# Near-real-time estimates of daily CO2 emissions from 1500 cities worldwide

Da Huo  Department of Earth System Science, Tsinghua University, Beijing,
100084, China  corresponding authors: Da Huo (dh2107@tsinghua.edu.cn), Zhu Liu
<EMAIL_ADDRESS> Xiaoting Huang  Department of Earth System Science,
Tsinghua University, Beijing, 100084, China  Xinyu Dou  Department of Earth
System Science, Tsinghua University, Beijing, 100084, China  Philippe Ciais
Laboratoire des Sciences du Climate et de l’Environnement LSCE, Orme de
Merisiers 91191 Gif-sur-Yvette, France  Yun Li  Department of Earth System
Science, Tsinghua University, Beijing, 100084, China  Zhu Deng  Department of
Earth System Science, Tsinghua University, Beijing, 100084, China  Yilong Wang
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographical Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, 100101, China  Duo Cui  Department of Earth System Science,
Tsinghua University, Beijing, 100084, China  Fouzi Benkhelifa  Nexqt, City
Climate Intelligence, France  Taochun Sun  Department of Earth System Science,
Tsinghua University, Beijing, 100084, China  Biqing Zhu  Department of Earth
System Science, Tsinghua University, Beijing, 100084, China  Geoffrey Roest
School of Informatics, Computing, and Cyber Systems, Northern Arizona
University, Flagstaff, AZ, 86011, USA  Kevin R. Gurney  School of Informatics,
Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ,
86011, USA  Piyu Ke  Department of Earth System Science, Tsinghua University,
Beijing, 100084, China  Rui Guo  Department of Earth System Science, Tsinghua
University, Beijing, 100084, China  Chenxi Lu  Department of Earth System
Science, Tsinghua University, Beijing, 100084, China  Xiaojuan Lin  Department
of Earth System Science, Tsinghua University, Beijing, 100084, China  Arminel
Lovell  CDP Worldwide, London, UK  Kyra Appleby  CDP Worldwide, London, UK
Philip L. DeCola  Department of Atmospheric and Oceanic Sciences, University
of Maryland, College Park, MD, 20742, USA  Steven J. Davis  Department of
Earth System Science, University of California, Irvine, 3232 Croul Hall,
Irvine, CA, 92697-3100, USA  Zhu Liu  Department of Earth System Science,
Tsinghua University, Beijing, 100084, China  corresponding authors: Da Huo
(dh2107@tsinghua.edu.cn), Zhu Liu<EMAIL_ADDRESS>
###### Abstract

Building on near-real-time and spatially explicit estimates of daily carbon
dioxide (CO2) emissions, here we present and analyze a new city-level dataset
of fossil fuel and cement emissions. Carbon Monitor Cities provides daily,
city-level estimates of emissions from January 2019 through December 2021 for
1500 cities in 46 countries, and disaggregates five sectors: power generation,
residential (buildings), industry, ground transportation, and aviation. The
goal of this dataset is to improve the timeliness and temporal resolution of
city-level emission inventories and includes estimates for both functional
urban areas and city administrative areas that are consistent with global and
regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan,
and CDP) were performed, and we estimate the overall uncertainty range to be
±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset
that includes cities around the world, including the first estimates for many
cities in low-income countries. A more complete description of this dataset is
published in Scientific Data https://doi.org/10.1038/s41597-022-01657-z.

## Background & Summary

More than 60% of global fossil-fuel CO2 emissions are produced in cities [1,
2], and high-quality city-level emissions inventories are urgently needed to
support international climate mitigation efforts [3, 4, 5]. For example, many
cities have adopted goals of reaching net-zero emissions by 2030 or 2050,
which require them to monitor and report emissions on a timely basis [6].
Unfortunately, a global, open, and harmonized dataset of city-level emission
inventories is yet lacking [7, 8]. Instead, most CO2 emission inventories are
conducted at the country level, as city-level fossil fuel consumption data are
more difficult to acquire [9]. Furthermore, many inventories–including
national inventories reported to the United Nations Framework Convention on
Climate Change (UNFCCC) often lag reality by one years or more [10]. Thus,
many city-level mitigation efforts are hampered by a lack of timely and high-
quality emissions data with which to set benchmarks and monitor progress [11,
12, 13, 14].

City-level CO2 emissions may refer to either the CO2 emissions produced within
the territory of a city or emissions related to all the goods and services
consumed in a city, which often include substantial emissions produced outside
the city boundary [15, 16, 8]. The in-boundary emissions are typically
referred to as scope 1, emissions from imported electricity as scope 2, and
all other trans-boundary emissions associated with other city activities are
referred to as scope 3 [16, 17]. Three conventional approaches have been used
to attribute CO2 emissions to cities: purely geographic production-based
accounting, community infrastructure-based accounting (geographic-plus), and
consumption-based accounting [15, 16]. These approaches can provide good
estimates for major cities that disclose high-quality energy consumption data
[15]. However, they can not be readily applied to a larger scale, where city-
specific data are largely absent, especially for smaller cities [18].
Downscaling represents a solution to the scalability issue. Some recent
studies use economic input-output (IO) tables down-scaled from national
statistics to attribute emissions to cities [19, 17], and other studies use
spatial proxies to disaggregate national or sub-national emissions to finer
scales. Popular spatial proxies include night-time light imagery and existing
gridded emission maps, such as the Emission Database for Global Atmospheric
Research (EDGAR) [20, 21, 22], which also relies on other spatial data like
population density and road networks. Downscaling has been used to construct
multiple city-level datasets that cover a large number of cities [20, 16], and
we adopt a similar approach in this study.

Cities that disclose their emissions typically follow protocols or standards
such as the Global Protocol for Community-Scale Greenhouse Gas Emission
Inventories (GPC) [23], the International Council for Local Environmental
Initiatives (ICLEI) [24] or the ICLEI-USA [15]. However, the reliability of
these self-reported inventories is difficult to assess due to the lack of
peer-review [8, 25, 5]. Inter-dataset comparison is also difficult due to
inconsistent definitions of spatial and temporal scales, protocols, sector
coverage, activity data sources, and accounting methods [26, 27, 8, 5, 24],
and many original input data are untraceable [8]. Therefore, a methodological
framework that supports inter-dataset comparisons and calibration is yet to be
developed for cities.

Where city-level emissions inventories exist, they often rely on data provided
by organizations such as the China Emission Accounts and Datasets (CEADs:
https://www.ceads.net/ [28]), the Multi-resolution Emission Inventory (MEIC,
http://meicmodel.org/ [29, 30]), and the Carbon$n$ Climate Registry
(https://carbonn.org/) [31, 8], or inventory warehouses like the CDP worldwide
(CDP: https://www.cdp.net/). But the coverage, timeliness, and temporal
resolution of these data are not always sufficient to support agile and
informed decision making. For example, although several high-quality datasets
are available for high-income countries, such as the Covenant of Mayors
database (https://www.globalcovenantofmayors.org/ [32, 24]) and OpenGHGmap
(https://openghgmap.net/ [18]) for EU cities, and the Vulcan and Hestia
datasets for the US cities (https://vulcan.rc.nau.edu/,
https://hestia.rc.nau.edu/ [33, 4, 5]), the tension between human development
and decarbonization requires an increasing focus on rapidly expanding cities
in low-income and emerging regions in South America, South and Southeast Asia,
Africa and the Middle East where high-quality emission inventories are lacking
[34, 35]. Moreover, most existing city-level inventories have the issues of
long time lag and low temporal resolution. Recently, the methodological
frameworks for estimating near-real-time (NRT) daily CO2 emissions have been
developed and successfully used for studying the impacts of COVID-19 on global
CO2 emissions [36, 37]. Here, we build on these approaches to provide NRT
daily emission estimates for hundreds of cities worldwide, including many in
low-income regions.

The dataset presented in this paper also provides a possible solution to
address the inconsistency between administrative emissions versus community-
wide emissions. Differences in spatial scope and accounting methods inevitably
complicate comparisons among cities [16, 8], and one possible solution is to
compile the inventories based on different functional zones of the city, such
as differentiating the core and commuting zones [24]. We provide a global
harmonized dataset that consistently quantifies production-based emissions
from core administrative areas in top emitting countries and, separately,
emissions from the world’s major metropolitan/functional urban areas.

## Methods

### Workflow

Carbon Monitor Cities (CM-Cities) is downscaled from the Carbon Monitor, which
is a NRT national level emission dataset at a global scale [10]. Specifically,
CM-Cities is produced following a four-stage workflow (Fig. 1). The first
stage mainly involves the construction of Global Gridded Daily CO2 Emission
Datasets (GRACED) [38], which are daily emission maps generated by
spatializing Carbon Monitor daily emissions using the Global Carbon Grid
(GID), the Emissions Database for Global Atmospheric Research (EDGAR) and
TROPOspheric Monitoring Instrument (TROPOMI). GRACED covers seven sectors
(power, industry, residential and commercial buildings, ground transportation,
domestic aviation, international aviation, and international shipping) and
provides NRT emission maps for fossil fuel combustion and cement production
with a global spatial resolution of 0.1∘ by 0.1∘ and a temporal resolution of
one day. GRACED is an intermediate gridded dataset between the Carbon Monitor
and CM-Cities, and the methods for generating this gridded dataset is
described in a later section.

In the second stage, we disaggregated the gridded daily emissions into cities
based on two types of city areas : Global Administrative Areas (GADM) and
Functional Urban Areas (FUA) to address the definition differences of ’a city’
in different countries. The FUA is defined by the Organisation for Economic
Co-operation and Development (OECD) and the European Union as the high-density
urban centres plus their surrounding commuting zones [39]. For OECD countries,
we used the OECD FUA, which provides higher quality FUA for OECD countries
(https://www.oecd.org/regional/regional-statistics/functional-urban-areas.htm
[40]). For other countries, the Global Human Settlement FUA is used
(https://ghsl.jrc.ec.europa.eu/ghs_fua.php [41]). The GADM level-2 is used for
prefecture-level cities in China and counties in the United States. The
details of the features and the usage of FUA and GADM datasets are described
in later sections. The spatial downscaling/disaggregation is performed by
first converting the FUA and GADM shapefiles into raster datasets with a
unique ID assigned to each city. Then the raster city area maps are used as
masks to extract the matching grid cells in the GRACED emission maps. We then
aggregate emission values for grid cells that correspond to the same city mask
to yield the total sectoral emission value for a given city.

In the third stage, we use city-level data to correct for the residential and
ground transport sectors to address the bias in raw city-level inventories
from the second stage. We use city-specific TomTom congestion data and daily
heating degree days (HDD) for the corrections.

The fourth stage involves error correction and data validation. We first
identify and remove outliers (which are mostly errors introduced by previous
processing steps and/or from the source data) using statistical approaches. We
then collected city-level inventories from other datasets (mostly annual data)
and compared them to our results for validation. The detailed procedures for
these processes are described in later sections.

CM-Cities includes city-level emission inventories from 01/01/2019 to
31/12/2021 for five main sectors: 1. power generation, 2. residential and
commercial buildings, 3. industrial production, 4. ground transportation, and
5. aviation. These five sectors combined account for over 70% of fossil fuel
CO2 emissions from a city [33].

### Coverage

CM-Cities covers 1500 cities in 46 counties (Fig. 2). Most of the cities are
clustered in Europe, Asia, North and South America. Major cities in Oceania
and Africa are also included. Figure 2 also shows comparisons between the FUA
and the GADM for Los Angeles (US) , Hangzhou (China), and Melbourne
(Australia). The FUA typically covers a larger area than the administrative
area, but for cities in some countries, such as China, the FUA is typically
smaller than the administrative city area. The use of both area definitions
facilitates dataset comparisons, which is highlighted for cities in China and
the United States. These two different spatial scopes also provide critical
information for differentiating administrative emissions versus community-wide
emissions.

### Near-Real-Time Daily Emissions by Sector

CM Cities is downscaled from the GRACED dataset, in which spatial distribution
and daily variations of emissions are combined. This section describes the
methods for estimating NRT daily emissions from a temporal perspective, and
the next section describes the spatial gridding procedure. The estimation of
daily emission variations follows the Carbon Monitor national dataset [10,
36], which provides daily fossil fuel CO2 emissions since January 1st, 2019 on
the global and national levels, with detailed estimates in 7 main sectors,
i.e., power, industry, ground transport, residential (including commercial),
domestic aviation, international aviation, and international shipping .
Emissions from international bunkers (including the international aviation
sector and international shipping sector) are only accounted for at the global
level and usually excluded from the national territorial emissions according
to the IPCC guidelines. Thus, CM-Cites consider the other 5 sectors, i.e.,
power, industry, ground transport, residential, and domestic aviation.

#### Power Sector

Daily power generation data are acquired from multiple open data sources
depending on the country (Table 1), which provides live power generation data
with a daily or hourly resolution, and accounts for more than 70% of the total
CO2 emissions in the power sector [10]. The emission factors are estimated
using EDGAR’s electricity emissions, divided by our collection of coal-fired
electricity data in various countries. The daily emissions are estimated as:

$Emis_{power,daily}=Emis_{power,yearly}(AD_{power,daily}/AD_{power,yearly}),$
(1)

where $AD$ is the power generation. For emissions from other countries
(countries not listed in Table 1), we assumed a linear relationship between
daily global emission and daily total emissions from these countries, and then
adjusted the emissions for countries that adopted lock-down measures during
the COVID-19 following the method used by the Carbon Monitor national dataset
[10].

#### Industry Sector

For the industry sector, the daily emissions are calculated from the monthly
industrial production index and the daily power generation data. Monthly
industrial production data are acquired from several datasets (Table 2). The
monthly CO2 emissions estimated from the Industrial Production Index (IPI) are
then disaggregated into a daily scale using daily power generation data. This
approach is based on two assumptions: 1. A linear relationship exists between
daily industrial production and industrial fossil fuel use. 2. A linear
relationship exists between daily industry activity and daily electricity
production [10].The monthly and daily industry emissions are estimated
following:

$Emis_{ind,monthly,currentyear,c}=Emis_{ind,yearly,2019,c}(IPI_{monthly,currentyear,c}/IPI_{yearly,2019,c}),$
(2)

$Emis_{ind,daily}=Emis_{ind,monthly}(Elec_{daily}/Elec_{monthly}),$ (3)

where $Emis_{ind,monthly,currentyear,c}$ is the monthly industry emissions for
country $c$ in current year, $Emis_{ind,yearly,2019,c}$ is the yearly industry
emissions for country $c$ in 2019 (year of the latest update of baseline
emissions), $IPI$ is the corresponding Industrial Production Index.
$Emis_{ind,daily}$ and $Emis_{ind,monthly}$ are the daily and monthly industry
emissions, respectively. $Elec_{daily}$ and $Elec_{monthly}$ are the daily and
monthly electricity production, respectively. For countries not listed in
Table 2, the industry sector emissions are estimated in the same way as for
the power sector.

#### Ground Transport Sector

Daily emissions from ground transportation are estimated using TomTom live
congestion index and EDGAR road transportation emissions. The TomTom traffic
congestion level represents the extra time spent on a trip in congested
conditions, as a percentage, compared to uncongested conditions. TomTom
congestion level data were obtained for more than 400 cities around the world
at a temporal resolution of one hour. This approach permits the estimation of
NRT emissions from ground transportation with a temporal resolution up to one
hour. The TomTom live congestion level data was proven to be highly accurate
for most cities, and Carbon Monitor has successfully adopted this approach
[10]. Note that a zero-congestion level means the traffic is fluid or ‘normal’
but does not mean there are no vehicles and zero emissions. The lower
threshold of emissions when the congestion level is zero was estimated using
real-time data from an average of 60 roads in the city of Paris [42]. TomTom
data accurately depicts the traffic volume using a sigmoid function-based
regression (Eq. 4), and Figure 3 is a comparison between the actual and TomTom
estimated hourly car counts on the measured roads in Paris. The estimated
traffic volume is then used to allocate the EDGAR on-road emissions to each
day (Eq. 5).

$Q_{d}=a+\frac{\beta X^{\gamma}}{{\lambda}^{\gamma}+X^{\gamma}},$ (4)

$Emis_{trans,d}=Q_{d}\frac{Emis_{onroad}}{\sum_{d=1}^{n}Q_{d}},$ (5)

where $Q_{d}$ is the mean vehicle number per hour in day $d$, $X$ is the daily
mean TomTom congestion level data, and $a$, $\beta$, $\gamma$, $\lambda$ are
regression parameters, $Emis_{trans,c,d}$ is the ground transport emissions in
day $d$, $Emis_{onroad}$ is the annual EDGAR road transportation emissions,
$n$ is the number of days in a year. For cities not covered by TomTom, we
assumed that the emission changes follow the mean changes of other cities in
the country. If no city in the country has TomTom data, then the relative
emission changes are assumed to follow the same pattern of the total emissions
from all TomTom-covered countries.

#### Residential Sector

Carbon Monitor uses the fluctuation of air temperature to capture the daily
variations in the energy consumption of residential and commercial buildings.
The assumption associated with this method is that the heating demand, which
is the largest contribution to the daily variability in emissions for this
sector, is strongly governed by air temperature [43], which determines the HDD
(cooling in summer mainly consumes electricity that is covered in the power
sector). This approach uses population-weighted HDD for different geographic
locations for each day based on the ERA-5 reanalysis of air temperature [44]
and also accounts for temperature-independent cooking emissions following
EDGAR. The EDGAR residential emissions are then downscaled to daily values
based on daily variations in population-weighted heating degree days.

$Emis_{res,c,d}=Emis_{res,c,m}R_{heating,c,m}\frac{HDD_{c,d}}{\sum_{m}HDD_{c,d}}+Emis_{res,c,m}\frac{1-R_{heating,c,m}}{N_{m}},$
(6)

$HDD_{c,d}=\sum_{g}R_{pop,g}He(18-T_{g,d}),$ (7)

where $Emis_{res,c,d}$ and $Emis_{res,c,m}$ are the residential emissions for
country $c$ in day $d$ and month $m$ respectively, $R_{heating,c,m}$ is the
percentage of residential emissions from heating demand in country $c$ in
month $m$, $HDD_{c,d}$ is the population-weighted heating degree day for
country $c$ in day $d$, $N_{m}$ is the number of days in month $m$,
$R_{pop,g}$ is the ratio of the population in grid $g$ to the total national
population, which is acquired from the Gridded Population of the World,
version 4 [45], $He$ is a Heaviside step function that converts any negative
values to zero, $T_{g,d}$ is the average air temperature in Celsius for grid
$g$ in day $d$ at 2 meters derived from ERA5 [44], and 18 is a HDD reference
temperature of 18°C.

#### Aviation Sector

Emissions in the aviation sector are computed from individual commercial
flights data from the Flightradar24 database (https://www.flightradar24.com).
This sector covers domestic flights, and all airports around a city were
selected even if they are not part of the FUA, but some airports are not
covered by the GADM. The daily CO2 emissions were estimated as the product of
distance flown and a constant emission factor ($EF_{avi}$).

$Emis_{avi}=DF\cdot EF_{avi},$ (8)

where $DF$ is distance flown, which is computed using great circle distance
between the take-off, cruising, descent, and landing points for each flight
and are cumulated over all flights. The emission factor per kilometer flown is
assumed to be a constant for the mix of all aircraft from an airport
(including regional, narrowbody passenger, widebody passenger, and freight
operations) as the share of flight types has not significantly changed since
2019.

### Gridded Daily CO2 Emissions

Carbon Monitor Cities disaggregates the Carbon Monitor national emissions to
cities using the GRACED dataset developed by the Carbon Monitor team [38],
which consists of emission maps generated by spatializing and gridding the
daily national emission inventories from Carbon Monitor into grid cells. This
was achieved by estimating spatial distribution proxies from satellite data
and existing gridded products while maintaining consistency between ‘bottom-
up’ accounting results and the spatial sum of the gridded results. Three
datasets were used in producing GRACED: 1. The Global Carbon Grid (GID), which
provides global CO2 emissions data from major industry and power plant point
sources with a resolution of 0.1∘ in 2019, 2. The Emissions Database for
Global Atmospheric Research (EDGAR), which provides sectoral emissions as
specified by the IPCC guidelines. 3. The NO2 thermal chemical vapor deposition
retrieval product from the Tropospheric Monitoring Instrument (TROPOMI)
onboard the Sentinel-5 Precursor satellite. Given that GID has higher data
quality in fine-grained spatial scales and point sources of industries and
power plants, the GID-based point sources and the EDGAR emission maps were
combined for constructing GRACED (Eq. 9). While the spatial emission patterns
derived from GID and EDGAR (with latest updates in 2019) cannot accurately
reflect the situation in 2020 and 2021, the NRT TROPOMI NO2 retrievals were
used as a proxy for CO2 to capture the daily variability in CO2 emission
following GRACED [38]. After several data processing steps, such as rolling-
average and thresholding, the NO2 data can reasonably indicate the spatial
distribution of CO2 sources [46]. Table 3 lists the gridded data used for
producing GRACED. For the aviation sector, EDGAR’s monthly data are used for
spatial distribution (Eq. 10). Thus, the gridded emissions $EmiGrid_{g,d,s}$
for grid $g$, date $d$ and sector $s$ were estimated as:

$EmiGrid_{g,d,s}=CM_{c,d,s}\frac{GID_{g,s}}{\sum_{i=1}^{n}GID_{i,s}}\frac{EDGAR_{g,m,s}}{\sum_{j=1}^{12}EDGAR_{g,j,s}}\cdot
12,$ (9)

$EmiGrid_{g,d,avi}=CM_{c,d,avi}\frac{EDGAR_{g,m,avi}}{\sum_{i=1}^{n}EDGAR_{i,m,avi}},$
(10)

where $CM_{c,d,s}$ represents the value of Carbon Monitor national emission
for country $c$, day $d$ and sector $s$. $s$ includes the power, industry,
residential, and ground transport sectors. $avi$ is the aviation sector.
$GID_{g,s}$ is the value of GID gridded CO2 emissions for grid $g$ and sector
$s$. $n$ is the total number of grids within this country and $j$ is the total
number of month. $EDGAR_{g,m,s}$ represents the EDGAR gridded CO2 emissions
for grid $g$, sector $s$, and month $m$ which date $d$ belongs to.

### City-level Spatial Disaggregation

The spatial disaggregation is performed by first converting the city area
shapefiles (FUA or GADM) into raster datasets with a unique ID assigned to
each city. Then the raster city area maps are used as masks to extract the
matching grid cells in GRACED emission maps. We then aggregate emission values
for grid cells that correspond to the same city mask to yield the total
sectoral emission value for a given city. For the aviation sector, emissions
from all planes within the city’s territory are included. The international
shipping sector is not included in this dataset because most of the emissions
from this sector occurred in the open ocean that cannot be allocated to
specific cities. The jurisdiction issue also applies to the aviation sector,
but we keep the territorial-based allocation approach in the dataset for
completeness.

We use both the administrative areas and FUA because boundary definition has
always been a problem in city-level inventory completion [16], as the
administrative city areas in most countries do not reflect emissions from the
larger commuting zones of a city, which may constitute a large part of the
emissions, meanwhile, FUA represents the most viable spatial dataset for
covering the more complete urban areas. In addition, FUA is clearly-defined
and produced using a consistent method for cities worldwide, while the
definition of administrative city areas may vary significantly across
different countries. Therefore, this use of both spatial scopes represents a
potential solution to differentiate administrative emissions versus community-
wide emissions and makes inter-dataset comparisons easier as demonstrated in
the validation section.

### City-level Corrections

The disaggregation from EDGAR spatial distributions is insufficient especially
for the residential and ground transport sectors, because EDGAR uses a
disaggregation of national sectoral totals per population for residential, and
per road network for ground transport, which introduces bias to cities.
Therefore, we correct these two sectors at individual city level. The ground
transport sector emission is corrected using city-specific TomTom data (by
applying Eqs. 4,5 at city scale) for 416 cities worldwide that have their own
NRT TomTom indices (list of these cities can be found in the documentation on
Carbon Monitor website), which represent more accurate ground transport
emission estimates for these cities. For cities that do not have their own
TomTom data, we spatially disaggregate the national mean estimates following
Eqs. 4,5,9.

The residential sector is corrected using city-level HDD to overcome the bias
of downscaling from national inventory. Specifically, we first calculate the
daily mean HDD for each city from the population-weighted HDD grid (Eq. 7),
and then use it as the baseline to compute a correction factor for each city
by comparing it with the mean national HDD to update the emissions for the
residential sector:

$Emis_{res,i}=Emis0_{res,i}\left(1-\frac{HDD_{c}-HDD_{i}}{max(HDD_{c})}\right),$
(11)

where $Emis_{res,i}$ and $Emis0_{res,i}$ represent the corrected and
uncorrected residential emissions, respectively for city $i$, $HDD_{c}$ is the
mean daily HDD for the country, and $HDD_{i}$ is the mean daily HDD for city
$i$.

### Outlier Correction

Outliers exist in the data mainly due to errors in the source datasets, such
as mistakes in unit conversions or data entry, etc. To correct these outliers,
we apply a statistical method based on intrinsic properties of the
distribution of the emissions in the database. This allows more accurate
identification of outliers that are likely to be the results of incorrect data
entry. Similar statistical approaches have been successfully applied to
correct for outliers in emission datasets [24]. The outlier identification
method is based on standard deviation (STD). Specifically, we consider an
emission value as an outlier if the differences between the current value and
its daily neighbours are both greater than 3 times the yearly STD for that
sector (Eq. 12). This threshold is determined by experimenting with data with
known error and data with periodical high variation (e.g., weekday versus
weekends for the ground transport emissions). These experiments determined the
lower and upper bounds of the threshold such that it correctly identifies
outliers and keeps the inherent variance within the data.

$|x-x_{n+1}|>3\cdot STD\;and\;|x-x_{n-1}|>3\cdot STD.$ (12)

### Limitations

This dataset focuses on improving the timeliness, temporal resolution, and
coverage of city-level inventories for studying NRT emission dynamics and also
providing emission estimates for many cities in low-income regions. This
dataset is derived from the gridded Carbon Monitor which is based on
downscaled national inventories, combined with point sources and spatial
distributions from GID and EDGAR, therefore, one limitation is the lack of
using city-specific bottom-up activity data except for the ground transport
sector, which may introduce additional uncertainties. We also noted that some
input data may contain inherent errors and missing values (other than the
above-mentioned outliers and discontinuities), especially for cities in less
developed nations, we do not intend to fix this kind of errors in the source
data without enough background information of the specific city, but we
consider our results represent a meaningful first-order estimate for many of
these cities that are lacking any emission inventories. Estimating NRT daily
emissions is a relatively new research direction and require ongoing efforts
to calibrate and update the workflow to improve data quality in the future.

## Data Records

CM-Cities provides NRT city-level emission inventories from 01/01/2019 to
31/12/2021 for 1500 cities in 46 counties. All data have gone through a
validation process, in which we estimated the uncertainties and corrected
errors. The attributes of the final dataset are listed in Table 4, and the
emission data are organized into spreadsheets. The definitions for sectors are
consistent with the Carbon Monitor national inventories. Brief descriptions of
the methods, sectors, coverage and uncertainty are also provided in Table 5.
Latest updates for selected cities and related information are available for
view and download on our website https://cities.carbonmonitor.org. At the time
of writing this article, this dataset has been updated to December 31, 2021
and the full dataset can be downloaded at Figshare [47]. Future updates will
also be available on our website.

  * •

The file that contains functional urban area results for all cities (carbon-
monitor-cities-all-cities-FUA.csv) has 8,114,886 data records (some cities
have missing values). Separate data files are also provided for each of the 46
countries (carbon-monitor-cities-’CountryName’.csv).

  * •

The file that contains all administrative area results for Chinese cities
(carbon-monitor-cities-China-GADM-prefecture.csv) has 1,885,120 data records,
including 344 prefecture-level cities, and each city has 5480 data records.

  * •

The file that contains U.S. county-level results (carbon-monitor-cities-US-
Counties.csv) has 1,720,720 data records, including 314 counties, and each
county has 5480 data records.

### Data Examples

Daily CO2 emission variations from a city reveal its geographic and socio-
economic characteristics. Figure 4 shows the sectoral breakdown of daily CO2
emissions for some major cities in different regions of the world, including
East Asia, Middle East, Southeast Asia, West Europe, East Europe, Oceania,
South America, North America, and Africa. As an example of the geographic
influence on the emissions, cities in the Southern Hemisphere, such as Sydney
in Australia, and Cape Town in Africa, exhibit higher emissions in the
residential sector during the southern winter (northern summer) due to the
increase in heating demand. Daily emissions also reveal certain events such as
holidays and the COVID-19 outbreak. The emissions from the power sector show a
surge in summer for many cities, which is likely due to the increased power
consumption for cooling. As an example, we show the impact of COVID-19 on city
emissions for Greater New York in the U.S. and Ahmedabad in India (Fig. 5),
note the significant drop in emissions for the ground transport sector in
spring 2020 (as indicated by the dashed lines) during the lockdown period and
during the 2021 second wave of COVID-19 pandemic in India.

## Technical Validation

The quality of this dataset is evaluated by comparing it against existing
datasets (Table 5). We also performed uncertainty analysis for our data and
for each sector based on a synthesis analysis of input data uncertainties and
the methodology used. Significant outliers were identified and corrected as
shown in the examples of Figure 6. The outlier occurrence rate for this
dataset is 0.012%.

### Validation Against Other Datasets

Multiple datasets are used to validate our results, including 1. City
inventories from the CDP, 2. Vulcan dataset for US counties, 3. CEADs and MEIC
dataset for China, and 4. individual reports released by city governments. For
cities in China, we validated our dataset by comparing it with the CEADs and
MEIC datasets. CEADs provides annual provincial emission inventories for China
for 2019, and we validated the data for each province by summing up emissions
from all prefecture-level cities in each province (for China, the GADM level-2
is exactly the area of prefecture-level cities, and the sum of all prefecture-
level cities within a province equals the total area of that province). Figure
7 depicts the comparison results for all the Chinese provinces including
municipalities and most autonomous regions. The result indicates a good
agreement between the CEADs and CM-Cities, with less than 10% difference in
annual emissions for most of the provinces. Statistics (Table 5) indicate that
the coefficient of determination ($R^{2}$) values between CEADs and CM-Cities
are 0.96, 0.76, and 0.92 for total, power, and industry sectors, respectively,
and the corresponding mean relative difference ($Rd$) are 11%, 30%, and 28%.
$R^{2}$ values between MEIC and CM-Cities are 0.93 and 0.62 for the power
sector and ground transport sector, respectively, and the corresponding $Rd$
are 21% and 31%. Other sectors were not compared due to the large differences
in sector definition and coverage. The mean relative differences are all
within the uncertainty ranges, which indicates a relatively high accuracy for
Chinese cities.

For cities in other countries, many datasets do not have recent (2019 or
later) inventories, for example, the latest Vulcan dataset provides emissions
in 2015 for United States counties, and the latest CDP inventories may range
from 2010 to 2021 depending on reporting status of each city. For the
completeness of the validation, we adjusted the area of accounting for CM-
Cities to be as consistent as possible with these datasets and compared city
inventories from all available data sources regardless of the time of
accounting. Note that GADM level-2 in the United States represents exactly the
area of counties, so our GADM results were used for comparison with Vulcan
county-level inventories. Figure 8 and Figure 9 show examples of the annual
total emission comparisons between CM-Cities and these datasets.

Comparison between Vulcan (2015) and CM-Cities (2019) covers top 50 counties
with the highest emissions in the United States. We used the coefficient of
determination ($R^{2}$) and mean relative difference ($Rd$) to evaluate the
comparison results. $R^{2}$ values are 0.82, 0.60, 0.58, 0.82, 0.90, and 0.69
for total, power, industry, residential (and commercial buildings), ground
transport, and aviation sectors, respectively, and the corresponding $Rd$
values are 26%, 114%, 67%, 35%, 41%, and 58%, respectively (Table 5). The
differences are mainly due to 1. the difference in the year of accounting, as
the earliest estimates of CM-Cities for the year 2019 is compared to the
latest Vulcan for the year 2015, multiple factors that govern emissions could
have changed during the period, 2. the different accounting methods, as CM-
Cities uses a territorial downscaling approach, while Vulcan uses a
consumption-based bottom-up accounting approach, and 3. the differences in
sector coverage definitions and source data (Tables 1, 2, 5), which partially
explains why the total emission comparison show a better good agreement than
the sectoral comparisons.

Direct comparisons with CDP were difficult due to several reasons: 1. CDP
inventories are city self-reported data, which were typically estimated using
different methods. 2. Most cities follow the GPC protocol and report in scopes
rather than in sectors, therefore, we only compared total emissions. 3. CDP
has not independently calculated the uncertainty range for these self-reported
inventories, and self-reported uncertainties are expected to be variable. For
example, 45% of cities reported “high confidence” in their emissions data for
2021, 35% reported “medium confidence”, and 3% reported “low confidence”. 4.
Spatial coverage is unclear, as the definition of a ’city’ can vary across
different countries, some cities report based on administrative areas, but
others include adjacent areas, but no shapefiles or raster maps were provided
to clarify the exact city boundary or area of accounting. Nonetheless, we
performed comparisons for 24 large cities in different regions with available
CDP inventories, the total emission comparisons for these cities show an
agreement with $R^{2}$ = 0.74 and $Rd$ = 31% (Fig. 9, Table 5).

### Uncertainty Analysis

The uncertainties in this dataset have two sources: 1. The uncertainties
inherited from Carbon Monitor and GRACED. 2. The uncertainty introduced by the
spatial downscaling process. The uncertainty analysis was conducted based on
the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. For the
power sector, uncertainty mainly comes from the emission factor and the
variability of energy mix for power generation, the 1-sigma uncertainty of
power emission from fossil fuel is estimated as ±10.0%. For the industry
sector, monthly production data is the main source of uncertainty, especially
the production in China, which accounts for more than 60% of world total
industrial CO2 emissions. Monte Carlo simulations were used to determine the
confidence interval based on regression models between estimated monthly
emissions and officially reported emissions. The 1-sigma uncertainty for the
industry sector is estimated as ±36.0%. For the ground transport, uncertainty
is estimated by applying the regression between the TomTom congestion index
and traffic flux to other cities (other than Paris). The 1-sigma uncertainty
for the ground transport sector is estimated as ±9.3%. For the residential
sector, the uncertainty is calculated based on comparisons between estimated
emissions and consumption-based accounting results for several countries in
Europe. The 1-sigma uncertainty for the residential sector is estimated as
±40.0%. For the aviation sector, 1-sigma uncertainty is estimated as ±10.2%.
These uncertainty estimates follow the methods used by Carbon Monitor [10].

Spatial downscaling introduces additional uncertainty because of the
rasterization of city areas. Spatial computations are based on raster
(gridded) files, but most cities and urban areas have irregular-shaped
boundaries that are not fully overlapped with gridded cells. Area
discrepancies are found along all city boundaries, and smaller cities
typically suffer from higher levels of dissimilarities because few grid cells
account for a large portion of the total urban area. We computed the area
discrepancies for all cities in the dataset, and found that 44.53% of cities
show an area difference of 0%-5% and the count decreases as the discrepancy
percentage gets higher. The mean area discrepancy for all cities is 13.55%. We
then estimated the overall uncertainty by first applying the error propagation
equation provided by IPCC[48], and then combining the uncertainties of all
sectors and the city area uncertainty:

$U_{overall}=\sqrt{\frac{\sum{(U_{s}\alpha_{s})}}{{\sum\alpha_{s}}^{2}}}+U_{a},$
(13)

where $U_{s}$ and $\alpha_{s}$ are the percentage and quantity (daily mean
emissions) of the uncertainty for sector $s$, respectively, and $U_{a}$ is the
city area uncertainty. Finally, the overall uncertainty range of CM-Cities is
estimated as ±21.66%.

## Usage Notes

The generated datasets are available from
https://doi.org/10.6084/m9.figshare.19425665.v1.The main data file has more
than one million lines of data, which will take a long time to load in Excel.
We recommend loading the data with a script that can handle large datasets. We
have provided an example of Python code to help users read in and plot
emissions for any city in the dataset (https://github.com/dh107/Carbon-
Monitor-Cities/). Users should also note that the unit of emissions in this
dataset is $ktCO_{2}$. Filename indicates whether the data is based on
administrative areas (GADM) or functional urban areas (default). The next
update to this dataset is scheduled for May 2022, which will update the
dataset to Feb 28, 2022.

## Code availability

Python code for producing, reading and plotting data for any city in the
dataset is provided at https://github.com/dh107/Carbon-Monitor-Cities/.

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## Acknowledgements

Authors acknowledges support from the CDP Worldwide.

## Author contributions statement

D.H. and Z.L. designed the research. D.H., X.H. and X.D. conducted the data
processing and wrote the manuscript. D.H., P.C. Z.D. and Z.L. designed the
methods, and all authors contributed to data collection, discussion and
analysis.

## Competing interests

The authors declare no competing interests.

## Figures & Tables

Figure 1: Flowchart illustrates the main workflow and data used in each stage. Figure 2: Map showing all the cities covered in this dataset. Purple dots indicate cities with emissions estimated based on functional urban areas (FUA), and blue dots indicate cities with emissions estimated based on both FUA and administrative areas (GADM). Subplots depict examples of the comparison between the administrative city area versus the functional urban areas for cities in different regions. Figure 3: Comparison between the actual and TomTom estimated hourly car counts on the measured roads in Paris. TomTom-based estimates accurately depicted the drop in traffic during the lock down period in 2020. Figure 4: Daily by sector CO2 emissions for cities (FUA) in different regions of the world. Including Tokyo in East Asia, Ankara in the Middle East, Bangkok in Southeast Asia, London in West Europe, Moscow in East Europe, Greater Sydney in Oceania, São Paulo in South America, Houston in North America, and Cape Town in Africa. Figure 5: Daily city-level CO2 emissions show the impact of COVID-19 for (A) Greater New York in the U.S. and (B) Ahmedabad in India. The emissions from ground transportation and aviation decreased significantly during the lockdown period (between the dashed lines) in spring 2020, and also during the second wave between March 2021 to June 2021 in India. Figure 6: Examples of outlier identification and correction for the ground transport data. (A) Two outliers clearly fall out of the typical range of weekday-weekend variation before the correction. (B) Outliers removed after the correction. Figure 7: Dataset comparison for cities in China. (A) Comparison of the sum of all prefecture-level cities within each Chinese province (including municipalities and autonomous regions) against the CEADs provincial datasets for year 2019. Note that the sum of all prefecture-level cities within a province equals the total area of that province in China (B) Sectoral comparison between CM Cities, CEADs and MEIC for sectors that have similar coverages. Figure 8: Comparison of sectoral emissions between Vulcan dataset and CM-Cities for (A) selected counties in the United States, and (B) county-level and FUA-level comparison. The year of accounting is 2015 for Vulcan inventories and 2019 for CM-Cities, which could partially explain the differences. Figure 9: City annual total emission comparisons between CM-Cities, CDP, Vulcan and some other city self-reported inventories. Magnitudes represent total emissions from each dataset. The area of accounting is adjusted to be as consistent as possible across datasets. Country/Region |  Data source |  Description
---|---|---
China |  National Grid Daily Electric Load |  Daily thermal production
India |  Power System Operation Corporation Limited (https://posoco.in/reports/daily-reports/) |  Daily thermal production (from coal, lignite, gas, naphtha and diesel)
United States |  Energy Information Administration’s (EIA) Hourly Electric Grid Monitor (https://www.eia.gov/beta/electricity/gridmonitor/) |  Hourly thermal production (from coal, petroleum, and natural gas)
EU27 |  ENTSO-E Transparency platform (https://transparency.entsoe.eu/dashboard/show) |  Hourly thermal production
United Kingdom |  Balancing Mechanism Reporting Service (BMRS) (https://www.bmreports.com/) |  Hourly power generation
Russia |  United Power System of Russia (http://www.so-ups.ru/index.php) |  Total hourly generation
Japan |  Organization for Cross-regional Coordination of Transmission Operators (OCCTO) (https://www.occto.or.jp/en/) |  Hourly thermal generation
Brazil |  Operator of the National Electricity System (http://www.ons.org.br/Paginas/) |  Hourly thermal production
Table 1: Data sources for the power sector. Country/Region |  Sector |  Data |  Data Source
---|---|---|---
China |  Steel industry |  Crude steel production |  World Steel Association (https://www.worldsteel.org/)
|  Cement industry |  Cement and clinker production |  National Bureau of Statistics (http://www.stats.gov.cn/english/)
|  Chemical industry |  Sulfuric acid, caustic soda, soda ash, ethylene, chemical fertilizer, chemical pesticide, primary plastic and synthetic rubber |  National Bureau of Statistics (http://www.stats.gov.cn/english/)
|  Other industry |  Crude iron ore, phosphate ore, salt, feed, refined edible vegetable oil, fresh and frozen meat, milk products, liquor, soft drinks, wine, beer, tobaccos, yarn, cloth, silk and woven fabric, machine-made paper and paperboards, plain glass, ten kinds of nonferrous metals, refined copper, lead, zinc, electrolyzed aluminum, industrial boilers, metal smelting equipment, and cement equipment |  National Bureau of Statistics (http://www.stats.gov.cn/english/)
India |  - |  Industrial Production Index (IPI) |  Ministry of Statistics and Programme Implementation (http://www.mospi.nic.in) Trading Economics (https://tradingeconomics.com)
United States |  - |  Industrial Production Index (IPI) |  Federal Reserve Board (https://www.federalreserve.gov)
EU27 and UK |  - |  Industrial Production Index (IPI) |  Eurostat (https://ec.europa.eu/eurostat/home) Trading Economics (https://tradingeconomics.com)
Russia |  - |  Industrial Production Index (IPI) |  Federal State Statistics Service (https://eng.gks.ru)
Japan |  - |  Industrial Production Index (IPI) |  Ministry of Economy, Trade and Industry (https://www.meti.go.jp)
Brazil |  - |  Industrial Production Index (IPI) |  Brazilian Institute of Geography and Statistics (https://www.ibge.gov.br/en/institutional/the-ibge.htm)
Table 2: Data sources for industrial production. Data |  Data Description |  Resolution
---|---|---
Global Carbon Grid (GID) v1.0 |  Global Grid in the Global Infrastructure Emission Database. Including power, industry, residential, transport, shipping, and aviation sectors with high data quality in spatial fine-grained maps (http://gidmodel.org) |  0.1∘ × 0.1∘ global, Annual
Emission Database for Global Atmospheric Research (EDGARv5.0) |  EDGAR v5.0 FT2019, covers major fossil CO2 sources globally, with monthly emissions provided per main source category (https://edgar.jrc.ec.europa.eu) |  0.1∘ × 0.1∘ global, Monthly
TROPOMI NO2 Retrievals |  NO2 thermal chemical vapor deposition retrievals acquired by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite, launched in 2017 |  0.1∘ × 0.1∘ global, Daily
Table 3: List of gridded data used for producing Global Gridded Daily CO2 Emissions Dataset (GRACED). Column |  Description
---|---
City |  Name of the city
Country |  Country where the city is located. The following countries are covered in this dataset: Argentina, Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile ,China, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, Hungary, India, Indonesia, Iran, Italy, Japan, Korea, Malaysia, Mexico, Myanmar, Netherlands, Nigeria, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Arab Emirates, United Kingdom, United States, Vietnam
Date |  Date (YYYY-MM-DD) on which the emissions were estimated. Currently, the dataset provides emissions from 2019-01-01 to 2021-12-31
Sector |  Sector for which the emissions were estimated, including power, industry, residential, ground transport, aviation
Value |  Magnitude of daily emissions with a unit of $ktCO_{2}$
Timestamp |  Unix timestamp at 00:00:00 (GMT+0000) on each day for scientific visualization
Table 4: Data attributes. Dataset |  CM Cities |  CEADs |  MEIC |  CDP |  Vulcan
---|---|---|---|---|---
Spatial coverage |  Global cities |  China national, provincial, prefectural |  China national, provincial |  Global cities |  U.S. counties
Temporal coverage |  2019-2021 |  1997-2019 |  2000-2017 |  2010-2021 |  2010-2015
Temporal resolution |  Daily |  Monthly |  Annual |  Annual |  Annual, hourly
Protocol |  - |  - |  - |  Various |  -
Overall uncertainty |  ±21.7% |  -15% to 30% |  ±15% |  All data is self-reported, CDP does not assess the uncertainty |  Sectoral uncertainties provided below
Area definition |  GADM, FUA |  N/A |  Population density, GDP |  Mostly city administrative, some include adjacent areas |  Administrative county area
Total emissions comparison (with CM Cities) |  - |  $R^{2}$=0.96, $Rd$=11%, $n$=30 |  - |  $R^{2}$=0.74, $Rd$=31%, $n$=24 |  $R^{2}$=0.82, $Rd$=26%, $n$=50
Power sector method |  Daily power generation downscaling. $\Delta$= ±10% |  Energy consumption for production and supply of electric power, steam and hot water |  Unit-level power generation. $\Delta$= -15% to 16% |  City report (scope 1-3 for relevant GPC stationary energy subsectors, including residential and commercial buildings, industry, agriculture, forestry and fishing) |  CAMD, DOE/ EIA fuel, EPA NEI point electricity production. $\Delta$= ±13%
Power comparison (with CM Cities) |  - |  $R^{2}$=0.76, $Rd$=30%, $n$=30 |  $R^{2}$=0.93, $Rd$=21%, $n$=30 |  - |  $R^{2}$=0.60, $Rd$=114%, $n$=50
Industry sector method |  Industrial production index downscaling. $\Delta$= ±36% |  Energy consumption for individual manufacturing sectors |  - |  City report (direct scope 1 emissions from industrial processes and product use) |  EPA NEI industrial point sources. $\Delta$= ±12.8%
Industry comparison (with CM Cities) |  - |  $R^{2}$=0.92, $Rd$=28%, $n$=30 |  - |  - |  $R^{2}$=0.58, $Rd$=67%, $n$=50
Residential sector method |  HDD. $\Delta$= ±40% |  - |  - |  City report (scope 1-3 for relevant GPC stationary energy subsectors, including residential and commercial buildings) |  EPA NEI residential and commercial nonpoint buildings. $\Delta$= ±12.8%
Residential comparison (with CM Cities) |  - |  - |  - |  - |  $R^{2}$=0.82, $Rd$=35%, $n$=50
Ground transport sector method |  TomTom congestion index. $\Delta$= ±9.3% |  - |  Vehicle ownership statistics and digital road map |  City report (scope 1-3 for GPC transportation subsectors, including on-road, railways, waterborne navigation, aviation, and off-road) |  EMFAC, EPA NEI onroad. $\Delta$= ±14.2%
Ground transport comparison (with CM Cities) |  - |  - |  $R^{2}$=0.62, $Rd$=31%, $n$=30 |  - |  $R^{2}$=0.90, $Rd$=41%, $n$=50
Aviation sector method |  Flightradar24 flight data. $\Delta$= ±10.2% |  - |  - |  City report aviation under transportation sector |  EPA NEI point airport. $\Delta$= ±7.8%
Aviation comparison (with CM Cities) |  - |  - |  - |  - |  $R^{2}$=0.69, $Rd$=58%, $n$=50
References |  - |  [28] |  [29, 30] |  - |  [4]
Table 5: Summary of city emission datasets ($\Delta$ is uncertainty) and
comparison statistics including coefficient of determination ($R^{2}$), mean
relative difference ($Rd$), and sample size ($n$) when compared with CM
Cities.