ArticlePDF Available

The carbon emissions of Chinese cities


Abstract and Figures

As increasing urbanization has become a national policy priority for economic growth in China, cities have become important players in efforts to reduce carbon emissions. However, their efforts have been hampered by the lack of specific and comparable carbon emission inventories. Comprehensive carbon emission inventories for twelve Chinese cities, which present both a relatively current snapshot and also show how emissions have changed over the past several years, were developed using a bottom-up approach. Carbon emissions in most Chinese cities rose along with economic growth from 2004 to 2008. Yet per capita carbon emissions varied between the highest and lowest emitting cities by a factor of nearly 7. Average contributions of sectors to per capita emissions for all Chinese cities were 65.1% for industrial energy consumption, 10.1% for industrial processes, 10.4% for transportation, 7.7% for household energy consumption, 4.2% for commercial energy consumption and 2.5% for waste processing. However, these shares are characterized by considerable variability due to city-specific factors. The levels of per capita carbon emissions in China's cities were higher than we anticipated before comparing them with the average of ten cities in other parts of the world. This is mainly due to the major contribution of the industry sector in Chinese cities.
Content may be subject to copyright.
Atmos. Chem. Phys., 12, 6197–6206, 2012
© Author(s) 2012. CC Attribution 3.0 License.
and Physics
The carbon emissions of Chinese cities
H. Wang1,2, R. Zhang1, M. Liu1, and J. Bi1,2
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University,
Nanjing 210046, China
2Institute for Climate and Global Change Research, Nanjing University, Nanjing 210046, China
Correspondence to: J. Bi (
Received: 2 February 2012 – Published in Atmos. Chem. Phys. Discuss.: 21 March 2012
Revised: 26 June 2012 – Accepted: 27 June 2012 – Published: 18 July 2012
Abstract. As increasing urbanization has become a national
policy priority for economic growth in China, cities have be-
come important players in efforts to reduce carbon emis-
sions. However, their efforts have been hampered by the
lack of specific and comparable carbon emission invento-
ries. Comprehensive carbon emission inventories for twelve
Chinese cities, which present both a relatively current snap-
shot and also show how emissions have changed over the
past several years, were developed using a bottom-up ap-
proach. Carbon emissions in most Chinese cities rose along
with economic growth from 2004 to 2008. Yet per capita car-
bon emissions varied between the highest and lowest emit-
ting cities by a factor of nearly 7. Average contributions of
sectors to per capita emissions for all Chinese cities were
65.1% for industrial energy consumption, 10.1% for indus-
trial processes, 10.4% for transportation, 7.7% for house-
hold energy consumption, 4.2% for commercial energy con-
sumption and 2.5% for waste processing. However, these
shares are characterized by considerable variability due to
city-specific factors. The levels of per capita carbon emis-
sions in China’s cities were higher than we anticipated before
comparing them with the average of ten cities in other parts
of the world. This is mainly due to the major contribution of
the industry sector in Chinese cities.
1 Introduction
As global climate changes become more apparent, efforts to
control and reduce greenhouse gas (GHG) emissions have
become a focus of worldwide attention. But the outcome of
COP 15 in Copenhagen in December 2009 has made it clear
that differences in the circumstances and interests of individ-
ual countries will make it difficult to agree on common GHG
reduction targets and strategies at the international level.
Nonetheless, bottom-up approaches to this problem are
emerging in many countries (Gurney et al., 2009), and cities
are taking on important roles in global efforts to address cli-
mate change (Hillman and Ramaswami, 2010; Koehn, 2008).
Since cities contribute over 67% of the global GHG emis-
sions from fossil fuel use (Satterthwaite, 2008), developing
benchmarks and more comprehensive carbon emission in-
ventories at the city level has become necessary in the context
of global efforts to mitigate climate change (Gertz, 2009; Ra-
maswami et al., 2008). Creating carbon emissions inventories
and improving the understanding of how and why cities dif-
fer in terms of emissions are essential to success in this realm
(Kennedy et al., 2009). To make progress, however, a com-
mon and comparable carbon accounting system is needed.
Previous efforts have employed accounting systems like
the one developed by the International Council for Local
Environmental Initiatives (ICLEI, 2008). But most of these
studies have calculated GHG emissions for a specific city or a
particular year, and they have not compared the components
of GHG inventories using a consistent methodology. Life-
cycle and demand-centered methodologies are thought to be
able to assign emissions to political jurisdictions more accu-
rately (Ramaswami et al., 2008; Larsen and Hertwich, 2009;
Schulz, 2010). However, the lack of data on comprehensive
consumptions at the city-scale and especially for cities in de-
veloping countries (Hillman and Ramaswami, 2010) consti-
tutes a severe problem for studies of this kind. Moreover, dif-
ferent understandings of the definition and boundary of the
life-cycle (Matthews et al., 2008) make GHG emission data
for cities difficult to compare and lead to risks of double-
counting in a spatial and temporal sense. To solve these
Published by Copernicus Publications on behalf of the European Geosciences Union.
6198 H. Wang et al.: The carbon emissions of Chinese cities
problems, we propose a comprehensive carbon accounting
approach, which is comparable to ICLEI’s and with data
availability for Chinese cities (Bi et al., 2011).
China has adopted the target of reducing CO2emissions
per unit of GDP by 40–45% relative to 2005 levels by 2020.
But urbanization is considered a national policy priority in
efforts to spur economic and industrial growth in China, and
the government aims to increase the urbanization rate from
40 % in 2005 to 60% by 2030 (UN, 2007). As rising incomes
make urban dwellers’ lifestyles more energy intensive and
as migrants to the cities demand greater per capita energy
than their rural counterparts (Dhakal, 2009), controlling en-
ergy consumption and GHG emissions becomes more diffi-
cult. Although there are several studies of GHG emissions for
cities in China, they are usually based on calculations of ag-
gregate city energy consumption using top-down approaches
(Dhakal, 2009; Li et al., 2010; Lin et al., 2010). They give us
proxies of the total GHG emissions. But they are usually not
able to present enough information for the local governments
to define operable measures to reduce carbon emissions com-
pared with bottom-up approaches. Furthermore, these fac-
tors also make it difficult for the researchers to analyze the
differences of carbon emissions between Chinese cities and
other cities in the world. Bottom-up carbon emissions inven-
tories for Chinese cities based on comparable accounting ap-
proaches are urgently needed.
This paper aims to analyze the GHG emission charac-
teristics of China’s mega-cities. We develop comprehensive
and comparable carbon emissions inventories for twelve Chi-
nese cities based on bottom-up approaches. Then, we ana-
lyze and compare the characteristics of GHG emissions in
those cities. We also examine similarities and differences in
GHG emissions between the cities in China and cities located
in other countries. This is the first systematic accounting of
Chinese GHG emissions at the city-scale based on a bottom-
up methodology where the emissions are compared with ten
cities in the world. The results provide a benchmark in dis-
cussions of the effectiveness of strategies designed to reduce
carbon emissions.
2 Methodology
2.1 Chinese cities
In order to represent various sizes and development charac-
teristics, we analyze twelve Chinese cities: Beijing, Shang-
hai, Tianjin, Chongqing, Guangzhou, Hangzhou, Nanjing,
Zhengzhou, Shenyang, Wuhan, Wuxi and Lanzhou. These
cities are situated in different geographical regions of China
as indicated in Fig. 1. Basic information about these cities
and ten world cities used in the comparison is included in
Table S1 (Supplement).
In China, major environmental policies are usually made
at the national government level and implemented by local
governments, such as provinces and cities. These cities are
not the same as urban areas. They are administrative units,
which usually include urban, town and rural populations.
2.2 Components of a carbon emissions inventory
In this study, we calculate carbon emissions, expressed in
carbon dioxide equivalents (CO2e), for six sectors of a city’s
GHG inventories, including industrial energy consumption,
transportation, household energy consumption, commercial
energy consumption, industrial processes and waste. The first
4 include emissions related to energy consumption, while the
last two involve process and waste related emissions. Due
to the high uncertainties and typically small contributions of
agriculture, forest and other land use (AFOLU) to the total
carbon emissions of cities (Kennedy et al., 2010), these emis-
sions have not been included in this study. Because carbon
emissions from biomass burning is largely offset by annual
vegetation re-growth (Houghton and Hackler, 1999), the con-
tribution of biomass use is often omitted in long-term anal-
yses of atmospheric CO2data (Yantosca et al., 2004; IPCC,
2006). We have adopted this procedure in our study. The spe-
cific methods used in calculating carbon emissions for each
sector are discussed in our previous paper (Bi et al., 2011),
and only the most salient details are provided here. The car-
bon accounting scope of this study is illustrated in Table S2.
2.2.1 Energy consumption
This sector includes primary and secondary energy con-
sumption relating to industrial, transportation, residential and
commercial activities. In this study, GHG emissions from en-
ergy consumption are calculated by multiplying energy con-
sumption of subsectors (e.g. coal and oil for energy types)
and corresponding emission factors, which are summarized
in Eq. (1).
Ci,j ·EFj(1)
where, irepresents subsectors in a typical sector (e.g. the
transportation sector can be divided into passenger cars,
heavy duty trucks, buses, etc.); jrepresents energy types
(e.g. coal, oil, electricity, etc.); GHG is the sector’s total CO2e
emissions, ton; Ci,j is energy consumption per sub-sector
(the units correspond to various energy type, such as tons
for coal, m3for the natural gas, kWh for the electricity, etc.);
EFjrepresents the CO2e emission factors for specific energy
2.2.2 Industrial processes
Carbon emissions from industrial processes mainly refer to
those emitted from the chemical or physical transformation
of materials during industrial production, such as cement
Atmos. Chem. Phys., 12, 6197–6206, 2012
H. Wang et al.: The carbon emissions of Chinese cities 6199
Fig. 1. Carbon emissions of the 12 cities in China from 2004 to 2008, million tons. 471
2004 2005 2006 2007 2008 0
2004 2005 2006 2007 2008
2004 2005 2006 2007 20
2004 2005 2006 2007 20
2004 2005 2006 2007 2008
2004 2005 2006 2007 20
2004 2005 2006 2007 2008
2004 2005 2006 2007 2008
2004 2005 2006 2007 2008
2004 2005 2006 2007 2008
2004 2005 2006 2007 2008
2004 2005 2006 2007 20
Energy consumption Waste treatment
Industry process
Fig. 1. Carbon emissions of the 12 cities in China from 2004 to 2008.
manufacturing and limestone consumption. Emissions asso-
ciated with combustion to produce energy for industrial use
are excluded from this sector. The CO2e emissions from this
sector are calculated according to the outputs of various prod-
ucts. Because of constraints relating to the availability of in-
formation regarding industrial processes at the city level, we
focused on the carbon emissions from three major sources
(cement and glass, chemical, and metal productions).
2.2.3 Waste
We applied IPCC’s (2006) First Order Decay approach to
account for carbon emissions from landfill waste. This ap-
proach ideally requires at least 20yr of landfill data and
good estimates of decay coefficients. The data on industrial
solid waste were obtained from the city statistical yearbooks.
Municipal solid waste production was estimated based on
the population of cities. Because there are few studies with
methane emissions from landfills in Chinese cities, we ap-
plied the IPCC’s recommended parameters for developing
2.3 Overall carbon emissions
In this study, the ICLEI (2008) metrics were applied as much
as possible to maximize the comparability of our results
with those from other cities in the world. Comparisons of
the carbon accounting scopes between ICLEI and this study
are shown in Table S2. A city’s overall carbon emissions
include emissions from fossil fuel combustion and indus-
trial processes occurring within the city boundaries, elec-
tricity use, and waste disposal. Although some cities export
their solid waste beyond their boundaries, methane emissions
from landfills are still included here.
In order to allow comparisons across cities and to assess
the consistency of GHG emissions across various scales, we
normalized the total GHG emissions for the 12 Chinese cities
on a per capita or per gross domestic product (GDP) basis
and compared them with the emissions of other cities in the
world. If not noted specifically, GDP has already been ad-
justed for purchasing power parity (PPP). We also divided
per capita carbon emissions into non-industry and industry
related emissions to reflect the impacts of personal consump-
tion and industrial production on the carbon emissions of
Chinese cities. Atmos. Chem. Phys., 12, 6197–6206, 2012
6200 H. Wang et al.: The carbon emissions of Chinese cities
2.4 Data sources
We collected data on industrial energy consumption, indus-
trial production, vehicle population, GDP and population
from the statistical yearbook of each city. Detailed references
are provided in our supporting information. We calculated
carbon emission factors for each fossil fuel used in Eq. (1)
using the IPCC (2006) recommended methods. Vehicle miles
traveled (VMT) and fuel economies were acquired from our
previous studies (Wang et al., 2010a, 2011) and The First
Census of Pollution Source in China.
We calculated carbon emissions from electricity on con-
sumption basis to avoid double counting. This means that
we excluded emissions from power plants in computing the
total carbon emissions for a city. Therefore, carbon emis-
sions from electricity depend on the amount consumed (not
production) and the carbon emission intensity of the supply
mix: (1) the electricity consumption is for all types of sec-
tors (e.g. industrial, transportation, residential and commer-
cial), which could be acquired from each city’s statistics. (2)
It is important to determine carbon emission factors of the
electricity supply mix. There are six large power grids in
China, named for the regions they serve: Northeast China,
North China, Central China, East China, Northwest China,
and South China. These grids are not strictly independent, as
one grid may buy power from another if needed. In this study,
we applied the electricity generation fuel mixes for these six
power grids from 2004 to 2008 (SSBC, 2009) to calculate the
carbon emission factors in different years. Power exchanges
between the grids have also been considered. Coal and hy-
dro are the two major energy sources for power generation
in China, and the split between them varies by region. Coal-
based power dominates in Northeast and North China gen-
eration mixes, with the proportion reaching as high as 95–
98%. Although coal remains dominant, the Northwest, Cen-
tral, and South mixes include 22% or more of hydro power.
The South and East China grids also include 5% nuclear
power (Huo et al., 2010). The carbon emission factors from
2004 to 2008 for the cities included in this study are pre-
sented in Table S3.
3 Results and discussion
3.1 Profiles of carbon emissions of Chinese cities
3.1.1 Trends of carbon emissions
China consists of eight economic regions (Chen et al., 2009),
including the North East (Heilongjiang, Liaoning and Jilin),
the North Coast (Beijing, Tianjin, Hebei and Shandong),
the East Coast (Shanghai, Jiangsu and Zhejiang), the South
Coast (Guangdong, Hainan and Fujian), the Middle Yangtze
River (Anhui, Hubei, Hunan and Jiangxi), the Middle Yellow
River (Henan, Shanxi, Inner Mongolia, Shaanxi), the South
West (Sichuan, Guizhou, Yunnan, Chongqing and Guangxi)
and the North West (Gansu, Ningxia, Qinghai, Tibet and Xin-
jiang). The 12 cities included in this study are distributed in
these 8 economic regions as shown in Fig. 1.
Total carbon emissions for all the Chinese cities other than
Beijing show growing emissions during the five years from
2004 to 2008. However, the growth rate in 2008 is not as high
as experienced previously, and carbon emissions have even
decreased for individual cities. Except for the improvement
of energy use efficiencies, this is related to the global reces-
sion of 2008–2009. As shown in Fig. S1, the growth rates of
exports and their contributions to the total GDP decreased in
2008 for most cities, especially those cities that have large
export activities such as Shanghai, Guangzhou and Wuxi.
As China’s capital city, Beijing’s carbon emissions de-
creased in recent years mainly due to measures associated
with the 2008 Olympic Games. To ensure good air quality
for the games, Beijing’s municipal government adopted an
Air Quality Guarantee Plan for the 29th Olympic Games”
(Wang et al., 2010b). Starting in 2000, many energy intensive
or heavy polluting industrial facilities (e.g. oil refineries and
steel plants) were relocated; numerous coal-fired boilers and
domestic stoves were modified to use natural gas, and older
vehicles were replaced with newer, cleaner vehicles. Beijing
also implemented other temporary measures during the pe-
riod of the games, such as odd-even number permit policies
for private cars on Beijing’s roads (i.e. vehicles with a li-
cense plate ending in an odd number were allowed only on
odd-number days while even numbers were allowed only on
even-number days) (Zhou et al., 2010b) as well as production
controls for some energy-intensive industries. Capital Iron
and Steel General Corporation and Beijing Yanshan Petro-
Chemical Corporation, for example, were required to reduce
their operations by 30–50 %. Overall, Beijing’s carbon emis-
sions in 2008 were 19.32 % below emissions in 2004 with an
annual decrease rate of 5.22%.
3.1.2 Preliminary analysis of factors influencing carbon
The total carbon emissions of Chinese cities were found to
correspond closely to GDP. The linear regression between
these two variables is statistically significant (tstat =7.11,
sig.<0.001) and has an R2of 0.47 (Table 1). Further re-
gressions were conducted to analyze whether the popula-
tion impacted city’s carbon emissions. As shown in Table 1,
the inclusion of population in a regression produced a bet-
ter linear fit (R2=0.53). In the improved model, city popu-
lation was statistically significant (tstat =2.74, sig.=0.008)
but still secondary to GDP (tstat =5.54, sig.<0.001). Per
unit area carbon emissions also correlated strongly with pop-
ulation density in China’s cities (tstat =15.38, sig.<0.001;
R2=0.80). As the per capita carbon emissions of most Chi-
nese cities have grown during the past five years (Fig. 3b),
China’s carbon emissions will inevitably increase in the near
Atmos. Chem. Phys., 12, 6197–6206, 2012
H. Wang et al.: The carbon emissions of Chinese cities 6201
Table 1. Linear Regression Analysis for the Carbon Emissions of 12 Chinese cities.
Variable coefficient tstat sig. 95 % CI
Total carbon emission (thousand tons) (R2=0.47)
Constant 49 162 8.09 0.000 36 991 to 61334
GDP (million $US in PPP) 0.67 7.11 0.000 0.48 to 0.86
Total carbon emission (thousand ton) (R2=0.53)
Constant 41 984 6.63 0.000 29 305 to 54663
GDP (million $US in PPP) 0.55 5.61 0.000 0.36 to 0.75
city population (thousand people) 1.25 2.74 0.008 0.34 to 2.17
Carbon emission density (ton km2) (R2=0.80)
Constant 602 0.91 0.369 728 to 1932
city population density (people km2)8.97 15.38 0.000 7.80 to 10.14
Log per GDP carbon emission (R2=0.70)
Constant 15.29 22.72 0.000 13.94 to 16.63
Log GDP (million $US in PPP) 0.73 11.59 0.000 0.85 to 0.60
future with more and more people crowding into big cities
and rapid development of economy.
The carbon emission intensities (per GDP carbon emis-
sions) of Chinese cities were found to decrease as a func-
tion of economic development (Table 1). The linear regres-
sion of log (per GDP carbon emission) and log (GDP) is sta-
tistically significant (tstat = −11.59, sig.<0.001) with good
fit (R2=0.70). Combined with Fig. 3a, this indicates that
there have already been some efforts by Chinese cities to re-
duce their carbon emission intensities to achieve the central
government’s objective, declared at COP 15 in Copenhagen,
of reducing carbon emissions per unit of GDP by 40–45%.
As we indicated above, however, these efforts were coun-
teracted by rapid economic development and urban popula-
tion growth. As a result, total emissions have continued to
increase during the past five years. To achieve absolute car-
bon emission reductions, China will have to adopt stronger
measures to save energy and reduce emissions.
There is little correlation between per capita carbon emis-
sions and per capita GDP (tstat =0.13, sig.=0.89; R2=
0.001), a finding that seems to conflict with the idea of the
environmental Kuznets curve. Although China is experienc-
ing rapid economic growth, development is extremely unbal-
anced with energy structures and technology levels differing
greatly across regions. This may explain why per capita car-
bon emissions (or energy consumption) do not correspond
with per capita GDP in China’s cities.
3.2 Carbon emission inventories and intercity
comparisons among Chinese cities
3.2.1 Carbon emissions inventories
Figure 2a shows carbon emissions from the six sectors for
each of the twelve cities normalized on a per capita basis
in 2008. Overall, carbon emissions covered a broad range
from 3.72 to 22.54 tons CO2e per person. Average contribu-
tions from individual sectors to total per capita carbon emis-
sions and the ranges observed for each sector are displayed in
Fig. 2b. Carbon emissions from industrial energy consump-
tion represent the largest source, contributing 64.34% to to-
tal per capita CO2e emissions. These emissions also exhibit
the greatest variation across cities. Emissions from build-
ings, which include household and commercial sectors, are
the second largest source with a contribution of 12.33% of
total carbon emissions. The third largest contributor is trans-
portation at 10.58%, followed by the industry process sector
at 10.23%. The waste sector contributes only 2.51% of the
total per capita carbon emissions on average.
3.2.2 Intercity comparisons
Although total carbon emissions of most Chinese cities have
increased during the past five years (Fig. 1), the carbon emis-
sion intensities have decreased in all 12 cities (Fig. 3a). Aver-
age reduction is 25.86%, which covers a range from 3.13%
(Chongqing) to 63.64% (Beijing). It means that GDP in-
crease faster than the carbon emissions of these cities. Be-
cause the energy and economic structures for most of the
cities did not change in an obvious manner (see Figs. S2 and
S3), this indicates the energy utilization efficiencies of Chi-
nese cities have improved during the past five years. Atmos. Chem. Phys., 12, 6197–6206, 2012
6202 H. Wang et al.: The carbon emissions of Chinese cities
 473
(a) 474
(b) 476
Fig. 2. Per capita carbon emissions (ton CO2e) by sector for all 12 China’s cities in 2008: (a) 477
Individual city data of per capita carbon emissions; (b) Sector contribution shares (mean and 478
range values) across the 12 cities. 479
0.00 5.00 10.00 15.00 20.00 25.00
Per capita carbon emissions(t-CO
Industry Energy
Industry Process
Transportat ion
Wast e
Per capita carbon emission contributions
Fig. 2. Per capita carbon emissions (ton CO2e)by sector for all 12
China’s cities in 2008: (a) individual city data of per capita carbon
emissions; (b) sector contribution shares (mean and range values)
across the 12 cities.
Structure changes within the industrial sector could also
influence cities’ energy uses. But this kind of impact could
be positive or negative in individual cities. For example, the
industrial carbon emissions of Beijing decreased through the
transfer of energy intensive industries (e.g. oil refiners and
big steel plants) to other cities (Fig. S4) and the develop-
ment of tertiary industries (Fig. S3). On the other hand, the
industrial carbon emissions of Tianjin increased as the city
received some oil refiners and big steel plants from Beijing,
even though the energy utilization efficiencies of these plants
improved greatly.
The Yangtze River Delta (YRD), the Pearl River Delta
and Beijing-Tianjin-Hebei (BTH) are the three most devel-
oped regions in China. They have the most active economies
and most advanced technologies. As the core cities in these
regions, Guangzhou (PRD), Beijing (BTH) and Shanghai
(a) 481
(b) 483
Fig. 3. Carbon emission characteristics of the 12 Chinese cities from 2004 to 2008: (a) Per GDP 485
carbon emissions; (b) Per capita carbon emissions. 486
2004 2005 2006 2007 2008
Per GDP carbon emissions (t-CO
/million USD)
2004 2005 2006 2007 2008
Per capita carbon emissions (t-CO
Fig. 3. Carbon emission characteristics of the 12 Chinese cities from
2004 to 2008: (a) per unit of GDP carbon emissions; (b) per capita
carbon emissions.
(YRD) have the lowest per unit of GDP carbon emissions
(Fig. 3a). By contrast, Lanzhou, a city in west China, has an
economy mainly dependent on heavy industry, and its energy
efficiency technologies lagged behind those of the most de-
veloped regions. Thus, Lanzhou’s per GDP emissions were 4
to 10 times of the cities’ in the three most developed regions.
As shown in Fig. 3b, per capita carbon emissions for all
the cities other than Beijing show growth trends from 2004
to 2008, although there are fluctuations among cities. This
will put huge pressure on the local governments as they seek
to realize their carbon mitigation ambitions. Per capita car-
bon emissions of most cities are within the range of 5 to
15tons. Lanzhou’s total carbon emissions are comparable to
Guangzhou. However, its population is around 3 million and
less than half of Guangzhou’s. Therefore, the per capita car-
bon emissions differed by more than a factor of two between
Atmos. Chem. Phys., 12, 6197–6206, 2012
H. Wang et al.: The carbon emissions of Chinese cities 6203
these cities. As the biggest municipality in China, Chongqing
has the largest population of around 28 million, more than
Shanghai (18 million) and Guangzhou (8 million) combined.
At the same time, Chongqing is a less developed city and de-
pends more on primary industry comparing to the other cities
in China (see Fig. S3). These explain why the per capita car-
bon emission of Chongqing is the lowest among the 12 Chi-
nese cities.
3.3 Comparisons with other cities in the world
To provide context, we compared carbon emission levels in
Chinese cities with data on ten cities elsewhere in the world
used in Kennedy’s analysis (2009), which employs a sim-
ilar accounting procedure to that used here. As Table 2 il-
lustrates, eight of the twelve Chinese cities have per capita
carbon emissions over 8.0 t-CO2e, making them compara-
ble or even higher than the ten world cities. Carbon emis-
sions in seven of the Chinese cities exceed the average value
for the ten world cities. Several factors account for this phe-
1. The definition of a city in China differs from the def-
inition used in the United States. The Chinese defini-
tion is broader, encompassing more than the areas en-
compassed in the ten world cities (Table S1). As a re-
sult, many rapidly developing and highly energy inten-
sive industries are included within cities in China. As
industrial sectors, including industry energy consump-
tion and industry processes, account for over 75% of
the total carbon emissions in China’s cities, it is easy
to understand the higher per capita carbon emissions in
Chinese cities compared to the world cities. On the other
hand, according to IEA (2007), average per capita car-
bon emissions from energy use in China and the United
States are respectively 6 and 25 t-CO2e. This means that
per capita carbon emissions of cities are higher than the
national average in China, while the reverse is true for
cities in the United States included in the comparison.
2. Calculations for seven of the ten world cities did not
include emissions from industry processes in the com-
putation of total carbon emissions. However, industry
process is the third largest sector in China, contributing
2–24% of total carbon emissions in individual cities.
Seven of the 12 Chinese cities would have lower per
capita carbon emissions than the average level for the
ten non-Chinese cities if the contribution of industry
processes were omitted.
3. The results of this study demonstrate that carbon emis-
sions in China’s cities are higher than we anticipated.
But it is important to note that the 12 cities included
in our assessment are among the most developed cities
in each province of China. As we have noted, a city’s
carbon emissions are highly correlated with GDP. In-
clusion of a wider selection of cities in China would
reduce the level of average emissions. The analysis also
revealed that the average per capita carbon emissions of
China is small, but in the case of developed cities, such
as some cities in this study, carbon emissions could be
well above other cities in the developed world.
Because China is a major exporter, per capita carbon emis-
sions may not be the best way to compare cities, for indus-
trial emissions are the major contributor and vary widely
among individual cities. In order to explore this issue, we
divided per capita carbon emissions into per capita indus-
trial and per capita non-industrial emissions. As can be de-
rived from Table 2, the average per capita industrial emission
is 8.25 t-CO2e for Chinese cities, which is three times that
of the ten world cities. However, the average for per capita
non-industrial emissions is only 37% of the ten cities’ in the
world. This illustrates that the carbon emissions of Chinese
cities are mainly caused by industrial production and that the
carbon emissions from a citizen’s everyday life are far below
the average in ten world cities.
China’s vehicle population (not including motorcycles)
has increased nearly 3 times during the past decade. How-
ever, vehicle ownership is only 50 per 1000 persons, which
is only 40 % of the world average and just 5% of the average
in the United States. For this reason, most cities in China (ex-
cept the highly developed ones like Beijing and Guangzhou)
have a much lower per capita carbon emissions resulting
from ground transport compared with the ten world cities.
This explains why ground transportation generates only 10 %
of total carbon emissions in China’s cities, well below the
17–40% of emissions in the ten world cities. This is also
supported by the fact that vehicles only consumed 6–7% of
the total energy in China, while in the developed countries
vehicles consumed 20–30 % of total energy (He et al., 2005).
3.4 Uncertainties
A carbon emissions inventory for a city has inherent uncer-
tainties because it simplifies complex real-world processes.
The uncertainties may arise from many sources, some of
which are common to all carbon emissions inventories, such
as errors in emission factors caused by real-world emission
variability. In this study, the main uncertaintiesin the carbon
emission inventory for Chinese cities may come from the fol-
lowing areas:
1. The uncertainty associated with estimates of carbon
emission factors for various types of energies/products.
For example, the IPCC default emission factors were
applied to the industrial processes and solid waste sec-
2. The reality that some activity data, such as municipal
solid waste generation, are rarely collected and reported
in China’s local statistics, and are difficult to obtain di-
rectly. We used waste generation rates at the national Atmos. Chem. Phys., 12, 6197–6206, 2012
6204 H. Wang et al.: The carbon emissions of Chinese cities
Table 2. Summary of the Per Capita Carbon Emissions from 12 Chinese Cities in 2005 and other Ten Cities in the World (t-CO2e/capita)a.
Electricity Heating and Ground Industry
City Use industrial fuel use Transportation Process Waste Summary
Industry Non Total
Bangkok 2.77 2.49 2.27 unknown 1.23 2.63 6.13 8.76
Barcelona 0.67 0.85 0.77 unknown 0.24 0.76 1.77 2.53
Cape Town 3.38 1.15 1.44 unknown 1.78 2.33 5.43 7.75
Denver 9.10 4.12 6.31 unknown 0.59 6.04 14.08 20.12
Geneva 0.35 3.45 1.85 unknown 0.38 1.81 4.22 6.03
London 2.50 2.58 1.22 unknown 0.21 1.95 4.56 6.51
Los Angeles 2.46 1.37 4.92 0.22 0.49 2.84 6.62 9.46
New York 3.01 3.13 1.53 unknown 0.35 2.41 5.61 8.02
Prague 3.31 3.20 1.44 0.43 0.11 2.55 5.94 8.49
Toronto 2.47 3.30 4.05 0.57 0.33 3.22 7.50 10.72
Beijing 3.40 3.01 1.46 0.61 0.14 5.12 3.50 8.62
Tianjin 3.53 7.07 0.90 0.42 0.16 10.04 2.03 12.07
Shanghai 4.82 3.77 0.95 0.83 0.19 7.44 3.13 10.57
Hangzhou 3.42 3.71 1.03 1.05 0.66 7.61 2.26 9.87
Nanjing 3.34 1.99 0.64 1.62 0.12 5.31 2.40 7.71
Wuxi 7.00 6.23 1.13 1.72 0.38 12.78 3.67 16.45
Guangzhou 3.27 1.87 1.57 0.98 0.17 4.47 3.39 7.86
Zhengzhou 3.10 3.39 0.81 1.44 0.50 6.32 2.92 9.24
Wuhan 1.16 11.16 0.77 0.60 0.16 12.59 1.25 13.84
Chongqing 0.76 1.22 0.35 0.43 0.13 2.03 0.85 2.88
Lanzhou 3.51 15.13 0.3b2.26 0.13 20.71 0.32 21.04
Shenyang 1.93 3.44 1.01 0.12 0.17 4.62 2.06 6.68
aThe carbon emissions data of bold italic marked cities are from this study and the data of other ten cities in the world are from Kennedy’s study (2009).
bBecause no information about vehicle population was found for Lanzhou city, the analogy analysis between Zhengzhou and Lanzhou was applied to
calculate the carbon emissions.
cFor the ten cities in the world used for comparison in this study, information on industry-related emissions is lacking. Thus it is assumed that industry
contributes 30 % of total per capita carbon emissions, which is similar to the proportion of average US level (the Fifth US Climate Action Report,
average level, which may be different from the actual
situation in specific cities.
3. Uncertainty associated with the integrity of carbon ac-
counting. Restricted by data availability, we only cal-
culated carbon emissions from key industrial processes,
including the mineral products industry (cement, glass,
etc.), chemicals, and metal production. The conse-
quence would be to underestimate the carbon emissions
from industrial processes. Quantitative analyses of these
types of uncertainties will be conducted in our future
studies, when more basic information on carbon emis-
sions in Chinese cities is available.
4 Conclusions and future work
In this study, carbon emissions of 12 Chinese cities from
2004 to 2008 were calculated using a bottom-up methodol-
ogy. In sum:
1. Total carbon emissions, per capita and per unit of GDP
emissions varied greatly due to city-specific factors,
such as energy structures, economic development and
structures, populations, and first of all the structures of
industry sectors in each city. Emissions in most Chinese
cities rose along with economic growth from 2004 to
2008. For most cities, per unit of GDP emissions de-
clined while per capita emissions grew. Total carbon
emission, per capita and per unit of GDP emissions var-
ied between the highest and lowest emitting cities by
factors of nearly 7, 10 and 4, respectively.
2. Contributions of individual sectors to total per capita
carbon emissions were as follows: industrial energy
consumption (65.1%), industrial processes (10.1%),
transportation (10.4%), household energy consump-
tion (7.7%), commercial energy consumption (4.2%),
and waste processing (2.5%). However, these shares
are also characterized by large variability due to city-
specific factors.
3. Levels of per capita carbon emissions in China’s cities
were higher than we anticipated due to the higher con-
tribution of the industrial sector to the total carbon
Atmos. Chem. Phys., 12, 6197–6206, 2012
H. Wang et al.: The carbon emissions of Chinese cities 6205
emissions. If we exclude the impact of industrial carbon
emissions, the average per capita non-industrial emis-
sion of Chinese cities is only 37% of the ten world
cities. This illustrates that the carbon emissions of Chi-
nese cities are mainly attributable to industrial activities
and carbon emissions from citizen’s everyday life are
far below the average levels of global countries.
This study preliminarily analyzed the carbon emission char-
acteristics of 12 Chinese cities. Other factors, such as a
city’s climate (e.g. geographical location influences heat-
ing/cooling energy consumption in cities), industrial struc-
ture, energy structure and energy prices, may also play a sub-
stantial role in accounting for total GHG emissions (Kennedy
et al., 2009; Zhao et al., 2010a). To develop a better under-
standing of the factors determining the emission trends in
China a quantitative decomposition analysis should be per-
formed (Dhakal, 2009; Minx et al., 2011). We plan to per-
form such an analysis in future studies.
As industry is the major contributor to the total carbon
emissions of the cities in China, more work is needed to
analyze the opportunities to improve industrial energy and
material consumption efficiencies in this sector. With con-
tinuing rapid urbanization, the development of benchmarks
of GHG emissions caused by city activities will definitely
have a large impact on climate action plans for China and the
world. Also, it should be noted that we have not attempted
to include carbon emissions from cross-boundary activities,
such as international air travel or embodied energy consump-
tion associated with products produced or consumed in cities
(e.g. food, water and fuel). These are also major contribu-
tors to emissions in developed countries (Hillman and Ra-
maswami, 2010; Kennedy et al., 2009). As China is a ma-
jor producer of products and commodities consumed abroad,
adopting a consumption perspective would significantly af-
fect comparisons between China’s cities and the ten world
cities. This topic will merit attention in future research.
Supplementary material related to this article is
available online at:
Acknowledgements. This work was supported by China National
Program on Key Basic Research Project (973 Program, Project
No. 2010CB950704), China National Nature Science Foundation
(Project No. 51008155) and Foundation Research Project of
Jiangsu Province (The Natural Science Fund No. BK2011017).
We also thank the two anonymous referees for their constructive
comments to improve our paper. The contents of this paper are
solely the responsibility of the authors and do not necessarily
represent official views of the sponsors.
Edited by: M. Gauss
Bi, J., Zhang, R., Wang, H., Liu, M., and Wu, Y.: The Benchmarks
of Carbon Emissions and Policy Implications for China’s Cities:
Case of Nanjing, Energy Policy, 39, 4785–4794, 2011.
Chen, Y., Yang, Z., Fang, S., Hu, Z., Matin, M., and Sujit, B.: A
patent based evaluation of technological innovation capability in
eight economic regions in PR China, World Patent Information,
31, 104–110, 2009.
Dhakal, S.: Urban energy use and carbon emissions from China and
policy implications, Energy Policy, 37, 4208–4219, 2009.
Gertz, M.: New research aims to create accurate account of a city’s
carbon burden, Environ. Sci. Technol, 43, 8006–8007, 2009.
Gurney, K. R., Mendoza, D. L., Zhou, Y., Fischer, M. L., Miller, C.
C., Geethakumar, S., and de la Rue du Can, S.: High resolution
fossil fuel combustion CO2emission fluxes for the United States,
Environ. Sci. Technol., 43, 5535–5541, 2009.
He, K., Huo, H., Zhang, Q., He, D., An, F., Wang, M., and Walsh,
M.: Oil consumption and CO2emissions in China’s road trans-
port: current status, future trends, and policy implications, En-
ergy Policy, 33, 1499–1507, 2005.
Hillman, T. and Ramaswami, A.: Greenhouse gas emission foot-
prints and energy use benchmarks for eight U.S. cities, Environ.
Sci. Technol, 44, 1902–1910, 2010.
Houghton, R. A. and Hackler, J. L.: Emissions of carbon from
forestry and land-use change in tropical Asia, Global Change
Biol., 5, 481–492, 1999.
Huo, H., Zhang, Q., Wang, M., Streets, D., and He, K.: Environ-
mental implication of electric vehicles in China, Environ. Sci.
Technol., 44, 4856–4861, 2010.
ICLEI: Cities for Climate Protection, Toronto, Canada: Interna-
tional Council of Local Environmental Initiatives, available at: (last access: December
2010), 2008.
IEA (International Energy Agency): CO2Emissions from Fuel
Combustion 1971–2005, Paris: Organization for Economic Co-
operation and Development, 2007.
IPCC (Intergovernmental Panel on Climate Change): 2006 IPCC
Guidelines for National Greenhouse Gas Inventories, Prepared
by the National Greenhouse Gas Inventories Programme, edited
by: Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., and Tan-
abe, K., IGES: Japan, 2006.
Kennedy, C., Steinberger, J., Casson, B., Hansen, Y., Hillman, T.,
anek, M., Pataki, D., Phdungsilp, A., Ramaswami, A., and
Mendez, G.: Greenhouse gas emissions from global cities, Envi-
ron. Sci. Technol, 43, 7297–7302, 2009.
Kennedy, C., Steinberger, J., Casson, B., Hansen, Y., Hillman,
T., Havr´
anek, M., Pataki, D., Phdungsilp, A., Ramaswami, A.,
and Mendez, G.: Methodology for inventorying greenhouse gas
emissions from global cities, Energy Policy, 38, 4828–4837,
Koehn, P. H.: Underneath Kyoto: emerging subnational government
initiatives and incipient issue-bundling opportunities in China
and the United States, Global Environ. Politics, 8, 53–57, 2008.
Larsen, H. and Hertwich, E.: The case for consumption-based ac-
counting of greenhouse gas emissions to promote local climate
action, Environ. Sci. Policy, 12, 791–798, 2009.
Li, L., Chen, C., Xie, S., Huang, C., Cheng, Z., Wang, H., Wang, Y.,
Huang, H., Lu, J., and Dhakal, S.: Energy demand and carbon
emissions under different development scenarios for Shanghai, Atmos. Chem. Phys., 12, 6197–6206, 2012
6206 H. Wang et al.: The carbon emissions of Chinese cities
China, Energy Policy, 38, 4797–4807, 2010.
Lin, J., Cao, B., Cui, S., Wang, W., and Bai, X.: Evaluating the
effectiveness of urban energy conservation and GHG mitigation
measures: The case of Xiamen city, China, Energy Policy, 38,
5123–5132, 2010.
Matthews, H. S., Hendrickson, C. T., and Weber, C. L.: The im-
portance of carbon footprint estimation boundaries, Environ. Sci.
Technol., 42, 5839–5842, 2008.
Minx, J. C., Baiocchi, G., Peters, G. P., Weber, C. L., Guan, D.,
and Hubacek, K.: A “Carbonizing Dragon”: China’s Fast Grow-
ing CO2Emissions Revisited, Environ. Sci. Technol., 45, 9144–
9153, 2011.
Ramaswami, A., Hillman, T., Janson, B., Reiner, M., and Thomas,
G.: A demand-centered, hybrid life cycle methodology for city-
scale greenhouse gas emissions, Environ. Sci. Technol., 42,
6455–6461, 2008.
Satterthwaite, D.: Cities’ contribution to global warming: notes on
the allocation of greenhouse gas emissions, Environment and Ur-
banization, 20, 539–549, 2008.
Schulz, N.: Delving into the carbon footprints of Singapore-
comparing direct and indirect greenhouse gas emissions of a
small and open economic system, Energy Policy, 38, 4848–4855,
SSBC (State Statistical Bureau of China): Electricity Yearbook of
China 2009, China Statistics Press: Beijing, China, 2009.
UN: World urbanization prospects: the 2007 revision, United Na-
tions Population, Population Division of the Department of Eco-
nomic and Social Affairs, available at: (last ac-
cess: November 2010), 2007.
Wang H., Fu L., Zhou, Y., Du, X., and Ge, W.: Trends in vehicular
emissions in China’s mega cities from 1995 to 2005, Environ.
Pollut., 158, 394–400, 2010a.
Wang, S., Zhao, M., Xing, J., Wu, Y., Zhou, Y., Lei, Y., He, K., Fu,
L., and Hao, J.: Quantifying the air pollutants emission reduc-
tion during the 2008 Olympic Games in Beijing, Environ. Sci.
Technol., 44, 2490–2496, 2010b.
Wang, H., Fu, L., and Bi, J.: CO2and pollutant emissions from
passenger car in China, Energy Policy, 39, 3005–3011, 2011.
Yantosca, M., Xiao, Y., Evans, J., Streets, G., Vay, L., and Sachse,
W.: Improved quantification of Chinese carbon fluxes using
CO2//CO correlations in Asian outflow, J. Geophys. Res., 109,
D18S18, doi:10.1029/2003JD004362, 2004.
Zhao, M., Tan, L., Zhang, W., Ji, M., Liu, Y., and Yu, L.: De-
composing the influencing factors of industrial carbon emissions
in Shanghai using the LMDI method, Energy, 35, 2505–2510,
Zhou, Y., Wu, Y., Yang, L., Fu, L., He, K., Wang, S., Hao, J., Chen,
J., and Li, C.: The impact of transportation control measures on
emission reductions during the 2008 Olympic Games in Beijing,
China, Atmos. Environ., 44, 285–293, 2010b.
Atmos. Chem. Phys., 12, 6197–6206, 2012
... Under this principle, there are two accounting methods, bottom-up and top-down. The bottom-up approach, in which energy emissions datasets are created from energy data, includes industrial energy consumption, household energy consumption, industrial processes [3], electricity and maritime transport [4]. This approach calculates cities' carbon emissions and demonstrates the overall potential for mitigating climate change in cities. ...
Full-text available
The “14th Five-Year Plan” period is a critical period and a window to obtain emission peak and carbon neutrality in China. The Yellow River Basin, a vital location for population activities and economic growth, is significant to China’s emission peak by 2030. Analyzing carbon emissions patterns and decomposing the influencing factors can provide theoretical support for reducing carbon emissions. Based on the energy consumption data from 2000–2019, the method recommended by Intergovernmental Panel on Climate Change (IPCC) is used to calculate the carbon emissions in the Yellow River Basin. The Logarithmic Mean Divisia Index (LMDI) decomposition method decomposes the influence degree of each influencing factor. The conclusions are as follows: First, The Yellow River Basin has not yet reached the peak of carbon emissions. Regional carbon emissions trends are different. Second, Shandong, Shanxi, Henan and Inner Mongolia consistently ranked in the top four in total carbon emissions, with low carbon emission efficiency. Third, Economic development has the most significant contribution to carbon emissions; other factors have various effects on nine provinces.
... This paper draws on emissions data from Wang et al. (2019), whose analysis is based on the Intergovernmental Panel on Climate Change (IPCC) territorial emissions (IPCC, 2006). Emissions from agriculture, forestry and other land use, as well as waste, have been excluded due to their high uncertainties and relatively small influence on urban emissions (Wang et al., 2012). Data on national carbon emissions used for prediction comes from CEADS database (CEADS, 2019). ...
Full-text available
Continued urban population expansion will be a defining challenge for climate change mitigation, and global sustainability more generally, over the coming decades. In this context, an important but underexplored issue concerns the relationship between the scale of urban areas and their carbon emissions. This paper employs the urban Kaya relation and Reduced Major Axis regression to look at urban emission patterns in China from 2000 to 2016. Our results reveal that larger cities tend to have lower per capita emissions. Thus, population agglomeration may be able to contribute to climate change mitigation and a wider transition to sustainability. The inverse-U shape between carbon emissions and population size is found. In addition, we observe unique scaling patterns in different regions, revealing how the relationship between emissions and population can be influenced by economic geography. City consumption weakens the role of population agglomeration in reducing carbon emissions in the East region, therefore it should be placed top priority in carbon emissions mitigation. These findings are important for China which looks to achieve carbon neutrality by 2060 against the backdrop of intertwined interplay between population agglomeration and city consumption.
... Previous studies have applied top-down [12][13][14] and bottom-up [15,16] methods in the development of a city-level GHG emission inventory. The bottom-up method divides and solves the problem based on small and practical parts, which considers a finer spatial scale [17]. ...
Full-text available
Low carbon city development and greenhouse gas (GHG) emission mitigation in urban communities are urgent. There is great potential to improve the GHG inventory at the community level. Meanwhile, building zero-waste cities and improving waste treatment efficiency have been significant environmental issues due to the rapid increase of waste generation. This research aims to develop a community-scale GHG emission inventory of the waste sector and improve its accuracy and consistency through applying the bottom-up approach. This study covers both direct and indirect emissions categories of the waste sector with the goal of building a zero-waste community. Honjo Waseda community, located in Japan, was used as a case study community. Energy consumption waste treatment sectors were evaluated and calculated through first-hand field data. GHG emission estimation of the waste sector included waste incineration, residential wastewater, and waste transport. The highest emissions originated from Beisiagate supermarket due to the large waste amount produced, and the CO2-biomass carbon emissions reached approximately 50% of the total emissions. Furthermore, a quantitative analysis of the implementation of new technologies was also conducted. This study created proposals for GHG emission reduction toward a zero-waste community through the comparison of three cases. Case 1 was business as usual; Case 2 proposed a combination of incineration bio-gasification (MBT); Case 3 introduced a combination of solid recovered fuel (SRF) and a bio-gasification system. SRF contributed the most to emission reduction, and Case 3 exhibited the highest energy recovery. Furthermore, comparing the GHG emissions produced by the use of SRF for power generation and heat supply revealed that using SRF as a heat supply reduced more GHG emissions than using SRF for power generation.
... In a greenhouse gas emission inventory, greenhouse gas emissions caused by human activities in a certain area are calculated and summarized (Stechemesser and Guenther, 2012). Research on city-level greenhouse gas emission inventories has mainly focused on the accounting of city-level carbon dioxide emissions (Cai et al., 2019b;Mi et al., 2016;Shan et al., 2018;Wang et al., 2012) and analyzed the current status and characteristics of carbon dioxide emissions from different departments and on different scales to explore future trends in carbon dioxide emissions and propose reasonable emission reduction policies Shan et al., 2017;Tian et al., 2018;Wang et al., 2020). In addition, research on city-level greenhouse gas accounting has mostly focused on comparing international cities or typical cities, and few studies have performed greenhouse gas accounting for all cities within a country. ...
China attaches great importance to methane (CH 4) emissions, and the "14th Five-Year Plan" will formulate actions related to CH 4 emission reduction. Accounting for and understanding CH 4 emissions at the city level are of great importance because cities are the focus of future mitigation and adaptation activities to address climate change, especially in developing nations. However, existing studies have not yet provided a complete and updated emissions database of city-level CH 4 emissions for China. To promote research on CH 4 emissions accounting , this paper applies a production-based method to calculate anthropogenic CH 4 emissions for 340 Chinese prefecture-level administrative units in 2015 based on multisource statistical information. The CH 4 emission sources considered in this paper are mainly divided into five categories: underground coal mining, rice cultivation, livestock management, straw burning, and waste treatment. The results show that the characteristics of CH 4 emissions in Chinese cities vary significantly and different emission sources present different spatial distribution characteristics. Moreover, there is an obvious single-source dominant pattern in the CH 4 emission structure of Chinese cities. More than 50% of the total CH 4 emissions in more than two-thirds of Chinese cities are from a single source. The accounting results are basically consistent with the official results, and the error range is within 20%, which is within the emission uncertainty range. The main contribution of this paper is the construction of a basic database to support China's efforts to achieve carbon neutrality by 2060.
The economy, society, and environment of the world are undergoing profound changes. Achieving sustainable development has become the common aspiration of the international communities. In recent years, sustainability issues are gaining increasing prominence for cities to formulate long-term development strategies. The Yangtze River Delta (YRD) urban agglomeration, one of China’s most developed and densely populated industrial areas, has encountered environmental problems. Assessment of city sustainability of the YRD is a prominent issue in the process of urban construction. The paper used multi-criteria decision making (MCDM) framework to evaluate the sustainability level of 26 cities in the YRD by selecting 24 indicators from the economic, social, and environmental dimensions. In the weighting process, the paper proposed a new subjective and objective integrated weighting method based on stochastic simulation techniques. Relative results including pairwise priority matrix (PPM), comprehensive priority index (CPI), and the most likely ranking were obtained to reflect the superiority degree between two cities by adequate comparisons. The assessment results indicate that all the cities showed an upward trend from 2015 to 2019. Shanghai had overwhelming advantages over the other cities. The CPIs of Suzhou, Nanjing, Wuxi, Changzhou in Jiangsu province, and Hangzhou, Ningbo, Zhoushan in Zhejiang province were above 50 in 2015 and increased to over 85 in 2019. Additionally, most cities had unbalanced development on the three dimensions. Each city should firstly pay more attention to their own weak points, and then achieve balanced development and long-term sustainability.
China is committed to developing a low-carbon economy that will contribute to achieving the national strategic target of carbon peak and carbon neutrality. However, changes in energy-based carbon dioxide (CO2) emissions at both long-term and global-local scales remain poorly revealed. This study explored the temporal-spatial distribution characteristics of energy-based CO2 emissions calculated by the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method during the period of 2004, 2010 and 2019, covering 30 provinces in China. Then, this presented study examined the impact degree of socio-economic factors concerning energy-based CO2 emissions at the global and local levels using the expanded Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and geographically weighted regression (GWR) model, respectively. The results indicated that the total CO2 emissions have significant spatial spillover effect in northeast region, where the cluster pattern of high total CO2 emissions and high CO2 emissions from coal mainly occur. Moreover, both urbanization and Gross Domestic Product (GDP) are significantly responsible for the increase in CO2 emissions. This proposed research framework can be promoted to explore the temporal-spatial characteristics of energy-based CO2 emissions in the city, the county, and even town levels to successfully realize CO2 emissions reduction.
A city’s economic structure and energy mix would change when the city is developed to accommodate more residents, visitors, and activities. This paper reviews Macao’s economic growth, energy use, and greenhouse gases (GHG) emission from 1985 to 2020. Specifically, Macao’s gross domestic product (GDP), energy use, and GHG emission have surged after the gaming industry was liberalized in 2002. The official data show that Macao’s GDP was MOP 11 billion in 1985, increased by four-fold to MOP 54 billion in 2000, and then surged rapidly to MOP 445 billion in 2019. Additionally, Macao’s total energy use increased from 8,840[Formula: see text]TJ in 1985 to 48,330[Formula: see text]TJ in 2019 while Macao’s GHG emission increased from 0.70[Formula: see text]Mt of CO 2 -equivalent in 1985 to 6.13[Formula: see text]Mt of CO 2 -equivalent in 2019. Macao’s GHG emission from all local sources per capita and GDP per capita exhibit an inverted U-shaped relationship, showing an environmental Kuznets curve. Due to the negative impact of COVID-19 pandemic, Macao’s GDP dropped by 56% to MOP 194 billion while its total energy use and GHG emission dropped by 33% and 17% to 32,198[Formula: see text]TJ and 5.06[Formula: see text]Mt of CO 2 -equivalent, respectively, in 2020.
China has become the world's largest carbon dioxide emitter and energy consumer, with serious environmental problems. Therefore, low-carbon economy has become the best choice for its development. In this paper, the economy, society, energy and environment are taken into account to build a low-carbon economy system (LCE). It is used to analyze the drivers of CO2 emissions and impact of low-carbon transition in China. Bayesian network and scenario analysis are applied. The results show that the increase of per capita consumption expenditure, energy consumption per unit of GDP, energy consumption per capita and CO2 emissions per capita is the reason for the continuous growth of CO2 emissions; the improve of energy efficiency, CO2 emissions per unit of GDP and CO2 emissions per unit of energy consumption inhibits CO2 emissions. China's low-carbon development does not come at the expense of its economic growth. Actually, the low-carbon transition is accompanied by the growth of population, consumer spending, energy consumption and energy efficiency. Urbanization rate will be up to 75.70%–97.44%. Compared with 2017, energy efficiency increases by 4.51%; energy consumption per unit of GDP reduces by 0.4%. Transformation of energy subsystem is the key to a low-carbon road.
Full-text available
Developing countries like Pakistan are facing issues of environmental degradation. The use of non-renewable energy consumptions for economic growth sets the ground for environmental degradation, and its consequences cannot be ignored. It is, therefore, necessary to identify such determinants, model the nexus and explore the feedback effect that may play a constructive role in mitigating the environmental issues. This study aims to probe the robust nexus between carbon emission, energy consumption, and economic progress both in bivariate and multivariate models for 1971–2019 in Pakistan. Variables’ robustness and interaction are investigated using the Johansen co-integration, and Auto-regressive Distributed Lag bound testing method for estimating the long-run relationship at the significance level of 5%, respectively. The examined results of Auto-regressive Distributed Lag shows that energy consumption, economic growth, urbanization, research and development, and forest area have a positive and significant impact on carbon emissions of Pakistan in the long run while in the short run urbanization and forest area have found a negative effect on carbon emissions The empirical results of Granger causality are studied in a Vector Error Correction Model framework entail that bidirectional causality exists between energy consumption and CO2 emissions across the three causality tests namely short run, long run, and the strong causality.Furthermore, the results indicate a positive and significant impact of a fast-growing economy and energy consumption on CO2 emissions, suggesting that economic progress and high energy consumptions are barriers to improve environmental quality in the long run and their impact on the environment is inevitable. Based on the estimated results, it is recommended that introducing cautious energy regulating policy by the assessment of traditional old energy resources and related emissions for long-run economic progress would assist towards a low-carbon prospect. Findings further suggest that the government needs a strong green growth policies framework that encourages energy efficiency and technology innovations for renewable energy targets by eliminating the subsidies over fossil fuel, raised to substitute energy conversion as technology innovations are permanent, and minimize the risk of policy volatility.
Carbon neutrality has attracted tremendous attention. Cities contribute the most to CO2 emissions. However, the contribution of vegetation to fossil-fuel-related CO2 emissions in urban agglomeration is unclear. Clarifying the trade-off role of vegetation can disaggregate carbon reduction targets down to sub-units to adapt to and even mitigate global warming. In this study, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), one of the world's largest metropolitan areas, was studied using our proposed inter-calibration method. The results showed that the inter-calibration method is satisfactory and that the EANTLI model effectively decreases the blooming and saturation effects of nighttime light. In addition, fossil-fuel-related CO2 emissions increased significantly (P < 0.0001) in the GBA during 2000–2018, while the variation in CO2 sequestrations was far lower than that in the increase in emissions. CO2 sequestrations by vegetation fully offset fossil-fuel-related CO2 emissions in 2000, while the status reversed after 2001. Our findings illustrate the role of vegetation carbon sequestration in offsetting fossil-fuel-related CO2 emissions and emphasize the importance of a CO2 budget. Additionally, the one city, one policy strategy is a good choice for further adapting to and mitigating global warming.
Full-text available
This paper suggests that the contribution of cities to global anthropogenic greenhouse gas emissions is often overstated. Many sources suggest that cities are responsible for 75—80 per cent of all such emissions. But as statistics drawn from the IPCC's Fourth Assessment show, this considerably understates the contributions from agriculture and deforestation and from heavy industries, fossil-fuelled power stations and high-consumption households that are not located in cities. It is likely that, worldwide, less than half of all anthropogenic greenhouse gas emissions are generated within city boundaries. However, if greenhouse gas emissions from power stations and industries are assigned to the location of the person or institution who consumes them (rather than where they are produced), cities would account for a higher proportion of total emissions. But it would be misleading to attribute this to “cities” in general, since these emissions would be heavily concentrated in cities in high-income nations and they should be ascribed to the individuals and institutions whose consumption generates them, not to the places where they are located.
Full-text available
We use observed CO2:CO correlations in Asian outflow from the TRACE-P aircraft campaign (February-April 2001), together with a three-dimensional global chemical transport model (GEOS-CHEM), to constrain specific components of the east Asian CO2 budget including, in particular, Chinese emissions. The CO2/CO emission ratio varies with the source of CO2 (different combustion types versus the terrestrial biosphere) and provides a characteristic signature of source regions and source type. Observed CO 2/CO correlation slopes in east Asian boundary layer outflow display distinct regional signatures ranging from 10-20 mol/mol (outflow from northeast China) to 80 mol/mol (over Japan). Model simulations using best a priori estimates of regional CO2 and CO sources from Streets et al. [2003] (anthropogenic), the CASA model (biospheric), and Duncan et al. [2003] (biomass burning) overestimate CO2 concentrations and CO2/CO slopes in the boundary layer outflow. Constraints from the CO2/CO slopes indicate that this must arise from an overestimate of the modeled regional net biospheric CO2 flux. Our corrected best estimate of the net biospheric source of CO2 from China for March-April 2001 is 3200 Gg C/d, which represents a 45% reduction of the net flux from the CASA model. Previous analyses of the TRACE-P data had found that anthropogenic Chinese CO emissions must be ∼50% higher than in Streets et al.'s [2003] inventory. We find that such an adjustment improves the simulation of the CO2/CO slopes and that it likely represents both an underreporting of sector activity (domestic and industrial combustion) and an underestimate of CO emission factors. Increases in sector activity would imply increases in Chinese anthropogenic CO2 emissions and would also imply a further reduction of the Chinese biospheric CO2 source to reconcile simulated and observed CO2 concentrations.
One of the challenges faced by local governments in the work with municipal climate action plans concerns accounting for the greenhouse gas (GHG) emissions—what emissions should be targeted, development of emissions over time, and how to effectively measure the success of local climate action. In this paper, we present challenges in developing a GHG emissions inventory related to the provision of municipal services. We argue that a consumption-based perspective, illustrated through the use of the carbon footprint (CF), rather than more conventional production-based inventory, provides a more useful and less misleading indicator. We present an analysis of the CF of municipal services provided by the city of Trondheim. The use of data directly from the city's accounting system ensures a reliable calculation of indirect emissions, and, with some minor modifications, also accurate data on direct emissions. Our analysis shows that approximately 93 percent of the total CF of municipal services is indirect
The net emissions of carbon from forestry and changes in land use in south and southeast Asia were calculated here with a book-keeping model that used rates of land-use change and associated per hectare changes in vegetation and soil to calculate changes in the amount of carbon held in terrestrial ecosystems and wood products. The total release of carbon to the atmosphere over the period 1850–1995 was 43.5 PgC. The clearing of forests for permanent croplands released 33.5 PgC, about 75% of the total. The reduction of biomass in the remaining forests, as a result of shifting cultivation, logging, fuelwood extraction, and associated regrowth, was responsible for a net loss of 11.5 PgC, and the establishment of plantations withdrew from the atmosphere 1.5 PgC, most of it since 1980. Based on comparisons with other estimates, the uncertainty of this long-term flux is estimated to be within ±30%. Reducing this uncertainty will be difficult because of the difficulty of documenting the biomass of forests in existence >40 years ago. For the 15-y period 1981–1995, annual emissions averaged 1.07 PgC y–1, about 50% higher than reported for the 1980s in an earlier study. The uncertainty of recent emissions is probably within ± 50% but could be reduced significantly with systematic use of satellite data on changes in forest area. In tropical Asia, the emissions of carbon from land-use change in the 1980s accounted for approximately 75% of the region’s total carbon emissions. Since 1990 rates of deforestation and their associated emissions have declined, while emissions of carbon from combustion of fossil fuels have increased. The net effect has been a reduction in emissions of CO2 from this region since 1990.
The development of urbanization is accelerating in China, and there are great pressures and opportunities in cities to reduce carbon emissions. An emissions inventory is a basic requirement for analyzing emissions of greenhouse gases (GHGs), their potential reduction and to realize low-carbon development of cities. This study describes a method to establish a GHGs emissions inventory in Chinese cities for 6 emission sources including industrial energy consumption, transportation, household energy consumption, commercial energy consumption, industrial processes and waste. Nanjing city was selected as a representative case to analyze the characteristics of carbon emissions in Chinese cities. The results show that carbon emissions in Nanjing have increased nearly 50% during the last decade. The three largest GHGs contributors were industrial energy consumption, industrial processes and transportation, which contributed 37-44%, 35-40% and 6-10%, respectively, to the total GHGs emissions. Per GDP carbon emissions decreased by 55% from 2002 to 2009, and the per capita and per GDP carbon emissions were comparable or even lower than the world average levels. These results have important policy implications for Chinese cities to control their carbon emissions.
Traffic congestion and air pollution were two major challenges for the planners of the 2008 Olympic Games in Beijing. The Beijing municipal government implemented a package of temporary transportation control measures during the event. In this paper, we report the results of a recent research project that investigated the effects of these measures on urban motor vehicle emissions in Beijing. Bottom–up methodology has been used to develop grid-based emission inventories with micro-scale vehicle activities and speed-dependent emission factors. The urban traffic emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NOx) and particulate matter with an aerodynamic diameter of 10 μm or less (PM10) during the 2008 Olympics were reduced by 55.5%, 56.8%, 45.7% and 51.6%, respectively, as compared to the grid-based emission inventory before the Olympics. Emission intensity was derived from curbside air quality monitoring at the North 4th Ring Road site, located about 7 km from the National Stadium. Comparison between the emission intensity before and during the 2008 Olympics shows a reduction of 44.5% and 49.0% in daily CO and NOx emission from motor vehicles. The results suggest that reasonable traffic system improvement strategies along with vehicle technology improvements can contribute to controlling total motor vehicle emissions in Beijing after the Olympic Games.
Urban areas contain 40% of the population and contribute 75% of the Chinese national economy. Thus, a better understanding of urban energy uses is necessary for Chinese decision-makers at various levels to address energy security, climate change mitigation, and local pollution abatement. Therefore, this paper addresses three key questions: What is the urban contribution to China's energy usage and CO2 emissions? What is the contribution of large cities, and what alternate energy–economy pathways are they following? How have energy uses and CO2 emissions transformed in the last two decades in key Chinese cities? This three-tier analysis illustrates the changes in urban energy uses and CO2 emissions in China. The results show that the urban contributions make up 84% of China's commercial energy usage. The 35 largest cities in China, which contain 18% of the population, contribute 40% of China's energy uses and CO2 emissions. In four provincial cities, the per capita energy usage and CO2 emissions have increased several-fold. Rapid progress was made in reducing the carbon intensity of economic activities in cities throughout the 1990s, but alarmingly, such progress has either slowed down or been reversed in the last few years. These results have important policy implications.
In this paper, Shanghai's CO2 emissions from 1995 to 2006 were estimated following the IPCC guidelines. The energy demand and CO2 emissions were also projected until 2020, and the CO2 mitigation potential of the planned government policies and measures that are not yet implemented but will be enacted or adopted by the end of 2020 in Shanghai were estimated. The results show that Shanghai's total CO2 emissions in 2006 were 184 million tons of CO2. During 1995–2006, the annual growth rate of CO2 emissions in Shanghai was 6.22%. Under a business-as-usual (BAU) scenario, total energy demand in Shanghai will rise to 300 million tons of coal equivalent in 2020, which is 3.91 times that of 2005. Total CO2 emissions in 2010 and 2020 will reach 290 and 630 million tons, respectively, under the BAU scenario. Under a basic-policy (BP) scenario, total energy demand in Shanghai will be 160 million tons of coal equivalent in 2020, which is 2.06 times that of 2005. Total CO2 emissions in 2010 and 2020 in Shanghai will be 210 and 330 million tons, respectively, 28% and 48% lower than those of the business-as-usual scenario. The results show that the currently planned energy conservation policies for the future, represented by the basic-policy scenario, have a large CO2 mitigation potential for Shanghai.
With the rapid economic growth in China, the Chinese road transport system is becoming one of the largest and most rapidly growing oil consumers in China. This paper attempts to present the current status and forecast the future trends of oil demand and CO2 emissions from the Chinese road transport sector and to explore possible policy measures to contain the explosive growth of Chinese transport oil consumption. A bottom-up model was developed to estimate the historical oil consumption and CO2 emissions from China's road transport sector between 1997 and 2002 and to forecast future trends in oil consumption and CO2 emissions up to 2030. To explore the importance of policy options of containing the dramatic growth in Chinese transport oil demand, three scenarios regarding motor vehicle fuel economy improvements were designed in predicting future oil use and CO2 emissions. We conclude that China's road transportation will gradually become the largest oil consumer in China in the next two decades but that improvements in vehicle fuel economy have potentially large oil-saving benefits. In particular, if no control measures are implemented, the annual oil demand by China's road vehicles will reach 363 million tons by 2030. On the other hand, under the low- and high-fuel economy improvement scenarios, 55 and 85 million tons of oil will be saved in 2030, respectively. The scenario analysis suggests that China needs to implement vehicle fuel economy improvement measures immediately in order to contain the dramatic growth in transport oil consumption. The imminent implementation is required because (1) China is now in a period of very rapid growth in motor vehicle sales; (2) Chinese vehicles currently in the market are relatively inefficient; and (3) the turnover of a fleet of inefficient motor vehicles will take a long time.