aggregated into daily electricity generation data. The data cleaning and aggregation method: Electricity generation data can be divided into 3 categories in terms of the time interval of data sampling: 15 minutes, 30 minutes and 60 minutes, divided into 2 categories in terms of data availability: missing values ( represented by n/e in the entire database ), not-a-number values (represented by N/A and void in the entire database). For duplicate data in the same time, the average value for each power sectors is used for further calculation. Prior to aggregation, data cleaning is conducted by preserving missing values for further per-sector analysis and detecting anomaly values in the data for every time interval of one day (24 hours) and for each power sector, which is named as daily power matrix. For each daily power matrix, the anomaly detection algorithm first assumes all not-a-number values as 0, subsequently uses a detection criteria called modified MAD (Median Absolute Deviation): ))(()23(2 1
AmedianAmedianerfcinvmMAD
Industry and cement production.
For China, the industrial sector was divided into four sub-categories including steel industry, cement industry, chemical industry, and other industries, based on the structure of industrial emissions calculations conducted by IEA Ground transportation (4) where a, b, c and d are the regression parameters. It is shown that the regression can reflect large drop down in the ground transportation due to the lockdown and the recovery afterwards. We assume that the daily emissions were proportional to this relative magnitude of daily mean car counts. Then, we applied the regression built for Paris to other cities included in TomTom dataset, assuming that the relative magnitude in car counts (and thus emissions) follow the similar relationship with TomTom. We compared the time series of TomTom congestion level in the first quarter of 2019 and 2020. The emission changes were first calculated for individual cities, and then weighted by city emissions to aggregate to national changes. The weighting emissions are taken from the gridded EDGARv4.3.2 emission map for the “road transportation” sector (1A3b) (https://edgar.jrc.ec.europa.eu/) for the year 2010, assuming that the spatial distribution of ground transport do not change significantly within a country. For countries not included in the TomTom dataset, we assume that the emission changes follow the mean changes of other countries. For example, Cyprus, as an EU member country, is not reported in TomTom dataset, and its relative emission change was assumed to follow the same pattern of the total emissions from other EU countries included in TomTom dataset (which covers 98% of EU total emissions). Similarly, the relative emission changes of countries in ROW but were not reported by TomTom were assumed to follow the same pattern of the total emissions from all TomTom countries (which cover 85% of global total emissions). The uncertainty in the TomTom-based Q, and thus emissions were quantified by the prediction interval of the regression. Aviation.
Ships
According to the Third IMO GHG Study29, CO2 emissions from international shipping accounts for 87% of global shipping emissions, domestic and fishing accounts for 9% and 4%, respectively. We estimated global CO2 shipping emissions from 2016-2018 with the EDGAR’s international emissions and the ratio between international shipping and global shipping emissions. And we extrapolated emissions from 2007-2018 to Residential and commercial buildings
The calculation of emissions was performed in three steps: 1) Calculation of population-weighted heating degree days for each country and for each day based on the ERA5 reanalysis of 2-meters air temperature, 2) Using the EDGAR estimates of 2018 residential emissions as the baseline. For each country, the residential emissions were split into two parts, i.e., cooking emissions and heating emissions, according to the EDGAR guidelines. The emissions from cooking were assumed to remain stable, while the emissions from heating were assumed to depend on and vary by the heating demand. 3) Based on the change of population-weighted heating degree days in each country, we scaled the EDGAR 2018 residential emissions to 2019 and 2020. Since the index of heating degree days are daily values, we can get daily emissions update for the residential sources globally. Note that the effect of increased time spent in households on residential buildings and decreased time in commercial and public buildings was not accounted for, since we did not have fuel consumption data for urban areas and building types. Our estimates of residential emissions changes are consistent with those obtained from the City of Paris, based on individual electricity use (https://data.enedis.fr/) and population surveys (Y. Françoise pers. comm.).
Disaggre gation of the subset of data available on a monthly basis into daily variations
For industrial sector, we calculate the monthly changes based on the monthly statistics, and disaggregate the monthly industrial emissions into daily industrial emissions by daily electricity generation on national level.
Unce rtainty e stimate s
We followed the 2006 IPCC Guidelines for National Greenhouse Gas Inventories to conduct the uncertainty analysis of the data. Firstly, the uncertainties were calculated for each sector: Power sector: the uncertainty is mainly from inter-annual variability of coal emission factors. Based the UN statistics the inter-annual variability of fossil fuel is within (±1.5%), which been used as uncertainty of the CO2 from power sectors. Industrial sector: Uncertainty of CO2 from Industry and cement production comes from the monthly production data. Given CO2 from Industry and cement production in China accounts for more than 60% of world total industrial CO2, and the fact that uncertainty of emission in China is t Uncertainty from monthly statistics was derived from 10000 Monte Carlo simulations to estimate a 68% confidence interval (1-sigma) for China. from monthly statistics was derived from 10000 Monte Carlo simulations to estimate a 68% confidence interval (1-sigma) for China. We calculated the 68% prediction interval of linear regression models between emissions estimated from monthly statistics and official emissions obtained from annual statistics at the end of each year, to deduce the one-sigma uncertainty involved when using monthly data to represent the whole year’s change. The squared correlation coefficients are within the range of 0.88 (e.g., coal production) and 0.98 (e.g., energy import and export data), which represent that only using the monthly data can explain 88% to 98% of the whole year’s variation34, while the remaining variation not covered yet reflect the uncertainty caused by the frequent revisions of China’s statistical data after they are first published. Ground Transportation: The emissions in ground transportation sector is estimated by assuming that the relative magnitude in car counts (and thus emissions) follow the similar relationship with TomTom. So the emissions were quantified by the prediction interval of the regression. Aviation: The uncertainty of aviation sector comes from the difference of daily emission data estimated based on the two methods. We calculate the average difference between the daily emission results estimated based on the flight route distance and the number of flights, and then divide the average difference by the average of the daily emissions estimated by the two methods to obtain the uncertainty of CO2 from aviation sector. Shipping: We used the uncertainty analysis from IMO as our uncertainty estimate for shipping emissions. According to Third IMO Greenhouse Gas study 201431, the uncertainty of shipping emissions was 13% based on bottom-up estimates. Residential: The 2-sigma uncertainty in daily emissions are estimated as 40%, which is calculated based on the comparison with daily residential emissions derived from real fuel consumptions in several European countries including France, Great Britain, Italy, Belgium, and Spain. The uncertainty of emission projection in 2019 is estimated as 2.2%, by combining the reported uncertainty of the projected growth rates and the EDGAR estimates in 2018. Then we combine all the uncertainties by following the error propagation equation from IPCC. Equation 5 is used to derive for the uncertainty of the sum, which could be used to combine the uncertainties of all sectors: 𝑈 𝑡𝑜𝑡𝑎𝑙 = √∑(𝑈 𝑠 ∙ 𝜇 𝑠 )|∑ 𝜇 𝑠 | (5) Where 𝑈 𝑠 and 𝜇 𝑠 are the percentage uncertainties and the uncertain quantities (daily mean emissions) of sector 𝑠 respectively. Equation 6 is used to derive for the uncertainty of the multiplication, which is used to combine the uncertainties of all sectors and of the projected emissions in 2019: 𝑈 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = √∑ 𝑈 𝑖2 (6) Table 6
Percentage uncertainties of all items. Items Uncertainty Range
Power ±1.5% Ground Transport ±9.3% Industry ±36.0% Residential ±40.0% Aviation ±10.2% International Shipping ±13.0% Projection of emission growth rate in 2019 ±0.8% EDGAR emissions in 2018 ±5.0%
Overall ±6.8%
Sate llite obse rvation and data source s:
To validate the response of the atmosphere, including CO concentration and air quality, to the decreased fossil fuel burning and transportation, we collected NO , aerosol optical depth (AOD) and column-averaged dry air mole fraction of CO (XCO ) data from satellites (NO from OMI, AOD from MODIS and XCO from GOSAT) and surface daily average nitrogen dioxide (NO , μg/m ), carbon monoxide (CO, μg/m ) from 1600 air quality monitoring sites (China and US, SI Figure S7) in to investigate the impact of COVID-19 on air quality and atmospheric CO data from the Ozone Monitoring Instrument (OMI) provided by Tropospheric Emission Monitoring Internet Service (TEMIS), which has with a spatial resolution of 0.125° x 0.125° and a temporal coverage from October 2004 to March 2020. We only included the data from January 2013 to March 2020 in the work (SI Figure S8). For AOD, we chose daily Level 2 MOD 04 data from MODIS and then calculated the monthly averaged AOD f from January 2013 to March 2020. Only “good” and “very good” data (in AOD_550_Dark_Target_Deep_Blue_Combined_QA_Flag 2 and 3) were kept in the calculation. At last, we calculated the monthly XCO data with a resolution of 2.5° x 2.5° from the Greenhouse Gases Observing Satellite "IBUKI" (GOSAT). Because of the delay in the data processing at National Institute for Environmental Studies (NIES), we used a bias-uncorrected version V02.81 for the period of January 2013 to March 2020. With the consideration of the focus on an abnormal event due to COVID-19, the bias-uncorrected data is proper for this study. All of the monthly averaged data were re-gridded to 1° x 1°. We focused on four emitting regions, China, USA, EU4 (UK, France, Germany, and Italy), and Indian, and then calculated the country level monthly averaged NO , AOD, and XCO values. Surface air pollution in China was significantly reduced during the epidemic period (SI Figure 7). A deep reduction of NO2 by 31.7% was observed on January 24th 2020, one day after the lockdown for many provinces (Si Figure S7b). The reduction rates were 13.7% for PM2.5 and 16.5% for CO on the same day. A clear rebound (U shape) could be found for all pollution after the spring festival (February 5th) in 2019. However, such recovery was missing in 2020 due to the lockdown, leading to a decreasing trend all through the first quarter. On average, pollution concentrations decreased by 23.0% for NO2, 15.4% for PM2.5, and 12.5% for CO during January-March 2020 relative to the same period in 2019. Pollution level in U.S. was also reduced by the epidemic but with smaller magnitude compared to that in China. Surface PM2.5 decreased in all first three months in 2020 relative to 2019 with the largest reduction of 20.6% in March. NO2 also exhibited large reductions of 9.0% on March 2020 compared to 2019, however, such reduction seemed affected by the limited site numbers (only 20). For example, one site in Salt Lake, Utah reported >200 ppb (normally <40) NO2 during March 20-23, 2020. Such episodes were likely caused by fires but weakened the reduction rate of NO2 after Middle March (SI Figure 7e). Changes of CO were also limited in U.S., with opposite signs in January and March. Such tendencies could also be biased due to the limited site numbers (only 31). The observed tropospheric nitrogen dioxide (NO2) column concentration data from satellite observation and surface air quality data from ground monitoring networks have exhibited a decrease (SI Table S9) consistent with reduction of fossil carbon fuels emissions. In China, January, February, March, and April 2020 decreased by -32.27%, -34.22%, -4.53%, -3.59% respectively compared to 2019. Overall, NO2 decreased over China by -21.55% from January to April 2020 compared to 2019. In the US, the decrease of NO first started in Feb and continued to decrease at least until March 2020. Compared to the same period of the year in 2019, NO over the US decreased by -23.08% and - 14.32% in February and March 2020, respectively (SI Table S9). For the UK, France, Germany, and Italy, we observe similar NO decreases than over the USA India had weaker decline in NO than other regions. The decline rate of NO (-21.55%) based on atmospheric observations can be used to check the consistency of the decrease of NO emission from the inventory, and given the NO2 is mainly contributed by fossil fuel combustion with life time short than one day, the temporal change of NO2 emission can could verify the decrease of the fossil fuel combustion and the associated CO emissions. For China where the most significant decrease of tropospheric NO column concentration observed, the inventory- based estimates of power generation (-6.8%), transportation (-37.2%) and industry(-8.1%) are adopted with result of weight mean -23.94% % NO2 emission in first quarter of 2020 when comparing with 2019. These three sectors together account for 96% of China’s total NO2 emissions. The -23.42% decline of the NO emissions from our bottom-up inventory is consistent with the satellite observed -26% decrease of column NO , and with the -23% decrease of near surface concentrations at the 1680 ground-based stations. For US, the inventory-based estimates of power generation (-4.9%), transportation (-2.7%) and industry(-2.2%) are adopted with result of -2.57% NO2 emission in first quarter of 2020 when comparing with 2019, slightly smaller than -4.76% tropospheric NO column concentration, but difference with the site observation data ( -8.98% in March and +0.34%for first quarter), which may be affected by site numbers (only 20 sites in US). We calculated 1° x 1° monthly mean of NO2, AOD, and XCO from OMI, MODIS, and GOSAT, respectively (SI Table S9). For satellite observations, the overall uncertainty of tropospheric NO2 columns monthly mean is 10% . The uncertainty of AOD is approximately 0.03+0.20τ M , where τ M is AOD at 550 nm . In other words, the uncertainties in percentage in low AOD regions (US and EU4) is higher in high AOD regions (China and India). The standard deviations of XCO monthly mean over land are about 0.5-1.5 ppm . Here we conservatively considered uncertainty of monthly XCO as 1.5 ppm. To estimate the uncertainty of changes of 2020 compared to 2019 from January to March, we input above uncertainties of monthly means and run Monte Carlo simulations of 10000 trials to calculate the 68% confidence intervals (i.e., one sigma range) which are shown in Table 1. Data Availability State me nt
All data generated or analyzed during this study are included in this article.
Code Availability
Comple ting Inte re sts statement
Authors declare no competing interests.
Author contribution:
Zhu Liu and Philippe Ciais designed the research, Zhu Deng coordinated the data processing. Zhu Liu, Philippe Ciais and Zhu Deng contributed equally in this research, all authors contributed to data collecting, analysis and paper writing.
Authors acknowledge Paul O. Wennberg for insightful comments to improving this paper.5
Authors acknowledge Paul O. Wennberg for insightful comments to improving this paper.5 Re fe re nces: Le Quéré, C., Jackson, R.B., Jones, M.W., Smith, A.J., Abernethy, S., Andrew, R.M., De -Gol, A.J., Willis, D.R., Shan, Y., Canadell, J.G. and Friedlingste in, P., 2020. Temporary reduction in daily global CO 2 emissions during the COVID-19 forced confinement.
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SI Figure S1. Daily CO emissions in 2019 and the first four months of 2020 by countries. SI Figure S2 Daily CO2 emissions in 2019 and the first four months of 2020 by countries SI Figure S3 | Daily electricity generation in 2020 in US, India, Russia, France, Germany, Italy, Spain, other European countries (Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, and Sweden), UK, Brazil, Japan and China, after being corrected by daily temperature (See Methods) . SI Figure S4 | Monthly emission changes in transport sector in February, March and the first quarter of 2020. (See SI Table 3) SI Figure S6.
The relationship between TomTom congestion level and the actual car counts (Q) for Paris. a) the regression between TomTom congestion level (x-axis) and the car counts (y-axis); b) the Q calculated based on TomTom congestion level (red) and the actual Q. SI Figure S6. Change in CO2 emissions for aviation.5
The relationship between TomTom congestion level and the actual car counts (Q) for Paris. a) the regression between TomTom congestion level (x-axis) and the car counts (y-axis); b) the Q calculated based on TomTom congestion level (red) and the actual Q. SI Figure S6. Change in CO2 emissions for aviation.5 SI Figure S7.
Daily variations of surface (a, d) PM , (b, d) NO , (c, f) CO concentrations from (a-c) China and (d-f) U.S. during the first quarters of 2019 and 2020. The bold lines are the mean values from all quality-controlled sites, with shadings indicating one standard deviation. The data on February 29 th SI Figure S8 . The monthly series of a) NO2, b) AOD and c) XCO2 over China, US, EU4(UK, Germany, Italy and France), and India SI Figure S9 Tropospheric column NO2 Observation in January – April of 2020 SI Figure S10 | Tropospheric column NO Observation in January – April of 2020 SI Figure S11. Anomaly of NO2 from OMI in the first quarter of 2020
The anomaly maps conducted by apply the same algorithm on every grid point. The anomaly defined as the deseasonalized value. For NO2 (Figure S11), the anomaly along the eastern coast of China was negative in January and February 2020, then partially become positive. About half of the anomalies over U.S. and Europe were positive in January 2020, then most areas over U.S. and Europe became negative, which also matches the COVID-19 epidemic delays compared to China. SI Figure S12 Anomaly of AOD from MODIS in the first quarter of 2020
The anomaly maps conducted by apply the same algorithm on every grid point. The anomaly defined as the deseasonalized value. For AOD (Figure S12), the negative anomaly area along the eastern coast of China expanded from January to March 2020. For US and Europe, AOD anomalies on land did not change too much. The shutdown of COVID-19 may not affect AOD over them since their AOD was always Low. SI Table S1 Mapping table of sectors between this study and EDGAR . This Study EDGAR Power Electricity and heat production Industry (from direct fuel combustion) Manufacturing industries and construction Other energy industries Ground Transport Road transportation Rail transportation Inland navigation Other transportation Residential Residential and other sectors
SI Table S2.
Sectoral changes in the first quarter in 2020 comparing to the same periods in 2019 by countries or regions.
Emission Decline (MtCO , 2020F4) Power Transport Industrial (with Process) Residential Domestic Aviation
Sum
Growth Rates (%) China -91.1 -84.4 -43.8 -7.5 -7.8 -234.5 -6.9%
India -39.7 -21.4 -22.1 -0.7 -76.6 -8.5% US -43.8 -78.3 -17.1 -14.8 -8.3 -162.4 -9.5% Europe (EU27 & UK) -82.0 -26.8 -15.1 -13.3 -1.2 -138.3 -12.0%
Russia -7.0 -3.6 -8.1 -0.3 -18.7 -3.4%
Japan -5.5 -4.3 -5.5 -1.9 -0.2 -17.3 -4.3%
Brazil -8.3 -2.2 -0.5 -9.9 -7.0%
Row -24.6 -113.1 -30.7 -5.1 -10.3 -183.7 -5.4%
Sum -292.5 -340.0 -136.2 -43.4 -29.2 -841.4 -7.5%
Growth Rates (%) -6.4% -15.5% -4.4% -2.7% -23.4% -7.5% International aviation -63.6 -32.4% International shipping -32.6 15.0% Global -937.6 -7.8% SI Table S3 . Monthly changes in power sector in 2020 comparing to the same periods in 2019 by countries or regions.
Countries/Regions Jan Feb Mar Apr Jan-Apr China -3.6% -14.4% -8.0% -6.0%
India -0.3% -12.8% -29.9% -9.2% US -10.6% -2.8% -8.6% -8.4% -7.7% Europe (EU27&UK) -18.0% -26.6% -10.7% -36.6% -22.5%
Russia -3.4% -2.9% -3.9% -2.4%
Japan -5.4% -0.1% -2.1% -3.8% -2.9%
Brazil -10.0% -18.8% -24.3%
ROW -1.8% -7.3% -2.0%
World -4.3% -6.4% -6.5% -8.9% -6.4% SI Table S4 . Monthly mobility changes in 2020 comparing to the same periods in 2019 by countries or regions.
Countries/Regions Jan Feb Mar Apr Jan-Apr China -18.5% -53.4% -25.9% -16.3% -28.1%
India -25.9% -65.6% -22.3% US -22.8% -50.0% -13.9% Europe (EU27&UK) -16.6% -32.1% -9.2%
Russia -3.5% -26.1% -4.7%
Japan -3.0% -7.2% -17.5% -6.8%
Brazil -0.2% -15.1% -37.7% -12.9%
ROW -22.0% -42.2% -15.4%
World -3.3% -20.9% -39.0% -15.5%
SI Table S6 . Growth rates of industrial emissions comparing to the same periods of last year in 2020.
Countries/Regions Jan Feb Mar Apr Jan-Apr China -7.5% -6.2% -5.0% -3.5%
India -20.6% -14.8% -7.9% US -0.7% -0.1% -5.9% -18.8% -6.4% Europe (EU27&UK) -1.2% -1.3% -12.0% -14.8% -7.3%
Russia -9.4%
Japan -2.4% -5.6% -5.3% -10.6% -6.0%
Brazil -0.4% -4.2% -16.0% -5.0%
ROW -2.0% -2.0% -3.1% -6.2% -3.4%
World -3.4% -2.8% -6.1% -4.8% -4.4% SI Table S9.
The observation of air quality and dry column CO (XCO ) (Full Data file attached separately) China US EU4 India O MI NO 2 Jan -32.26% ± 12.03% 22.98% ± 16.02% 15.78% ± 15.24% -8.96% ± 13.63%
Feb -34.22% ± 11.87% -23.08% ± 12.63% -25.12% ± 12.40% -13.79% ± 13.36%
Mar -4.53% ± 13.77% -14.32% ± 13.24% -15.56% ± 13.22% -13.37% ± 13.29% -3.59% ± 13.97% -2.16% ± 14.07% -23.40% ± 12.48% -11.10% ± 13.50%
Jan-Mar -21.55% ± 12.87% -4.22% ± 13.75% -12.73% ± 13.26% -11.78% ± 13.41%
MO DIS AO D Jan
Feb -7.88% ± 41.08% -3.98% ± 82.64% -1.95% ± 70.78% 7.29% ± 39.89%
Mar
April
January-April
GO SAT XCO 2 Jan
Feb
Mar
April
Jan-Mar
TRO PO MI CO Jan
Feb
Mar
Jan-Mar
Site NO 2 Jan -18.05%±23.90% -3.80%±12.80%
Feb -30.33%±21.78% 14.98%±68.82%
Mar -23.03%±17.29% -8.98%±44.17%
Jan-Mar -23.00%±14.97% 0.34%±79.05%
Site PM2.5 Jan -2.67%±41.56% -8.77%±49.43%
Feb -26.71%±26.94% -14.78%±59.12%
Mar -21.80%±17.51% -20.55%±39.30%
Jan-Mar -15.39%±19.06% -14.68%±40.49%
Site CO Jan -5.89%±22.22% -12.35%±23.12%
Feb -19.60%±20.49% -6.19%±45.39%
Mar -14.24%±19.77% 4.94%±74.21%
Jan-Mar -12.51%±15.41% -5.11%±26.53%
Inventory NO 2 Jan -0.99% (-1.36~-0.86%)
Feb
Mar -15.49% (-21.20~-13.46%) -7.72% (-10.58~-6.72%)
Jan-Mar -17.47% (-23.94~-15.20%) -2.57% (-3.52~-2.24%)5