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1SCIENTIFIC DATA | (2021) 8:301 | https://doi.org/10.1038/s41597-021-01086-4
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Monthly direct and indirect
greenhouse gases emissions
from household consumption in
the major Japanese cities
Yin Long 1 ✉, Yida Jiang2, Peipei Chen3, Yoshikuni Yoshida1, Ayyoob Sharifi 4,
Alexandros Gasparatos 5,6 ✉, Yi Wu 3, Keiichiro Kanemoto 7, Yosuke Shigetomi8 &
Dabo Guan 3,9
Urban household consumption contributes substantially to global greenhouse gases (GHGs) emissions.
Urban household emissions encompass both direct and indirect emissions, with the former associated
with the direct use of fossil fuels and the latter with the emissions embodied in the consumed goods
and services. However, there is a lack of consistent and comprehensive datasets outlining in great
detail emissions from urban household consumption. To bridge this data gap, we construct an emission
inventory of urban household emissions for 52 major cities in Japan that covers around 500 emission
categories. The dataset spans from January 2011 to December 2015 and contains 12,384 data records
for direct emissions and 1,543,128 records for indirect emissions. Direct emission intensity is provided in
g-CO2/JPY to facilitate both future studies of household emission in Japan, as well as act as a reference
for the development of detailed household emission inventories in other countries.
Background & Summary
Cities currently account for the bulk of the ever-increasing greenhouse gas (GHG) emissions globally1, with
large variability between countries and regions2. Further to the increasing levels of GHG emissions from direct
energy use in cities, urban household consumption has emerged as an even greater (and o!en hidden) source
of emissions. For example, the GHG emissions embodied in household consumption can account for as much
as 70–80% of the total "nal emissions for the world’s three largest (and highly urbanized) economies, namely
USA3,4, China5–9, and Japan10,11. Furthermore, studies have pointed that urban household consumption con-
tributes to more than 70% of GHG emissions from cities5,12, accounting for a large fraction of total national
emissions13.
Reducing the carbon footprint of urban household consumption has been identi"ed as one of the most
promising climate change mitigation strategies. For example, in some developed countries such as Japan, it
could reduce nearly 40% of national GHG emission by 203014. However, estimating the GHG emissions of urban
households is rather complicated.
To start, the "eld of urban carbon accounting is still nascent. Major improvements would be needed before
obtaining more accurate and consistent results considering that cities are open systems that continuously
1Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan. 2Graduate
Program in Sustainability Science - Global Leadership Initiative, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa, Chiba, 277-8563, Japan. 3The Bartlett School of Sustainable Construction, University College London,
London, WC1E 7HB, UK. 4Graduate School of Humanities and Social Science. 1–31- Kagamiyama, Hiroshima
University, Higashi, Hiroshima, 739-8530, Japan. 5Institute for Future Initiatives, University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo, 113-8654, Japan. 6Institute for the Advanced Study of Sustainability (UNU-IAS), United Nations
University, 5-53- Jingumae, Shibuya-ku, Tokyo, 150-8925, Japan. 7Research Institute for Humanity and Nature, 457-4
Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047 Japan, Kyoto, Japan. 8Faculty of Environmental Science, Nagasaki
University, 1-14 Bunkyo-machi, Nagasaki, 852-8521, Japan, Nagasaki University, Nagasaki, Japan. 9Department
of Earth System Science, Tsinghua University, Beijing, 100084, China. ✉e-mail: longyinutokyo@gmail.com;
gasparatos@ifi.u-tokyo.ac.jp
DATA DESCRIPTOR
OPEN
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interact with their hinterlands15. Accounting for direct GHG emissions has traditionally been conducted at the
national level using energy use data or at the global level based on satellite observations. Several well-established
databases currently provide estimates of global emissions at somewhat granular spatial and temporal scales, e.g.
the Open‐source Data Inventory for Anthropogenic CO2 (ODIAC)16, and the Emission Database for Global
Atmospheric Research (EDGAR)17. Top-down approaches that employ atmospheric inversion techniques can
estimate urban GHG $uxes and concentrations18,19. Downscaling methods using proxies such as population
density and commercial activity have been used to estimate city-level GHG emissions using total global or
national emissions data available in the above-mentioned databases2.
Using combinations of the above-mentioned approaches, some e%orts have been made to estimate urban
GHG emissions at city level20,21. Globally, some of the more noteworthy developments have been city-level emis-
sion datasets using data compiled by organizations such as the Carbon Disclosure Project (CDP: https://www.
cdp.net/en) and the Carbon Climate Registry (https://carbonn.org/)22 and the China Emission Accounts and
Datasets (CEADs: https://www.ceads.net/). However, some scholars have expressed major concerns about the
reliability of urban emission datasets that are based on data self-reported from city governments and other
similar organizations, as studies have found that such inventories sometimes omit certain fuels and types of
emissions, while they estimate transport-related emissions di%erently23.
Households, and their consumption, pose quite severe complications for city-level GHG accounting, and are
rarely if ever included in city-level GHG emission datasets such as the ones mentioned above. &is is because
household emissions are very diverse, ranging from emissions associated with direct fuel use for household
heating, cooking, and transport (i.e. direct emissions), to emissions embodied to the multitude of goods (e.g.
food, durable goods) and services (e.g. entertainment) (i.e. indirect emissions) that urban household con-
sumes24–26. Top-down approaches such as the ones mentioned above cannot be mobilized e%ectively to cap-
ture household-level emissions considering the signi"cant component of indirect emissions, while downscaling
approaches become particularly uncertain due to the large national heterogeneities27,28.
Methodologically, recent developments in the "eld of environmental extended input-output modelling have
been increasingly used to estimate the indirect emission component of household consumption in countries
such as China5,7,26, India24,29, and Japan13,30,31. &is emerging literature has explored diverse topics such as the
aggregate emissions of households32–34, single-city case studies35–37, multi-city case studies38,39, or even speci"c
phenomena such as the inequality of GHG emissions across houseolds40,41. However, such techniques require
the careful combination and cross-mapping of consumption inventories and input-output tables in order to
provide reliable estimates of indirect emissions. Although the consumption inventories usually de"ne the level
of detail of the matching process, input-output tables tend to contain fewer sectors than inventories.
&e above suggests that city-level GHG emission accounting is still not well developed and lags behind
national-level accounting. &is is due to various issues associated with resource intensiveness, and limited access
to comprehensiveness and veri"able data, especially related to indirect household emissions. Currently, there is a
scarcity of multi-city databases that quantify both direct and indirect emissions in a comprehensive and consist-
ent quality manner. Apart from the methodological constraints outlined above, this is compounded by the very
diverse capability of di%erent cities to collect data.
Against this background, this Data Descriptor provides a comprehensive and consistent database of the
direct and indirect emissions of urban households in the major Japanese cities. &e database created contains
several hundred emission categories for the period January 2011 to December 2015 based on the monthly con-
sumer expenditure survey and input-output lifecycle inventory. To our knowledge, both are the "nest-scale open
data currently available in Japan cognizant of the timespan, number of commodities, and free availability. (see
Limitations and Acknowledgement). &e proposed Data Descriptor is therefore the most comprehensive data-
base of urban household emissions for Japan (i.e. 12,384 records and 1,538,136 records for direct and indirect
emissions, respectively).
Methods
Dataset scope. &e dataset outlined in this Data Descriptor contains the monthly direct and indirect emis-
sions of urban households in major Japanese cities for the period between 2011 and 2015 (Fig. 1). &e basis of
the dataset is the monthly “Family Income and Expenditure Survey” (FIES) collected by the Statistics Bureau of
Japan42. &is survey quanti"es in a consistent manner the expenditures of Japanese households for around 500
distinct categories of goods and services. &e consumption data is coupled with the 2011 and 2015 databases of
the Embodied Energy and Emission Intensity Data for Japan Using Input-Output Tables (3EID)43–46.
&is results in a unique dataset that quanti"es consistently over time and across di%erent cities the direct
and indirect GHG emissions of urban households. &e direct emissions are due to the direct combustion of
fossil fuels in housing, transport, and other activities (see “Direct Emissions” below) (Fig. 1). &e indirect emis-
sions re$ect the emissions embodied in approximately 500 categories of goods and services consumed by urban
households (see “Indirect Emissions”) (Fig. 2).
&e spatial scope of the dataset is the major Japanese cities: 51 major cities (2011 and 2012 data), or 52
major cities (2013, 2014, and 2015 data). &ese cities encompass all of the capitals of the 47 prefectures, as
well as four major cities that are not prefectural capitals, namely Kawasaki (Kanagawa Prefecture), Sagamihara
(Kanagawa Prefecture, from 2013 to 2015 data), Hamamatsu (Shizuoka Prefecture), Sakai (Osaka Prefecture),
and Kitakyushu (Fukuoka Prefecture).
&e "les of the di%erent annual FIES are "rst processed in order to extract the relevant parts of data for both
direct and indirect emission calculation. &e following sub-sections include more information about the direct
and indirect emissions included in the dataset, as well as its limitations.
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Direct emissions. &e fossil fuels associated with direct household emissions are gasoline, kerosene, liq-
ue"ed petroleum gas (LPG), and city gas. As outlined in more detail below we convert the monthly household
expenditures for these fuels elicited from the FIES, to g-CO2e. In summary, for each fuel, the household expendi-
tures extracted from the FIES are "rst converted into corresponding mass or volume using retail fuel prices (see
below). Fuel volume or mass is then converted into g-CO2e by multiplying with respective emission coe(cients
(see below).
Weekly retail prices for gasoline and kerosene come from a weekly survey conducted by the Ministry of
Economy, Trade and Industry of Japan on retail prices at "lling stations47. Monthly prices for gasoline and ker-
osene are obtained by averaging the prices of the weeks within each month. For kerosene, we adopt prices for
on-site purchase, as the 2006 survey of kerosene and LPG consumption48 indicates that on-site purchases are
be the principal means through which households purchase kerosene in Japan. For LPG, prices come from the
monthly survey of retail prices conducted by the Oil Information Center, at the Institute of Energy Economics,
Japan49. &e LPG prices for the cities across the 47 prefectures are recorded as the overall prices of the corre-
sponding geographical regions. As LPG retail prices are recorded in a stepwise manner for volumes (at 5 m3,
10 m3, 20 m3 and 50 m3), prefectural unit prices of LPG are set as the per unit price at one of the four recorded
volumes just greater than the average volume purchased per month. Due to the lack of actual consumption data
to distinguish the stepwise price window, we de"ne this price window spatially using the most recent consump-
tion data on LPG consumption in each of the 47 prefectures50. For instance, the average monthly consumption
of LPG is 12.6 m3 per household in Chiba Prefecture. &erefore, the per cubic meter price of LPG (JPY/m3)
purchased at 20 m3 is set to correspond to the per unit price for Chiba, as 20 m3 is the volume gradient that is
Fig. 1 Structure of the dataset.
Fig. 2 Average monthly direct and indirect emissions by city and year.
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immediately greater than 12.6 m3. Prices for city gas for the period 2011–2014 are captured by the Japanese
Government Statistics51. As there is no data for the 2015 city gas price from the same data source we convert
2014 prices using Japan’s Consumer Price Index52.
Emission coefficients of gasoline, kerosene, LPG and city gas are provided by Japan’s Ministry of the
Environment53. As LPG’s emission coe(cient in the dataset is expressed with the unit tCO2/ton while LPG
retail prices are expressed with the unit JPY/m3, a conversion is performed to convert the weight of liquid LPG
(ton) into the volume of gaseous LPG (m3) using the conversion coe(cient recommended by Ministry of the
Environment54.
Indirect emissions. &e estimation of the indirect emissions embodied in the goods and services consumed
by the household sector requires a cross-mapping and matching of the emission categories (and their intensities)
from the 3EID dataset with the consumption categories of the FIES dataset.
&e calculation of the indirect carbon emission intensity (Ei) in the 3EID model is as follows:
!
!
= − − −D I I M A
E
E
E
( ( ) )
(1)
k
n
1
1
where D is the direct emission matrix, I is the unit matrix, =Amn
x
x
mn
m
represents the output of industry m
required to produce one unit of output from industry n, and M is a diagonal matrix representing the import
portion of the direct requirement coe(cient. Due to its structure, the 3EID considers only domestic production
(see “Limitations and Acknowledgements”). Further details of the input-output table and applications can be
found elsewhere10,37,55–60.
As the classi"cation of industries in the 3EID database di%ers from the classi"cation of consumption ele-
ments in the FIES expenditure data, we matched the data following the general approach outlined elsewhere33,
and outlined for the year 2015 in one of the supporting documents (see “Data Records”). In its emission intensity
dataset, the 2011 3EID contains 395 items and the 2015 3EID contains 390 items. When cross-mapping the 3EID
datasets with the corresponding FIES datasets, we end up with an emission inventory of 2011 to 2014 have 495
items for the period 2011–2014, and 512 items for the year 2015.
Even though the base data for the goods and services consumed by households that fall under indirect
emissions are generated for each month between January 2011 and December 2015 under the FIES data (see
“Dataset scope”), the indirect emission intensities relevant for each of these indirect emission categories a!er
cross-mapping (see above) are generated only for the 2011 and 2015 input-output tables. &is is because the
3EID databases that are used for the emission intensities are released every "ve years, and are thus only available
for the years 2011 and 2015. To estimate the indirect emission intensities for each study items for the years 2012,
2013, and 2014 we use linear interpolation. &erefore, the values are obtained via an interpolation method as
follows:
= +
= +
= +
E E E
E E E
E E E
3
4
1
4
1
2
1
2
1
4
3
4 (2)
i i i
i i i
i i i
2012 2011 2015
2013 2011 2015
2014 2011 2015
where Eij indicates the carbon emission intensity of i in the year of j. Ei2011 and Ei2015 are generated from
3EID43,44, which respectively applied the 2011 and 2015 Japan input-output table.
It should be noted that electricity is treated as an indirect emission, as there is no direct emission upon its
consumption (in contrast to the fossil fuels discussed in the previous section). Although the di%erent cities
contained in this dataset are supported from di%erent electricity companies, we employ the national standard
electricity intensity generated through the input-output tables.
It is worth noting that there is not always a perfect matching between (a) FIES and 3EID categories, and
(b) within 2011 and 2015 3EID categories. &ese items are matched based on similarities in their properties.
For (a) some 3EID categories such as waste management are not distinct household components in FIES. For
this reason, they have been linked to relevant services items such as municipal services. However, some of the
FIES miscellaneous expenses that are not expected to have indirect emissions such as allowances, grants for
religious services, and donations have been omitted. For (b), some examples include categories in the 2011 3EID
such as “small dried sardines”, “sewing machines” and “cloth tailoring” that do not have a perfect match in the
2015 3EID, and have been thus matched in similar categories such as “other salted food”, “consumer electrical
equipment” and “other personal services” in 2015 3EID, respectively. &is logic is also used when interpolating
between years.
Limitations and Acknowledgements. First, the FIES that is the base of the dataset used in this Data
Descriptor does not cover single-person households. Single-person households are very prevalent in Japan42,
and have very distinct consumption patterns, which o!en lead to higher per capita emissions in Japan61. &is
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means that there is possibly an underestimation of the "ndings outlined in the “Technical Validation” section.
Considering the comparatively high prevalence of single-person households in Japan62, there should be some
caution or acknowledgement when using this dataset. It should be noted that similar to the FIES, the National
Survey of Family Income and Expenditure (NSFIE)63 records the monthly consumption expenditures per house-
hold. Although the NSFIE covers a larger household sample than FIES (including single-person households), it
does not record the consumption expenditures continuously. In particular the expenditures are based on a survey
covering September to November for two- or more-person households, and one in November for single-person
households. Furthermore, it is not conducted every year but every "ve years. Although this dataset has been
utilized for household emissions at the city64,65, the fact remains that it has a lower periodicity and is not available
publicly.
Second, the 3EID is an emission inventory generated through the Japanese single-regional input-output
(SRIO) table. &is means that the emission intensities used in this study re$ect only domestic goods and ser-
vices. By applying domestic emission intensities for imported goods, inserts uncertainty to the dataset, which is
to a large degree unavoidable considering the lack of options to create a "ne-grained dataset that also includes
international emission intensities (see below). In particular, we select the 3EID, rather than a multi-regional
input-output (MRIO) because of its higher sectoral resolution. In more detail, the 3EID has a much higher sec-
toral resolution (390 sectors), which brings it closer to the structure of the FIES that contains 500 consumption
categories. &is is a much more extensive coverage compared with other MRIOs such as WIOD (56 sectors)
and EXIOBASE (200 sectors). &is inability to consider properly emission intensities for imported goods and
services might underestimate the actual GHG emissions for some consumption categories, as imported goods
tend to have longer value chains, and thus higher GHG emissions when compared to similar domestic goods24.
Regarding the regional heterogeneity, using a domestic subnational MRIO66 would be more preferable than
3EID cognizant of domestic regional heterogeneity65. However, there are no MRIOs covering recent time-series
(e.g. 2011–2015) available yet.
&ird, the latest available version of the 3EID input-output table covers emissions until the year 2015. &us
the dataset included in this Data Descriptor covers the period until December 2015. Furthermore, due to the
use of city-scale statistics, it is not possible to disaggregate the dataset by income level or age group, which would
have increased the explanatory power of the dataset.
Data Records
&e dataset contains monthly direct and indirect GHG emissions for 51–52 Japanese cities from 2011 to 2015.
&e direct emissions are recorded as Natural Gas, Gasoline, LPG, and Kerosene. &e emissions are expressed
in “per capita” terms. Overall, there is a total of 1,555,512 items, which include 1,543,128 items for indirect
emissions and 12,384 items for direct emissions. Table 1 o%ers a summary of the data items for each study year.
&e entire dataset is made public in Figshare, and is named “Monthly direct and indirect greenhouse gases
emissions from household consumption in Japanese cities”67. It consists of 17 excel files (Table 2), which
are explained in more detail below. For each study year, the data is included in two excel "les, one for direct
emissions (labeled as: “direct_20XX depending on the year) and one for indirect emissions (labeled as: “indi-
rect_20XX depending on the year). &us, the dataset spans a total of 10 excel "les (Files 1–10) (Unit: g-CO2)
&e "les for direct emissions, apart from the emission dataset itself, also contain the unit prices for direct energy
consumption for each city for each month, provided by a separate "le named Direct Emission Intensity.xlsx
(Files 11–14). Here, direct emission intensity is provided in g-CO2/JPY, to facilitate future calculations on direct
emission calculation at the city-scale (Files 11–14). Next, for reference (Files 15–16) we provide two excel "les
highlighting the cross-mapping of FIES and 3EID for the year 2015 (named Mapping.xlsx) and sector details
(named FIES_items_Eng_2011-15.xlsx). Last, the household size information (File 17), includes total household
size, children under 18 years old, aging above 65 years old, are given by a separate "le named Household size
information.xlsx.
Technical Validation
Figure 2 shows the monthly average emissions of the cities included in this dataset for the period between 2011
and 2015. &e results show that indirect emissions per capita are much higher than direct emissions per capita,
accounting for 81.2% of total emissions. Naha and Sapporo are the lowest and highest per capita emitting cities,
respectively. Lower per capita emitting cities are generally located in western Japan and Kyushu (e.g. Osaka,
Fukuoka, Kumamoto, Nagasaki, Miyazaki and Naha). On the contrary, the higher per capita emitting cities are
located in northeastern Japan (e.g. Sapporo and Akita). &e cities in northeastern Japan have consistently higher
direct and indirect emissions per capita compared to other cities in the country.
Year Cities Months
Indirect emission
items
Direct emission
items
Indirect emission
data records
Direct emission
data records
Total data
records
2011 51 12 495 4 302,940 2,448 305,388
2012 51 12 495 4 302,940 2,448 305,388
2013 52 12 495 4 302,940 2,496 311,376
2014 52 12 495 4 308,880 2,496 311,376
2015 52 12 512 4 319,488 2,496 321,984
Table 1. Data records for each study year.
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Figure 3 shows the monthly emission variation, averaged across cities, between 2011 and 2015. In particular,
Fig. 3(a), shows the total emission averaged for all cities across years, and suggests higher total emissions for
colder months such as December, January, and February. &is trend is visible for both direct and indirect emis-
sions, Fig. 3(b,c). However, two emission peaks are visible in March and August, which are mostly generated by
indirect emissions. Figure 4 provides a simple break-down of indirect emissions averaged across 512 consump-
tion elements for the year 2015. Approximately, 50.2% of total indirect emissions is due to electricity and other
utilities, followed by food (19.8%). When disaggregating the food-related indirect emissions, emission from
eating accounts for 16% of the total food-related emission, followed by processed food (15%), meat (14%), and
cereals (13%) (Fig. 4).
Although there is no directly comparable dataset for validation purposes, we see a good correspondence
of the direct emissions estimated through our dataset, with the relevant constituents of the Greenhouse Gas
Inventory O(ce of Japan (GIO), at the National Institute for Environmental Studies (NIES) (Fig. 5). In Fig. 5
the pink shadow area shows the maximum and minimum value of our dataset (i.e. highest- and lowest-emitting
cities), which falls within the average national estimates of GIO for direct emissions.
File number File name File content
1 indirect_2011 Indirect emission for 2011
2 indirect_2012 Indirect emission for 2012
3 indirect_2013 Indirect emission for 2013
4 indirect_2014 Indirect emission for 2014
5 indirect_2015 Indirect emission for 2015
6 direct_2011 Direct emission for 2011
7 direct_2012 Direct emission for 2012
8 direct_2013 Direct emission for 2013
9 direct_2014 Direct emission for 2014
10 direct_2015 Direct emission for 2015
11 city_gas_intensity Direct emission intensity for city gas for 2011–2015
12 gasoline_intensity Direct emission intensity for gasoline for 2011–2015
13 kerosene_intensity Direct emission intensity for kerosene for 2011–2015
14 lpg_intensity Direct emission intensity for LPG for 2011–2015
15 Mapping Cross-mapping of data items between FIES and 3EID dataset
16 FIES_items_Eng_2011–15 Names of FIES items
17 Household size information Household information in terms of household member
Table 2. Description of dataset "les.
Fig. 3 Monthly variation of GHG emissions averaged across cities.
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To note, for this validation we only use the direct emissions component of our dataset, since the indirect
emissions are not available in any other currently available o(cial statistics or other studies for validation pur-
poses. However, we have to note that the indirect emission intensities in 3EID vary little over short timescales,
such as the ones in this Data Descriptor (period between 2011–2015). For example, the emission intensities of
rice in 2011 and 2015 are 8.1t-CO2e/M-JPY and 7.9 t-CO2e/M-JPY, respectively. &erefore, the variance is only
2.65% in a "ve-year period. Furthermore, the emission intensities for some durable goods changes even less, as
production processes do not change dramatically. &us we expect that the interpolation will not introduce major
uncertainties in the calculation of indirect emissions for the years 2012, 2013 and 2014.
&e authors of this Data Descriptor have used portions of this dataset to estimate the emissions of Japanese
households for the years 2005 and 201133,68. &ese previous studies have estimated for 2011 total emissions that
range between 3.41–5.00 t-CO2e/cap, which are in accordance with the 2011 emission estimated through the
current Data Descriptor. Total emission range between 2.98–4.39 t-CO2e/cap across 2011 to 2015, with cities’
average emission being 3.76, 3.86, 3.87, 3.96, and 3.85 t-CO2e/cap for each study year.
Usage Notes
As cities have emerged as major actors in climate mitigation e%orts in the past decades, there have been multi-
ple initiatives to both estimate and quantify the contribution of cities to national and/or global emissions (see
Background and Summary), as well as to develop city-level climate change mitigation strategies. For exam-
ple, the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report pointed that progressive
Fig. 4 Breakdown of indirect emissions for 2015 across 512 consumption categories.
Fig. 5 Comparison of direct emissions with GIO data.
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cities across the world have demonstrated signi"cant political leadership by initiating meaningful strategies and
actions to tackle climate change69. In this context the Urban Carbon Footprint (UCF) has been recognized as
one of the more useful methodological options to inform decision-makers about environmental sustainability,
both within and beyond city limits70.
&at said, and mindful of its limitations (see Methods), this dataset can provide a very useful resource to
urban researchers interested in analyzing di%erent aspects of UCFs in a temporal and spatially di%erentiated
manner. &e data structure (i.e. direct and indirect emissions) and the method used to develop the emissions
factors in this Data Descriptor have been discussed (and to some degree used) in previous UCF studies33,68. In
this sense the method is rather universal in its approach.
However, what sets this Data Descriptor apart is the quality and comprehensiveness of the underlying data,
both in relation to urban consumption (FIES data), as well as the emission intensity factors (3EID data). &us,
this dataset can allow the exploration of consumption patterns in a very disaggregated manner (>500 consump-
tion items) and over di%erent periods of time (i.e. monthly, annually). Due to data limitations at the urban scale,
few studies have managed to calculate city-level household emission inventories in such a comprehensive man-
ner. As outlined below, this dataset can appeal to researchers globally, as well as practitioners and policy-makers
in the covered cities, and Japan more broadly.
In terms of research, some possible applications could be to identify, among others, (a) di%erences in the
UCFs of cities24,71, (b) di%erences in consumption structure21, (c) di%erences in drivers of emissions by month,
year, or city33,72, or (d) di%erences in emissions by income26,73, education74,75, or age11,31,76. Beyond city-level pat-
terns, the dataset can be used to understand broader phenomena related to the environmental impacts of urban
consumption. For example, the indirect emission component could be connected with other datasets focusing
on speci"c demand to understand better emerging topics in urban studies such as urban tele-connections77,78,
the transboundary environmental impacts of cities79, or inequalities in emissions26,80. Ideally this dataset can
become an input to ongoing and future global reports on urban carbon mitigation, such as the reports prepared
by the IPCC’s Working Group III.
In terms of policy and practice, the granular and location-speci"c data for various constituents of consump-
tion can be used to identify potential priority areas for GHG emission reduction and facilitate better-informed
and evidence-based mitigation actions by policy-makers in the covered cities. For example in some cities in
northern Japan such as Sapporo and Aomori, kerosene consumption accounts for a large percentage of the
direct GHG emissions. &is suggests that the electri"cation of heating could be an important GHG mitigation
measure33,68. By providing the emission inventories for each month, it is possible to facilitate the understanding
of seasonal emission patterns, providing an even "ner print of household emissions, and the development of
decarbonization measures through behavioral change. Furthermore, this dataset can be used to explore di%er-
entiated emission pro"les across households with di%erent characteristics such as income, age or education.
By identifying better the di%erent emission pro"les of such groups it can help city governments create more
nuanced and targeted measures to a%ect consumption and emission behavior across di%erent types of house-
holds or investigate decarbonization scenarios in a more nuanced way. &e above could inform the generation of
good practices for how to use such high-resolution data to track household carbon footprints according to daily
consumption behaviors, which can possibly be applied in other urban contexts around the world.
Code availability
No code was used in the generation of the data.
Received: 11 March 2021; Accepted: 1 November 2021;
Published: xx xx xxxx
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Acknowledgements
&is research is supported by the UK Natural Environment Research Council (NE/P019900/1) and the National
Natural Science Foundation of China (41921005). Alexandros Gasparatos acknowledges support from the Asia-
Paci"c Network for Global Change Research (APN) (Project Reference: CRRP2017-01MY). Ayyoob Shari"
was supported by JSPS KAKENHI Grant Number 19K20497. Keiichiro Kanemoto is supported by the Research
Institute for Humanity and Nature (project no. 14200135).
Author contributions
Y.L., A.G. and D.G. designed the dataset; Y.L. and Y.J. collated the data. Y.L., A.G. and A.S. wrote the manuscript
with inputs from K.K., Y.S., P.C. and Y.W. D.G. and Y.Y. supervised the research.
Competing interests
&e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to Y.L. or A.G.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
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Monthly direct and indirect
greenhouse gases emissions
from household consumption in
the major Japanese cities
Yin Long 1 ✉, Yida Jiang2, Peipei Chen3, Yoshikuni Yoshida1, Ayyoob Sharifi 4,
Alexandros Gasparatos 5,6 ✉, Yi Wu 3, Keiichiro Kanemoto 7, Yosuke Shigetomi8 &
Dabo Guan 3,9
Urban household consumption contributes substantially to global greenhouse gases (GHGs) emissions.
Urban household emissions encompass both direct and indirect emissions, with the former associated
with the direct use of fossil fuels and the latter with the emissions embodied in the consumed goods
and services. However, there is a lack of consistent and comprehensive datasets outlining in great
detail emissions from urban household consumption. To bridge this data gap, we construct an emission
inventory of urban household emissions for 52 major cities in Japan that covers around 500 emission
categories. The dataset spans from January 2011 to December 2015 and contains 12,384 data records
for direct emissions and 1,543,128 records for indirect emissions. Direct emission intensity is provided in
g-CO2/JPY to facilitate both future studies of household emission in Japan, as well as act as a reference
for the development of detailed household emission inventories in other countries.
Background & Summary
Cities currently account for the bulk of the ever-increasing greenhouse gas (GHG) emissions globally1, with
large variability between countries and regions2. Further to the increasing levels of GHG emissions from direct
energy use in cities, urban household consumption has emerged as an even greater (and o!en hidden) source
of emissions. For example, the GHG emissions embodied in household consumption can account for as much
as 70–80% of the total "nal emissions for the world’s three largest (and highly urbanized) economies, namely
USA3,4, China5–9, and Japan10,11. Furthermore, studies have pointed that urban household consumption con-
tributes to more than 70% of GHG emissions from cities5,12, accounting for a large fraction of total national
emissions13.
Reducing the carbon footprint of urban household consumption has been identi"ed as one of the most
promising climate change mitigation strategies. For example, in some developed countries such as Japan, it
could reduce nearly 40% of national GHG emission by 203014. However, estimating the GHG emissions of urban
households is rather complicated.
To start, the "eld of urban carbon accounting is still nascent. Major improvements would be needed before
obtaining more accurate and consistent results considering that cities are open systems that continuously
1Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan. 2Graduate
Program in Sustainability Science - Global Leadership Initiative, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa, Chiba, 277-8563, Japan. 3The Bartlett School of Sustainable Construction, University College London,
London, WC1E 7HB, UK. 4Graduate School of Humanities and Social Science. 1–31- Kagamiyama, Hiroshima
University, Higashi, Hiroshima, 739-8530, Japan. 5Institute for Future Initiatives, University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo, 113-8654, Japan. 6Institute for the Advanced Study of Sustainability (UNU-IAS), United Nations
University, 5-53- Jingumae, Shibuya-ku, Tokyo, 150-8925, Japan. 7Research Institute for Humanity and Nature, 457-4
Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047 Japan, Kyoto, Japan. 8Faculty of Environmental Science, Nagasaki
University, 1-14 Bunkyo-machi, Nagasaki, 852-8521, Japan, Nagasaki University, Nagasaki, Japan. 9Department
of Earth System Science, Tsinghua University, Beijing, 100084, China. ✉e-mail: longyinutokyo@gmail.com;
gasparatos@ifi.u-tokyo.ac.jp
DATA DESCRIPTOR
OPEN
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interact with their hinterlands15. Accounting for direct GHG emissions has traditionally been conducted at the
national level using energy use data or at the global level based on satellite observations. Several well-established
databases currently provide estimates of global emissions at somewhat granular spatial and temporal scales, e.g.
the Open‐source Data Inventory for Anthropogenic CO2 (ODIAC)16, and the Emission Database for Global
Atmospheric Research (EDGAR)17. Top-down approaches that employ atmospheric inversion techniques can
estimate urban GHG $uxes and concentrations18,19. Downscaling methods using proxies such as population
density and commercial activity have been used to estimate city-level GHG emissions using total global or
national emissions data available in the above-mentioned databases2.
Using combinations of the above-mentioned approaches, some e%orts have been made to estimate urban
GHG emissions at city level20,21. Globally, some of the more noteworthy developments have been city-level emis-
sion datasets using data compiled by organizations such as the Carbon Disclosure Project (CDP: https://www.
cdp.net/en) and the Carbon Climate Registry (https://carbonn.org/)22 and the China Emission Accounts and
Datasets (CEADs: https://www.ceads.net/). However, some scholars have expressed major concerns about the
reliability of urban emission datasets that are based on data self-reported from city governments and other
similar organizations, as studies have found that such inventories sometimes omit certain fuels and types of
emissions, while they estimate transport-related emissions di%erently23.
Households, and their consumption, pose quite severe complications for city-level GHG accounting, and are
rarely if ever included in city-level GHG emission datasets such as the ones mentioned above. &is is because
household emissions are very diverse, ranging from emissions associated with direct fuel use for household
heating, cooking, and transport (i.e. direct emissions), to emissions embodied to the multitude of goods (e.g.
food, durable goods) and services (e.g. entertainment) (i.e. indirect emissions) that urban household con-
sumes24–26. Top-down approaches such as the ones mentioned above cannot be mobilized e%ectively to cap-
ture household-level emissions considering the signi"cant component of indirect emissions, while downscaling
approaches become particularly uncertain due to the large national heterogeneities27,28.
Methodologically, recent developments in the "eld of environmental extended input-output modelling have
been increasingly used to estimate the indirect emission component of household consumption in countries
such as China5,7,26, India24,29, and Japan13,30,31. &is emerging literature has explored diverse topics such as the
aggregate emissions of households32–34, single-city case studies35–37, multi-city case studies38,39, or even speci"c
phenomena such as the inequality of GHG emissions across houseolds40,41. However, such techniques require
the careful combination and cross-mapping of consumption inventories and input-output tables in order to
provide reliable estimates of indirect emissions. Although the consumption inventories usually de"ne the level
of detail of the matching process, input-output tables tend to contain fewer sectors than inventories.
&e above suggests that city-level GHG emission accounting is still not well developed and lags behind
national-level accounting. &is is due to various issues associated with resource intensiveness, and limited access
to comprehensiveness and veri"able data, especially related to indirect household emissions. Currently, there is a
scarcity of multi-city databases that quantify both direct and indirect emissions in a comprehensive and consist-
ent quality manner. Apart from the methodological constraints outlined above, this is compounded by the very
diverse capability of di%erent cities to collect data.
Against this background, this Data Descriptor provides a comprehensive and consistent database of the
direct and indirect emissions of urban households in the major Japanese cities. &e database created contains
several hundred emission categories for the period January 2011 to December 2015 based on the monthly con-
sumer expenditure survey and input-output lifecycle inventory. To our knowledge, both are the "nest-scale open
data currently available in Japan cognizant of the timespan, number of commodities, and free availability. (see
Limitations and Acknowledgement). &e proposed Data Descriptor is therefore the most comprehensive data-
base of urban household emissions for Japan (i.e. 12,384 records and 1,538,136 records for direct and indirect
emissions, respectively).
Methods
Dataset scope. &e dataset outlined in this Data Descriptor contains the monthly direct and indirect emis-
sions of urban households in major Japanese cities for the period between 2011 and 2015 (Fig. 1). &e basis of
the dataset is the monthly “Family Income and Expenditure Survey” (FIES) collected by the Statistics Bureau of
Japan42. &is survey quanti"es in a consistent manner the expenditures of Japanese households for around 500
distinct categories of goods and services. &e consumption data is coupled with the 2011 and 2015 databases of
the Embodied Energy and Emission Intensity Data for Japan Using Input-Output Tables (3EID)43–46.
&is results in a unique dataset that quanti"es consistently over time and across di%erent cities the direct
and indirect GHG emissions of urban households. &e direct emissions are due to the direct combustion of
fossil fuels in housing, transport, and other activities (see “Direct Emissions” below) (Fig. 1). &e indirect emis-
sions re$ect the emissions embodied in approximately 500 categories of goods and services consumed by urban
households (see “Indirect Emissions”) (Fig. 2).
&e spatial scope of the dataset is the major Japanese cities: 51 major cities (2011 and 2012 data), or 52
major cities (2013, 2014, and 2015 data). &ese cities encompass all of the capitals of the 47 prefectures, as
well as four major cities that are not prefectural capitals, namely Kawasaki (Kanagawa Prefecture), Sagamihara
(Kanagawa Prefecture, from 2013 to 2015 data), Hamamatsu (Shizuoka Prefecture), Sakai (Osaka Prefecture),
and Kitakyushu (Fukuoka Prefecture).
&e "les of the di%erent annual FIES are "rst processed in order to extract the relevant parts of data for both
direct and indirect emission calculation. &e following sub-sections include more information about the direct
and indirect emissions included in the dataset, as well as its limitations.
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Direct emissions. &e fossil fuels associated with direct household emissions are gasoline, kerosene, liq-
ue"ed petroleum gas (LPG), and city gas. As outlined in more detail below we convert the monthly household
expenditures for these fuels elicited from the FIES, to g-CO2e. In summary, for each fuel, the household expendi-
tures extracted from the FIES are "rst converted into corresponding mass or volume using retail fuel prices (see
below). Fuel volume or mass is then converted into g-CO2e by multiplying with respective emission coe(cients
(see below).
Weekly retail prices for gasoline and kerosene come from a weekly survey conducted by the Ministry of
Economy, Trade and Industry of Japan on retail prices at "lling stations47. Monthly prices for gasoline and ker-
osene are obtained by averaging the prices of the weeks within each month. For kerosene, we adopt prices for
on-site purchase, as the 2006 survey of kerosene and LPG consumption48 indicates that on-site purchases are
be the principal means through which households purchase kerosene in Japan. For LPG, prices come from the
monthly survey of retail prices conducted by the Oil Information Center, at the Institute of Energy Economics,
Japan49. &e LPG prices for the cities across the 47 prefectures are recorded as the overall prices of the corre-
sponding geographical regions. As LPG retail prices are recorded in a stepwise manner for volumes (at 5 m3,
10 m3, 20 m3 and 50 m3), prefectural unit prices of LPG are set as the per unit price at one of the four recorded
volumes just greater than the average volume purchased per month. Due to the lack of actual consumption data
to distinguish the stepwise price window, we de"ne this price window spatially using the most recent consump-
tion data on LPG consumption in each of the 47 prefectures50. For instance, the average monthly consumption
of LPG is 12.6 m3 per household in Chiba Prefecture. &erefore, the per cubic meter price of LPG (JPY/m3)
purchased at 20 m3 is set to correspond to the per unit price for Chiba, as 20 m3 is the volume gradient that is
Fig. 1 Structure of the dataset.
Fig. 2 Average monthly direct and indirect emissions by city and year.
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immediately greater than 12.6 m3. Prices for city gas for the period 2011–2014 are captured by the Japanese
Government Statistics51. As there is no data for the 2015 city gas price from the same data source we convert
2014 prices using Japan’s Consumer Price Index52.
Emission coefficients of gasoline, kerosene, LPG and city gas are provided by Japan’s Ministry of the
Environment53. As LPG’s emission coe(cient in the dataset is expressed with the unit tCO2/ton while LPG
retail prices are expressed with the unit JPY/m3, a conversion is performed to convert the weight of liquid LPG
(ton) into the volume of gaseous LPG (m3) using the conversion coe(cient recommended by Ministry of the
Environment54.
Indirect emissions. &e estimation of the indirect emissions embodied in the goods and services consumed
by the household sector requires a cross-mapping and matching of the emission categories (and their intensities)
from the 3EID dataset with the consumption categories of the FIES dataset.
&e calculation of the indirect carbon emission intensity (Ei) in the 3EID model is as follows:
!
!
= − − −D I I M A
E
E
E
( ( ) )
(1)
k
n
1
1
where D is the direct emission matrix, I is the unit matrix, =Amn
x
x
mn
m
represents the output of industry m
required to produce one unit of output from industry n, and M is a diagonal matrix representing the import
portion of the direct requirement coe(cient. Due to its structure, the 3EID considers only domestic production
(see “Limitations and Acknowledgements”). Further details of the input-output table and applications can be
found elsewhere10,37,55–60.
As the classi"cation of industries in the 3EID database di%ers from the classi"cation of consumption ele-
ments in the FIES expenditure data, we matched the data following the general approach outlined elsewhere33,
and outlined for the year 2015 in one of the supporting documents (see “Data Records”). In its emission intensity
dataset, the 2011 3EID contains 395 items and the 2015 3EID contains 390 items. When cross-mapping the 3EID
datasets with the corresponding FIES datasets, we end up with an emission inventory of 2011 to 2014 have 495
items for the period 2011–2014, and 512 items for the year 2015.
Even though the base data for the goods and services consumed by households that fall under indirect
emissions are generated for each month between January 2011 and December 2015 under the FIES data (see
“Dataset scope”), the indirect emission intensities relevant for each of these indirect emission categories a!er
cross-mapping (see above) are generated only for the 2011 and 2015 input-output tables. &is is because the
3EID databases that are used for the emission intensities are released every "ve years, and are thus only available
for the years 2011 and 2015. To estimate the indirect emission intensities for each study items for the years 2012,
2013, and 2014 we use linear interpolation. &erefore, the values are obtained via an interpolation method as
follows:
= +
= +
= +
E E E
E E E
E E E
3
4
1
4
1
2
1
2
1
4
3
4 (2)
i i i
i i i
i i i
2012 2011 2015
2013 2011 2015
2014 2011 2015
where Eij indicates the carbon emission intensity of i in the year of j. Ei2011 and Ei2015 are generated from
3EID43,44, which respectively applied the 2011 and 2015 Japan input-output table.
It should be noted that electricity is treated as an indirect emission, as there is no direct emission upon its
consumption (in contrast to the fossil fuels discussed in the previous section). Although the di%erent cities
contained in this dataset are supported from di%erent electricity companies, we employ the national standard
electricity intensity generated through the input-output tables.
It is worth noting that there is not always a perfect matching between (a) FIES and 3EID categories, and
(b) within 2011 and 2015 3EID categories. &ese items are matched based on similarities in their properties.
For (a) some 3EID categories such as waste management are not distinct household components in FIES. For
this reason, they have been linked to relevant services items such as municipal services. However, some of the
FIES miscellaneous expenses that are not expected to have indirect emissions such as allowances, grants for
religious services, and donations have been omitted. For (b), some examples include categories in the 2011 3EID
such as “small dried sardines”, “sewing machines” and “cloth tailoring” that do not have a perfect match in the
2015 3EID, and have been thus matched in similar categories such as “other salted food”, “consumer electrical
equipment” and “other personal services” in 2015 3EID, respectively. &is logic is also used when interpolating
between years.
Limitations and Acknowledgements. First, the FIES that is the base of the dataset used in this Data
Descriptor does not cover single-person households. Single-person households are very prevalent in Japan42,
and have very distinct consumption patterns, which o!en lead to higher per capita emissions in Japan61. &is
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means that there is possibly an underestimation of the "ndings outlined in the “Technical Validation” section.
Considering the comparatively high prevalence of single-person households in Japan62, there should be some
caution or acknowledgement when using this dataset. It should be noted that similar to the FIES, the National
Survey of Family Income and Expenditure (NSFIE)63 records the monthly consumption expenditures per house-
hold. Although the NSFIE covers a larger household sample than FIES (including single-person households), it
does not record the consumption expenditures continuously. In particular the expenditures are based on a survey
covering September to November for two- or more-person households, and one in November for single-person
households. Furthermore, it is not conducted every year but every "ve years. Although this dataset has been
utilized for household emissions at the city64,65, the fact remains that it has a lower periodicity and is not available
publicly.
Second, the 3EID is an emission inventory generated through the Japanese single-regional input-output
(SRIO) table. &is means that the emission intensities used in this study re$ect only domestic goods and ser-
vices. By applying domestic emission intensities for imported goods, inserts uncertainty to the dataset, which is
to a large degree unavoidable considering the lack of options to create a "ne-grained dataset that also includes
international emission intensities (see below). In particular, we select the 3EID, rather than a multi-regional
input-output (MRIO) because of its higher sectoral resolution. In more detail, the 3EID has a much higher sec-
toral resolution (390 sectors), which brings it closer to the structure of the FIES that contains 500 consumption
categories. &is is a much more extensive coverage compared with other MRIOs such as WIOD (56 sectors)
and EXIOBASE (200 sectors). &is inability to consider properly emission intensities for imported goods and
services might underestimate the actual GHG emissions for some consumption categories, as imported goods
tend to have longer value chains, and thus higher GHG emissions when compared to similar domestic goods24.
Regarding the regional heterogeneity, using a domestic subnational MRIO66 would be more preferable than
3EID cognizant of domestic regional heterogeneity65. However, there are no MRIOs covering recent time-series
(e.g. 2011–2015) available yet.
&ird, the latest available version of the 3EID input-output table covers emissions until the year 2015. &us
the dataset included in this Data Descriptor covers the period until December 2015. Furthermore, due to the
use of city-scale statistics, it is not possible to disaggregate the dataset by income level or age group, which would
have increased the explanatory power of the dataset.
Data Records
&e dataset contains monthly direct and indirect GHG emissions for 51–52 Japanese cities from 2011 to 2015.
&e direct emissions are recorded as Natural Gas, Gasoline, LPG, and Kerosene. &e emissions are expressed
in “per capita” terms. Overall, there is a total of 1,555,512 items, which include 1,543,128 items for indirect
emissions and 12,384 items for direct emissions. Table 1 o%ers a summary of the data items for each study year.
&e entire dataset is made public in Figshare, and is named “Monthly direct and indirect greenhouse gases
emissions from household consumption in Japanese cities”67. It consists of 17 excel files (Table 2), which
are explained in more detail below. For each study year, the data is included in two excel "les, one for direct
emissions (labeled as: “direct_20XX depending on the year) and one for indirect emissions (labeled as: “indi-
rect_20XX depending on the year). &us, the dataset spans a total of 10 excel "les (Files 1–10) (Unit: g-CO2)
&e "les for direct emissions, apart from the emission dataset itself, also contain the unit prices for direct energy
consumption for each city for each month, provided by a separate "le named Direct Emission Intensity.xlsx
(Files 11–14). Here, direct emission intensity is provided in g-CO2/JPY, to facilitate future calculations on direct
emission calculation at the city-scale (Files 11–14). Next, for reference (Files 15–16) we provide two excel "les
highlighting the cross-mapping of FIES and 3EID for the year 2015 (named Mapping.xlsx) and sector details
(named FIES_items_Eng_2011-15.xlsx). Last, the household size information (File 17), includes total household
size, children under 18 years old, aging above 65 years old, are given by a separate "le named Household size
information.xlsx.
Technical Validation
Figure 2 shows the monthly average emissions of the cities included in this dataset for the period between 2011
and 2015. &e results show that indirect emissions per capita are much higher than direct emissions per capita,
accounting for 81.2% of total emissions. Naha and Sapporo are the lowest and highest per capita emitting cities,
respectively. Lower per capita emitting cities are generally located in western Japan and Kyushu (e.g. Osaka,
Fukuoka, Kumamoto, Nagasaki, Miyazaki and Naha). On the contrary, the higher per capita emitting cities are
located in northeastern Japan (e.g. Sapporo and Akita). &e cities in northeastern Japan have consistently higher
direct and indirect emissions per capita compared to other cities in the country.
Year Cities Months
Indirect emission
items
Direct emission
items
Indirect emission
data records
Direct emission
data records
Total data
records
2011 51 12 495 4 302,940 2,448 305,388
2012 51 12 495 4 302,940 2,448 305,388
2013 52 12 495 4 302,940 2,496 311,376
2014 52 12 495 4 308,880 2,496 311,376
2015 52 12 512 4 319,488 2,496 321,984
Table 1. Data records for each study year.
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Figure 3 shows the monthly emission variation, averaged across cities, between 2011 and 2015. In particular,
Fig. 3(a), shows the total emission averaged for all cities across years, and suggests higher total emissions for
colder months such as December, January, and February. &is trend is visible for both direct and indirect emis-
sions, Fig. 3(b,c). However, two emission peaks are visible in March and August, which are mostly generated by
indirect emissions. Figure 4 provides a simple break-down of indirect emissions averaged across 512 consump-
tion elements for the year 2015. Approximately, 50.2% of total indirect emissions is due to electricity and other
utilities, followed by food (19.8%). When disaggregating the food-related indirect emissions, emission from
eating accounts for 16% of the total food-related emission, followed by processed food (15%), meat (14%), and
cereals (13%) (Fig. 4).
Although there is no directly comparable dataset for validation purposes, we see a good correspondence
of the direct emissions estimated through our dataset, with the relevant constituents of the Greenhouse Gas
Inventory O(ce of Japan (GIO), at the National Institute for Environmental Studies (NIES) (Fig. 5). In Fig. 5
the pink shadow area shows the maximum and minimum value of our dataset (i.e. highest- and lowest-emitting
cities), which falls within the average national estimates of GIO for direct emissions.
File number File name File content
1 indirect_2011 Indirect emission for 2011
2 indirect_2012 Indirect emission for 2012
3 indirect_2013 Indirect emission for 2013
4 indirect_2014 Indirect emission for 2014
5 indirect_2015 Indirect emission for 2015
6 direct_2011 Direct emission for 2011
7 direct_2012 Direct emission for 2012
8 direct_2013 Direct emission for 2013
9 direct_2014 Direct emission for 2014
10 direct_2015 Direct emission for 2015
11 city_gas_intensity Direct emission intensity for city gas for 2011–2015
12 gasoline_intensity Direct emission intensity for gasoline for 2011–2015
13 kerosene_intensity Direct emission intensity for kerosene for 2011–2015
14 lpg_intensity Direct emission intensity for LPG for 2011–2015
15 Mapping Cross-mapping of data items between FIES and 3EID dataset
16 FIES_items_Eng_2011–15 Names of FIES items
17 Household size information Household information in terms of household member
Table 2. Description of dataset "les.
Fig. 3 Monthly variation of GHG emissions averaged across cities.
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To note, for this validation we only use the direct emissions component of our dataset, since the indirect
emissions are not available in any other currently available o(cial statistics or other studies for validation pur-
poses. However, we have to note that the indirect emission intensities in 3EID vary little over short timescales,
such as the ones in this Data Descriptor (period between 2011–2015). For example, the emission intensities of
rice in 2011 and 2015 are 8.1t-CO2e/M-JPY and 7.9 t-CO2e/M-JPY, respectively. &erefore, the variance is only
2.65% in a "ve-year period. Furthermore, the emission intensities for some durable goods changes even less, as
production processes do not change dramatically. &us we expect that the interpolation will not introduce major
uncertainties in the calculation of indirect emissions for the years 2012, 2013 and 2014.
&e authors of this Data Descriptor have used portions of this dataset to estimate the emissions of Japanese
households for the years 2005 and 201133,68. &ese previous studies have estimated for 2011 total emissions that
range between 3.41–5.00 t-CO2e/cap, which are in accordance with the 2011 emission estimated through the
current Data Descriptor. Total emission range between 2.98–4.39 t-CO2e/cap across 2011 to 2015, with cities’
average emission being 3.76, 3.86, 3.87, 3.96, and 3.85 t-CO2e/cap for each study year.
Usage Notes
As cities have emerged as major actors in climate mitigation e%orts in the past decades, there have been multi-
ple initiatives to both estimate and quantify the contribution of cities to national and/or global emissions (see
Background and Summary), as well as to develop city-level climate change mitigation strategies. For exam-
ple, the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report pointed that progressive
Fig. 4 Breakdown of indirect emissions for 2015 across 512 consumption categories.
Fig. 5 Comparison of direct emissions with GIO data.
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cities across the world have demonstrated signi"cant political leadership by initiating meaningful strategies and
actions to tackle climate change69. In this context the Urban Carbon Footprint (UCF) has been recognized as
one of the more useful methodological options to inform decision-makers about environmental sustainability,
both within and beyond city limits70.
&at said, and mindful of its limitations (see Methods), this dataset can provide a very useful resource to
urban researchers interested in analyzing di%erent aspects of UCFs in a temporal and spatially di%erentiated
manner. &e data structure (i.e. direct and indirect emissions) and the method used to develop the emissions
factors in this Data Descriptor have been discussed (and to some degree used) in previous UCF studies33,68. In
this sense the method is rather universal in its approach.
However, what sets this Data Descriptor apart is the quality and comprehensiveness of the underlying data,
both in relation to urban consumption (FIES data), as well as the emission intensity factors (3EID data). &us,
this dataset can allow the exploration of consumption patterns in a very disaggregated manner (>500 consump-
tion items) and over di%erent periods of time (i.e. monthly, annually). Due to data limitations at the urban scale,
few studies have managed to calculate city-level household emission inventories in such a comprehensive man-
ner. As outlined below, this dataset can appeal to researchers globally, as well as practitioners and policy-makers
in the covered cities, and Japan more broadly.
In terms of research, some possible applications could be to identify, among others, (a) di%erences in the
UCFs of cities24,71, (b) di%erences in consumption structure21, (c) di%erences in drivers of emissions by month,
year, or city33,72, or (d) di%erences in emissions by income26,73, education74,75, or age11,31,76. Beyond city-level pat-
terns, the dataset can be used to understand broader phenomena related to the environmental impacts of urban
consumption. For example, the indirect emission component could be connected with other datasets focusing
on speci"c demand to understand better emerging topics in urban studies such as urban tele-connections77,78,
the transboundary environmental impacts of cities79, or inequalities in emissions26,80. Ideally this dataset can
become an input to ongoing and future global reports on urban carbon mitigation, such as the reports prepared
by the IPCC’s Working Group III.
In terms of policy and practice, the granular and location-speci"c data for various constituents of consump-
tion can be used to identify potential priority areas for GHG emission reduction and facilitate better-informed
and evidence-based mitigation actions by policy-makers in the covered cities. For example in some cities in
northern Japan such as Sapporo and Aomori, kerosene consumption accounts for a large percentage of the
direct GHG emissions. &is suggests that the electri"cation of heating could be an important GHG mitigation
measure33,68. By providing the emission inventories for each month, it is possible to facilitate the understanding
of seasonal emission patterns, providing an even "ner print of household emissions, and the development of
decarbonization measures through behavioral change. Furthermore, this dataset can be used to explore di%er-
entiated emission pro"les across households with di%erent characteristics such as income, age or education.
By identifying better the di%erent emission pro"les of such groups it can help city governments create more
nuanced and targeted measures to a%ect consumption and emission behavior across di%erent types of house-
holds or investigate decarbonization scenarios in a more nuanced way. &e above could inform the generation of
good practices for how to use such high-resolution data to track household carbon footprints according to daily
consumption behaviors, which can possibly be applied in other urban contexts around the world.
Code availability
No code was used in the generation of the data.
Received: 11 March 2021; Accepted: 1 November 2021;
Published: xx xx xxxx
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Acknowledgements
&is research is supported by the UK Natural Environment Research Council (NE/P019900/1) and the National
Natural Science Foundation of China (41921005). Alexandros Gasparatos acknowledges support from the Asia-
Paci"c Network for Global Change Research (APN) (Project Reference: CRRP2017-01MY). Ayyoob Shari"
was supported by JSPS KAKENHI Grant Number 19K20497. Keiichiro Kanemoto is supported by the Research
Institute for Humanity and Nature (project no. 14200135).
Author contributions
Y.L., A.G. and D.G. designed the dataset; Y.L. and Y.J. collated the data. Y.L., A.G. and A.S. wrote the manuscript
with inputs from K.K., Y.S., P.C. and Y.W. D.G. and Y.Y. supervised the research.
Competing interests
&e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to Y.L. or A.G.
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