June, 2014
Journal of Resources and Ecology
J. Resour. Ecol. 2014 5 (2) 115-122
DOI:10.5814/j.issn.1674-764x.2014.02.003
www.jorae.cn
Vol.5 No.2
Article
Increased CO2 Emissions from Energy Consumption Based on
Three-Level Nested I-O Structural Decomposition Analysis for
Beijing
ZHANG Wang1,2, SHEN Yuming1* and ZHOU Yueyun2
1 College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
2 Global Joint Research Centre for Low Carbon City, Hunan University of Technology, Zhuzhou 412007, China
Abstract: The first task in ensuring a reduction in CO2 emissions is to quantitatively measure the factors and
their effect size on increasing CO2 emissions due to fossil fuel consumption. An extension of the buying and
import-noncompetition economy-energy-CO2 emission input-output model was designed to analyze CO2 emission
increases for Beijing from 1997–2007. The increase in CO2 emissions because of energy consumption was broken
down into nine kinds of effects including the change in energy consumption intensity and structure, and economic
scale expansion. We found that the effect of economic scale expansion such as consumption investment, export
and selling were the main factors increasing CO2 emissions. The effect of the change in energy consumption
intensity was the dominant factor reducing CO2 emissions. CO2 emissions increased rapidly from 2002. The
first increase in carbon emissions was related to the service industry, adjustment in industrial structure and the
priority given to tertiary industries. High energy consumption manufacturing was the industrial branch driving
CO2 emissions; the main industry driving CO2 emission reductions was the energy industry. The new round of
industrialization with ‘high carbon’ features meant that CO2 emissions increased rapidly from 2002–2007. The
quantity and direction of the nine focal effects varied across industries and different industrial sectors.
Key words: I-O SDA method; energy consumption; carbon dioxide emissions; Beijing
1 Introduction
In Beijing, 93.78% of greenhouse gas emissions arose
from energy production from 1970–2007 (Zhu 2009). It is
therefore necessary to control and reduce CO2 emissions due
to energy consumption and build low carbon city for Beijing.
Basic research in this area is needed, including quantitatively
calculating growth factors and size effects for CO2 emissions
resulting from fossil fuel use and scientific screening in
important industries and key industrial sectors.
Since Leontief (1970) first used an input-output model
to estimate emissions from energy consumption in the USA
and develop policy suggestions for controlling growth in
energy consumption, structural decomposition analysis
(SDA) has gradually become an active decomposition
technique in economic and social research areas, energy
and the environment (Chang and Lin 1997; Ang et al.
2003; Alcantara and Duarte 2004; Limmeechokchat and
Suksuntornsiri 2007; Sun et al. 2005; Liang et al. 2007; Li
and Zhang 2008; Zhao and Hong 2009). Research in this
area has mostly been limited to a single state, and research
on urban or metropolitan areas is rare. Single level structural
decomposition methods on energy consumption or CO 2
emissions are generally used, and the final use effect is
classified into three parts: consumption, investment and
export/import.
Here, we use reference research ideas and decomposition
methods to analyze CO2 emission growth in China from
1992–2007 (Guo 2010), by focusing on the mega-city region
of Beijing. We considered that the domestic region also
features products of domestic buying and selling, so the
amount of supply was further decomposed to buying up and
selling alternative effects. This formed a Three-Level Nested
I-O SDA technique. Using this method, changes in CO2
Received: 2014-03-25 Accepted: 2014-04-23
Foundation: the National Twelfth-Five Year Science and Technology Support Program (2011BAJ07B03-06).
* Corresponding author: SHEN Yuming. Email: [email protected].
116
Journal of Resources and Ecology Vol.5 No.2, 2014
emissions from energy consumption could be decomposed
totally to nine species effects including structural change
effect of energy consumption (SCE), intensity change effect
of energy consumption (ICE), consumption expansion effect
(CEE), investment expansion effect (IEE), selling expansion
effect (SEE), export expansion effect (EXE), buying up
alternative effect (BUE), imports alternative effect (IME)
and the Leontief coefficient change effect (LCE).
2 Methods
2.1 The I-O SDA method
An input-output table distinguishes three parts as local
production, buying and selling, but these have not been
determined, so the table assumes no difference among
buying, selling and products produced in Beijing. Therefore,
an extensional competition input-output table on economyenergy-CO2 emission (buying, imported) was adopted (Table
1).
Because the department of energy consumption is not
matched between the input-output table and energy statistics
data, the industrial sector as a whole was divided into 28
sub-sectors with serial number (Table 2). Then the endconsumer energy was divided into 19 types according to the
China Energy Statistical Yearbook (Table 3). Thus in Table
Table 1 Simple table for buying and import competition economy-energy-CO2 emissions input-output.
Total intermediate
use
Intermediate input
Total value-added
Total input
Energy consumption
Structure of energy consumption
CO2 emission
AX
V
XT
FEX
F
QT=cFEX
Final use (Y)
Total
Buying up Imported
Total
Gross fixed capital
output
Selling Export
consumption
formation
C
I
SE
EX
BU
IM
X
Note: A is direct consumption coefficient matrix; XT and QT is separately transposed matrix of X and Q; as CO2 emission coefficient, c is very stable in the
short time and was assumed to remain unchanged in this research.
Table 2 Industry classification and numbering.
Industry
Farming, forestry, animal husbandry, fishery
Mining and washing of coal
Extraction of petroleum and natural gas
Mining and processing of metal ores
Mining and processing of nonmetal ores
Number
1
2
3
4
5
Manufacture of foods and tobacco
6
Manufacture of textile
Manufacture of textile wearing apparel, footwear, caps
leather, fur, feather and related products
Processing of timber, manufacture of wood, bamboo, rattan,
palm, straw and products furniture
Manufacture of paper and paper products, printing,
reproduction of recording media, manufacture of articles
for culture, education and sport activity
Processing of petroleum, coking, processing of nuclear fuel
Chemical industries
Manufacture of non-metallic mineral products
Smelting and pressing of metals
7
8
9
Industry
Number
Manufacture of metal products
15
Manufacture of general and special purpose machinery
16
Manufacture of transport equipment
17
Manufacture of electrical machinery and equipment
18
Manufacture of communication equipment, computers
19
and other electronic equipment
Manufacture of measuring instruments and machinery
20
for cultural activity and office work
Manufacture of artwork and other manufacturing
21
Production and distribution of electric power and heat
22
power
Production and distribution of gas
23
10
Production and distribution of water
24
11
12
13
14
Construction
Transport, storage and post
Wholesale, retail trade and hotel, restaurants
Others services
25
26
27
28
Note: For further research, 28 industries were merged as four kinds of industries: Agriculture (number 1), industry (number 2–24), construction (number
25) and service (number 26–28) as per section 3.2. According to, more or less, the production and consumption of the energy industry, these 28 industries
were divided into four kinds of industrial sector as energy industry (number 2, 3, 11, 22, 23), low energy consumption industry (number 6–10), high
energy consumption industry (number 12–20) and other industry (number 4, 5, 21, 24) as per section 3.3.
ZHANG Wang, et al.: Increased CO2 Emissions from Energy Consumption Based on Three-Level Nested I-O Structural Decomposition Analysis for Beijing
117
Table 3 Kinds of final energy consumption and average low calorific value, poetical carbon emission factor, carbon oxidation
rate and conversion factor.
Kinds of consumption
Raw coal
Cleaned coal
Other washed coal
Briquettes
Coke
Coke oven gas
Other gas
Crude oil
Gasoline
Kerosene
Diesel oil
'Fuel oil
LPG
Refinery gas
Natural gas
Other petroleum products
Other coking products
Heat
Electricity
Average low calorific value
(kJ kg-1 or m-3 or kWh-1)
20 934
26 377
8274
20 500
28 470
16 746
5277
41 868
43 124
43 124
42 705
41 868
47 472
46 055
35 588
41 816
28 200
3596
Poetical carbon emission Carbon oxidation Conversion factor (kg sce kg-1
or m-3 or MJ-1 or kWh-1)
(tc 10-12 J-1)
rate
26.80
0.922
0.7143
25.80
0.940
0.9000
25.80
0.940
0.4286
33.60
0.900
0.6000
29.41
0.928
0.9714
13.00
0.990
0.6143
13.00
0.990
0.1786
20.08
0.979
1.4286
18.90
0.980
1.4714
19.60
0.986
1.4714
20.17
0.982
1.4571
21.09
0.985
1.4286
17.20
0.989
1.7143
18.20
0.989
1.5714
15.32
0.990
1.3300
20.00
0.980
1.4286
29.41
0.928
1.2437
0.0341
0.1219
Note: Average low calorific value, poetical carbon emission factor and carbon oxidation rate of fossil fuels are derived from test results from the Energy
Saving and Environment Protection Center in Beijing; conversion factor of each fuel resulted from the China Energy Statistical Yearbook 2011, emission
factor of heat equivalent about SCE was 2.46 t CO2 tsce-1; emission factors of outside electricity derived from the marginal electricity emission factor
OM regional grid of baseline emission factor in northern China for China regional grid published by National Development and Reform Commission as:
1.0585 t CO2 MWh-1 before 2007, and 1.1208 t CO2 MWh-1 in 2007.
1, F was 19×28 matrix of energy consumption structure
in production sector; c was the CO2 emission coefficient
for 19 kinds of energy. A diagonal matrix including 1×19
vector elements and 28×28 band for E was formed, where
eii represented direct energy intensity on output of sector i,
which means the ratio of final energy consumption about
each department to its total output. Thus Q=cFEX and S=cF
was supposed, so CO2 emissions were Q=SEX, written in
matrix form as Q = Ŝ Ê X. So: ΔQ = Ŝt Êt Xt− Ŝ0 Ê0 X0 .
where, the subscript of t, 0 represented separate variables
in the reporting and base period; and Δ represented the
value change of variable. Due to more variables, in this
paper the polar decomposition method was used for factor
decomposition (Dietzenbacher and Los 1998), thus:
corresponding to the column vector X of Total Output,
the column vector SE of Selling, the column vector EX of
Export, the column vector BU of Buying up, the column
vector IM of Import. In light of methods of Liao (2009) for
decomposing changes in economic sale ΔX, it assumed that
R0=(I− Û 0 A0)−1, Rt=(I− Û t At)−1, then
ΔX =
1
2
+
+
ΔQ =(Δ Ŝ Ê0 X0+Δ Ŝ Êt Xt)/2+( ŜtΔ Ê X0+ Ŝ0Δ Ê Xt)/2
+( Ŝ0 Ê0 ΔX+ Ŝt Êt ΔX)/2
(1)
According to the balanced relation of regional inputoutput tables, changes in economic scale could be further
decomposed into CEE, IEE, SEE, EXE, BUE, IME
and LCE. If u i meant the supply ratio of each regional
department, the formula was ui= (xi–si–ei) / (xi–si–ei+bi+mi).
Among them, xi, si, ei, bi, mi represented separate elements
+
(R0 Û 0+Rt Û t)ΔC +
1
2
1
2
(R0+R1)ΔSE +
1
2
1
2
(R0 Û 0+Rt Û t)ΔI
(R0+R1)ΔEX
[R0Δ Û(AtXt+Ct+It)+RtΔ Û(A0X0+C0+I0)]
1
(R0 Û 0ΔAXt+Rt Û tΔAX0)
(2)
2
For ui=(xi–si–ei) / (xi–si–ei+bi+mi)=1–[bi / (xi–si–ei+bi+mi)
+ m i / (x i–s i–e i+b i+m i)], it assumed that u bi=b i / (x i–s i–
ei+bi+mi), umi= mi / (xi–si–ei+bi+mi), thus
Δui= –(Δubi+Δumi)
(3)
(2) and (3) were put in (1), and it assumed that k=( Ŝt Êt
118
Journal of Resources and Ecology Vol.5 No.2, 2014
SCE = (Δ Ŝ Ê0X0+Δ Ŝ Êt Xt)/2
(4)
ICE = ( Ŝ tΔ Ê X0+Δ Ŝ0 Ê Xt)/2
(5)
CEE = k (R0 Û 0+Rt Û t) ΔC/2
(6)
IEE = k (R0 Û 0+Rt Û t) ΔI/2
(7)
eliminate the impact of price factors, annual input-output
tables were converted to price in 1997 by the GDP index of
corresponding years for related industries (farming, forestry,
animal husbandry and fishery; secondary industries;
construction; industry; and the tertiary industry). While it
lacked the price index of each producer or GDP index for
industrial sectors, the industrial value was converted by
a uniform index of total industrial GDP. The coefficient
formula of CO2 emissions caused by energy combustion
was: c = average low calorific value for unit fuel× poetical
carbon emission factor × carbon oxidation rate in the
combustion processing × 44 / 12 (IPCC 2007).
SEE = k (R0+Rt)ΔSE / 2
(8)
3 Results
EXE = k (R0+Rt)ΔEX / 2
(9)
+ Ŝ0 Ê0 )/2, (4)–(12) as formulas on structural decomposition
of Three-Level Nested can be derived. Finally, regional CO2
emission changes at different periods can be subjected to
factor analysis:
BUE =−k[R0ΔÛb(AtXt+Ct+It)+RtΔÛb(A0X0+C0+I0)]/2 (10)
IME =−k[R0ΔÛm(AtXt+Ct+It)+RtΔÛm(A0X0+C0+I0)]/2 (11)
LCE = k(R0 Û 0ΔAXt+Rt Û tΔAX0)/2
(12)
2.2 Data sources and processing mode
This increase in CO 2 emissions caused by energy
consumption were decomposed in Beijing over two periods:
1997–2002 and 2002–2007. Because data collection was
doubly limited for industrial energy consumption statistics
and input-output tables, the input-output tables relate
to the current price in 1997, 2002 and 2007 and energy
consumption tables of industrial departments for these
three years. The input-output tables of the current price
in 1997, 2002 and 2007 were sorted according to official
data published by the Beijing Statistics Bureau; and
industrial final energy consumption data originated from the
Beijing Statistical Yearbook and China Energy Statistical
Yearbook for the focal years. Based on requirements
for data matching, industrial departments of energy
consumption were adjusted to 28 sectors (Table 2). To
3.1 Whole state
CO 2 emissions increased 51.6499×10 4 t in the 10 year
focal period in Beijing (Table 4). The pulling factors
for this increase sorted by size were SEE (82.5929×10 4
t), IME (57.6191×10 4 t), CEE (29.7872×10 4 t) and IEE
(16.0009×104 t); cutting factors of CO2 emission reduction
were ICE (–1.25×104 t), LCE (–11.648×104 t) and BUE
(–7.9373×10 4 t). The growth factors of economic scale
such as consumption, investment, selling and exports
were major factors in emission growth, whose SEE was
the deciding factor. Whereas ICE was the major factor in
curbing growth in CO2 emissions. Regarding change in
CO2 emissions with time, there has been a soaring trend
since 2002. CO2 emissions increased 44.6291×104 t from
2002–2007, which accounted for 86.41% of the cumulative
increase from 1997–2007 (a 6.36 times increase). IME for
1997–2002 and 2002–2007 were all positive, indicating that
import structure had deteriorated CO2 emissions. During
the 10 years, LCE was negative, indicating that transform
of economic development played more of a role, including
relying on technology development, management intensive
and optimization of industry structure. The value shifted to
positive in 2002–2007, which increased CO2 emissions by
1.6854×104 t. This meant that general technical progress
had a significant setback and economic development was
Table 4 SDA to decompose the CO2 emission increase in Beijing from 1997–2007.
Influence factors
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
Increase in CO2 emissions (104 t equivalent CO2)
1997–2002
2002–2007
1997–2007
–467.14
3.71
–463.44
–6014.19
–6233.41
–1.25×104
–34.06
3012.78
2978.72
742.92
857.17
1600.09
5424.36
2834.93
8259.29
101.07
1133.47
1234.54
2234.31
–3028.04
–793.73
48.15
5713.76
5761.91
–1333.34
168.54
–1164.80
702.08
4462.91
5164.99
1997–2002
–66.54
–856.62
–4.85
105.82
772.61
14.40
318.24
6.86
–189.91
100
Contribution rate (%)
2002–2007
0.08
–139.67
67.51
19.21
63.52
25.40
–67.85
128.03
3.78
100
1997–2007
–8.97
–237.13
57.67
30.98
159.91
23.90
–15.37
111.56
–22.55
100
ZHANG Wang, et al.: Increased CO2 Emissions from Energy Consumption Based on Three-Level Nested I-O Structural Decomposition Analysis for Beijing
more extensive in this period. Although initiatives since
2004 such as “replacing coal with gas” and “replacing
coal with oil” to optimize energy structure were adopted
(Li 2011), SCE was less obvious. It reversed to a positive
value in 2002–2007 and the accumulated reduction for CO2
emissions was only 4.6344×104 t from 1997–2007.
For the contribution to increasing CO2 emissions caused
by expansion of consumption, investment, buying and
export in Beijing, the biggest increase was for SEE from
1997–2002 and CEE from 2002–2007. The top two for
annual cumulative increase was also SEE and CEE from
1997–2007. This indicates that selling and consumption
exceeded investment and exports, and the first two were
the major contributors of CO2 emissions growth. For BUE
and IME, the biggest increase was BUE from 1997–2002;
the biggest increase was IME, but BUE was negative for
119
2002–2007 and annual cumulative incremental was IME >
BUE from 1997–2007. On the one hand this indicates that
IME was always positive instead of pulling CO2 emission
growth; on the other hand it shows that the BUE shift to
negative directly reduced CO2 emissions in Beijing from
2002–2007 but indirectly increased CO2 emissions in other
regions. This conclusion is consistent with the findings
of Huang et al. (2012) who suggested that source areas
of embodied energy, particularly the provinces and cities
around Beijing, exert more pressure on resources and
the environment and very likely experience a number of
environmental problems.
3.2 Various industries
As shown in Table 5, the CO2 emission increase for the
agriculture, industry, construction and service industries
Table 5 SDA to decompose CO2 emission increases in different industries in Beijing from 1997–2007.
Period
1997–2002
2002–2007
1997–2007
Influence
factors
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
Increment of CO2 emission (104 t equivalent CO2)
Agriculture
Industry
Construction
Service
12.69
–183.37
–11.66
–284.80
91.33
–7497.36
83.60
1308.25
–4.71
–194.25
3.31
161.59
36.97
447.38
24.17
234.40
8.10
4865.48
10.34
540.43
13.30
120.87
3.56
–36.66
13.69
2028.53
4.70
187.38
–0.05
26.44
2.16
19.60
–4.28
–-1533.74
2.18
202.50
167.04
–1920.02
122.36
2332.70
2.34
96.35
4.81
–99.80
–17.21
–5522.86
26.69
–720.03
82.52
2622.44
1.43
306.39
–30.54
566.86
38.28
282.58
4.64
1641.64
–19.31
1207.96
9.70
695.25
–2.72
431.25
–58.77
–3543.72
–9.21
583.66
87.05
5260.82
17.54
348.35
109.77
808.71
1.31
–751.24
189.49
2625.50
58.82
1589.10
15.04
-87.02
–6.85
–384.61
74.12
–1.30×104
110.29
588.22
77.81
2428.18
4.75
467.98
6.42
1014.24
62.44
516.98
12.75
6507.12
–8.96
1748.39
23.00
816.12
0.84
394.58
–45.08
–1515.19
–4.51
771.05
86.99
5287.27
19.70
367.95
105.49
–725.03
3.48
–548.75
356.54
705.48
181.17
3921.80
Agriculture
7.60
54.67
–2.82
22.13
4.85
7.96
8.19
–0.03
–2.56
100.00
1.24
–9.08
43.55
–16.12
2.45
5.12
–31.01
45.94
57.93
100.00
4.22
20.79
21.82
1.80
3.57
6.45
–12.64
24.40
29.59
100.00
Contribution rate (%)
Industry Construction
9.55
–9.53
390.48
68.32
10.12
2.71
–23.30
19.75
–253.41
8.45
–6.30
2.91
–105.65
3.84
–1.38
1.77
79.88
1.78
100.00
100.00
3.67
8.18
–210.35
45.37
99.88
2.44
21.59
65.07
62.53
–32.83
26.48
–4.63
–134.97
–15.66
200.37
29.82
30.80
2.22
100.00
100.00
–12.33
–3.78
–1845.58
60.87
344.19
2.62
143.77
34.47
922.37
–4.95
115.68
0.46
–214.77
–2.49
749.46
10.88
–102.77
1.92
100.00
100.00
Service
–12.21
56.08
6.93
10.05
23.17
–1.57
8.03
0.84
8.68
100.00
–6.28
–45.31
19.28
17.78
76.02
27.14
36.73
21.92
–47.27
100.00
–9.81
15.00
11.93
13.18
44.58
10.06
19.66
9.38
–13.99
100.00
Note: An effect of contribution rate for some industries = (CO2 emission increment of an effect about some industries / total increment of CO2 emission
about some industries) × %; see Table 6.
120
was respectively 356.54×104 t, 705.48×104 t, 356.54×104 t
and 39.218×104 t from 1997–2007, and their contribution
ratio to the overall increase was 6.90%, 13.66%, 3.51% and
75.93%. This indicates that CO2 emission increases in the
service sector dominated, and the increase here was greater
than the total increase for the other three industries.
As for each industry, expansion of economic scale led
to IEE, CEE, SEE and EXE, which were major factors for
increasing CO 2 emissions. The contribution rate varied
across different industries: IEE and EXE were less than
CEE in agriculture and industry; CEE and EXE were less
than IEE in construction; and CEE + IEE + EXE < SEE
in industry and services. The main effects that led to a
reduction in CO2 emissions were not the same in different
industries: BUE was the only factor in agriculture for
–45.08×104 t; ICE was that in industry for –1.30×104 t and
the contribution rate was up to –1845.58%; every kind of
effect was more weaker in construction, whose contribution
rate were less than –5%; and SCE and LCE were main
factors in service. It is worth mentioning that increased
industrial CO 2 emissions converted from negative to
positive during 2002–2007, and exceed that of service more
than 1000 × 104 t, which meant the industries with character
of high energy consumption and high emission had been
increasing since 2002. Moreover IME of each industry was
positive, which indicated the situation for import structure
had worsened seriously.
The direction and power of playing a part for each effect
were different in every industry in 1997–2002 and 2002–
2007. The pulling factors of CO2 emission increase were:
LCE and CEE in agriculture and industry; SCE, IME, IEE
in construction; and EXE, SEE, IME in service. While the
major factors for curbing the growth in CO2 emissions were:
ICE and BUE in agriculture; BUE and SEE in industry;
SEE and ICE in construction; and ICE and LCE in service.
3.3 Industrial sectors
The whole industry was divided into four types: energy
industry, low energy consumption industry, high energy
consumption industry and other industry (classification
basis shown in Table 2).The increase in CO2 emissions in
various industries was decomposed in order to identify the
characteristics of CO2 emission growth for each industrial
sector (Table 6).
Table 6 shows the CO2 emission increase for the energy
industry, low energy consumption industry, high energy
consumption industry and other industry as –7004.05 ×
104 t, 402.27 × 104 t, 7271.82 × 104 t and 35.44 × 104 t for
1997–2007, respectively. These data suggest that the key
industrial sector for increasing CO2 emissions was the high
energy consumption industry. Therefore, the most effective
way to reduce industrial emissions in Beijing is eliminating
and constraining high energy consumption sectors. This
would reduce the proportion of added value for the energyintensive sector in the secondary industry (Liu et al. 2010),
Journal of Resources and Ecology Vol.5 No.2, 2014
and this concretely started from those three major industries
Smelting and Pressing of Metals, Manufacture of Nonmetallic Mineral Products and Chemical Industries. The
CO2 emission increases of low energy consumption industry
should not be ignored, particularly the Manufacture of
Foods and Tobacco as it was 225.97 × 104 t and 32.03% of
total industrial increment. Energy industry was an important
sector for cutting CO2 emission growth, mainly concentrated
in Processing of Petroleum, Coking, Processing of Nuclear
Fuel (–42.7137 × 104 t), and the Production and Distribution
of Electric Power and Heat Power (–27.0465 × 104 t).
The direction and power for each effect were different for
every industrial sector. The pulling factors for CO2 emission
growth were: SEE and IME in energy industry; ICE and
IME in low energy consumption industry; IME and SEE
in high energy consumption industry; and IME and ICE in
other industry. While the major factors for curbing growth
in CO2 emissions were: ICE was the greatest contribution
in the energy industry; LCE and BUE in the low energy
consumption industry (contribution rate less than –10%);
and BUE in the high energy consumption industry and other
industry.
The direction and power for every effect were also
different for each industrial sector from 1997–2002 and
2002–2007. The pulling factors for CO2 emission increases
were: CEE and ICE in energy industry; IME and ICE in
low energy consumption industry; and LCE and IME in
high energy consumption industry and other industry. While
major factors for curbing the growth in CO2 emissions were:
BUE and SEE in energy industries; BUE and SEE in low
energy consumption industry; BUE and ICE in high energy
consumption industry; and BUE in other industry.
4 Conclusions and discussion
(1) The pulling factors of increased CO 2 emissions
were the growth of economic scale such as consumption,
investment, buying and export. The major factor curbing
the growth in CO2 emissions was the intensity change effect
of energy consumption. The imports alternative effect
was positive, but the structural change effect of energy
consumption was not clear. As for the allocation structure of
final demand, buying and consumption exceeded investment
and export, which were two major contributors to CO2
emission increases.
(2) CO 2 emissions increased sharply in 2002 when
new industrialization and ‘high carbon’ features were
implemented. The increase in CO2 emissions caused by
economic expansion fully offset curbing by the intensity
change effect of energy consumption, which remains. The
Leontief coefficient changes effect was converted from
negative to positive, which resulted in further CO2 emission
increases.
(3) Service was the largest sector for increasing CO 2
emissions, but the growth of industrial CO 2 emissions
converted from negative to positive in 2002 and exceeded
ZHANG Wang, et al.: Increased CO2 Emissions from Energy Consumption Based on Three-Level Nested I-O Structural Decomposition Analysis for Beijing
121
Table 6 SDA to decompose CO2 emission increases in different industry sectors in Beijing from 1997–2007.
Increment of CO2 emission (104 t equivalent CO2)
Period
1997–2002
2002–2007
1997–2007
Influence
factors
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
SCE
ICE
CEE
IEE
SEE
EXE
BUE
IME
LCE
Total
Energy
industry
30.52
–9373.24
–214.78
386.38
3203.68
72.07
1195.58
13.40
–11.06
–4697.45
–416.82
–5294.86
1123.64
291.85
1173.51
437.57
–1640.58
2102.89
–83.81
–2306.61
–386.30
–14668.09
908.86
678.23
4377.18
509.64
–445.00
2116.29
–94.87
–7004.05
Low energy
consumption
industry
High energy
consumption
industry
–10.26
68.39
–37.17
–18.26
76.21
–33.17
43.27
-9.76
–19.64
59.62
–7.93
184.01
25.58
13.45
27.63
16.22
–71.79
169.74
–14.28
342.64
–18.18
252.40
–11.59
–4.80
103.84
–16.95
–28.52
159.98
–33.92
402.27
–200.91
1766.68
54.38
80.25
1586.39
83.02
784.38
5.27
–1466.83
2692.62
521.99
–440.93
1471.91
262.30
392.11
217.75
–1570.21
2803.43
920.84
4579.20
321.08
1325.75
1526.29
342.55
1978.50
300.77
–785.83
2808.70
–545.99
7271.82
that of service generally. The key sector for CO2 emission
increases focused on the high energy consumption industry,
while the key sector for curbing CO2 emissions was the
energy industry. The direction and power of each effect
were different for various industries and each industrial
sector from 1997–2002 and 2002–2007.
Because data on the energy consumption of service
sectors could not be obtained in 1997, 2002 and 2007,
changes in CO 2 emissions for service sectors were not
analyzed. These limitations should be managed in future
research and relative data gained when the service
proportion has passed 75% of GDP in Beijing.
Other
industry
–2.72
40.81
3.31
–1.00
–0.80
–1.04
5.30
17.53
–36.21
25.18
–0.89
28.92
1.31
–0.74
48.39
23.70
–261.14
184.76
–14.04
10.26
–3.61
69.72
4.62
–1.74
47.59
22.66
–255.84
202.30
–50.26
35.44
Contribution rate (%)
Energy
industry
–0.65
199.54
4.57
-8.23
–68.20
–1.53
–25.45
–0.29
0.24
100.00
18.07
229.55
–48.71
–12.65
–50.88
–18.97
71.13
–91.17
3.63
100.00
5.52
209.42
–12.98
–9.68
–62.50
–7.28
6.35
–30.22
1.35
100.00
Low energy High energy
consumption consumption
industry
industry
–17.20
114.71
–62.34
–30.62
127.83
–55.64
72.58
–16.37
–32.94
100.00
–2.31
53.70
7.47
3.93
8.06
4.73
–20.95
49.54
–4.17
100.00
–4.52
62.74
–2.88
–1.19
25.81
–4.21
–7.09
39.77
–8.43
100.00
–7.46
65.61
2.02
2.98
58.92
3.08
29.13
0.20
–54.48
100.00
11.40
–9.63
32.14
5.73
8.56
4.76
–34.29
61.22
20.11
100.00
4.42
18.23
20.99
4.71
27.21
4.14
–10.81
38.62
–7.51
100.00
Other
industry
–10.81
162.07
13.16
–3.96
–3.16
–4.14
21.05
69.63
–143.82
100.00
–8.69
281.82
12.74
–7.23
471.64
231.04
–2545.27
1800.81
–136.87
100.00
–10.20
196.74
13.04
–4.91
134.30
63.94
–721.91
570.81
–141.81
100.00
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基于结构分解法的北京能源碳排放增量分析
张 旺1, 2, 申玉铭1, 周跃云2
1 首都师范大学 资源环境与旅游学院, 北京 100048;
2 湖南工业大学 全球低碳城市联合研究中心, 株洲 412007
摘 要: 确保减碳的首要任务是定量测度化石能源消费碳排放的增量影响因素及其大小。为分析北京市1997-2007年的碳排
放增量, 本文构建了一个扩展的（调入、进口）竞争型经济—能源—碳排放投入产出模型, 从整体特征、不同产业、工业行业3
个方面, 对1997-2007年北京能源消费的碳排放增量进行了结构分解。分析发现: 经济规模增长要素（消费、投资、调出和出口
等）是拉动碳排放增长的主导因素, 能源强度变动效应却是碳减排的决定性因素; 在规模扩张因素中, 消费和调出超过投资和出
口, 是碳排放增长的主要贡献者; 2002以来新一轮“高碳”特征的工业化导致CO2排量呈急增之势; 产业结构调整、三产比重最
大使得服务业成为碳排放增长的最大部门, 但工业排放的增长却后来居上; 碳增排的重点行业是高能耗业, 而碳减排的却是能源
工业; 两时段各效应在不同产业、不同工业行业的影响方向和大小不一。
关键词: I-O SDA法; 能源消费; CO2排放; 效应; 北京市