Proceedings of 6th Annual American Business Research Conference

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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
Provincial Responsibilities of Power Generation Emission in China
Feng Wang and Xin Liu
Accurate calculation of CO2 emissions in every province in China is the basis for the
development of regional energy policies. Based on the carbon emission flow in networks theory,
this paper proposes an approach to calculate provincial CO 2 emissions as a result of fossil
energy consumption from the perspective of final secondary energy consumption. This approach
calculates the CO2 emissions from fossil energy consumption in every province based on the
final energy consumption after considering cross-provincial secondary energy trading and
cross-provincial electric power trading in the regional power grid. Given the uneven distribution of
energy resources and imbalanced energy consumption, cross-provincial secondary energy
trading in China is significant, especially in the power industry. Compared with the traditional
approach that calculates provincial CO2 emissions from fossil energy consumption based on the
consumption of primary energy, provincial carbon intensity and corresponding energy policy can
be modified by adopting the approach proposed in this paper.
Key words: CO2 emissions, Regional grid structure, Provincial Carbon Intensity
1. Introduction
Energy and environment are important for the future of human society. The global climate
change as a result of extensive use of fossil fuel as well as CO 2 emissions has become the most
significant challenge facing humanity in the 21st century. As a party to UNCCFC and Kyoto Protocol,
China attaches great importance to energy conservation and emission reduction. In November 2009,
the State Council announced that China would reduce its CO 2 emission per unit of GDP by 40% to
45% of the emissions level 2005 by 2020, and this goal has been included in the mid- and long-term
plans of China for national and social development as a mandatory target (Wang et al., 2013; Yang
et al., 2014). China started to allocate CO2 emission reduction target at the provincial and municipal
levels during the 11th Five-Year Period. In 2012, the State Council released the 12th Five-Year Plan
for Energy Saving and Emission Reduction. By considering a variety of factors including the level of
economic development, industrial structure, emerging conservation potential, environmental
capacity, and national industrial arrangement into overall consideration, the Plan establishes
-------------------------------------------------------------------------------------------------------------------Feng Wang and Xin Liu
School of Economics and Business Administration, Chongqing University, Chongqing, CHINA.
 Corresponding author. Tel.:+ 86 1388 383 5370. Email addresses: liuxin@cqu.edu.cn (X. Liu)
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
reasonable energy conservation and emission reduction goals by region and industry, and
strengthens the evaluation and appraisal of goal implementation. Defining the pollutant discharge
allowances and develop energy and environment policies for different provinces and municipalities
according to the level of economic development and environmental carrying capacity have become
realistic and significant topics for research on the sustainable development of China (Auffhammer
and Carson, 2008; Yi et al., 2011; Wei, Ni and Du, 2012; Liu, Jayanthakumaran and Neri, 2013;
Wang et al., 2013; Yu, Wei and Wang, 2014).
Existing studies on the calculation of CO2 emissions from fossil energy consumption generally adopt the
IPCC approach (Intergovernmental Panel on Climate Change, 2006), that is, to estimate CO2 emissions
following the amount of fossil energy consumed. However, given the vast territory of China, different
regions vary in natural resource endowment and level of economic development, and significant
cross-provincial secondary energy trading has been observed (Zhang et al., 2013). For example, a huge
amount of secondary energy1, such as electricity and coal products, is traded from Shanxi, Inner Mongolia,
Shaanxi, and Guizhou to Beijing, Tianjin, Shanghai, Zhejiang and Guangdong. If the calculation of CO2
emissions is based on the consumption of primary energy, then the CO2 emission of provinces that sell
energy will be overestimated because the secondary energy traded to other provinces will be counted as
consumption by energy-selling provinces. Such overestimation may affect the establishment of the proper
goals and timeframes for energy conservation and emission reduction. Therefore, changing the traditional
mindset to observe the tangible “outlets” of CO2 emissions and recalculating the CO2 emissions of different
provinces or municipalities according to the final energy consumption by looking at CO 2 emissions from the
perspectives of “source to outlet” flows, distribution patterns, and cross-provincial trading of secondary
energy are necessary (Meng et al., 2011).
1
This paper adopts the classification of energy provided in the Chinese Energy Statistical Yearbook 2011. According to the yearbook, secondary
energy includes coal products (including cleaned coal, other washed coal, briquettes, coke, coke over gas, blast furnace gas, converter gas, and
other gases), petroleum products (including gasoline, kerosene, diesel, fuel oil, LPG, refinery gas, and other petroleum products), electricity and
heat, and primary energy including crude oil, raw coal, natural gas, and LNG.
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
The newly emerging theory on carbon emission flow in networks introduces the concept of network flow
into the CO2 emission analysis, and thereby revealing the characteristics and patterns of CO 2 emission flow
hidden in the energy flow (Kang et al., 2012). The adoption of this concept enables the calculation of carbon
emissions and carbon intensity of the power industry not only from the perspective of electricity production,
but also from the perspective of power consumption, and the carbon emissions of both electricity production
and power consumption are related to each other via carbon emission flows of power grids. Kang et al. (2012)
define carbon emission flow of the power industry as a virtual network flow formed from the carbon
emissions that are attached to power flow and used in the power industry to sustain any branch power flow,
and thereby linking the analysis of power industry CO2 emissions to the power flow calculation and
accurately reflect the characteristics of power industry CO2 emission flow. Their studies show that compared
with the power generation carbon emission evaluation results, the largest cross-regional carbon emission
flow in 2010 accounted for approximately 10% of the regional carbon emissions, and this percentage will
increase to approximately 40% by 2020 because of the continuous improvement of the power network and
increase in power transmission scale in China.
Using the carbon emission flow in networks theory, we recalculate the CO 2 emissions from fossil energy
consumption in various provinces and municipalities based on the final energy consumption and
cross-provincial secondary energy trading. The CO2 emissions from the production and use of the secondary
energy transferred from energy selling provinces to energy using provinces are counted as the hidden CO2
emissions of the latter. In the production and use of secondary energy, the use of heat is characterized by
vicinity and generally does not involve cross-provincial secondary energy trading. Therefore,
cross-provincial secondary energy trading mainly involves electricity production, coking and oil refining,
and resulting products. Electricity production has unique technological and economic characteristics, and
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
regional power grid covers more than one province/municipality. In addition, power trading also exists
between regional power grids (Zhu et al., 2005). Therefore, a new approach to calculate CO 2 emissions from
power consumption in different provinces and municipalities should be developed based on the carbon
emission flow in networks theory. The trading data on cross-provincial power purchases and key
cross-regional power transmission channels published by the State Electricity Regulatory Commission on its
website also provide the basis for this study (State Electricity Regulation Commission, 2012).
In the second part, we present the approach to calculate the provincial CO2 emission that considers
cross-provincial secondary energy trading. The third part discusses the methods for calculating the
hydropower-to-thermal-power ratio and the CO2 emission coefficient of thermal power, as well as the
methods for calculating hidden CO2 emission flow in cross-provincial power trading according to the carbon
emission flow in networks theory. The fourth part calculates the final energy consumption based on CO2
emission by province/municipality in 2010 and the total amount of CO2 emission flows after considering the
cross-provincial energy trading. CO2 emissions from power consumption are calculated after considering the
cross-provincial power trading. Using the new result of CO2 emission by province/municipality, the fifth part
analysis the provincial carbon intensity and contribution to national carbon intensity. The last part presents
the conclusion of this study.
2 Approach to Calculate Provincial CO2 Emissions that Considers Cross-Provincial
Secondary Energy Trading
2.1 CO2 emission calculation approach and CO2 emission coefficients
The Energy Balance Table by Region (Physical Quantity) contained in the Chinese Energy Statistical
Yearbook 2011 (Department of Energy Statistics, National Bureau of Statistics, 2012) is used to calculate the
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
provincial CO2 emissions from fossil energy consumption. In the balance tables, the total energy
consumption comprises final energy consumption, losses in processing and transformation, and energy losses.
The energy consumption data used to calculate the provincial CO2 emissions is the sum of the final energy
consumption and energy losses. Energy losses refer to the losses of energy in transportation, distribution, and
storage and various losses because of objective reasons in a given time period. For example, the Electricity
Balance Sheet shows that the final consumption of electricity in 2010 was 3936.63 billion KWh and the
losses in power transmission and distribution were 256.82 billion KWh, accounting for approximately 6% of
the final consumption. The CO2 emissions caused by burning fossil energy to generate electric power are
allocated to provinces and municipalities in this paper as the hidden CO2 emissions in power consumption.
The calculations in this paper include 20 energy products found in the energy balance table of the
Chinese Energy Statistical Yearbook 2011, including raw coal, cleaned coal, other washed coal, briquettes,
coke, coke over gas, blast furnace gas, converter gas, other gases, crude oil, gasoline, kerosene, diesel, fuel
oil, LPG, refinery gas, natural gas, LNG, heat, and electricity. Gangue, other petroleum products, other
coking products and other energy sources are not included in this paper.
According to the IPCC approach (Intergovernmental Panel on Climate Change, 2006), the total regional
CO2 emission from energy consumption is the sum of CO2 emissions from the use of primary and secondary
energies. The formula is as follows:
n
n
i 1
i 1
Tcoa2   Tcoa2 ,i   Ei  NCVi  CEFi  COFi  (44 /12)
(1)
where Tcoa2 is the total regional CO2 emission from energy consumption, i represents the different
energy products, E is the consumption of a certain energy product, NVC is the net calorific value of a certain
energy product, and for the purpose of this paper, the average low calorific value provided in the Chinese
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
Energy Statistical Yearbook 2011 is used as NCV. CEF is the carbon emission coefficients offered by IPCC,
but IPCC does not provide the coefficient for raw coal. The method developed by Chen(2009) is used, that
is, to take the weighted average of the carbon emission coefficients of bituminous coal and anthracite coal
provided by IPCC. COF is the carbon oxidation factor (0.99 for coal and 1 for other energy products); 44 and
12 are the molecular weights of CO2 and C, respectively2.
2.2 CO2 emissions after considering cross-provincial secondary energy trading
The secondary energy production mainly covers electricity production, heating, coking, and oil refinery.
In this paper, CO2 emissions from the production, transportation, and use of secondary energy that is
transferred from secondary energy selling provinces to energy using provinces are regarded as emissions of
the provinces where secondary energy is used. The change in CO2 emissions after considering secondary
energy (excluding power) trading is calculated as follows:
n
n
i 1
i 1
Tcob 2   Tcob 2,i   ( I co2.i  Oco2.i ) ,
(2)
where Tcob 2 is the change in CO2 emissions of a province after considering secondary energy trading,
I co2.i is the CO2 emissions from the production and transportation of purchased secondary energy, Oco2.i is
the CO2 emissions from the production and transportation of sold secondary energy, i is the secondary energy
products involving cross-provincial trading.
As the production and use of heat are characterized by vicinity, no cross-provincial trading is involved,
and no changes in CO2 emissions attributed to the consideration of cross-provincial energy trading. Therefore,
the CO2 emissions from the use of primary energy for local heat production are used for the purpose of this
2
See Exhibit 1 for CO2 emission coefficients of various energy products used in this paper.
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
paper. Given the unique technological and economic characteristics of electricity production, regional power
grids vary in hydropower-to-thermal-power ratio, thermal power production efficiency, and thermal power
carbon emission coefficient. Therefore, the impact of regional power grid structures and cross-provincial
power trading on provincial CO2 emissions from fossil energy consumption will be separately discussed
based on carbon emission flow in networks theory in the next section.
According to Weng (2009), the energy consumption per unit of coke production in the coking industry
of China is 146.49 kgce/t. The raw material for coking is raw coal. The CO 2 emission coefficient of raw coal
at 406.16 kg/t can be used to calculate the CO2 emission per unit of coke production (Zhou et al., 2012). The
calculations of CO2 emissions from rail, ship, and highway transportation of coal by Kang et al (2009).
Based on the carbon emission factors of commonly used means of transport and loss ratios in transportation
suggest that the CO2 emission ratio of coal transportation from Shanxi, Shaanxi, and Jiangxi to eastern China
ranges between 2.05% and 2.88%. Given that the energy balance tables used in this study only provides data
on purchased and sold secondary energy of different provinces and municipalities and further details on
cross-provincial secondary energy trading (particularly carbon emissions and transportation losses in the
transportation of secondary energy) is not available, the CO2 emissions from the transportation of primary
and secondary energies are not included in the emissions of energy using provinces.
Oil refining is more complicated than coking, and different products are produced at different stages. In
addition, the products also vary because of the difference in the processes of refining facilities. According to
Li, Qiao, and Zheng (2010), different refineries and different refining products vary in energy consumption
and CO2 emission coefficient. However, this paper adopts a simple averaging method to estimate the average
energy consumption in the production of LPG, gasoline, diesel, fuel, and kerosene, and then calculates the
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
CO2 emissions from the production processes because of the lack of detailed data on refining plants in
different provinces and municipalities.
CO2 emissions after considering cross-provincial secondary energy trading can be calculated by adding
the total regional CO2 emission from energy consumption and the change in CO2 emission after considering
cross-provincial secondary energy trading, as shown in Eq. (3).
Tco2  Tcoa2  Tcob 2  Eco2 ,
(3)
where Tco2 provincial CO2 emissions after considering secondary energy trading. Tcoa2
and Tcob 2 refer
to total regional CO2 emission from energy consumption and the change in CO2 emission after considering
cross-provincial secondary energy trading, respectively. Eco2 CO2 emissions from power consumption after
considering regional power grid structure and cross-provincial power trading.
3 Power Industry Carbon Emission Flow Based on Power
Consumption CO2 Emission
Calculation Method
The carbon emission calculation based on the traditional primary energy consumption approach focuses
on thermal power plants in the power industry as the only sources of emission. This approach does not reflect
the network characteristics of power grids and is not linked to the flow based calculation. The approach is
inappropriate for China given the large-scale cross-regional transmission of electric power. According to the
carbon emission flow in networks theory, the proportional sharing principle is applicable to the analysis of
the correlation between power generation units and load carbon flow. The principle specifies that for a
certain loading node in the power industry, the contribution of carbon inflows from all generation units in the
system to the load carbon flow rate is equal to the contribution of the same node to carbon flow rate (Kang et
al., 2012). Building on such a principle, we develop the approach for allocating the CO2 emissions from the
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
production of thermal power within a certain power grid to different provinces and municipalities that are
end users of thermal power.
3.1 Regional power grid structure and CO2 emission flow
When China implemented the electricity industry reform in 2002, the Chinese power grid has become a
structure consisting of regional, provincial, and independent power grids. Transmission of power among
provinces covered by the same regional power grids enjoys priority over cross-regional power transmission.
Currently, the operations of the State Grid Corporation include East China Grid, Central China Grid,
Northwest China Grid, Northeast Grid, and North China Grid, whereas China Southern Power Grid
Corporation includes power grids in the southern part of China. Except for a few provinces that have
provincial power grids, the provincial grid companies under the State Grid Corporation operate in a region
that is basically same as the provincial administrative division, which provides necessary conditions for using
the power data contained in the provincial energy balance tables to observe the cross-provincial CO2
emission flow.
Consistent with the overall strategy for Western Development and West-to-East Power Transmission of
China, the overall trend of cross-regional power trading is west-to-east transmission of power. The power
plants are mainly based in central and western regions that have access to abundant coal and hydroelectric
resources, and power buyers are mainly Beijing, Tianjin, Hebei, Shandong, Zhejiang, and Pearl River Delta
Region that are more developed economically. To satisfy the power demands of these economically
developed regions, the large scale power transmission leaves the less developed western regions to account
for the pollution and emissions attributed to the production of secondary energy. From the perspective of
coordinated regional economic development, to recalculate the provincial power consumption CO2 emissions
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
can provide important information for the decision making process concerning sustainable economic growth
in China.
Thermal power plants vary in energy input and energy conversion efficiency.
CO2 emission coefficient
of power generation in different provinces and within regional power grids also varies. Regional power grids
purchase power from thermal power plants that emit a considerable amount of CO2 and clean power plants
(such as hydropower plants) with nearly zero emission. The flow of hidden CO2 emission of the same
amount of electricity inputted from different nodes also varies from one node to another because regional
power grids also vary in hydropower-to-thermal-power ratio. For example, the flow of hidden CO2 emission
of the same amount of electricity transmitted from Shanxi (where mainly coal-fired power is generated) to
Beijing is different from that of the same amount of electricity transmitted from Hubei (where the share of
hydropower is higher) to Beijing. Therefore, developing new methods based on the carbon emission flow in
networks theory to calculate provincial power consumption CO2 is necessary.
3.2 Regional power grid structure based on power consumption CO2 emission calculation method
Given the unique technological and economic features of the power industry, i.e., the simultaneous
completion of electricity production, power supply, and use of power, to identify where the power traded
between provinces on regional power grids originate is very difficult. Based on the characteristics of carbon
emission flow distribution in the power network, we look at the cross-provincial power trading within the
same regional power grid as a whole when we discuss the energy conversion efficiency and emission
coefficients, and then proportionally allocates the CO2 emission flow hidden in cross-provincial power
trading to power purchasing provinces within the same regional power grid. As for power selling provinces,
the power consumption CO2 emission is calculated by multiplying the amount of thermal power consumption
in the total power consumption (total power consumption is the difference between the amount of power
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
generated and the amount of power traded and transmitted to other provinces) by thermal power carbon
emission coefficient.
Regarding cross-regional power trading, we first calculate the hidden CO2 emission of the cross-regional
power trading according to the thermal power carbon emission coefficient and hydro-to-thermal power ratio
of the power supplying grid, and then allocate the emission to the provinces covered by the power purchasing
grid. For example, the North China Grid, as a power purchasing grid, covers a number of provincial power
grids, including Beijing and Tianjin, and purchases power from Western Inner Mongolia Grid and Northwest
China Grid in addition to the large scale power transmission from Shanxi, a province covered by the North
China Grid. Therefore, when the thermal power CO2 emissions of the provinces and municipalities covered
by the North China Grid are calculated, the hidden CO2 emission of the power transmitted from Shanxi,
Western Inner Mongolia Grid, and Northwest China Grid should be allocated to the power purchasing
provinces. The thermal power carbon emission coefficients and hydro-to-thermal power ratios of different
power grids involved are important parameters for the calculation.
Like coal and oil products, electricity is subject to losses in transmission and distribution. The carbon
emission flow in networks theory can determine the CO2 emission flow hidden in the power grid structures,
as well as the CO2 emission attributed to the transmission and distribution losses. In the national electricity
and provincial energy balance tables of the Chinese Energy Statistical Yearbook 2011 that are used in this
paper, provincial power consumption comprises final power consumption and transmission and distribution
losses. The amount of electricity production (power available for consumption) is equal to the sum of final
power consumption and transmission and distribution losses. Therefore, the allocation of CO2 emissions to
different provinces based on the amount of electricity production implied that the CO2 emissions because of
the transmission and distribution losses have already been allocated to corresponding provinces.
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
Provincial power consumption CO2 emission can be calculated as follows:
(1) Power consumption CO2 emission of power selling provinces
Eco2  SCE j  Pj 
TC j
TPj
(4)
,
Where Eco2 = power consumption CO2 emission of a province as calculated from Eq. (3), SCE j =
total power consumption of province j (final power consumption + transmission and distribution losses), Pj
= proportion of thermal power consumption in the total power consumption of province j. In this paper, the
ratio of thermal power output to power output of province j is used as a substitute for the proportion of
thermal power consumption in the total power consumption, that is, Pj  TPj / E j ;
TC j
TPj
= CO2 emission
coefficient of thermal power, where TC j = total CO2 emission of province j as a result of its primary energy
input for thermal power production. The power consumption CO2 emission after considering the regional
power grid structure and cross-provincial power trading is a part of the total CO2 emission as a result of the
primary energy input for thermal power production because the power consumption of power selling
provinces is smaller than the power output.
(2) Power consumption CO2 emission of power purchasing provinces
Assuming province j purchases power from other provinces covered by the same regional power grid, 5
then its consumption of thermal power purchased from other provinces within the same regional power grid
can be expressed as:
5
Currently, some projects pilot point-to-grid cross-provincial power trading. For example, the Erlang Power Plant Phase 1 (1200 MW) jointly
developed by Chongqing and Guizhou will directly supply electricity to Chongqing Power Grid. As these projects remain under construction,
they have not have any impact on the power market structure of China.
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
n
 j  MIE j 
 P  SOE
j
j 1
j
 SOE
j 1
(5)
,
n
j
where  j = the amount of thermal power purchased from other provinces within the same regional
power grid, n = number of provinces within the same regional power grid, MIE j = amount of power
purchased by province j, SOE j = amount of power sold by province j. SOE j is 0 if j is a power
n
 P  SOE
purchasing province.
j
j 1
= proportion of thermal power in the amount of power traded among
n
 SOE
j 1
j
j
provinces within the same regional power grid.
The thermal power CO2 emission coefficient of a regional power grid  can be expressed as:
n

TC j
 TP
j 1
 SOE j  Pj
j
 SOE
j 1
(6)
.
n
j
 Pj
The CO2 emission of province j hidden in the power purchased from the regional power grid can be
calculated by multiplying the consumption of thermal power purchased from the regional power grid by the
thermal power CO2 emission coefficient:
rj   j   .
(7)
Then, the power consumption CO2 emission of province j is:
Eco2  ( SCE j  MIE j )  Pj 
TC j
TPj
 rj
(8)
n
Eco2  ( SCE j  MIE j )  Pj 
TC j
TPj
 MIE j 
 SOE
j 1
 Pj 
n
 SOE
j 1
13
j
j
TC j
TPj
(9)
Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
(3) Power consumption CO2 emission in the event of cross-regional power trading
The amount of power consumed can be used to calculate the power consumption CO2 emission of a
regional power grid that sells power to other grid(s). Similarly, the power consumption CO2 emission of a
power-selling province within a regional power grid that sells power to other grid(s) can also be calculated
by using the amount of power consumed. However, the power consumption CO2 emission of a power
purchasing province within a regional power grid that purchases power from other grid(s) should be
calculated by bringing rj (CO2 emission hidden in the power purchased from the regional power grid) as
calculated using Eq. (10) into Eq. (8).
n
n
 SOE
j 1
rj  MIE j 
j
 Pj 
TC j
TPj
n
  ROEk 
 SOE
m,k
m 1
n
m 1
n
 SOE   ROE
j 1
j
k 1
TCm,k
TPm,k
 SOE
k 1
n
 Pm,k 
m,k
,
(10)
k
where ROEk = amount of power purchased by the regional power grid that covers province j from
another regional power grid k. m,k refers to the mth province within regional power grid k. By bringing rj into
Eq. (8), the power consumption CO2 emission of the power purchasing province can be calculated.
4 Final Energy Consumption Based CO2 Emission by Province in 2010
4.1 Provincial CO2 emission and CO2 emission flow because of cross-provincial secondary energy
trading
The Energy Balance Table by Region (Physical Quantity) contained in the Chinese Energy Statistical
Yearbook 2011 is used to calculate the provincial fossil energy consumption CO2 emissions in 2010. Without
considering the cross-provincial secondary energy trading, the calculation based on the consumption of
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Proceedings of 6th Annual American Business Research Conference
9 - 10 June 2014, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-52-8
primary and secondary energies of each province/municipality show that the total fossil energy consumption
based on the CO2 emission of China in 2010 was 9.35483 billion tons. After considering cross-provincial
secondary energy trading and CO2 emissions from the production of secondary energy, the calculation
showed that the total fossil energy consumption based on CO2 emission of China in 2010 was 9.5586 billion
tons.
[Insert Table 1 Here]
Table 1 above shows the carbon trading and emission flows at provincial level in China. The data
suggests geographic-energy trading relation. China’s energy supply and demand distribution shows high
inequality. As mentioned in earlier sections of the paper, there is huge fissile fuel reserve and production
capacity in the west, while the under-developed social and economic status cause lower demand. The higher
developed eastern coastal regions of China show dynamic economic momentum and hence high energy
demand. However, the reserve are insufficient to support the economic and social development. Thus energy
tradings are flourishing between the energy rich regions and high energy consuming regions. There are
private trading corporations active between the regions, as well as government-led projects like West East
Gas Transmission Project. Thus even though some provinces or municipalities are big producers of fossil
energy, the carbon emission does not occur on their turf. What’s more, even though the emission does occur
in a certain provinces, with the national projects like West-East Electricity Transmission Project, other
provinces or municipalities consume the electric power and should be responsible for the carbon emission.
CO2 emission flow caused by cross-provincial secondary energy trading has a significant effect on the
fossil energy consumption CO2 emissions of some provinces and municipalities. After considering the CO2
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Proceedings of 6th Annual American Business Research Conference
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emission flow caused by the cross-provincial secondary energy trading, Hebei Province has a substantial
increase in CO2 emission (99.1479 million tons, up 13.09%), followed by Beijing (61.5981 million tons, up
53.14%), Liaoning (61.5559 million tons, up 11.86%), and Guangdong (61.4361 million tons, up 12.17%)
because CO2 emission flow is a result of secondary energy purchase. Inner Mongolia has a substantial
decline in CO2 emission (-11.56903 million tons, down 20.66%), followed by Shanxi (-55.7982 million tons,
down 12.43%), Guizhou (39.36214 million tons, down 17.96%), and Anhui (32.5696 million tons, down
10.87%) because CO2 emission flow is a result of secondary energy sales.
In 2010, Beijing purchased 24,554,500 tons of raw coal, 1,986,200 tons of cleaned coal, 759,200 tons of
coke, 12,550,100 tons of petroleum products, 7.503 billion m3 of natural gas, and 56.564 billion KWh of
electricity from other provinces. According to traditional CO2 emission calculation approach, the CO2
emissions from the production of secondary energy are attributed to secondary energy producing provinces
other than Beijing. However, according to the approach proposed in this paper that considers the CO2
emission flow attributed to cross-provincial secondary energy trading, the CO2 emission in Beijing increased
by 61.60 million tons, of which 56.94 million tons are attributed to the purchase of power from other
provinces. Under the new approach, the green gas emissions from electricity production, heat production,
coking, and refining in the secondary energy producing provinces are attributed to the end users of energy,
and thereby increasing the emission reduction responsibility of the end users of secondary energy, Beijing in
this case. The new CO2 emission calculation approach that considers cross-provincial secondary energy
trading will improve the reallocation of the emission reduction responsibility among energy selling provinces
and energy purchasing provinces because the end users in China are generally more developed.
4.2 CO2 emission flow because of cross-provincial secondary energy trading
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Electricity is one of the most important energy products in modern society. Although the traditional CO2
emission calculation approach attributes the CO2 emissions from the production of thermal power to the
power producing provinces, the new approach proposed in this paper attributes such emission to the power
using provinces. In China, cross-provincial power trading is a major cause of CO2 emission flow. In 2010,
the total power consumption CO2 emission of China reached 3.49038 billion tons, accounting for 36.5% of
national fossil energy consumption CO2 emission and 75.5% of national secondary energy consumption CO2
emission. Table 2 shows the power trading and provincial power consumption CO2 emission in 2010.
[Insert Table 2 Here]
Table 2 shows that Inner Mongolia, Shanxi, Hubei, and Guizhou were the largest power sellers and
Guangdong, Hebei, Beijing, and Liaoning were the largest power buyers in 2010. These
provinces/municipalities also saw the highest CO2 emission flows because of cross-provincial power trading.
In 2010, the amount of power sold by 12 provinces in western China (including Sichuan, Chongqing,
Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, and Inner Mongolia), which
are the least developed parts of China, added up to 216.392 billion KWh. By considering the CO2 emission
flows, the power consumption CO2 emissions of these provinces will decrease by 227.4227 million tons.
Energy resources are not evenly distributed in the western regions. Some provinces, for example, Inner
Mongolia and Guizhou, are important power suppliers, and some provinces, such as Chongqing, which
purchased 21.677 billion KWh of power in 2010, are power buyers.
Eastern China (including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong, Guangdong, and Hainan) are the most developed provinces and large power buyers as well. In
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2010, eastern provinces/municipalities purchased 332.388 billion KWh of power. By considering the CO2
emission flows, the power consumption CO2 emissions of these provinces/municipalities will increase by
262.8118 million tons.
[Insert Figure 1 Here]
From figure 1, we can clearly observe carbon emission flows from the western inland regions to the
eastern coastal regions. The western provinces are considered less developed in China, and they are major
domestic energy suppliers. The coast provinces are economically stronger provinces with high energy
demand. It is worthwhile to know that the places known as world factory floor including Beijing, Shanghai,
Guangdong are all located in the eastern coastal regions.
The thermal power production efficiency is a factor that has an important impact on national CO2
emission and its geographic distribution. High efficiency implies low hidden CO2 emissions in electricity
production and consumption. The efficiency and CO2 emission rates are subject to two determinants, namely,
coal-fired electricity production and primary energy used for thermal power production. In the coal-fired
electricity production and integrated use technology, the current processing and conversion efficiency of
Chinese thermal power plants and power plant heating are much lower than world average. In 2011, the
national average CO2 emission coefficient of thermal power plants was 1.042t CO2/KWh. By benchmarking
Beijing that ranked No. 1 in terms of thermal power production efficiency (0.739 CO2/KWh), the potential
for energy conservation and emission is significant. For the primary energy used for thermal power
production, the main input for thermal power production in China is coal. As a result, the CO2 emission rate
of thermal power plants is relatively high. However, the CO2 emissions of thermal power plants in Hainan
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are effectively reduced, achieving relatively low thermal power production efficiency (0.847 CO2/KWh)
because natural gas accounted for 12.29% of the input for thermal power production.
At present, Beijing ranks No. 1 in terms of thermal power production and CO2 emission efficiencies,
followed by Hainan, Jiangsu, and Guangdong, and these provinces are all located in the coastal regions in
Eastern China. By contrast, Yunnan, Inner Mongolia, Jilin, and Liaoning are all important coal and thermal
power producers with low thermal power production efficiency and CO2 emission efficiency. The large scale
power transmission from provinces with low thermal power production and CO2 emission efficiencies to
provinces with high thermal power production and CO2 emission efficiencies has caused substantial flows of
CO2 emission. As a result, the pollution and emissions from electricity production are affecting the central
and western provinces where the economy is less developed, and thereby affecting the coordinated regional
economic development. However, the CO2 emission flows also allowed the national government to adjust the
benefit sharing mechanism by encouraging eastern and coastal provinces to transfer funds and technologies
to energy selling provinces in central and western China, such that these provinces can improve the thermal
power production efficiency, and reduce the overall CO2 emissions.
5 Provincial Carbon Intensity and Contribution to National Carbon Intensity
Considering cross-provincial secondary energy trading and cross-provincial electric power trading, the
new calculation approach adjusts the result of provincial CO2 emissions. Accordingly, the provincial
productivity of the entirety social production has changed. Several methods are used to measure economic
efficiency with undesirable output, such as CO2 emissions. The most popular method is carbon intensity,
which is the ratio of CO2 emission and Gross Domestic Product (GDP). Higher carbon intensity is results in
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lower provincial productivity with CO2 emissions. Carbon intensity has thus been used as a key index in the
environmental targets of the Chinese government.
Provincial carbon intensity before and after considering cross-provincial secondary energy trading is
shown in Table 3. The rank with provincial carbon intensity has changed in many provinces. For example,
after considering cross-provincial secondary energy trading, the carbon intensity of Beijing increased by
53.7% because of massive electrical power transfer from other provinces. For provinces selling secondary
energy, the carbon intensity of Inner Mongolia declined by 20.6%.
[Insert Table 3 Here]
The Chinese government has announced that by 2020, China would reduce its carbon intensity by 40%
to 45% of 2005 emission levels. Given the current governance structure in Chinese government, the timely
realization of the national carbon intensity target depends on proper allocation of the target to provincial
governments. Whether the national target can be achieved depends on whether provincial targets can be met.
However, theoretical analysis indicates that national carbon intensity is affected by regional carbon intensity
and regional share in the national GDP. The national carbon intensity can be decomposed as Eq. (11).
CI 
n
n
n
C
C Y
C
  i   i i   CI iYSi
Y i 1 Y i 1 Yi Y i 1
(11)
where CI = national carbon intensity, CIi = carbon intensity of a region, Yi = GDP of a region, and YSi =
a region’s share of national GDP.
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The mainland of China (excludes Hong Kong Special Administrative Region, Macau Special
Administrative Region, and Taiwan Province) has been conventionally divided into three economic areas:
the eastern area, the central area and the western area (Guo, Zhang and Meng, 2012; Wang et al., 2012). The
eastern area covers 12 provinces (municipalities), including Beijing, Tianjin, Hebei, Liaoning, Shanghai,
Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central area covers 8 provinces, including
Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, He’nan, Hubei, and Hu’nan. The western area covers 12
provinces (municipalities) including Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet,
Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The effect of regional economic development and carbon
intensity on national carbon intensity can be calculated as Eq. (12).
CI 
Y
Y
Y
C Ce  Cc  Cw Ce Ye Cc Yc Cw Yw



+
 CI e e  CI c c +CI w w
Y
Y
Ye Y Yc Y Yw Y
Y
Y
Y
Y
 e
Y
11
Y
CI ieYSie  c

Y
ie 1
8
Y 12
CI icYSic  w  CI iwYSiw

Y iw1
ic 1
(12)
The subscripts e, c and w indicate the different economic area. Table 4 shows the result of decomposing
of national carbon intensity in three economic areas.
[Insert Table 4 Here]
Assuming no major changes in the share of the province to the national GDP in the short term,
provinces with large economic aggregates in the eastern region will contribute more significantly to the
reduction of national carbon intensity if they reduce the total carbon emission or carbon intensity.
Considering the large-scale west-to-east transmission of secondary energies (especially electricity) in China,
the adoption of the end-user-energy-consumption-based carbon emission measurement approach proposed in
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this paper to recalculate provincial carbon intensity and emission reduction targets is vital to the realization
of national carbon intensity targets, whose new approach increases the responsibility as regards CO2
emission reduction in the eastern region.
6 Conclusion
As a country with uneven distribution of energy resources and imbalanced regional economic
development, how China strikes a balance between energy selling provinces and energy purchasing
provinces in terms of CO2 emission reduction responsibility has become the key to coordinated regional
economic growth in a context of global climate change and CO2 emission reduction. This paper shows that
the secondary energy purchasing provinces should assume greater responsibility for CO2 emission reduction
because of the large scale cross-provincial secondary trading, and the calculation of provincial fossil energy
consumption CO2 emission should consider the final consumption of primary and secondary energies and the
secondary energy production process. This new approach will assist in the review of the benefit sharing
mechanism between secondary energy producing provinces and secondary energy using provinces, and the
energy and CO2 emission efficiencies of different provinces and municipalities could be calculated more
accurately.
Unlike other secondary energy, the end user of power does emit CO2. Only the thermal power
production is responsible for CO2 emissions. Therefore, the relationship among cross-provincial power
trading, cross-regional power trading, and CO2 emission flows is the key to the calculation of CO2 emission
flow attributed to cross-provincial secondary energy trading. This paper demonstrated that the differences
between different regional power grids in terms of hydropower-to-thermal-power ratio, thermal power
production efficiency, and grid structure have significant effects on CO2 emissions of different provinces and
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municipalities. The approach proposed in this paper can calculate the hydropower-to-thermal-power ratio and
carbon emission coefficient of thermal power according to the structure of regional power grids, and
determine the CO2 emission flows hidden in cross-provincial power trading. The use of this approach will
lead to more accurate calculation of CO2 emissions of different provinces and municipalities.
Acknowledgement
The work reported in the article is funded by National Natural Science Foundation of China (71133007,
71303270), Social Sciences Research Foundation of the Ministry of Education Of China (10XJC790004),
Fundamental Research Funds for the Central Universities(CQDXWL-2012-171).
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