China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies

China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies

Applied Energy xxx (2016) xxx–xxx Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy China...

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Applied Energy xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies Bo Zhang a,b,⇑, T.R. Yang a, B. Chen c, X.D. Sun a a

School of Management, China University of Mining & Technology (Beijing), Beijing 100083, PR China State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology (Beijing), Beijing 100083, PR China c State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, PR China b

h i g h l i g h t s  China’s CH4 emissions have significant contributions to global climate change.  The total CH4 emissions in 2010 amount to 44.3 Tg, half from energy activities.  Half of the national total direct emissions are embodied in interregional trade.  2/3 of the embodied emission transfers via domestic trade are energy-related.  A national comprehensive action plan to reduce CH4 emissions should be designed.

a r t i c l e

i n f o

Article history: Received 16 February 2016 Received in revised form 13 April 2016 Accepted 16 April 2016 Available online xxxx Keywords: CH4 emissions National and regional emission inventories Multi-regional input–output analysis Greenhouse gas emission mitigation China

a b s t r a c t Methane (CH4), the second largest greenhouse gas emitted in China, hasn’t been given enough attention in the country’s policies and actions for addressing climate change. This paper aims to perform a bottom-up estimation and multi-regional input–output analysis for China’s anthropogenic CH4 emissions from both production-based and consumption-based insights. As the world’s largest CH4 emitter, China’s total anthropogenic CH4 emissions in 2010 are estimated at 44.3 Tg and correspond to 1507.9 Mt CO2-eq by the lower global warming potential factor of 34. Energy activities as the largest contributor hold about half of the national total emissions, mainly from coal mining. Inherent economic driving factors covering consumption, investment and international exports play an important role in determining regional CH4 emission inventories. Interregional transfers of embodied emissions via domestic trade are equivalent to half of the national total emissions from domestic production, of which two thirds are energy-related embodied emissions. Most central and western regions as net interregional CH4 exporters such as Shanxi and Inner Mongolia have higher direct emissions, while the eastern coastal regions as net interregional importers such as Guangdong and Jiangsu always have larger embodied emissions. Since China’s CH4 emissions have significant contributions to global climate change, a national comprehensive action plan to reduce CH4 emissions should be designed by considering supply-side and demand-side emission characteristics, mitigation potentials and emission responsibilities. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction As the world’s largest greenhouse gas (GHG) emitter, China is under the global spotlight on climate change when it comes to the issue of emission mitigation [1–3]. The Chinese government has made the commitment that the country’s CO2 emissions per ⇑ Corresponding author at: School of Management, China University of Mining & Technology (Beijing), Beijing 100083, PR China. E-mail address: [email protected] (B. Zhang).

unit of gross domestic product (GDP) by 2020 will be 40–45% lower than in 2005, and 60–65% lower by 2030. China also intends to achieve the peak of its CO2 emissions around 2030 and make best efforts to peak early [4]. Meanwhile, the governments at all levels have taken measures to implement the national policies and set their targets for CO2 emission reduction [5]. In the academic field, there have been an increasing number of studies about China’s CO2 emission assessment or mitigation from various aspects [6–14].

http://dx.doi.org/10.1016/j.apenergy.2016.04.088 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Zhang B et al. China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.04.088

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Comparing to the CO2 emissions, non-CO2 GHG emissions still haven’t been given enough attention in China, especially in the country’s policy frameworks and associated targets for emission reduction [15]. Methane (CH4) is considered as the second largest GHG after CO2, and China is also the largest CH4 emitter in the world [16]. In the Initial [17] and Second National Communication on Climate Change of China [18], the CH4 emissions of China in 1994 and 2005 were reported as 34.3 Tg and 44.5 Tg, amounting to 19.4% and 13.2% of the total GHG emissions by the global warming potential (GWP) factor of 21, respectively. Zhang and Chen [16] reported the CH4 emissions in China 2007 were 964.1 Mt CO2-eq with the GWP factor of 25, equivalent to 14.8% of the total CO2 emissions and even larger than the total nationwide CO2 emissions of some developed countries such as the United Kingdom and Germany in 2007. In view of the importance of CH4 in the whole GHG emission inventories, increasing CH4 emissions could compromise China’s efforts to mitigate its GHG emissions [19]. Extensive studies have contributed to the CH4 emission accounting in China covering some main emission sources such as agricultural and energy activities [20–29]. Nevertheless, there are only a few studies providing national CH4 emission inventories [16–18,30–34], partly using the reliable bottom-up estimation methods [16,19,34]. In addition to supply-side direct emissions, quantifying demand-driven embodied GHG emissions has attracted increasing attention in recent decade to make consumption-side mitigation policies [35–38]. Input–output analysis has been recognized as a useful quantitative tool for identifying the GHG emissions induced by final demand [39,40], which facilitates a deeper appreciation of sector-specific direct/visible and indirect/hidden emission requirements [41,42]. A large number of studies have been performed to measure China’s CO2 emissions embodied in final consumption and international trade from different scales, using single-regional input–output models [42–46] and multiregional input–output (MRIO) models [47–56]. Since MRIO model presents the interaction and spatial linkages of industries within both intraregional and interregional economic system, Zhang et al. [57] have conducted a MRIO study on China’s regional CH4 emissions and calculated the interregional transfer of embodied emissions in 2007. However, the list of using input–output embodiment analysis on national or regional CH4 emissions is still very short [58–60]. The uncertainties of input–output embodiment analysis can be largely affected by the accuracy of emission data, especially for developing countries with limited reliable national inventories [15,61]. Given the methodologies and data for the compilation of China’s CH4 emission inventories have been updated in recent years, this paper aims to perform a bottom-up estimation and multi-regional input–output analysis for China’s anthropogenic CH4 emissions in 2010, based on the latest statistical data, emission estimation methods and recently available MRIO table for China 2010. Examining both production-based direct and consumption-based embodied CH4 emissions of different regions and industrial sectors will be useful for understanding and identifying the influence of industrial positions, final consumption demands and trades (interregional trade and exports) on national and regional CH4 emissions and related emission-reduction potentials in domestic supply chains. The rest of the paper is organized as follows. Section 2 introduces the methods for CH4 emission estimation and MRIO analysis as well as data sources and preparation. The results of direct CH4 emissions, embodied emissions in final demand and embodied emissions in interregional trade are presented in Section 3. Section 4 is the discussion of CH4 inventory characteristics and corresponding policy implications for emission mitigation. Concluding remarks will be addressed in the last section.

2. Material and methods 2.1. CH4 emission estimation methods Anthropogenic CH4 emissions in China are mainly from following sources as agricultural activities (i.e. enteric fermentation, manure management, rice cultivation, and field burning of crop residues), energy activities (i.e. coal mining, oil and natural gas system leakage, and bio-fuel combustion), and waste management (i.e. municipal solid waste management, industrial wastewater management, and domestic sewage management). The basic estimation methods for various emission sources considered in this study are adopted from IPCC [62] and Zhang and Chen [16,19], and further revised to fit Chinese situation better. For emissions from enteric fermentation and manure management, we use

Eenteric

or manure

¼

X ðPi;j  EF i Þ;

ð1Þ

i;j

in which Eenteric or manure is CH4 emissions from enteric fermentation or manure management; P i;j is the average annual livestock population; EF i the CH4 emission factors of enteric fermentation or manure management; i and j the animal category and region, respectively. To get P, we resort to the method provided by Hu and Wang [23]. For animals with slaughtering rates above 1, P is calculated by taking the average of the numbers of livestock at year-end in 2009 and 2010. For animals, such as swine and rabbit, with slaughtering rates below 1, P is estimated by P ¼ Di  ðNAPA=365Þ from IPCC [62]. Di is the living period (200 days for swine and 105 days for rabbit). NAPA is the annual slaughtering number. For rice cultivation, we use

Erice

cultiv ation

¼

X ðAi;j  EF i;j  t i;j Þ;

ð2Þ

i;j

in which Ai;j is the acreage of rice field; EF i;j the emission factors of rice; t i;j the growing period; i and j the rice category (early, middle and late rice) and region, respectively. For field burning of crop residues, the equation is

Efield

burning

¼

X ðPi;j  ri  f i;j  EF i Þ;

ð3Þ

i;j

in which Pi;j is crop production; ri the ratio of straw/crop; f i;j the percentage of straw burned in field; EF i the emission factors of field burning; and i and j the type of crop and region, respectively. CH4 emissions from coal mining can be calculated as

Ecoal ¼

 X X low P i  EF i  t þ Pi  EF low þ P high  EF high  t  r; i i

ð4Þ

i

where P i is coal production; Plow and Phigh the coal production from i i low-CH4 and high-CH4 coal mines, respectively; EF i the total CH4 emission factor of coal mining; EF low and EF high the CH4 emission factors for post coal mining of low-CH4 and high-CH4 coal mines, respectively; t the conversion coefficient of CH4 (Gg CH4/m3); r the utilization amount of CH4 captured from coal mining; and i represents region. For CH4 leakage from oil and gas systems, we use

Eoil

and gas

¼

X i

 X  off off f on v oil Pon Poil þ i  EF i þ P i  EF i i  EF i þ P i  EF i i

X ðEF 1 þ EF 2 þ EF 3 þ EF 4 Þ  Pproduction þ i i

þ

X ðEF 5 þ EF 6 þ EF 7 þ EF 8 Þ  Pconsumption ; i

ð5Þ

i

Please cite this article in press as: Zhang B et al. China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.04.088

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B. Zhang et al. / Applied Energy xxx (2016) xxx–xxx off in which P on represent the oil production from onshore and i and P i

offshore onshore

oil well, respectively; Poil i the total oil production from both off and offshore source; EF on the emission factors of i and EF i and offshore oil well, respectively; EF vi and EF if the emis-

onshore sion factors of venting and flaring during the process of oil extracP production i

tion, respectively;

xif ¼

X straw Bi  EF straw þ Bfirewood  EF firewood ; i

ð6Þ

30 X 30 30 X 2 30 X 30 X X X fs zfsij þ dit þ eif þ oif ¼ zfsij þ pif ; s¼1 j¼1

is natural gas production, while

is natural gas consumption; EF 1 the emission factor for Pconsumption i fugitive emissions of gas production; EF 2 the emission factor for flaring during gas production; EF 3 the emission factor for fugitive emissions of gas disposal; EF 4 the emission factor for flaring during gas disposal; EF 5 and EF 6 the emission factors for fugitive emissions and flaring of gas transportation, respectively; EF 7 and EF 8 the emission factor for gas storage and gas distribution, respectively; and i represents region. The calculation for emissions from bio-fuel combustion is

Ebiomass ¼

materials shows the basic format of the MRIO table. Detailed sectoral and regional information is presented in Tables A2 and A3. For the MRIO table, the basic row balance can be formulated as

s¼1 t¼1

ð10Þ

s¼1 j¼1

where xif is the total output of sector i in region f; zfsij the intermedifs

ate use of sector j in region s provided by sector i in region f; dit the final use of region s in consumption (t = 1) or investment (t = 2) supplied by sector i in region f; eif and oif represent exports and others of sector i in region f, respectively; and pif the total final use provided by sector i in region f. By considering the CH4 emissions embodied in both consumer goods and intermediate products for all sectors linked with the environment and the economy [64,65], Eq. (10) can be further revised as

i 30 X 30 X

esj  zfsji ¼

30 X 30 X

eif  zfsij þ

30 X 2 X

eif  dfsit þ eif  eif þ eif  oif

in which Bstraw and Bfirewood represent consumption amount of straw i i and firewood, respectively; EF straw and EF firewood the emission factors; and i stands for region. The formula of CH4 emissions from municipal solid waste landfill can be expressed as

cif þ

EMSW ¼

where cif is the direct CH4 emissions of sector i in region f; zfsji the

X MSW i  MCF i  DOC  DOC F  F  16=12;

ð7Þ

i

in which MSW i is the amount of municipal solid waste disposed by landfill; MCF i the correction factor of landfill in terms of different disposal methods and depths; DOC the fraction of degradable organic carbon in the solid waste; DOC F the fraction of the DOC that actually degrades; F the fraction of CH4 in the gases that come from solid waste degradation; 16/12 the conversion coefficient of CH4 (CH4/C); and i stands for region. At last, for wastewater management we use

Edomestic

sewage

¼

X ðBOD5 rei  EF  MCF re þ BOD5 disi  EF  MCF dis Þ;

ð8Þ

wastewater

¼

X CODdis CODre i  EF  MCF dis þ j  EF  MCF j ;

i

30 X 30 X

s¼1 t¼1

eif  zfsij þ eif  pif ;

ð11Þ

s¼1 j¼1

intermediate input from sector j in region s; and esj and eif the embodied emission (both direct and indirect) intensities of the output from sector j in region s and sector i in region f, respectively. Then for the entire MRIO model, we have the simultaneous equations as

8 P P30 s 1s P30 P30 1 1s 1 1 c11 þ 30 > s¼1 j¼1 ej  zj1 ¼ s¼1 j¼1 e1  z1j þ e1  p1 > > > P P30 s 1s P P30 P30 1 1s > > 1 1 < c12 þ 30 s¼1 j¼1 ej  zj2 ¼ s¼1 j¼1 e1  z2j þ e2  p2

c30 30 þ

P30 P30 s¼1

e  z30s j30 ¼

s j¼1 j

.. . P30 P30 s¼1

;

ð12Þ

30 30 e  z30s 30j þ e30  p30

30 j¼1 30

To get the embodied emission intensities, we have to simplify Eq. (12) into single matrix form using the following notations,

to calculate emissions from domestic sewage, and

Eindustrial

s¼1 j¼1

¼

> > > > > > :

i

X

s¼1 j¼1

20

j

ð9Þ to calculate emissions from industrial wastewater, in which BOD5 rei and BOD5 disi are the biological oxygen demand (BOD) in domestic and sewage that are removed and discharged, respectively; CODdis i CODre the chemical oxygen demand (COD) in industrial wastewater j discharged into environment and removed by wastewater treatment facilities, respectively; EF the emission factor (g CH4/g COD or g CH4/g BOD5); MCF re , MCF dis and MCF j the correction factors; i stands for region; and j stands for economic sector. 2.2. MRIO analysis for embodied emissions This study adopts the latest available China’s MRIO table for the year of 2010 [63], which provides economic flows of China’s 30 regions (excluding Hong Kong, Macau, Taiwan and Tibet) with 30 sectors in each region. Since the original MRIO table has differentiated domestically produced goods from imported ones to present the interregional trade dependency within China, the item for international imports has been excluded to focus on domestic interregional connection [64]. Table A1 in the supplementary

East

e11

13

C7 6B .. C 6B C7 6B . A7 7 [email protected] 7 6 1 6 e30 7 7 6 7 6 . 7; ¼6 . 7 6 . 7 60 6 e30 1 7 7 6 1 7 6B 7 6B . C 7 6 B .. C C [email protected] A5 20

e30 30

z11 11

6B .. 6B 6B [email protected] . 6 6 z11 130 6 6  6 Z ¼6 60 6 z130 6 6 B 11 6B . 6 B .. [email protected] z130 130

13 c11 C7 B 6B 7 6 B .. C [email protected] . C A7 7 6 7 6 6 c130 7 7 6 7 6 . 7; C ¼ 6 . 7 6 . 7 60 6 c30 1 7 7 6 1 7 6B 7 6B . C 7 6 B .. C C [email protected] A5 20



z11 301

..

.. .

.

1

c30 30

C C C  A

   z11 3030 .. . 

z130 301

..

.. .

.



z130 3030

1

..

.

C C C  A

0

z301 11

B B . B .. @ z301 130 0 B B B @

 ..

.



z3030 11

.. . 

.. .

..

z3030 130



.

z301 301

13 C C C A

7 7 7 7 7 301 z3030 7 7 7 7 7 7 1 7 3030 z301 7 C7 7 .. C 7 . C A5 z3030 3030 .. .

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and

20

30 X 30 X z1s þ p11 6B 6 B s¼1 j¼1 1j B 6 6B 6B 6B 6B 6B [email protected] 6 6 6 6 X¼6 6 6 6 6 6 6 6 6 6 6 6 6 4

1

3

C C C C .. C C . C 30 X 30 C X 1s 1 A z30j þ p30

7 7 7 7 7 7 7 7 7 7 7 7 7 7: 7 17 7 7 C7 C7 C7 C7 .. C7 C7 . C7 30 X 30 C7 X 30s 30 A 5 z þp

s¼1 j¼1

..

.

0 B B B B B B B B @

30 X 30 X 30 z30s 1j þ p1 s¼1 j¼1

30j

30

s¼1 j¼1

Then we have the matrix form of the equation as

C  þ Z   E ¼ X  E ;

ð13Þ

and the emission intensities can be obtained by the transformed equation of

E ¼ C  ðX  ZÞ1 ;

ð14Þ ⁄





In Eq. (14), E, C and Z are transposed matrix of E , C and Z , and the elements of X and Z can be extracted or calculated from the whole economic intermediate and final use data in the MRIO table, shown in Table A1. Thereafter, embodied emissions induced by any given final demand category, such as consumption and investment, can be obtained by multiplying the emission intensity matrix E by the corresponding final-use vector. As to the embodied emissions in interregional trade, this study adopts the model from Zhang et al. [65] and Meng et al. [66] to avoid double counting in measuring bilateral trade balance and associated embodied emissions across regions. The emissions embodied in interregional exports (EEIE) of region 1 can be expressed as 1

1

EEIE ¼ C  ðX  ZÞ

1

30 X 2 X s  dt þ es þ os

!

s¼2 t¼1

¼

30 X EEIT 1s ; ðs–1Þ; ð15Þ s¼2

  C ¼ ðc11 ;    c130 Þ; ð0;    ; 0Þ;    ; ð0;    ; 0Þ ; h    i 2 1;2 1;2 2;2 2;2 30;2 30;2 dt ¼ d1;t ;    ; d30;t ; d1;t ;    ; d30;t ;    ; d1;t ;    ; d30;t ; ðs ¼ 2Þ;     e2 ¼ ð0;    ; 0Þ; e21 ;    ; e230 ; ð0;    ; 0Þ;    ; ð0;    ; 0Þ ; ðs ¼ 2Þ;     o2 ¼ ð0;    ; 0Þ; o21 ;    ; o230 ; ð0;    ; 0Þ;    ; ð0;    ; 0Þ ; ðs ¼ 2Þ; 1

where EEIE1 is the CH4 emissions embodied in interregional exports of region 1; ðc11 ;    c130 Þ the direct CH4 emissions of every sector in s region 1 as the form of a row vector; dt the domestic final consump1;2

1;2

2

2

tion of region s, for instance, ðd1;t ;    ; d30;t Þ in dt (transpose of dt ) is the final use of region 2 supplied by region 1; ðe21 ;    ; e230 Þ and ðo21 ;    ; o230 Þ are exports and others of region 2, respectively; and EEIT 1s the interregional transfer of embodied emissions from region 1 to region s ðs–1Þ. Similarly, the emissions embodied in interregional imports (EEII) of region 1 can be obtained by

EEII1 ¼

30 X EEIT s1 ; ðs–1Þ;

ð16Þ

s¼2

in which EEIT s1 represents the embodied emissions from region s to region 1. Thereafter, we can calculate the net embodied emissions of interregional trade balance (EEIB) by the difference between EEIE and EEII. Regions with positive EEIB are net interregional exporters of embodied emissions, while negative EEIB indicate that they are net interregional importers. The total EEIE is equal to the total EEII at the national level. 2.3. Data sources and preparation The environmental resource data of individual region are assembled from multiple statistical yearbooks of China such as China Agriculture Yearbook [67], China Animal Husbandry Yearbook [68], China Energy Statistical Yearbook [69], China Marine Statistical Yearbook [70], China Urban Construction Statistical Yearbook [71], China Environmental Statistical Yearbook [72] and China Statistical Yearbook 2011 [73]. The emission factors and other parameters used in compiling direct CH4 emission inventories are mainly adopted from IPCC [62], Zhang and Chen [16,19], Zhang et al. [28] and relevant studies such as Zhou et al. [20], Fu and Yu [22], Ma and Gao [26], CCDNDRC [74] and Yang et al. [75]. To perform the MRIO modeling, we have to allocate the direct CH4 emission data that are related to economic activities to each industrial sector. CH4 emissions from agricultural activities, coal mining and oil & gas system leakage can be directly classified to corresponding economic sectors such as Agriculture, Coal Mining and Dressing, and Petroleum and Natural Gas Extraction. As to the allocation of regional emissions from municipal solid waste and domestic sewage to the detailed economic sectors, we take the assumption from Zhang et al. [57] that one third of the emissions can be attributed to the construction and service sectors, and further assign the emissions of each sector by its economic output. The regional emissions from industrial wastewater by sector are estimated based on the regional total emissions and sectorspecific ratio for this category at the national scale, due to the data availability. Detailed procedures to illustrate the compilation

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process of regional and sectoral CH4 emission inventories can be referred to Zhang et al. [57] and Zhang and Chen [16,19,58].

3. Results 3.1. Direct CH4 emissions The national total direct anthropogenic CH4 emissions in 2010 amount to about 44349.2 Gg. Table 1 presents the detailed emissions by source. It’s clear that energy activities hold about half (49.8%) of the national total emissions with an amount of 22098.9 Gg, mainly from coal mining. Agricultural activities are the second largest emission source with an amount of 16017.1 Gg (36.1% of the national total), mainly from enteric fermentation and rice cultivation. Waste management also contributes 6233.2 Gg CH4 emissions, accounting for 14.1% of the total. Regarding the direct emissions by region (Fig. 1), there are significant disparities among all the 30 regions. Shanxi (R4) presents to be the largest CH4 emitter with an emission volume of 3450.3 Gg (7.8% of the national total). Anhui (R12), Inner Mongolia (R5), Henan (R16), Guizhou (R24), Sichuan (R23) and Hunan (R18) are also top emitters with the emissions above 2000 Gg. Agricultural activities are the largest emission source for 12 regions, while energy activities for 13 regions and waste management for the rest

Table 1 China’s anthropogenic CH4 emissions in 2010. Emission source

CH4 emissions (Gg)

Fraction (%)

1. Agricultural activities Enteric fermentation Manure management Rice cultivation Field burning of agricultural residues 2. Energy activities Coal mining Oil system leakage Natural gas system leakage Straw combustion Firewood combustion 3. Waste management MSW landfill Industrial wastewater Domestic sewage Total

16017.1 8554.2 1482.7 5614.1 366.1 22098.9 19323.6 263.4 582.8 1478.4 450.6 6233.2 3953.2 1727.2 552.8 44349.2

36.1 19.3 3.3 12.7 0.8 49.8 43.6 0.6 1.3 3.3 1.0 14.1 8.9 3.9 1.2 100.0

3.2.1. Embodied emissions by source When eliminating the CH4 emissions which are not directly connected with economic activities, the total amount of direct emissions from all the economic sectors in the MRIO table remains as 38897.7 Gg. The sector of energy-related mining sectors and Agriculture contribute 20137.7 Gg and 15535.0 Gg, accounting for 51.8% and 39.9% of the total direct emissions from domestic production, respectively. Waste management holds the rest fraction of 8.3%. Fig. 2 further presents the emissions embodied in final demand by source and by region. The regional distribution of embodied emissions is very different from that of direct emissions. Guangdong (R19) has the largest embodied emissions in final demand with an amount of 3534.5 Gg. Jiangsu (R10), Henan (R16), Shandong (R15), Hunan (R18) and Sichuan (R23) are also significant emitters with the total volume each around 2000 Gg. Embodied emissions from energy activities contribute the major share to 19 regions’ emission composition and its fraction can be up to over 70% in some regions such as Chongqing (R22) and Ningxia (R29). The top six regions for embodied agricultural emissions are Guangdong, Hunan, Henan, Sichuan, Jiangsu and Shandong. The proportions of embodied emissions from waste management are relatively small and generally around or under 10% among 30 regions. Embodied emissions in final demand for each industrial sector can be obtained by adding the emission volume of the same sector of all the 30 regions. Fig. 3 presents the CH4 emissions embodied in final demand by source and by sector. The top four sectors of Agriculture (S1), Construction (S24), Food Production, Food Processing and Tobacco Processing (S6) and Other Service Activities (S30) contribute significant embodied emissions, summing up to 59.4% of the total. Agriculture and Food Production, Food Processing and Tobacco Processing have the largest embodied emissions from agricultural

4000.0

Agricultural activities Energy activities Waste management

Embodied CH 4 emissions (Gg)

4000.0

Direct CH 4 emissions (Gg)

3.2. Embodied CH4 emissions in final demand

3000.0

2000.0

1000.0

Agricultural activities Energy activities Waste management

3000.0

2000.0

1000.0

0.0 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30

0.0

Fig. 1. Direct CH4 emissions of China 2010 by region and by source.

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30

5000.0

5 regions. Hunan (R18) is the largest region of agricultural CH4 emissions, followed by Jiangxi (R14), Sichuan, Henan and Guangxi (R20). Shanxi, Anhui, Inner Mongolia, Guizhou and Henan are main energy-related CH4 emitters, summing up to 52.8% of the total energy-related emissions. Shandong (R15), Guangdong (R19), Jiangsu (R10) and Zhejiang (R11) are the top four regions for the emissions from waste management.

Fig. 2. Embodied CH4 emissions in final demand by region and by source.

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8000.0

Agricultural activities Energy activities Waste management

Embodied CH 4 emissions (Gg)

7000.0 6000.0 5000.0 4000.0 3000.0 2000.0 1000.0

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30

0.0

Fig. 3. Embodied CH4 emissions in final demand by sector and by source.

activities with the volume of 6898.1 Gg and 3594.6 Gg, respectively. Embodied emissions from agricultural activities are the major composition for the sectors which are either directly related to agricultural production or heavily dependent on agricultural products. Construction has the largest embodied energy-related emissions with an amount of 5944.2 Gg, followed by Electric Power, Steam and Hot Water Production and Supply (S22) of 2070.4 Gg and Other Service Activities of 1987.1 Gg. Embodied emissions from energy activities also hold the dominant share in most manufacturing sectors, mainly due to their high energy consumption. Most service sectors have the relatively high proportion of embodied emissions from waste management. The three sectors of Other Service Activities, Construction and Food Production, Food Processing and Tobacco Processing are responsible for more than half of the total embodied emissions from this category. 3.2.2. Embodied emissions by final demand category Fig. 4 shows the emissions embodied in final demand in terms of consumption, investment, exports and others. Consumption as the leading final demand category contributes 42.2% of the national total embodied emissions with 16418.9 Gg and is the top one category for 16 regions. Consumption-driven emissions

are extremely high in Guangdong (R19) with an amount of 1407.0 Gg, followed by Henan (R16), Sichuan (R23), Shanghai (R9) and Jiangsu (R10). Investment-driven embodied emissions, as the largest final demand category for 14 regions, reach 16208.4 Gg and account for 41.7% of the national total. Hunan (R18) holds the top position for this category, followed by Guangdong, Henan, Guangxi (R20) and Jiangsu. Export is responsible for only 14.3% of the national total. It’s obvious that eastern coastal regions have significant exports-driven embodied emissions, mainly due to their geographical advantage and economic openness. Prominently, Guangdong’s export-related emissions (1155.2 Gg) are much higher than other regions. Fig. 5 further presents the embodied CH4 emissions by final demand category and by sector. Consumption is a major driving factor for the embodied emissions of some sectors related to residents’ daily life such as food, garments, electricity, water and gas, and basic services. Agriculture (S1, 4247.5 Gg), Food Production, Food Processing and Tobacco Processing (S6, 3620.9 Gg), Other Service Activities (S30, 2811.6 Gg), Electric Power, Steam and Hot Water Production and Supply (S22, 1880.2 Gg) and Hotels and Catering Service (S27, 1251.8 Gg) contribute most of the emissions embodied in consumption. Construction (S24) is the largest sector for investment-related embodied emissions with an amount of 7259.4 Gg, responsible for 44.8% of the total national amount, followed by Agriculture with 2657.1 Gg. In particular, some manufacturing sectors have high proportions of embodied emissions in exports. Textile (S7) and Chemical Industry (S12) are the top two sectors with exports-driven embodied emissions of 767.0 Gg and 619.0 Gg, respectively. 3.3. Emissions embodied in interregional trade 3.3.1. Total interregional transfer of embodied emissions Multiregional input–output analysis for China’s regional CH4 emissions can capture the interregional emission spillover effect in domestic trade networks. The total direct emissions embodied in interregional trade are 19547.0 Gg, accounting for 50.3% of the total emissions from domestic production. Table 2 presents the emissions embodied in each region’s interregional export, import and trade balance. Shanxi (2872.1 Gg), Inner Mongolia (2277.9 Gg), Guizhou (1778.4 Gg), Anhui (1748.1 Gg) and Henan (1503.1 Gg) are the top five interregional exporters of embodied emissions, summing up to 52.1% of the total emissions embodied in

Consumpution Investment Exports Others 3000.0

2000.0

1000.0

8000.0 7000.0

Embodied CH 4 emissions (Gg)

Embodied CH4 emissions (Gg)

4000.0

Consumpution Investment Exports Others

6000.0 5000.0 4000.0 3000.0 2000.0 1000.0

0.0

Fig. 4. Embodied CH4 emissions in final demand by region and by final demand category.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30

0.0

Fig. 5. Embodied CH4 emissions in final demand by sector and by final demand category.

Please cite this article in press as: Zhang B et al. China’s regional CH4 emissions: Characteristics, interregional transfer and mitigation policies. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.04.088

B. Zhang et al. / Applied Energy xxx (2016) xxx–xxx Table 2 Embodied CH4 emissions in interregional trade by region. Region

EEIE/Gg

Fraction (%)

EEII/Gg

Fraction (%)

EEIB/Gg

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Total

40.8 70.1 622.9 2872.1 2277.9 359.5 457.6 969.3 47.8 282.4 143.4 1748.1 109.1 584.1 393.1 1503.1 320.1 762.8 281.3 422.4 41.7 436.8 749.7 1778.4 410.1 940.3 114.9 61.4 309.8 436.2 19547.0

0.2 0.4 3.2 14.7 11.7 1.8 2.3 5.0 0.2 1.4 0.7 8.9 0.6 3.0 2.0 7.7 1.6 3.9 1.4 2.2 0.2 2.2 3.8 9.1 2.1 4.8 0.6 0.3 1.6 2.2 100.0

665.5 666.3 1285.3 270.6 382.1 746.3 629.9 348.5 1571.5 1822.0 1376.3 423.1 423.6 302.0 990.1 701.4 482.3 410.2 2634.8 659.3 36.3 407.1 439.5 177.5 462.1 583.3 273.4 99.9 88.7 187.9 19547.0

3.4 3.4 6.6 1.4 2.0 3.8 3.2 1.8 8.0 9.3 7.0 2.2 2.2 1.5 5.1 3.6 2.5 2.1 13.5 3.4 0.2 2.1 2.2 0.9 2.4 3.0 1.4 0.5 0.5 1.0 100.0

624.7 596.2 662.4 2601.4 1895.8 386.8 172.3 620.8 1523.7 1539.6 1233.0 1325.0 314.5 282.1 597.1 801.7 162.3 352.6 2353.6 236.9 5.3 29.7 310.2 1600.9 52.0 356.9 158.5 38.5 221.1 248.4 0.0

7

interregional export. The top six interregional importers are Guangdong (2634.8 Gg), Jiangsu (1822.0 Gg), Shanghai (1571.5 Gg), Zhejiang (1376.3 Gg), Hebei (1285.3 Gg) and Shandong (990.1 Gg), covering 49.5% of the total embodied emissions in interregional import. There are 14 regions appearing as net interregional exporters of embodied emissions, while the rest 16 regions are net interregional importers. Presented in Fig. 6 are the specific interregional transfers of embodied CH4 emissions in interregional trade. It is explicit to see the major emission flows within regions are mainly between the large direct emitter such as Shanxi, Inner Mongolia, Guizhou and Henan and the economic developed eastern coastal region such as Guangdong, Jiangsu and Zhejiang. 3.3.2. Interregional transfers of embodied emissions by source As to embodied emissions by source, 12925.9 Gg (66.1% of the total) emissions embodied in interregional transfer are from energy activities, 5678.9 Gg (29.1%) from agricultural activities and 942.2 Gg (4.8%) from waste management. Fig. 7 shows the composition of regional EEIB by emission source. 17 regions are net interregional exporters of embodied agricultural CH4 emissions, and 11 regions are net interregional exporters of embodied energy-related emissions. It’s obvious that embodied emissions from energy activities hold the dominant composition in 25 regions, while agricultural activities only in other five regions. Shanxi (R4) is the largest net interregional exporter for embodied energy-related emissions, responsible for 30.3% of the total net interregional export, followed by Inner Mongolia (R5, 18.9%), Guizhou (R24, 16.4%) and Anhui (R12, 12.1%). Guangdong (R19), Jiangsu (R10), Shanghai (R9) and Zhejiang (R11) are leading net

Fig. 6. Interregional transfers of embodied CH4 emissions via domestic trade. Note: This graph is drawn by the Circos procedure from Krzywinski et al. [76]. For each region, the lines which have the same color with the outside arc stand for emissions embodied in this region’s export to other regions and the lines with other different colors stand for emissions embodied in this region’s import from other regions.

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B. Zhang et al. / Applied Energy xxx (2016) xxx–xxx

3000.0 2500.0

Agricultural activities Energy activities Waste management

4. Discussions and policy implications 4.1. Inventory contribution of China’s CH4 emissions

2000.0 1500.0

EEIB (Gg)

1000.0 500.0 0.0 -500.0 -1000.0 -1500.0 -2000.0 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30

-2500.0

Fig. 7. Composition of EEIB by source and by region.

Embodied CH 4 emissions (Gg)

4500.0

Agricultural activities Energy activities Waste management

3600.0

2700.0

1800.0

900.0

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S27 S28 S29 S30

0.0

Fig. 8. Embodied CH4 emissions in interregional trade by source and by sector.

interregional importers for embodied energy-related emissions, accounting for 21.1%, 14.2%, 11.4% and 10.5% of the total, respectively. Jiangxi (R14), Hunan (R18), Anhui, Heilongjiang (R8), Inner Mongolia and Sichuan (R23) are the top six net interregional exporters for embodied emissions from agricultural activities, summing to 61.9% of the total. Meanwhile, Shanghai, Guangdong, Zhejiang, Jiangsu, Shandong (R15) and Beijing (R1) are the top six net interregional importers for this category, together responsible for 73.8% of the total. Fig. 8 further shows the emissions embodied in interregional trade by source and by sector. The embodied emissions in interregional export and import of each economic sector are identical. 50.3% of the total embodied emissions in interregional trade are associated with the four sectors of Construction (S24, 4020.1 Gg), Food Production, Food Processing and Tobacco Processing (S6, 2319.1 Gg), Agriculture (S1, 1814.6 Gg), and Other Service Activities (S30, 1672.4 Gg). Construction, Other Service Activities, Electric Power, Steam and Hot Water Production and Supply (S22) and some manufacturing sectors are the dominant sectors for embodied energy-related emissions in interregional trade, and Food Production, Food Processing and Tobacco Processing and Agriculture for embodied emissions from agricultural activities.

The factor of GWP has been widely used to reveal the contribution of CH4 to GHG emission inventories. In the latest IPCC fifth assessment report [77], the GWP factors of CH4 are 86 for the time span of 20 years and 34 for 100 years. Accordingly, China’s total anthropogenic CH4 emissions in 2010 can be converted into 3814.0 Mt CO2-eq and 1507.9 Mt CO2-eq, being equivalent to respectively 53.4% and 21.1% of its total CO2 emissions from fuel combustion, 7137.3 Mt at the same year [78]. Even with the commonly using lower GWP factor of 21, China’s direct CH4 emissions reach 931.3 Mt CO2-eq in 2010, amounting to 13.0% of its energyrelated CO2 emissions. To illustrate the inventory status of China’s CH4 emissions, we further resort to the emission inventories of some developed countries from the United Nations Framework Convention on Climate Change (UNFCCC) [79]. China’s CH4 emissions are about 1.6 times larger than those of the United States, the largest CH4 emitter among the Annex I Parties with an emission volume of 27657.1 Gg, and nearly 2 times larger than those of Russia (23390.5 Gg). Moreover, the total CH4 emissions of China measured by the unit of CO2-eq have greatly exceeded the nationwide total GHG emissions of Canada, the United Kingdom, Australia, France and Italy in 2010. It is worth noting that the comprehensive bottom-up evaluation of China’s CH4 emissions in this study will unavoidably result in several areas of uncertainty. In the estimates of emission inventory for each source category, the uncertainties stem mainly from the accounting scope, statistic data, emission factors and calculation methods, as mentioned in IPCC [62] and discussed by some other reports [16–19]. Table 3 lists the representative inventories for China’s CH4 emissions. The latest official national CH4 emission inventory provided in SNCCCC [17] is for the year 2005. The results of our study (44.3 Tg) as a whole are close to those of SNCCCC and USEPA, though some differences exist when considering specific emission sources. However, the magnitudes of the emissions listed in EDGAR [32] are much higher than all the other studies, which may not be able to reflect the real status of China’s CH4 emissions. Compared with the 2005 national emission inventory, the emission data of coal mining given by USEPA and this study are much higher due to the rapidly increasing coal production in China, while those from agricultural activities are much smaller. The decrease of agricultural CH4 emissions can be found mainly from enteric fermentation, which can be partly explained by the fact that the cattle population decreased by 24.9% in China 2010 contrasting to the statistical data collected in 2005 [67,80]. Therefore, the estimates of regional CH4 emissions in our study represent a reasonable

Table 3 Some reports for China’s CH4 emission inventory (Gg). Emission sources

Year 2005 [18]

Year 2010 [33]

Year 2010 (This study)

Enteric fermentation Manure Management Rice cultivation Other agriculture sources Biomass combustion Coal mining Natural gas & oil system leakage Fossil fuel combustion Municipal landfill Wastewater management Total

14379 2864 7926 No data 2163 12922 218

10119 957 5933 48 2310 14071 200

8554.2 1482.7 5614.1 366.1 1929.0 19323.6 846.2

126 2204 1620 44419

1671 2243 6286 43838

No data 3953.2 2280.0 44349.2

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and detailed approximation with the data and resources available, which may provide fundamental information to understand of the CH4 emissions in China. 4.2. Production-based and consumption-based regional emission inventories There are two accounting principles when quantifying GHG emissions, the production-based and the consumption-based [37,53]. Table 4 shows regional CH4 emission inventories under different accounting principles, indicating that there are significant variations among all the 30 regions. As to the direct emissions, Shanxi, Inner Mongolia, Anhui, Henan and Guizhou are the largest emitters. When applying the consumption-based accounting principle, Guangdong and Jiangsu turn out to be the two largest emitters while Shanxi becomes one of the smallest emitters. Guangdong’s total embodied emissions exceed its direct emissions by 2353.6 Gg, while Shanxi’s embodied emissions are only 737.3 Gg, much smaller than its direct emissions. In Shanghai, Tianjin, Beijing, Zhejiang, Guangdong and Jiangsu, the eastern developed regions in China, the embodied emissions are 12.7, 6.5, 6.0, 3.4, 3.0 and 2.6 times of the direct emissions, respectively. By contrast, most inland regions have relatively higher direct emissions. For instance, the direct emissions of Shanxi, Inner Mongolia, Guizhou, Anhui, Ningxia and Heilongjiang are 4.5, 2.7, 2.7, 1.8, 1.6 and 1.6 times larger than their embodied emissions, respectively. The inventory discrepancies under production-based and consumption-based accounting principles can be explained by the interregional transfer of embodied emissions via domestic trade. The major net interregional importers of agricultural CH4 emissions and energy-related CH4 emissions are all eastern coastal regions (e.g., Guangdong, Jiangsu, Shanghai and Zhejiang), the most developed part of China. The final demands in the developed

provinces not only trigger a large amount of agricultural and industrial production activities within their own jurisdictional boundaries, but also impose great requirements to other regions via interregional supply chains [65], due to the large population base, high consumption capacity and high-speed economic development. In contrast, the less developed western and inland provinces such as Shanxi and Inner Mongolia depend heavily on their exports of mineral resources and agricultural products economically, thus appearing to have high direct CH4 emissions and relatively low embodied emissions. Therefore, eastern coastal regions should take more responsibilities on CH4 emission mitigation based on consumption-based inventories though their relatively lower volumes for production-based emissions. Regional emission indicators in terms of emissions per capita and per GDP are also strongly influenced by different accounting principles. From production-based perspective, Inner Mongolia has the largest per capital CH4 emissions of 122.8 kg, following by Shanxi (93.4 kg), Ningxia (92.4 kg), Qinghai (72.6 kg) and Guizhou (72.4 kg). Shanghai’s direct emissions per capita is the smallest, only 5.7 kg. The standard deviation of the direct per capita emissions is 29.0 kg. When applying the consumption-based accounting principle, the largest per capital emissions are from Qinghai (79.5 kg), Shanghai (71.8 kg), Ningxia (57.5 kg) and Tianjin (54.2 kg), while the smallest are from Shandong (19.4 kg) and Fujian (19.9 kg). It’s obvious that the disparities among regions are diminished to a certain extent and the standard deviation drops to 14.8 kg. For production-based emission accounting, Guizhou’s emission intensity is up to 5.5 g/CNY, following by Shanxi, Ningxia, Qinghai, Inner Mongolia and Anhui. As to consumptionbased emission intensities, Qinghai, Ningxia, Guizhou and Yunnan are the most prominent regions with the volume of 3.3 g/CNY, 2.2 g/CNY, 2.0 g/CNY and 2.0 g/CNY, respectively. The standard deviation varies from 1.3 g/CNY to 0.6 g/CNY from production-

Table 4 Regional emission inventories under different accounting principles. Region

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Total

Total emissions (Gg)

Emissions per capita (kg)

Emissions per GDP (g/CNY)

Production

Consumption

Production

Consumption

Production

Consumption

125.8 107.8 1032.6 3338.7 3036.0 1091.3 763.8 1709.3 130.3 938.7 510.2 2990.0 418.4 1498.8 1264.4 2885.6 1037.6 2180.4 1181.0 1130.9 191.7 1274.9 2129.5 2517.8 1366.9 1619.8 497.4 409.0 584.9 934.2 38897.7

750.5 704.0 1695.0 737.3 1140.2 1478.1 936.2 1088.5 1654.0 2478.2 1743.2 1665.0 732.9 1216.7 1861.4 2083.8 1199.9 1827.8 3534.5 1367.8 186.4 1245.2 1819.4 916.9 1418.9 1262.9 656.0 447.4 363.8 685.9 38897.7

6.4 8.3 14.4 93.4 122.8 24.9 27.8 44.6 5.7 11.9 9.4 50.2 11.3 33.6 13.2 30.7 18.1 33.2 11.3 24.5 22.1 44.2 26.5 72.4 29.7 43.4 19.4 72.6 92.4 42.8 29.2

38.3 54.2 23.6 20.6 46.1 33.8 34.1 28.4 71.8 31.5 32.0 28.0 19.9 27.3 19.4 22.2 21.0 27.8 33.9 29.7 21.5 43.2 22.6 26.4 30.8 33.8 25.6 79.5 57.5 31.4 29.2

0.1 0.1 0.5 3.6 2.6 0.6 0.9 1.7 0.1 0.2 0.2 2.4 0.3 1.6 0.3 1.3 0.7 1.4 0.3 1.2 0.9 1.6 1.2 5.5 1.9 1.6 1.2 3.0 3.5 1.7 0.9

0.5 0.8 0.8 0.8 1.0 0.8 1.1 1.1 1.0 0.6 0.6 1.4 0.5 1.3 0.5 0.9 0.8 1.1 0.8 1.4 0.9 1.6 1.1 2.0 2.0 1.3 1.6 3.3 2.2 1.3 0.9

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B. Zhang et al. / Applied Energy xxx (2016) xxx–xxx

based to consumption-based accounting, which indicates that the gap of regional emission intensity has been narrowed.

4.3. Policy implications for CH4 emission mitigation in China Unlike the high marginal cost in reducing CO2 from energy sector due to economic growth, CH4 have higher GWP and are relatively easier to mitigate than CO2 [81,82]. The marginal cost of CH4 emission mitigation is always cheaper than CO2 with environmental benefits and potential financial gains [83]. By studying the mitigation paths of the Annex I Parties, it is noteworthy that CH4 not only has been certainly reduced by various amounts in most of them, but also became an important part of their GHG mitigation processes. Table 5 presents the emission variation of CH4 and CO2 of some major countries listed in Annex I to the UNFCCC between 1990 and 2010. Ukraine, Germany and the United Kingdom had reduced their CH4 emissions by around 50% over this period, and Japan by 36.1% and Poland by 26.1%, respectively. Prominently, the amounts of CH4 reduction in Ukraine, Germany, the United Kingdom, Italy and France are equal to 22.3%, 27.6%, 60.6%, 68.0% and 83.1% of those of their CO2 reduction, respectively. According to the national inventory reports [79], the CH4 mitigation of Annex I Parties can be attributed to the reduction of emissions from oil & gas system leakage, coal mining and solid waste disposal. Since a large portion of China’s CH4 emissions can be found from the energy and waste sectors, it is critical for policy makers to recognize the great mitigation potentials and relevant contributions to climate action. Low-cost technologies and best practices to recover and utilization of CH4 are already widely available and used in main emission sectors, as discussed by many reports [81,82]. For instance, in the energy sector, the prevention of fugitive emissions by coal mining or leakage from oil & gas systems and CH4 collection for recycling are main ways of reduction [25]. As to the waste sector, using waste as a resource, reducing and recycling waste, converting combustible waste into energy, and generating power from collected landfill gases have promoted in policies and practicable measures. Increased meat consumption, centralized feeding modes and consumer preferences change on agricultural product demand continue to contribute increasing CH4 emissions in agricultural sector. Agricultural mitigation measures include adopting advanced irrigation management methods, planting hybrid rice with lower CH4 generation than common rice, converting livestock manure into energy, using animal feces for aerobic composting to produce fertilizer, improving the digestibility and quality of livestock feeds, etc. [83].

Except for end-reduction-oriented strategies, it’s important to compile consumption-based emission inventories, evaluate consumer’s emission responsibilities and then formulate effective mitigation policies [58]. A region’s GHG mitigation potentials depend on not only its intro-regional production technologies, but also its position and participation degrees in the domestic and global supply chains [66]. Inherent economic driving factors covering consumption, investment and international exports play an important role in determining the regional direct and embodied emissions [49]. The net transfers of embodied CH4 emissions from central and western inland regions to eastern coastal regions in interregional trade reveal cross-boundary potentials for visible and hidden emission mitigation in domestic supply chains. A regional collaborative mitigation mechanism should be established to balance the regional emission liabilities by addressing interregional transfers of emission responsibilities [53]. Particularly, the regions which are economically better off but rank high on the list of embodied emissions, such as Guangdong, Jiangsu, Zhejiang and Shanghai should take on consumer responsibilities and establish mitigation cooperation with their trade partners either financially or technologically. The inclusion of CH4 is critically important for finding the powerful and cost-effective ways to reduce the GHG emissions at the regional and sectoral levels. The Chinese government should design a national comprehensive action plan to reduce CH4 emissions by addressing the emission characteristics, mitigation potentials and emission responsibilities. In 2017, China will launch a national CO2 trading market covering power generation, steel, cement and other key industrial sectors. Under the emission trading scheme, we suggest that the governments can set the total amount of allowable greenhouse gas emissions including non-CO2 gases for each company and companies may achieve GHG mitigation targets through trading emission permits as well as voluntary mitigation efforts. In addition, considering insufficient statistics on CH4, enhancing data monitoring and statistical analysis at the national and regional level is urgently needed to establish precise emission data and actual mitigation potentials. 5. Concluding remarks China’s CH4 emissions have significant contributions to the country’s GHG inventories and global climate change. In 2010, the total direct anthropogenic CH4 emissions amount to 44.3 Tg, 3814.0 Mt CO2-eq or 1507.9 Mt CO2-eq by the GWP factors of 86 and 34 for the time horizon of 20 years and 100 years, respectively being equivalent to 47.7% or 18.8% of its total energy-related CO2 emissions in this year and even larger than the GHG emissions of

Table 5 The variation of CH4 and CO2 emissions in some Annex I Parties between 1990 and 2010 (Mt CO2-eq). Party

Australia Canada European Union France Germany Italy Japan Poland Russia U.K. U.S. Ukraine

CH4 emissions

CO2 emissions

1990

2010

1990–2010 (%)

1990

2010

1990–2010 (%)

115.2 72.0 601.2 59.8 108.8 43.8 32.4 55.9 593.4 109.1 633.2 162.4

109.1 88.6 408.8 53.4 50.1 37.2 20.7 41.3 491.2 56.7 580.8 66.5

5.3 23.1 32.0 10.7 54.0 15.1 36.1 26.1 17.2 48.0 8.3 59.1

276.1 459.0 4437.0 398.8 1042.1 434.7 1141.1 469.4 2505.4 591.5 5100.6 718.9

399.4 554.4 3907.8 391.1 829.4 425.0 1191.1 329.6 1602.4 505.0 5712.8 289.7

44.7 20.8 11.9 1.9 20.4 2.2 4.4 29.8 36.0 14.6 12.0 59.7

Note: The emission data are available from UNFCCC [79] excluding emissions/removals from land use land-use change and forestry.

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B. Zhang et al. / Applied Energy xxx (2016) xxx–xxx

some Annex I countries such as the United Kingdom and Germany. Energy activities, agricultural activities and waste management respectively contribute 49.8%, 36.1% and 14.1% to the national total emissions. Coal mining is the largest single contributor (43.6% of the total). When changing production-based accounting to the consumption-based, regional CH4 emission inventories change a lot owing to the impact and effect of domestic trade. Interregional transfers of embodied emissions via domestic trade are equivalent to half of the national total emissions from domestic production, of which two thirds are energy-related embodied emissions. Eastern coastal regions always have higher consumption-based emissions while western and central inland regions show higher production-based emissions. From consumption-based perspective, eastern coastal regions should take on more responsibility to China’s CH4 emissions. The large-scale urbanization and industrialization associated with the growth of energy demand, urban population and living standards will continue driving the increase of anthropogenic CH4 emissions in China unless substantial efforts are undertaken to reduce them in the future. USEPA [33] provided a projection that China’s CH4 emissions in 2030 will reach 52.1 Tg. All these reflect the unprecedented emergency of CH4 emission control in China. Since it’s more effective to reduce CH4 than CO2 for determination of potential greenhouse gas limitation/reduction targets, CH4 should be given enough attention in the national and regional GHG mitigation policies, such as the action plans on climate change in the 13th Five-Year (2016–2020). The mitigation paths of major Annex I nations show that CH4 can be effectively reduced in most emission sources. China deserves to draw experiences from developed countries though the special status of its CH4 emissions should also be taken into consideration. To assess all possible paths to achieving global GHG emission mitigation, it’s urgent to trace the geographical origins and interregional transfers of anthropogenic CH4 and other non-CO2 GHG emissions at different scales [83,84]. Considering production-based direct and consumptionbased embodied emission reduction for non-CO2 GHGs will be certainly helpful for accomplishing the national and regional GHG mitigation targets, especially for most developing countries.

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This study has been supported by the National Natural Science Foundation of China (Grant nos. 71403270 and 71373262) and the Foundation of State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology (Grant no. SKLCRSM14KFA03).

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Appendix A. Supplementary material [30]

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apenergy.2016. 04.088.

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