Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement

Abstract

Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades. COVID-19 pandemic lockdowns have altered global energy demands. Using government confinement policies and activity data, daily CO2 emissions have decreased by ~17% to early April 2020 against 2019 levels; annual emissions could be down by 7% (4%) if normality returns by year end (mid-June).

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DispatchDate: 08.05.2020 · ProofNo: 797, p.1
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Articles
https://doi.org/10.1038/s41558-020-0797-x
1School of Environmental Sciences and the Tyndall Centre for Climate Change Research, University of East Anglia, Norwich, UK. 2Earth System Science
Department, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA, USA. 3Applied Physics Department,
Stanford University, Stanford, CA, USA. 4CICERO Center for International Climate Research, Oslo, Norway. 5Integrated Research for Energy, Environment
and Society (IREES), Energy and Sustainability Research Institute Groningen University of Groningen, Groningen, the Netherlands. 6Global Carbon Project,
CSIRO Oceans and Atmosphere, Canberra, Australia. 7College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. 8LMD,
IPSL, ENS, PSL Université, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris, France. 9Mercator Research Institute on
Global Commons and Climate Change, Berlin, Germany. 10Technical University Berlin, Berlin, Germany. ✉e-mail: c.lequere@uea.ac.uk
Before 

the COVID-19 pandemic of 2020, emissions of carbon
dioxide were rising by about 1% per year over the previous
decade1–3, with no growth in 20194 (also updated from Peters
et al.3 (Methods)). Renewable energy production was expanding
rapidly amid plummeting prices5, but much of the renewable energy
was being deployed alongside fossil energy and did not replace it6,
while emissions from surface transport continued to rise3,7.







The emergence of COVID-19 was first identified on 30
December 20198 and declared a global pandemic by the World
Health Organization on 11 March 2020. Cases rapidly spread, ini-
tially mainly in China during January, but quickly expanding to
South Korea, Japan, Europe (mainly Italy, France and Spain) and the
United States between late January and mid-February, before reach-
ing global proportions by the time the pandemic was declared9.
Increasingly stringent measures were put in place by world gov-
ernments in an effort, initially, to isolate cases and stop the trans-
mission of the virus, and later to slow down its rate of spread. The
measures imposed were ramped up from the isolation of symptom-
atic individuals to the ban of mass gatherings, mandatory closure
of schools and even mandatory home confinement (Table1). The
population confinement is leading to drastic changes in energy use,
with expected impacts on CO2 emissions





.
Despite the critical importance of CO2 emissions for under-
standing global climate change, systems are not in place to monitor
global emissions in real time. CO2 emissions are reported as annual
values1, often released months or even years after the end of the cal-
endar year. Despite this, some proxy data are available in near-real
time or at monthly intervals. High-frequency electricity data are
available for some regions (for example, Europe10 and the United
Q2
Q3 Q4
Q5 Q6
Q7 Q8 Q9
States11), but rarely the associated CO2 emissions data. Fossil fuel
use is estimated for some countries at the monthly level, with data
usually released a few months later1,12. Observations of CO2 concen-
tration in the atmosphere are available in near-real time13,14, but the
influence of the natural variability of the carbon cycle and meteo-
rology is large and masks the variability in anthropogenic signal
over a short period15,16. Satellite measurements for the column CO2
inventory17 have large uncertainties and also reflect the variability
of the natural CO2 fluxes18, and thus cannot yet be used in near-real
time to determine anthropogenic emissions.
Given the lack of real-time CO2 emissions data, we take an alter-
native approach to estimate country-level emissions based on a con-
finement index (CI) that represents the effect of different policies.
The change in CO2 emissions associated with the confinement is
informative in multiple ways. First, the changes in emissions are
entirely due to a forced reduction in energy demand. Although in
this case the demand disruption was neither intentional nor wel-
come, the effect provides a quantitative indication of the potential
limits that extreme measures could deliver with the current energy
mix (for example, a higher rate of home working or reducing con-
sumption). Second, during previous economic crises, the decrease
in emissions was short-lived with a postcrisis rebound that restored
emissions to their original trajectory, except when these crises were
driven by energy factors such as the oil crises of the 1970s and
1980s, which led to significant 

shifts in energy efficiency and the
development of alternative energy sources19 (Fig.1). For example,
the 2008–2009 Global Financial Crisis saw global CO2 emissions
decline of –1.4% in 2009, immediately followed by a growth in
emissions of +5.1% in 201020, well above the long-term average.
Q10
Temporary reduction in daily global CO2 emissions
during the COVID-19 forced confinement
Corinne Le Quéré 1 ✉ , Robert B. Jackson 2, Matthew W. Jones 1, Adam J. P. Smith1,
Sam Abernethy 2,3, Robbie M. Andrew 4, Anthony J. De-Gol1, David R. Willis1, Yuli Shan5,
Josep G. Canadell 6, Pierre Friedlingstein 7,8, Felix Creutzig 9,10 and Glen P. Peters 4
Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world.
Many international borders were closed and populations were confined to their homes, which reduced transport and consump-
tion patterns. 

Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced
confinements. Daily global CO2 emissions decreased by –17% (–11 to –25%) by early April 2020 compared with the mean 2019
levels, primarily from changes in surface transport. At their peak, emissions in individual countries decreased by –27% on aver-
age. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%)
if prepandemic conditions return by mid-June, and a high estimate of –8% (–3 to –14%) if some restrictions remain worldwide
until the end of 2020. Government actions and economic incentives postcrisis will probably influence the global CO2 emissions
path for decades.
Q1
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Emissions soon returned to their previous path almost as if the cri-
sis had not occurred.
The economic crisis associated with COVID-19 is markedly
different from previous economic crises in that it is more deeply
anchored in constrained individual behaviour. At present it is
unclear how long and deep the crisis will be, and how the recovery
path will look, and therefore how CO2 emissions will be affected.
Keeping track of evolving CO2 emissions can help inform govern-
ment responses to the COVID-19 pandemic to avoid locking future
emissions trajectories in carbon-intensive pathways.
Results
In this analysis, we used a combination of energy, activity and policy
data available up to the end of April 2020 to estimate the changes in
daily emissions during the confinement from the COVID-19 pan-
demic, and its implications for the growth in CO2 emissions in 2020.
We compared this change in emissions to the mean daily emissions
for the latest available year (2019 for the globe) to provide a quantita-
tive measure of relative change compared to pre-COVID conditions.
Changes in CO2 emissions were estimated for three levels of
confinement and for six sectors of the economy, as the product of
the CO2 emissions by sector before confinement and the fractional
decrease in those emissions due to the severity of the confinement
and its impact on each sector (equation(1) in Methods). The analy-
sis is done over 69 countries, 50 US states and 30 Chinese provinces,
which represent 85% of the world population and 97% of global CO2
emissions.
The confinement index (CI) is defined on a scale of 0 to 3 and
allocates the degree to which normal daily activities were con-
strained for part or all of the population (Table1). Scale 0 indicates
no measures were in place, scale 1 indicates policies targeted at
small groups of individuals suspected of carrying infection, scale
2 indicates policies targeted at entire cities or regions or that affect
about 50% of society and scale 3 indicates national policies that sig-
nificantly 

restrict the daily routine of all but key workers and affect
approximately 80% of society (Supplementary Extended Methods).
During the early confinement phase around Chinese New Year in
China (starting 25 January 2020), around 30% of global emissions
were in areas under some confinement (Fig.1). This increased to
70% by the end of February, and over 85% by mid-March when con-
Q11
finement in Europe, India and the United States started, as China
relaxed confinement (Fig.1). At its peak in early April, 89% of
global emissions were in areas under some confinement.
The six economic sectors covered in this analysis are: (1) power
(44.3% of global fossil CO2 emissions), (2) surface transport
(20.6%), (3) industry (22.4%), (4) public buildings and commerce
(here shortened to ‘public’, 4.2%), (5) residential (5.6%) and (6) avia-
tion (2.8% (Methods)). We collected time-series data (mainly daily)
representative of activities that emit CO2 in each sector to inform
the changes in each sector as a function of the confinement level
(Fig.2). The data represent changes in activity, such as electricity
demand or road and air traffic, rather than direct changes in CO2
emissions. We made a number of assumptions to cover the six sec-
tors based on the available data and the nature of the confinement
(Table 2, Methods and Supplementary Tables 1–10). Changes in
the surface transport and aviation sectors were best constrained by
indicators of traffic from a range of countries, which included both
urban and nationwide data. Changes in power-sector emissions
were inferred from electricity data from Europe, the United States
and India. Changes in industry were inferred mainly from industrial
activity in China and steel production in the United States. Changes
in the residential sector were inferred from UK smart meter data,
whereas changes in the public sector were based on assumptions
about the nature of the confinement. All the activity changes are
relative to typical activity levels prior to the COVID-19 pandemic
(Supplementary Extended Methods).
Activity 



data show that the changes in daily activities were largest
in the aviation sector, with a decrease in daily activity of –75% (–60 to
–90%) during confinement level 3 (Table2). Surface transport saw its
activity reduce by –50% (–40 to –65%), whereas industry and public
sectors saw their activity reduce by –35% (–25 to –45%) and –33%
(–15 to –50%), respectively. Also during confinement level 3, power
saw its activity decrease by a modest –15% (–5 to –25%) and the resi-
dential sector saw its activity increase by +5% (0 to +10%). Activity
data also show substantial decreases in activity during confinement
level 2, and only small decreases during confinement level 1 (Table2).
Daily changes in CO2 emissions
The effect of the confinement was to decrease daily global CO2
emissions by –17 (–11 to –25) MtCO2 d−1, or –17% (–11 to –25%)
Q12 Q13
Table 1 | Definition of the CI
Level Description Policy examples
0 No restrictions
1 Policies targeted at long distance travel or groups of
individuals where outbreak first nucleates Isolation of sick or symptomatic individuals
Self-quarantine of travellers arriving from affected countries
Screening passengers at transport hubs
Ban of mass gatherings >5,000
Closure of selected national borders and restricted international travel
Citizen repatriation
2 Regional policies that restrict an entire city, region
or ~50% of society from normal daily routines Closure of all national borders
Mandatory closure of schools, universities, public buildings, religious or cultural buildings,
restaurants, bars and other non-essential businesses within a city or region
Ban of public gatherings >100 and social distancing >2 m
Perhaps also accompanied by recommended closures at a broader or national level
Mandatory night curfew
3 National policies that significantly restrict the daily
routine of all but key workers, ~80% of the workforce
Mandatory national ‘lockdown’ that requires household confinement of all but key workers
Ban public gatherings >2 and social distancing >2 m
The CI categorizes the level of restrictions to normal activities that have the potential to influence CO2 emissions. It is based on the policies adopted by national and subnational governments.
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by 7 April 2020 (Table 2), relative to the mean level of emissions in
2019. The change in emissions on 7 April was the largest estimated
daily change during 1 January to 30 April 2020. Daily emissions in
early April are comparable to their levels of 2006 (Fig.3). The val-
ues in MtCO2 d−1 are close to the value in percent coincidentally,
because we currently emit about 100 MtCO2 d−1. For individual
countries, the maximum daily decrease averaged to –27% (±9%
for ±1σ), although the maximum daily decrease did not occur dur-
ing the same day across countries, and hence the decrease is more
pronounced than the global maximum daily decrease. Estimated
Level 3
Level 2
Level 1
CI
Fraction of global CO2 emissions
produced in areas subject to confinement (%)
January February March April
Year 2020
0
20
40
60
80
100
Fig. 1 | Fraction of global CO2 emissions produced in areas subject to confinement. CO2 emissions from nations and states in each confinement level
(Table1) aggregated as a fraction of global CO2 emissions. CO2 emissions are from the Global Carbon Project1 (Methods).
European
countries
India US
states
China
coal
US
steel
City
congestion
Country
mobility
UK US
states
UK smart
meters
World
countries
–100
–80
–60
–40
–20
0
20
40
60
80
100
Change in activity (%)
Power
Industry
Surface transport
Residential
Aviation
Fig. 2 | Change in activity by sector during confinement level 3 (percent). The data includes: for the power sector, temperature-adjusted electricity trends
in Europe10, India39 and the US40; for the industry sector, coal use in industry in China23 and US steel production41; for the surface transport sector, city
congestion42, country mobility43, UK44 and US state45 traffic data; for the residential sector, UK smart meter data46 and for aviation, aircraft departures47.
Each data point (filled circles) represents the analysis of a full time series and shows the changes in activity compared to typical activity levels prior
to COVID-19, corrected for seasonal and weekly biases. These changes along with the nature of the confinement were used to set the parameters in
equation(1) in Methods. The data are randomly spaced to highlight the volume of some data streams. Open points represent the mean value among the
sample of data points, whereas the whiskers mark the standard deviation from the mean. The plotted violins represent the kernel density estimate of the
probability density function for each sample of data points.
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changes quantify the effect of confinement only, and are rela-
tive to underlying trends prior to the COVID-19 pandemic. The
daily decrease in CO2 emissions during the pandemic is as large
as the seasonal amplitude in emissions estimated from data pub-
lished elsewhere21,22 (–17 MtCO2 d−1), which results primarily from
the higher energy use in winter than in summer in the Northern
Hemisphere. The range in estimate reflects the range of parameter
values (Table2) based on the spread in the underlying data (Fig.2).
Global emissions from surface transport fell by –36% or –7.5
(–5.9 to –9.6) MtCO2 d−1 by 7 April 2020 and made the largest con-
tribution to the total emissions change (–43%; Fig.4 and Table2).
Emissions fell by –7.4% or –3.3 (–1.0 to –6.8) MtCO2 d−1 in the power
sector and by –19% or –4.3 (–2.3 to –6.5) MtCO2 d−1 in the industry
sector. Emissions from surface transport, power and industry were
the most affected sectors in absolute values, accounting for 86% of
the total reduction in global emissions. CO2 emissions declined by
–60% or –1.7 (–1.3 to –2.2) MtCO2 d−1 in the aviation sector, which
yielded the largest relative anomaly of any sector, and by –21% or
–0.9 (–0.3 to –1.4) MtCO2 d−1 in the public sector. The large relative
anomalies in the aviation sector correspond with the disproportion-
ate effect of confinement on air travel (Table 2). A small growth
in global emissions occurred in the residential sector, with +2.8%
or +0.2 (–0.1 to +0.4) MtCO2 d−1 and only marginally offsets the
decrease in emissions in other sectors.
The total change in emissions until the end of April is estimated
to amount to –1,048 (–543 to –1,638) MtCO2 (Supplementary
Table13). Of this, the changes are largest in China, where the con-
finement started, with a decrease of –242 (–108 to –394) MtCO2,
then in the United States, with –207 (–112 to –314) MtCO2, then
Europe, with –123 (–78 to –177) MtCO2, and India, with –98 (–47 to
–154) MtCO2. These changes reflect both that these regions emit high
levels of CO2 on average and that their confinements were severe in
the period through end of April. The integrated changes in emissions
over China MtCO2 are comparable in magnitude with the estimate
of –250 MtCO2 of Myllyvirta (2020)23 up to the end of March. The
global changes in emissions is also consistent with global changes in
the NO2 inventory from satellite data, although the concentration
data are complex to interpret (Supplementary Figs.1 and 2).
Implications for global fossil CO2 emissions in 2020
The change for the rest of the year will depend on the duration and
extent of the confinement, the time it will take to resume normal
Table 2 | Change in activity as a function of the confinement level (%)
Change in activity as a function of confinement level (equation (1))aResultsb
Level 1 Level 2 Level 3 Daily change 7 April 2020
Power 0 (0 to 0) –5 (0 to –15) –15 (–5 to –25) –7.4 (–2.2 to –14)
Industry –10 (0 to –20) –15 (0 to –35) –35 (–25 to –45) –19% (–10 to –29)
Surface transport –10 (0 to –20) –40 (–35 to –45) –50 (–40 to –65) –36% (–28 to –46)
Public –5 (0 to –10) –22.5 (–5 to –40) –32.5 (–15 to –50) –21% (–8.1 to –33)
Residential 0 (0 to 0) 0% (–5 to +5) +5 (0 to +10) +2.8% (–1.0 to +6.7)
Aviation –20 (0 to –50) –75 (–55 to –95) –75 (–60 to –90) –60% (–44 to –76)
Total –17% (–11 to –25)
The mean and uncertainty are shown. aParameters used in equation(1) for each sector (ΔAs). bResults for the globe on the day with the maximum change (4 April 2020). The change is estimated relative to
the mean level of emissions in 2019 (Methods).
20
40
60
80
100
a b
1970 1980 1990 2000 2010 2020
Year
Daily CO
2
emissions (MtCO
2
d
−1
)
Global daily fossil CO
2
emissions (MtCO
2
d
−1
)
20
40
60
80
100
Jan Feb Mar Apr May
2020
Fig. 3 | Global daily CO2 emissions (MtCO2 d−1). a, Annual mean daily emissions in the period 2000–2019 (black line), updated from the Global Carbon
Project1,3 (Methods), with uncertainty of ±5% (±1σ; grey shading). The red line shows the daily emissions in 2020 estimated here. b, Daily CO2 emissions
in 2020 (red line, as in a) based on the CI and corresponding change in activity for each CI level (Fig.2), and the uncertainty (red shading; Table2). Daily
emissions in 2020 are smoothed with a 7-day box filter to account for the transition between confinement levels.
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activities and the degree to which life will resume its preconfine-
ment course. At the time of press, most countries that were in con-
finement level 3 had announced dates when they anticipated some
confinement would be lifted. Dates ranged between mid-April and
mid-May. We used those dates where available, and for other coun-
tries we assumed an end of confinement that corresponded to those
neighbouring regions or states (Supplementary Tables15 and 16).
It is possible that the end of confinement will be delayed in some
countries and therefore these dates are probably the earliest possible
dates. Nevertheless, the mounting social24,25 and economic pres-
sure26, along with the improving management of healthcare, means
a systematic postponement is unlikely.
We assessed the effect of the recovery time by conducting three
sensitivity tests. Our sensitivity tests are not intended to provide
a full range of possibilities, but rather to indicate the approximate
effect of the extent of the confinement on CO2 emissions. Before
COVID-19, we expected global emissions to be similar to those
in 20192, so the effect of confinement on CO2 emissions provided
above might be approximately equivalent to the actual change from
2019 emissions. Our sensitivity tests do not attempt to quantify the
effects of multiple confinement waves, or of deeper and sustained
changes in the economy that could result from either the collapse of
tens of thousands of small and medium businesses or government
economic stimulus packages.
In the first sensitivity test, we assumed that after the announced
dates for initial deconfinement, activities will return to precrisis lev-
els within six weeks (around mid-June), as observed for coal use in
industry in China23. In this case, the decrease in emissions from the
COVID-19 crisis would be –1,524 (–795 to –2,403) MtCO2 or –4.4%
(–2.3 to –7.0%). In the second sensitivity test, we assumed it takes
12 weeks to reach preconfinement levels (around the second half of
July), because of the low productivity that results from social trauma
and low confidence. This longer period is more aligned with the
announcements of gradual deconfinements, for example, in France,
the UK and Norway, where a gradual deconfinement is planned
over the coming months, and with timescales for the expected pro-
gression of the illness27. In this case, the decrease in emissions from
the COVID-19 crisis would be –1,923 (–965 to –3,083) MtCO2 or
–5.6% (–2.8 to –9.0%).
In the third sensitivity test, we made the same assumption as
in the second test, but further assumed that confinement level 1
remains in place in all the countries examined until the end of the
year. This is consistent with the situation in China in general, where,
although measures were lifted at the end of February in most prov-
inces, there are still some restrictions on specific activities, such as
a restricted international travel. It is also more aligned with the lat-
est understanding of the dynamics of transmission of the disease,
which suggests prolonged or intermittent social distancing may be
necessary into 202228. In this case, the decrease in emissions from
the COVID-19 crisis would be –2,729 (–986 to –4,717) MtCO2 or
–8.0% (–2.9 to –14%).
At the regional levels, the low sensitivity test led to mid-point
decreases in emissions for year 2020 of –2.3%, –6.7%, –5.6% and
–5.3% respectively for China, the US, Europe (EU27 + UK) and
India, while the high sensitivity test led to midpoint decreases of
–5.1, –11.3, –9.3 and –8.8% for those same countries (Supplementary
Power Industry Surface transport
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
−10.0
−7.5
−5.0
−2.5
0.0
Change in global
daily fossil CO2 emissions (MtCO2 d−1)
Change in global
daily fossil CO2 emissions (MtCO2 d−1)
Public Residential Aviation
Jan Feb Mar Apr Jan Feb Mar Apr Jan Feb Mar Apr
−2.0
−1.5
−1.0
−0.5
0.0
0.5
Fig. 4 | Change in global daily fossil CO2 emissions by sector. The uncertainty ranges represent the full range of our estimates. Changes are relative to
annual mean daily emissions from those sectors in 2019 (Methods). Daily emissions are smoothed with a 7-day box filter to account for the transition
between confinement levels. Note that the different ranges on the y axes in the upper and lower panels.
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Articles Nature Climate ChaNge
Table 14). For comparison, for the United States alone, the
Energy Information Administration (EIA) provides a forecast
of a decrease in emissions of –7.5% in 202029, which takes into
account all projected economic factors, and is between our scenario
tests 1 and 2.
In spite of the broader effects on the economy that are not
included in our analysis, our 2020 estimates are similar to those
that can be inferred based on the projections of the International
Monetary Fund for 2020 of –3% reduction in global Gross Domestic
Product30 combined with an average CO2/GDP improvement of
–2.7% over the past decade31, which gives a –5.7% reduction in CO2
emissions in 2020. These independent global and US projections are
similar to the middle sensitivity test 2 of confinement that we pres-
ent in this publication (see Supplementary Table14), while the pro-
jection of the International Energy Agency (IEA) of –8% decrease
in CO2 emissions in 2020 aligns with our high-end test 332. The
International Monetary Fund and EIA further forecast that emis-
sions will rebound by +5.8 and +3.5% in 2021, respectively, for the
world and US economies.
Discussion
The estimated decrease in daily CO2 emissions from the severe and
forced confinement of world populations of –17% (–11 to –25%) at
its peak are extreme and probably unseen before. Still, these only
correspond to the level of emissions in 2006. The associated annual
decrease will be much lower (–4.4 to –8.0% according to our sen-
sitivity tests), which is comparable to the rates of decrease needed
year-on-year over the next decades to limit climate change to a
1.5 °C warming33,34. These numbers put in perspective both the large
growth in global emissions observed over the past 14 years and the
size of the challenge we have to limit climate change in line with the
Paris Climate Agreement.
Furthermore, most changes observed in 2020 are likely to be
temporary as they do not reflect structural changes in the economic,
transport or energy systems. The social trauma of confinement and
associated changes could alter the future trajectory in unpredictable
ways35, but social responses alone, as shown here, would not drive
the deep and sustained reductions needed to reach net-zero emis-
sions. Scenarios of low-energy and/or material demand explored for
climate stabilization explicitly aim to match reduced demand with
higher well-being35,36, an objective that is not met by mandatory
confinements. Still, opportunities exist to set structural changes in
motion by implementing economic stimuli aligned with low carbon
pathways.
Our study reveals how responsive the surface transportation
sector’s emissions can be to policy changes and economic shifts.
Surface transport accounts for nearly half the decrease in emis-
sions during confinement, and active travel (walking and cycling,
including e-bikes) has attributes of social distancing that are likely
to be desirable for some time28 and could help to cut back CO2 emis-
sions and air pollution as confinement is eased. For example, cities
like Bogota, New York and Berlin are rededicating street space for
pedestrians and cyclists to enable safe individual mobility, with some
changes likely to become permanent. Follow-up research could
explore further the potential of near-term emissions reductions in
the transport sector without an impact on societal well-being.
Several drivers push towards a rebound with an even higher
emission trajectory compared with the policy-induced trajecto-
ries before the COVID-19 pandemic, which include calls by some
governments37 and industry to delay Green New Deal programmes
and to weaken vehicle emission standards38, and to the disruption
of clean energy deployment and research from supply issues. The
extent to which world leaders consider the net-zero emissions tar-
gets and the imperatives of climate change when planning their eco-
nomic responses to COVID-19 is likely to influence the pathway of
CO2 emissions for decades to come.
Online content
Any methods, additional references, Nature Research reporting
summaries, source data, extended data, supplementary informa-
tion, acknowledgements, peer review information; details of author
contributions and competing interests; and statements of data and
code availability are available at https://doi.org/10.1038/s41558-
020-0797-x.
Received: 9 April 2020; Accepted: 1 May 2020;
Published: xx xx xxxx
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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
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Articles Nature Climate ChaNge
Methods
Changes in emissions. Changes in emissions ΔCO2c,s,d (MtCO2 d−1) for each
country/state/province (c), sector (s) and day (d) are estimated using equation

(1):
ΔCO2
c;s;d¼
CO2
c´δ
S
c´
ΔA
s;dðCI;cÞð1Þ
where CO2c (MtCO2 d−1) is the mean daily emissions for the latest available year
(2017–2019) updated from the Global Carbon Project for world countries1
(Supplementary Extended Methods), EIA48 for the United States and national
statistics49 for Chinese provinces. δSc is the fraction of emissions in each sector
using data from the IEA50 for world countries, EIA48 for the United States and
national statistics49 for Chinese provinces. 

ΔAs,d(CI) is the fractional change in
activity level for each sector compared with pre-COVID levels (Table2) as a
function of the CI for each day of the year and each country (Supplementary
Tables15 and 16). The combination of CO2 emissions data from the Global Carbon
Project and sector distribution from IEA enabled the use of a country’s own
reported emissions to the UNFCCC (United Nations Framework Convention on
Climate Change), building on our previous work51, and means that more recent
emissions could be used. Our analysis is done for 69 countries, which accounts for
97% of global emissions. We do not estimate the changes in other countries.
Parameter choices. The choice of parameters by sector is based on data that
represent changes in activity rather than directly changes in CO2 emissions, and
on assumptions about the nature of the confinement. Most data are available daily
up to 15 April 2020. All the data (Fig.2) are representative of changes compared to
a typical day prior to confinement, taking into account seasonality and day of the
week. The changes were calculated differently depending on the data availability
and the causes of the seasonality and weekly variability. Sectors and parameter
choices are described in detail in Supplementary Extended Methods with the key
elements summarized here.
The power sector (44.3% of global CO2 emissions) includes energy conversion
for electricity and heat generation. The change in electricity and heat assumes
this sector follows the change observed in electricity demand data for the United
States40, selected European countries10 and India39.
The industry sector (22.4%) includes the production of materials (for example,
steel) for manufacturing and cement. 

The change in industry is based on coal
consumption from six coal producers in China23 and on steel production in the
United States41.
The surface transport sector (20.6%) includes cars, light vehicles, buses and
trucks, as well as national and international shipping. The change in transport is
based on Apple mobility data43 for world countries, US45 and UK44 traffic data and
urban congestion data from TOMTOM42. The changes in shipping are based on
forecasts by the World Trade Organization.
The public sector (4.2%) includes public buildings and commerce. The change
in the public sector is based on surface transport for the upper limit, assuming it is
proportional to the change in the workforce. It is based on electricity changes for
the lower limit, with the central value interpolated between the two.
The residential sector (5.6%) represents mostly residential buildings. The
changes in residential sector is based on reports of residential use monitored with
UK smart meters46.
The aviation sector (2.8%) includes both domestic and international aviation. It
is based on the total number of departing flights by aircraft on ground47.
Data availability
Global Carbon Project CO2 emissions data are available at https://www.icos-cp.
eu/global-carbon-budget-2019. International Energy Agency IEA World
Energy Balances 2019 @IEA are available at http://www.iea.org/statistics/.
European Network of Transmission System Operators Electricity Transparency
Platform are available at https://transparency.entsoe.eu/. Power System
Q14
Q15
Q16
Operation Corporation Limited data are available at https://posoco.in/reports/
daily-reports/. EIA data are available at https://www.eia.gov/realtime_grid/.
CO2 emissions data for China are available at http://doi.org/10.1038/s41597-
020-0393-y/. Coal changes from China industry are available at https://www.
carbonbrief.org/analysis-coronavirus-has-temporarily-reduced-chinas-c
o2-emissions-by-a-quarter/. American Iron and Steel Institute data are available
at https://www.steel.org/industry-data/. TOMTOM Traffic Index are available at
https://www.tomtom.com/en_gb/traffic-index/. MS2 Corporation traffic data are
available at https://www.ms2soft.com/traffic-dashboard/. Apple Mobility Trends
data are available at https://www.apple.com/covid19/mobility/. UK traffic data
from the Cabinet Office Briefing are available at https://www.gov.uk/government/
collections/slides-and-datasets-to-accompany-coronavirus-press-conferences.
Octopus Energy Tech smart meter data are available at https://tech.octopus.energy/
data-discourse/2020-social-distancing/index.html. Aircraft on Ground OAG data
are available at https://www.oag.com/coronavirus-airline-schedules-data/.
References
48. Today in Energy (EIA; accessed 7 April 2020); https://www.eia.gov/
todayinenerg/
49. Shan, Y. L. & Huang, Q. & Guan, D. B. & Hubacek, K. China CO2 emission
accounts 2016–2017. Sci. Data 7, 54 (2020).
50. World Energy Balances 2019 (IEA, accessed 11 November 2019); http://www.
iea.org/statistics
51. Friedlingstein, P. etal. 

(2019).
Acknowledgements
We thank P. Hunter for insights on the evolution of the pandemic. C.L.Q. and D.R.W.
were funded by the Royal Society (grant no. RP\R1\191063). M.W.J., P.F., A.J.D.-G.,
R.M.A. and G.P.P. were funded by the European Union Horizon 2020 ‘4C’ project
(no. 821003), M.W.J. and A.J.P.S. by the ‘VERIFY’ project (no. 776810) and M.W.J.
by the ‘CHE’ project (no. 776186). R.B.J. was funded by the Gordon and Betty Moore
Foundation (GBMF5439). J.G.C. was funded by the Australian National Environmental
Science Program—Earth Systems and Climate Change Hub. This collaboration was
made possible by prior funding from the UK Natural Environment Research Funding
International Opportunities Fund (no. NE/I03002X/1), and by the Global Carbon
Project. We thank the UEA HPC team for support. This analysis is based in part on IEA
data from the IEA, http://www.iea.org/statistics.
Author contributions
C.L.Q., R.B.J., J.G.C., P.F. and G.P.P. conceived and designed the project. C.L.Q. and
A.J.P.S. conceived the CI and, together with Y.S., produced it. C.L.Q., R.B.J., M.W.J.,
S.A., R.M.A., A.J.D.-G., D.R.W. and F.C. provided and analysed data. C.L.Q. produced
the analysis. All the authors contributed to the interpretation of the results and
wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41558-020-0797-x.
Correspondence and requests for materials should be addressed to C.L.Q.
Peer review information Nature Climate Change thanks Hannah Ritchie and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
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