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ENVIRONMENTAL STUDIES
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License 4.0 (CC BY).
City-level climate change mitigation in China
Yuli Shan
1,2
, Dabo Guan
2,3
*, Klaus Hubacek
4,5,6
, Bo Zheng
3,7
, Steven J. Davis
3,8,9
*, Lichao Jia
10
,
Jianghua Liu
11
, Zhu Liu
2,3
, Neil Fromer
12
, Zhifu Mi
13
, Jing Meng
14
, Xiangzheng Deng
15,16
,
Yuan Li
2,17
*, Jintai Lin
18
, Heike Schroeder
2
, Helga Weisz
19,20
, Hans Joachim Schellnhuber
19,21
As national efforts to reduce CO
2
emissions intensify, policy-makers need increasingly specific, subnational
information about the sources of CO
2
and the potential reductions and economic implications of different possible
policies. This is particularly true in China, a large and econom
ically diverse country that has rapidly industrialized and
urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-
level estimates of CO
2
emissions for 182 Chinese cities, decomposed in
to 17 different fossil fuels, 46 socioeconomic
sectors, and 7 industrial processes. We find that more affl
uent cities have systematical
ly lower emissions per unit of
gross domestic product (GDP), supported by imports from less af
fluent, industrial cities located nearby. In turn, clusters
of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting
manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-
based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-
based analysis of each city suggests that technological improv
ements could be a practical and effective means of reducing
emissions while maintaining growth and
the current economic structure and energy system. We explore city-level emis-
sion reductions under three scenarios of technological prog
ress to show that substantial reductions (up to 31%) are pos-
sible by updating a disproportionately sm
all fraction of existing infrastructure.
INTRODUCTION
Under the Paris Agreement, China pledged that its emissions will peak
by 2030 and that it will decrease the carbon intensity [CO
2
emissions
per unit of gross domestic product (GDP)] of its economy by 60 to 65%
relative to 2005 levels (
1
). To fulfill these ambitious commitments in the
most cost-effective way, policy-makers seek to characterize the sources
of CO
2
in as much detail as possible and to assess the potential for emission
reductions and economic losses relat
ed to different policy approaches.
Urbanization is a major driver of economic growth in China, and
as elsewhere
cities produce most (85%) of China
sCO
2
emissions (
2
).
For this reason, China
s cities play an increasingly important role in its
efforts to reduce CO
2
emissions. For example, the newly launched
Emission Trading Scheme in 2017 intends to monitor and control
national CO
2
emissions and energy consumption at the city/firm level
as part of an emission peak by 2030. Cu
rrently, the Emission Trading
Scheme covers only the electricity sector owing to data accessibility. The
further success of the scheme will depend on accurate sector-level
accounts of city emissions, as well as the cooperation of city-level gov-
ernments where many officials are concerned about the economic im-
pacts of energy and emission constraints. However, in comparison to
national and provincial emissions, data to support city-level emission
inventories are generally less available and of lower quality.
Because of these data limitations, previous studies have focused on
megacities in developed countries for which energy data were consistent
and systematic, particularly cities in the United States. For example,
Ramaswami
et al.
(
3
) developed a hybrid life cycle
based methodology
for conducting city-scale greenhouse gas inventories, and their sub-
sequent studies (
4
) applied the method to eight U.S. cities. Kennedy
et al.
(
5
,
6
) also estimated the emissions of 10 megacities. Matese
et al.
(
7
)
estimated the CO
2
emissions of Florence, Italy, based on carbon flux ob-
servations. Later, attempts were also made to estimate the city-level
emissions in some developing countries. D
Almeida Martins and da
Costa Ferreira (
8
) analyzed emissions of two Brazil megacities: São
Paulo and Rio de Janeiro. Ali and Nitivattananon (
9
) discussed the
emissions from the city of Lahore (in Pakistan) from 1971 to 2010.
Only recently have studies begun t
o include large and rapidly grow-
ing Chinese cities (
10
,
11
), usually by adopting statistical downscaling
methods. For example, Creutzig
et al.
(
12
) built an energy/emission data
set of 274 global cities, assessing their aggregate potential for urban
climate change mitigation. Of the 274 cities, 37 are from China. How-
ever, the data in Creutzig
et al.
(
12
) were collected from multiple sources
of inconsistent statistical caliber and quality. Dhakal (
13
) similarly
estimated the emissions of provincial capital cities in China using pro-
vincial average energy intensity (energy per GDP energy consump-
tion) and GDP index, thus introducing large uncertainties. In contrast,
Wang
et al.
(
14
) analyzed the emissions from China
s provincial capital
cities based on the cities
statistical yearbooks. Ramaswami
et al.
(
15
)
1
School of Environment, Tsinghua University, Beijing 100084, China.
2
Water Security
Research Centre, Tyndall Centre for Climate Change Research, School of International
Development, University of East Anglia, Norwich NR4 7TJ, UK.
3
Department of Earth
System Science, Tsinghua University, Beijing 100080, China.
4
Department of Geograph-
ical Sciences, University of Maryland, College Park, MD 20742, USA.
5
Department of
Environmental Studies, Masryk University, Jo
š
tova 10, 602 00 Brno, Czech Republic.
6
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361
Laxenburg, Austria.
7
Laboratoire des Sciences du Climat et de l
Environnement,
CEA-CNRS-UVSQ, UMR8212, Gif-
sur-Yvette, Paris, France.
8
Department of Earth System
Science, University of California, Irvine, Irvine, CA 92697, USA.
9
Department of Civil and
Environmental Engineering, University of California, Irvine, Irvine, CA 92697, USA.
10
School of Materials Science and Engineering, State Key Lab of Material Processing
and Die and Mould Technology, Huazhong University of Science and Technology,
Wuhan, Hubei 430074, China.
11
Institute of Finance and Economics Research,
School of Urban and Regional Science, Shan
ghai University of Finance and Economics,
Shanghai 200433, China.
12
Resnick Sustainability Institute, California Institute of Tech-
nology, Pasadena, CA 911125, USA.
13
Bartlett School of Construction and Project Man-
agement, University College London, London WC1E 7HB, UK.
14
Department of Politics
and International Studies, University of Cambridge, Cambridge CB3 9DT, UK.
15
Institute
of Geographic Sciences and Natural Resource
s Research, Chinese Academy of Sciences,
Beijing 100101, China.
16
University of Chinese Academy of Sciences, Beijing 100049,
China.
17
College of Economics, Jinan Univ
ersity, Guangzhou 510632, China.
18
Labora-
tory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and
Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China.
19
Potsdam Institute for Climate Impact
Research, 14473 Potsdam, Germany.
20
Department of Cultural History and Theory and Department of Social Sciences,
Humboldt University of Berlin, Unter den Linden 6, 10117 Berlin, Germany.
21
Uni-
versity of Potsdam Stockholm Resilience Centre, Stockholm, Sweden.
*Corresponding author. Email: dabo.guan@uea.ac.uk (D.G.); sjdavis@uci.edu (S.J.D.);
y.li4@uea.ac.uk (Y.L.)
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further estimated emissions from 637 Chinese cities by downscaling
from national/provincial data (
16
).
Yet, each of these previous studies
assessed emissions using different
methods, scopes, and primary data (see Materials and Methods for
details), generating inconsistent results and preventing meaningful
comparisons across studies. For example, Parshall
et al.
(
17
)and
Markolf
et al.
(
18
) only estimated the scope 1 emissions, whereas
Ramaswami
et al.
(
3
) included both direct and indirect emissions and
Ramaswami
et al.
(
16
) estimated the scope 1 + scope 2 emissions for
cities. The scope 1 emissions refer to CO
2
emitted during energy com-
bustion or other human activities within a city boundary, while the
scope 2 emissions included CO
2
induced by imported electricity/heat
generation. Mi
et al.
(
19
) calculated the consumption-based (scope 3)
emissions of 13 Chinese cities using an input-output method. The
consumption-based emissions quantify the emissions embodied in
the consumption of final products.
Furthermore, most of the previous studies of city-level emissions
provided a single number for total emissions (
13
) or, alternatively,
included only emissions from selected key sectors. Thus, the sketchy
emission accounts of cities cannot support in-depth discussion of
city-level emission characteristics and of potential policies for emis-
sion reduction. In addition, emission sources reported in different
studies are disparate, increasing the uncertainties and discrepancies
across studies. For example, Ramaswami
et al.
(
3
)calculatedemissions
from
buildings and facilities
energy final consumption, transportation,
and embodied energy consumption of key urban materials.
On the
other hand, Kennedy
et al.
(
6
) calculated emissions from seven sectors:
electricity, heating and industrial fuels, ground transportation fuels,
aviation and marine transportation,
industrial processes, product use,
and waste.
In contrast, Wang
et al.
(
14
)calculatedcity-levelemissions
from
industries, transportation, household energy use, commerce, in-
dustrial processes, and waste.
Comprehensive and consistent inventories of city-level emis-
sions based on physical energy fl
ows are thus still badly needed,
including disaggregation of fossil fuel types and socioeconomic
sectors within cities
boundaries. Our study provided the CO
2
emission inventories for 182 Chinese cities by 17 fossil fuels and
46 socioeconomic sectors. We follow the Intergovernmental Panel
on Climate Change (IPCC) administ
rative territorial approach,
which is compatible and consistent with national and international
emission inventories. The 46 sectors are classified according to
China
s System of National Accounts (further details in Materials
and Methods). The sectors classification can also be mapped with other
countries/cities around the world (
20
). The result is a set of consistent
and directly comparable CO
2
emission inventories of cities that can pro-
vide robust and transparent data support for city-level emission con-
trol in China, as well as the nationwide Emission Trading Scheme.
The cities are defined as prefecture-level administrative units in
China (including both build-up city and administrative area). According
to the latest administrative planning report, there are currently 334
cities in China. We select 182 cities based on the data availability (see
Materials andMethods for datasource).The 182 case cities encompass
62% of China
spopulationaswellas77%ofitsGDPin2010.
We first classify cities into five g
roups according to their apparent
development pathway an
d then quantify the potential for emission re-
ductions among the city groups unde
r a range of technological scenar-
ios. Our results reveal the extent to which different policies may reduce
emissions while in large part maintai
ning the current industrial struc-
ture and energy mix of cities and thereby minimize economic impacts.
RESULTS
Emission inventories of 182 cities
Figure 1 shows the total CO
2
emissions of 182 cities, which are for
2010. Most of the 182 cities are located in the eastern half of the country
[that is, under the Aihui-Tengchong line, where more than 90% of
China
s population resides (
21
)]. In 2010, the 182 cities emitted 7610
million tons (Mt) of CO
2
in total. Industrial sectors make up the
largest share of emissions (6639 Mt of CO
2
or 87% of the cities
total),
especially power and heat producti
on, iron and steel production, and
nonmetal minerals (cement, glass, and ceramics). Nonindustrial sectors
make up the remaining 12% (971 Mt of CO
2
), with two-thirds of emis-
sions from farming and direct energy use in rural areas. The pie charts
in fig. S1 (A and B) show the sector mix of 182 cities
total CO
2
emis-
sions. Figure S1C shows that burning of coal is the source of 74%
[especially raw coal (57%) and coke (9%)] of the cities
total emissions,
with oil and natural gas combustion representing just 15 and 2%,
respectively, and the remaining 9% from industrial processes such
as cement production.
Population and socioeconomic development varies tremendously
among these cities: from 0.2 millio
n people living in Jiayuguan (Gansu
province, northwest China) to 28.7 in Chongqing (southwest) and
from per-capita GDP of just ¥9068 in Fuyang (Anhui province, east)
to ¥175,125 in Ordos (Inner Mongolia, north). Similarly, colors in
Fig. 1 indicate that the total CO
2
emissions of the 182 cities range
from <15 Mt (yellow) to >90 Mt (dark red; see also table S1). Higher
levels of emissions prevail in the north and east, and the top-emitting
cities represent a disproportionately large fraction of the total emis-
sions from the 182 cities. The top five emitting cities [Tangshan,
Shanghai, Suzhou (the one in Jiangsu province), Nanyang, and
Chongqing] accounted for 11% of the total in 2010. In addition, our
estimates of CO
2
emission intensity, calculated as total emissions
divided by GDP, range from <0.1 to >1.5 metric tons of CO
2
per ¥1000,
with a minimum of 0.04 and 0.05 metric tons of CO
2
per ¥1000 in
Shenzhen (Guangdong province, east coast) and Huangshan (Anhui
province, east), respectively; a maximum of 1.72 and 1.55 metric tons
of CO
2
per ¥1000 in Hegang (Heilongjiang province, northeast) and
Panzhihua (Sichuan province, southwest), respectively; and an average
of 0.22 metric tons of CO
2
per ¥1,000 (see table S1).
The detailed investigation of cities
emissions by sectors and fuels
helps in understanding the wide range in cities
carbon intensity: cities
such as Beijing and Shenzhen, whose emission intensities are just
0.07 and 0.04 metric tons of CO
2
per ¥1000, respectively, have small
manufacturing and energy sectors (20 and 44% of their GDP, respec-
tively) and larger service sectors (75 a
nd 53%, respectively). In contrast,
cities such as Maanshan, Tangshan, and Panzhihua have iron and steel
production as their
pillar
industries (21, 27, and 31% of their GDP,
respectively), with correspondingly high carbon intensities: 0.68, 0.43,
and 1.55 metric tons of CO
2
per ¥1000, respectively. Similarly, in cities
where energy production and mining of natural resources are the dom-
inant industries, such as Hegang, emission intensities a
re especially high
(1.72 metric tons of CO
2
per ¥1000) because the activities are emission-
intensive, but such cities produce low value-added energy products
(such as cleaned coal, coke, and electricity).
Five city groups
Recognizing these characteristic differences, we use formal cluster anal-
ysis to classify the cities into five distinct groups based on their GDP and
industrial output: service-based cities (
n
=8),high-techcities(
n
= 24),
energy production cities (
n
= 32), heavy manufacturing cities (
n
= 51),
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Fig. 2. Spatial distribution of five city groups.
Colors on the map indicate the categorization of each of the 182 cities into energy production (prod.) cities (red),
heavy manufacturing (manf.) cities (orange), light manufacturing cities (yellow), high-tech cities (green), and service-based cities (blue). Black circles and areas indicate
the location of coal and oil bases and common city cluster destinations for their energy exports.
Fig. 1. Total CO
2
emissions of 182 Chinese cities.
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and light manufacturing cities (
n
= 67) (see Materials and Methods).
Figure 2 shows the geographical distribution of the cities in each group.
Most service-based and high-tech cit
ies (blue and green in Fig. 2, respec-
tively) are located in east and south China, with 21 of the 32 gathered
into three city clusters (see map insets of Jing-Jin-Ji, the Yangtze River
Delta, and the Pearl River Delta). I
n contrast, the 32 energy production
cities (red in Fig. 2) are congregated in west and north China owing to
the location of fossil resources (mainly coal) in those regions. The heavy
and light manufacturing cities (orange and yellow in Fig. 2, respectively)
are more widely dispersed but with a large concentration in central
China.
Yet, none of the different types of cities are independent of the
others, and there is evidence of a division of labor among the city
groups. For example, although none of the three major city clusters
highlighted in Fig. 2 contain cities where energy production is the pillar
industry, each cluster is supported by energy imported from nearby
energy production centers: The Pea
rl River Delta cities are supported
by Maoming oil base, the Jing-Jin-Ji region obtains power from the
Shanxi
Inner Mongolia coal base, and the Yangtze River Delta cities
are supported by both the Huainan-Huaibei and Shanxi
Inner Mongolia
coal bases (Fig. 2, black circles and arrows, and fig. S2). Emission-
intensive activities of outer-lying heavy and light manufacturing cities
similarly support production and con
sumption activities of the major
city clusters (
19
,
22
).
Figure 3A shows the emission intensity
s mean values and SDs of
the five city groups. The average emiss
ion intensity is 0.47 metric tons of
CO
2
per ¥1000 in energy production ci
ties, 0.31 metric tons of CO
2
per
¥1000 in heavy manufacturing cities, 0.23 metric tons of CO
2
per ¥1000
in light manufacturing citi
es, 0.15 metric tons of CO
2
per ¥1000 in high-
tech cities, and 0.11 metric tons of CO
2
per ¥1000 in service-based cities.
We find that among the five city groups, the energy production cities
have the highest average emission intensity, while the service-based
cities have the lowest value. The
z
test of mean values shows that the
average emission intensities of the service-based and high-tech cities are
significantly lower than those of the energy production and heavy
manufacturing cities at the 0.05 level. In addition, the average emission
intensity of the light manufacturing cities is significantly lower than that
of the energy production cities at the
0.05 level as well (see Materials and
Methods). The SDs of the energy pro
duction and heavy manufacturing
cities
emission intensity are the largest among the five city groups (0.34
and 0.30 metric tons of CO
2
per ¥1000, respectively). The SDs of high-
tech and service-based cities
emission intensities are relatively small,
which are 0.08 and 0.07 metric tons of CO
2
per ¥1000, respectively.
Such a difference in city groups
energy intensity is determined by
the cities
economic structures. The energy production and heavy man-
ufacturing cities have more energy-insensitive sectors, which emit high
CO
2
with low economic outputs. Conversely, high-tech and service-
based cities rely more on the high-tech industries and service sectors.
This led them to lower emission intensities. Figure 3 (B to F) shows
the economic structures of the five city groups.
Figure 3 (B to F) and related
z
test of mean values (see Materials and
Methods) define the pil
lar industry of each city group. A city group
s
pillar industry has the highest share in economic structure compared
with other city groups. For example, the service sectors
average share
in GDP of the service-based cities (59%) is significantly higher than
those of energy production (34%), heavy manufacturing (36%), light
manufacturing (36%), and high-tech (43%) cities. Similarly, the energy
production sectors
average share in energy production cities (54%) is
the highest among the five city groups.
Superemitting city sectors and CO
2
reduction capacities in
industrial sectors
Given the strong influence of industrial activities on cities
CO
2
emissions, we next examine the potential for emission reductions in
industrial sectors through scenarios
of specific technological improve-
ment. As each city has 39 industrial sectors (5 energy production, 16
heavy manufacturing, 13 light manufacturing, and 5 high-tech in-
dustries), there are 7098 industrial city sectors in total. The term
city
sector
refers to each industrial sector of each city; for example,
the sector
coal mining and dressing
of Beijing is a city sector, while
the sector
food processing
of Shanghai is another. We calculate the
per
industrial output emissions for the 7098 industrial city sectors.
We then identify three levels of superemitters of those city sectors
based on the city sectors
per
industrial output emissions (
23
).
(1) Above-average emitters have per
industrial output emissions
greater than the sector mean.
(2) One SD superemitters have per
industrial output emissions
1
s
above the sector mean.
(3) Two SD superemitters have per
industrial output emissions 2
s
above the sector mean. The 2 SD superemitters represent the most
carbon-intensive city sectors.
The few superemitters of city secto
rs represent a disproportionately
large fraction of the total emissions. The top 2.5% of the 7098 industrial
city sectors in per
industrial output emissions contribute 70% of total
CO
2
emissions (Fig. 4A, emission-Lorenz curve). Figure 4 (B and C)
shows the top 10 and bottom 10 industrial city sectors, respectively,
in terms of emissions per industrial output.
To explore the potential emission re
duction capacities via applying
technical improvement to the three levels of superemitting city sectors,
we define three scenarios. Scenario #
3 is the strongest scenario when all
the above-average emitters improve their technology to the average
level, while scenario #1 is the mildest scenario.
(1) Scenario #1: 2 SD superemitting city sectors reach the current
national sector average emission intensity.
(2) Scenario #2: 1 SD superemitters reach the current sector average.
(3) Scenario #3: Above-average superemitters reach the current
sector average.
We then calculate the emission reduction capacities of the five city
groups
pillar industry, under the three scenarios (shown in fig. S3A).
Under the strongest scenario, #3, reductions to cities
pillar industries
alone could avoid emissions of 544 Mt of CO
2
in energy production
cities, 388 Mt of CO
2
in heavy manufacturing cities, 36 Mt of CO
2
in
light manufacturing cities, and 1 Mt of CO
2
in high-tech cities, or 52, 38,
40, and 11%, respectively, of those cities
pillar industry emissions.
Energy production and heavy manufacturing sectors are the primary
emission sources of every city group due to their high emission in-
tensities; therefore, we calculate the emission reduction capacities of the
cities
energy production (fig. S3B) and heavy manufacturing sectors
(fig. S3C) as well.
Figure 5 and Table 1 summarize comprehensive results of the
potential emission re
ductions of city groups
pillar industry, energy pro-
duction, and heavy manufacturing sectors. We presented the results
under three scenarios relative to a baseline of current emissions.
Although statistically few, mitigating emissions from only 2 SD
superemitters could nonetheless substantially reduce CO
2
emissions,
particularly in heavy manufacturing and light manufacturing cities
(the second bars of each city group in Fig. 5). The emissions reduced
under scenario #1 of the energy produc
tion, heavy manufacturing, light
manufacturing, high-tech manufacturing, and service-based cities
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Fig. 3. Mean and standard deviation of emission intensity and economic structure of each city group.
The bars present the mean value of the variables; the lines
above the bars show the +1 SD of the variables.
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are 61 Mt (or 4%), 122 Mt (6%), 242 Mt (13%), 0.3 Mt (0.03%), and
72 Mt (9%), respectively. If scenario #3 technical improvement is ap-
plied to cities
pillar, energy production, and heavy manufacturing in-
dustries, then the overall reductions are 2.4 gigatons (Gt) of CO
2
,or
31% of the cities
emissions in 2010 (shown in Fig. 5 and Table 1).
DISCUSSION
Updating and improving technologies might reduce emissions while
leaving the industrial structure of individual cities (and thus their re-
spective roles in the existing Chinese economy) unchanged. This is
critical, given the rapidity of targeted reductions; the central govern-
ment in China seeks to reduce national CO
2
emission intensity by 60
to 65% compared with the 2005 level by 2020 (
1
). If the percentage
reductions of scenario #3 in 2010 (31%) a
re assumed to still be available
in 2014, then China
s emission intensity in 2014 could be 48.25% lower
than the 2005 level (the real emission intensity in 2014 is 25% lower
than the 2005 level). This would allow China to meet its 60 to 65%
commitments easier. Other options for meeting these goals, such as
radical industrial restructuring or large-scale shifts in the country
s
energy mix (
24
), are unlikely to be feasible over the time span of just a
few years. For example, policies that simply shut down energy-intensive
Fig. 4. City sectors ranked by per-industrial output emissions.
Emission-Lorenz curve of 7098 industrial city sectors (
A
) and top/bottom 10 city sectors in per
industrial output emission (
B
and
C
). The numbers alongside the
y
axis in (B) and (C) are the per
industrial output emissions of the city sectors. The numbers in
parenthesesafterthecitynamedenotethesectorso
f the cities, which are consistent with the sectors
ID number in table S2. The colors of the bars indicate the city
sectors
categories (red, energy production; orange, heavy manufacturing; yello
w, light manufacturing; green, high-tech industry). For example, Shangqi
u(20)in(B)
refers to the
petroleum processing and coking
sector of Shangqiu and belongs to energy production (red); Beijing (34) in (C) refers to the
electronic and tele-
communications equipment
sector of Beijing and belongs to the high-tech industry (green).
Fig. 5. CO
2
emissions of city groups under three reduction scenarios.
Potential reductions in CO
2
emissions (in million tons) are shown for each of the five city
groups where the emission intensities of 2 SD, 1 SD, and above-average superemitters are brought down to the sector mean intensity (scenarios #1, #2, and #3,
respectively). The numbers on top of each scenario bar represent the potential reductions in CO
2
emissions under the scenarios compared with the baseline. The
magnitude of reductions under scenario #1 is greatest in the light manufacturing cities, while the energy production cities have the largest reduction magnitude under
scenario #3. The overall reductions under scenario #3 are 2.4 Gt of CO
2
, or 31% of the cities
emissions in 2010.
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industries would immediately an
d seriously affect the economies
of energy production and heavy industry cities, as well as the more
developed high-tech and service-based cities that depend on them.
Although it would be costly and di
sruptive for energy production
and heavy industry
based cities to reorganize their industrial structure
in the short term (for example, by closing or relocating emission-
intensive industries), more affluent,
service-based cities might be able
to quickly outsource their more emiss
ion-intensive industries without
economic hardship. However, such outsourcing by highly developed
cities could increase overall emissions; by virtue of their level of devel-
opment, these cities often have more advanced technologies in place,
and outsourcing would thus tend to move carbon-intensive, heavy-
polluting industries to less-developed regions with less-efficient technol-
ogies. For example, Shougang Corporation, one of the largest steelmaking
companies in China, has moved progressively from Beijing to Hebei
province (mainly to Tangshan) since 2010. Beijing
sCO
2
emissions de-
creased by 7.6 Mt during 2010 to 2015, but emissions in Hebei province
increased by 87.1 Mt during the same period (
25
), and Shougang Cor-
poration is one of the main causes of the increase. Whereas the emission
intensity of iron and steel production in Beijing was 1.4 metric tons of
CO
2
per ¥1000 in 2007, the intensity of
the same sector in Tangshan was
2.6 metric tons of CO
2
per ¥1000 in 2010 (86% higher). Although a few
affluent cities have reduced the proportion of coal in their energy mix
[for example, Beijing has reduced its coal consumption by 61%, or
18.2 Mt from 2007 to 2015 (
26
,
27
)] through a combin
ation of increased
renewables and natural gas, China
s large stocks of cheap coal and
equally large fleet of young, coal-burning power plants (
28
) are daunting
economic barriers to radical near-te
rm shifts in the Chinese energy mix.
Table 1. CO
2
emissions of five city groups by sector categories under the three reduction scenarios (2010, million tons).
City groups
Scenario
Energy
production
Heavy
manufacturing
Light
manufacturing
High-tech
Farming, construction,
services, and household
Total
Energy production cities
Baseline
1037.26
291.28
28.16
4.71
123.97
1485.37
Scenario #1
1022.23
245.35
28.16
4.71
123.97
1424.42
Scenario #2
897.47
236.99
28.16
4.71
123.97
1291.30
Scenario #3
493.63
126.42
28.16
4.71
123.97
776.89
Heavy manufacturing
cities
Baseline
912.60
1017.10
48.24
4.76
225.90
2208.61
Scenario #1
808.38
999.65
48.24
4.76
225.90
2086.94
Scenario #2
796.62
970.20
48.24
4.76
225.90
2045.73
Scenario #3
599.36
628.84
48.24
4.76
225.90
1507.11
Light manufacturing
cities
Baseline
928.88
629.15
90.87
7.22
246.99
1903.10
Scenario #1
723.50
592.64
90.34
7.22
246.99
1660.68
Scenario #2
670.52
572.87
89.37
7.22
246.99
1586.97
Scenario #3
562.16
399.96
54.86
7.22
246.99
1271.18
High-tech industry cities
Baseline
607.05
393.85
53.46
8.28
157.55
1220.18
Scenario #1
607.00
393.57
53.46
8.27
157.55
1219.84
Scenario #2
591.56
350.45
53.46
8.27
157.55
1161.29
Scenario #3
489.64
318.66
53.46
7.39
157.55
1026.68
Service-based cities
Baseline
341.79
190.15
23.02
20.94
216.45
792.36
Scenario
#1
270.51
189.75
23.02
20.94
216.45
720.68
Scenario
#2
270.51
189.55
23.02
20.94
216.45
720.48
Scenario
#3
256.67
123.91
23.02
20.94
216.45
640.99
Total (all 182 cities)
Baseline
3827.57
2521.53
243.75
45.90
970.86
7609.62
Scenario #1
3431.63
2420.95
243.22
45.90
970.86
7112.56
Scenario #2
3226.69
2320.05
242.26
45.90
970.86
6805.76
Scenario #3
2401.45
1597.79
207.74
45.01
970.86
5222.86
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On the basis of detailed analysis of cities and their industries, our
findings suggest that China
s near-term goals of reducing its emission
intensity may be feasibly accomplished by targeted technological im-
provements, buying time for the longer-term strategies of shifting
to non-fossil energy and a more service-based economy. Moreover, im-
proving and optimizing the energy a
nd carbon efficiency of industrial
production processes and operations could help lower the costs of
advanced technologies and thus facilitate their deployment in less-
developed cities and countries beyond China.
CONCLUSION
In order for China to cost-effectively reach its goal of reducing CO
2
emission intensity by 60 to 65% and running a nationwide Emission
Trading Scheme over the next few years, policy-makers in the country
need increasingly specific, subnational information about the sources
of CO
2
and the potential reductions and economic implications of dif-
ferent possible policies. By categorizing Chinese cities according to
their development stage and industrial makeup, we offer policy-makers
anopportunitytomeaningfullydif
ferentiate across the wide range in
city-level CO
2
emissions (from 1.6 to 194 Mt) and emission intensity
(from 0.04 to 1.72 metric tons of CO
2
per ¥1000). Further, because
the lower emission intensities of affluent cities (that is, high-tech and
service-based cities) are supported by
imports from less affluent, indus-
trial, and energy-producing cities located nearby, consumption-based
policies may allow more developed cities to subsidize emission reduc-
tions without undercutting the economic core of still-developing cities
by directly regulating their manuf
acturing and the electric sectors.
However, where policies directly targeting manufacturing and
electric power infrastructure are im
plemented, our sectoral analysis of
each city indicates that targeted technological improvements may be a
practical and effective means of reducing emissions while preserving
cities
current economic structure and energy systems. In particular,
by focusing efforts on superemitting industry sectors in each city,
roughly 30% of China
s carbon emissions might be eliminated.
Although the leveling off of China
sCO
2
emissions in recent years is
a tremendous watershed in the global effort to avoid dangerous climate
change, the progress reflects sweeping policies to improve the country
s
industrial technologies and energy systems. Further progress will in-
creasingly depend on policies that differentiate among cities according
to their economic structure, level of development, and infrastructure
and are carefully crafted to target the largest and most cost-effective
emission reductions.
In addition, rapidly growing cities with
new
investments in infra-
structure and institutions tend to emulate already-established cities and
thereby lock into the
old
development paths. Instead, promoting low-
carbon transitions at early stages of industrialization implies careful
consideration of
future fit
”—
how industries can be designed and run
to optimize or decouple the relationship between their economic
productivity and energy use (or environmental impact). The socio-
economic and environmental characteristics of the 182 cities repre-
sent various stages of industrialization, from the preindustrialization
stage (for example, energy production cities in Shanxi and Inner
Mongolia) to the postindustrialization stage (such as Beijing and
Shanghai). In turn, this cross section of Chinese cities we analyze
may provide important insights for other developing countries seeking
to target superemitting sectors/units in their cities, perhaps enabling
them to bypass or abbreviate the most emission-intensive phases of
industrialization.
MATERIALS AND METHODS
CO
2
emission accounts and data source
Scopes
Three approaches are usually used to account for the CO
2
emissions of
one country: the territorial-based, production-based, and consumption-
based approaches. According to the IPCC (
29
), the territorial-based
emissions are CO
2
emitted within one administrative unit. In the
production-approach, not only
emissions from international aviation
and shipping are typically allocated t
o the country of the relevant vessel
s
operator
[(
30
), p. 453] but also the
emissions from international tourism
are allocated based on where individua
l tourists are resident, rather than
their destination
[(
30
), p. 453]. The consumption-based emissions are
calculated according to the final products
consumption (
31
).
Similarly, three
scopes
aredefinedbytheWorldResourcesInsti-
tute (WRI) and the World Business Council for Sustainable Develop-
ment (
32
) to account for the regional CO
2
emissions. Scope 1 includes
emissions from in-boundary fossil fu
el combustion, industrial process/
product use, wastes, and other in-boundary activities. Scope 2 refers to the
in-boundary electricity/heat-related emissions induced by the purchased
electricity and heat. Scope 3 includes all the out-of-boundary emissions
such as emissions from aviation/marine and imported products/services
(
6
). Accordingly, four system boundaries for regional emission accounts
aredefinedonthebasisofthethreescopes:systemboundary1isequal
to scope 1 emissions; system boundary 2 includes both scope 1 and
scope 2 emissions; system boundary 3 is equal to scope 1 plus scope
3 emissions; while system boundary
4 is consumption-based emissions
(also called carbon footprint) (
33
).
Considering the higher uncertainties and incomparability of the scope
3 emissions, most of the previous studi
es on city-level emission accounts
focus mainly on the scope 1 territorial emissions (
34
). Here, we considered
the scope 1 territorial emission as well owing to the city-level data acces-
sibility in China. The territorial emissions describe the current CO
2
emission
induced within one country/region
s administrative boundary. The territorial
emissions are usually used for the emission feature analysis and for the
reduction policy-making.
Territorial emissions are the foundation of the
other emission approaches, in that p
roduction- and consumption-based
emissions are calculated on the basis of territorial emissions.
The territorial CO
2
emissions of cities in this study included both the
fossil fuel
related and process-related emissions. The fossil fuel
related
emissions were induced by the fossil fuel combustions. Per the defini-
tion of territorial direct CO
2
emissions, the cities
CO
2
emissions in this
study did not include the part induced by imported/purchased electricity
and heat. The emissions from the fossil fuel burnt in power plants for
electricity/heat generation were allocated to the power and heat sector
of the location cities. In addition, we removed the energy used as raw
materials during industrial processes (shown as non-energy use in
energy statistics) from the total consumption; this part did not emit
CO
2
either (
35
). The process-related emissions referred to CO
2
that
escaped from chemical reaction during the industrial processes (
36
),
rather than emissions from fossil f
uel combustion to gain power, which
were accounted as fossil fuel
related emissions. Above all, this study
calculated the cities
IPCC territorial administrative emissions from
17 kinds of fossil fuels (fossil fuel
related; see table S3) and 7 industrial
processes (process-related; see table S4).
CO
2
emission calculation method
IPCC (
29
) provided methods for CO
2
emission accounting based on
mass-balance theory. The emissions
were estimated as the product of
activity data (energy consumption or
industrial productions) and the cor-
responding emission factors. The method is widely used by researchers.
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On the basis of the IPCC method, the National Development and Re-
form Commission of China (NDRC) designed an emission account
system for Chinese provinces (
35
). Furthermore, in the
Global
Protocol for Community-Scale Greenhouse Gas Emission Inventories
and
International Local Government GHG Emissions Analysis
Protocol
,WRI
et al.
(
37
) and Local Governments for Sustainability
(ICLEI) (
38
) provided a bottom-up approach for higher-precision
city-level emission accounting. The ISO 14064 and 37120 series of
standards also provided guidelines f
or emission accounts at the enter-
prise level (
39
).
Alternative approaches were developed to account for the city-level
emissions. For example, Cai
et al.
(
40
) used spatial models to build a
bottom-up emission database for Chinese cities at 1 km × 1 km resolution.
Doll
et al.
(
41
), Ma
et al.
(
42
), and Meng
et al.
(
43
)usednighttimelight
imagery to estimate city CO
2
emissions. These approaches can potentially
provide more detailed emissions or full-coverage emissions for Chinese
cities. However, they have higher data requirements compared with the
IPCC methods, making them difficult to implement. Furthermore, city-
level emissions calculated by these methods are not comparable with the
national emission inventories due to
differences in methods and scopes.
Fig. 6. Mean test (
z
test) results of the five city groups.
n/a, not applicable.
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