Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Journal of Integrative Agriculture 2014, 13(6): 1393-1403 June 2014 RESEARCH ARTICLE Research on Spatial-Temporal Characteristics and Driving Facto...

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Journal of Integrative Agriculture 2014, 13(6): 1393-1403

June 2014

RESEARCH ARTICLE

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China TIAN Yun1, 2, ZHANG Jun-biao1, 2 and HE Ya-ya1, 2 1 2

College of Economics & Management, Huazhong Agricultural University, Wuhan 430070, P.R.China Hubei Rural Development Research Center, Huazhong Agricultural University, Wuhan 430070, P.R.China

Abstract Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy field, soil and livestock breeding, this paper firstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index (LMDI) model. The results show: (1) The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy field, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39% of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; (2) There is an obvious difference among regions: the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84 % from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into five types, namely agricultural materials dominant type, paddy field dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; (3) Compared with 1995, efficiency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively; while economy factor increase carbon emissions by 113.16%. Key words: China, agricultural carbon emissions, spatial-temporal characteristics, driving factor, LMDI model

INTRODUCTION Climate change has already become the most serious global environmental problem. The increasing concentration of CO2, N2O, CH4 and other greenhouse gases in the atmosphere is one of the roots that lead to global warming. Among them, the industrial sector and service

industry occupy a dominant position of carbon emission. However, the rapid development of agriculture is a vital inducing factor to accelerate climate warming. Therefore, more and more researchers have been conducted the study on agricultural carbon emissions, mainly around the following aspects: (1) the mechanism and characteristics analysis of agricultural carbon emissions, including its composition and estimates (Johnson 2007), and regional comparisons (Tasman 2009); (2) agricul-

Received 15 April, 2013 Accepted 31 July, 2013 TIAN Yun, E-mail: [email protected], Correspondence ZHANG Jun-biao, Tel: +86-27-87288381, E-mail: [email protected]

© 2014, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(13)60624-3

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tural carbon emissions research in specific perspectives, such as the agricultural operating model and agricultural carbon emissions (Lal 2004; Gomierol et al. 2008; Wise et al. 2009), land use change and ecosystem carbon balance (Joseph et al. 2008; Arevalo and Bhatti 2011; Gillian and Jerry 2011), and the relationship between agricultural technology and agricultural carbon emissions (Zaman et al. 2012); (3) studies on the building of agricultural carbon reduction mechanism and policy issues. The policy measures include carbon tax (Peters et al. 2011) and establishing agricultural carbon trading market (Mccarl and Schneider 2000). Besides, agricultural engineering and technical measures include increase in crop cultivation density (Paustian et al. 2000), changes in land use patterns, minimum tillage or no-till, enhancing soil carbon sequestration levels (Kragt et al. 2012), improvement of manure management level (Burney 2010), setting up the anaerobic system (Bracmort 2010), gradually replacing fossil fuels required for agricultural production through the sustainable use of bio-fuels (Steenblik and Moise 2010). As the world’s largest greenhouse gas emitting country, 17% of the carbon emissions in China was originated from the agricultural production activities (Zhao et al. 2010), which raises concerns and a lot of researches have been carried out: (1) The research on single agricultural carbon emissions. Part of the agricultural carbon sources, such as agricultural land use (Tian et al. 2011a), agricultural energy consumption (Li and Li 2010), livestock and poultry raising (Zhou et al. 2007), are selected to calculate their carbon emissions; (2) The carbon footprint research of farmland, including regional farmland carbon footprint research (Duan et al. 2011) and carbon footprint research of specific cropping pattern (Chen et al. 2011). There is no doubt that these researches has greatly enriched the China’s agricultural research system of carbon emissions, and laid a solid foundation for subsequent researches. However, with the deepening of the research, its limitations are gradually emerging: on one hand, the current research of agricultural carbon emissions in China is mostly based on single perspective, on the other hand, lacking of overall grasp of the present situation of the agricultural carbon emission. The objective of this study is to calculate and decompose the agricultural carbon emissions in China, and to analyze spatial-temporal characteristics. In order

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to achieve this aim, this paper is organized as follows: Firstly, to calculate China’s agricultural carbon emissions from 1995 to 2010 and analyze its composition and stage change characteristic. Then, estimating the agricultural carbon emissions of China’s 31 provinces and cities in 2010, and analyzing the spatial differences from three aspects, namely the total amount, structure and intensity. Next, we use logarithmic mean Divisia index (LMDI) model to analyze the driving factors of agricultural carbon emissions. Finally, make discussion based on analysis as well as give the final conclusion.

METHODLOGY AND DATA SOURCE The agricultural carbon emissions studied in this paper are caused by farmers when they are engaged in agricultural production activities. Three types of greenhouse gases have been examined in this paper, C, CH4, and N2O. In order to facilitate the analysis, we replaced CH4 and N2O with standard C in final calculation, according to IPCC (2007) and so all of those greenhouse gases emissions named carbon emissions. The source of carbon emissions we considered in this article mainly comes from following four aspects: (1) carbon emissions caused by the input of agricultural material (Zhao and Qian 2009); (2) N2O emissions due to the damage of soil surface when planting crops (Qi and Dong 1999); (3) CH4 and other greenhouse gas emissions in rice growth process (Johnson 2007); (4) livestock breeding emissions including CH4 emissions from enteric fermentation, CH4 and N2O emissions triggered by manure management (Johnson 2007).

Calculation method of agricultural production carbon emissions On the basis of carbon emissions equation some researchers (Song and Lu 2009; Zhang et al. 2010) have established, this paper construct agricultural carbon emissions formula as follows. E=∑Ei=∑Ti×δi (1) Where, E is the total carbon emissions of agricultural production, Ei is the carbon emissions of various types of carbon sources, Ti is the amount of carbon sources, δi is the coefficient for each source of carbon emissions. And this paper determines the specific carbon source factor © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

and its corresponding carbon emission coefficient into four aspects (namely agricultural materials, soil, paddy field, and livestock breeding) based on the source characteristics of agricultural carbon emissions, According to previous researches as well as consulting with experts, in this paper, we believe agricultural materials carbon emissions primarily caused by agricultural materials inputs directly or indirectly, such as fertilizers, pesticides, plastic sheeting, diesel oil, and electricity consumed by agricultural irrigation activities. Carbon emission coefficient of various carbon sources are shown in Table 1. These coefficients are from classic literatures in natural sciences (Zhi and Gao 2009, IPCC 2007, Tian 2011b, and Duan et al. 2011) which have been widely used and the accuracy has been recognized (Johnson 2007; Tian et al. 2011a; Min and Hu 2012). Table 1 The carbon emission coefficient of major agricultural sources Carbon sources Fertilizers Pesticides Plastic sheeting Diesel oil Irrigation

Carbon emission coefficient (kg C kg-1) Reference source 0.8956 Zhi and Gao 2009 4.9341 Zhi and Gao 2009 5.18 Tian 2011b 0.5927 IPCC 2007 266.48 Duan et al. 2011

Damage to the soil surface in the process of planting, easily lead to loss of the large amounts of greenhouse gases enter into atmosphere, among which N2O play the most important role. Compared with other greenhouse gases, N2O has many characteristics such as leading to much more warming potential, destroying the ozone layer and so on (Qi and Dong 1999). At present, the domestic researchers estimate soil N2O emission coefficient of main crop varieties in China through a large number of experiments, which are shown in Table 2. These N2O

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Table 2 Nitrous oxide emission coefficient of soil from all varieties of crops Crop varieties Paddy rice Spring wheat Winter wheat Soybeans Corn Vegtable Other dryland crops

N2O emission coefficient (kg ha-1) 0.24 0.4 2.05 0.77 2.532 4.21 0.95

Reference source Wang 1997 Yu et al. 1995 Pang et al. 2011 Xiong et al. 2002 Wang and Su 1993 Qiu et al. 2010 Wang 1997

emission coefficients have been widely recognized by the academia and with a large number of references in the research field of soil carbon emissions. Paddy field is one of the most important sources of CH4 emissions, which is also one kind of greenhouse gases. But at the same time, there is a big difference in the hydrothermal conditions in every district because of China’s vast territory, which lead to different CH4 emission rates in rice growth cycle in different regions. In order to make the results more accurate, this study will refer to the paddy field CH4 emission coefficient estimated by some researchers (Wang et al. 1998; Min and Hu 2012). This coefficient is gotten in accordance with the relevant model which input weather, soil, hydrological characteristics and other relevant parameters to estimate respectively of CH4 emission factors in each province of early rice, in-season rice and late rice, shown in Table 3. Livestock breeding, especially ruminants breeding, is another important source of CH4 and N2O, including CH4 emissions caused by enteric fermentation, CH4 and N2O emissions triggered by manure management system. In China, cattle, horses, donkeys, mules, camels, pigs, goat, and sheep are the main livestock breeds which produce CH4 and N2O. The respective coefficient of emissions is shown in Table 4.

Table 3 Methane emission factor of rice grow cycle from all regions in China (g m-2)1) Region Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang 1)

Early rice 0 0 0 0 0 0 0 0 12.41 16.07 14.37

Late rice 0 0 0 0 0 0 0 0 27.50 27.60 34.50

In-season rice 13.23 11.34 15.33 6.62 8.93 9.24 5.57 8.31 53.87 53.55 57.96

Region Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing

Early rice 16.75 7.74 15.47 0 0 17.51 14.71 15.05 12.41 13.43 6.55

Late rice In-season rice 27.60 51.24 52.60 43.47 45.80 65.42 0 21.00 0 17.85 39.00 58.17 34.10 56.28 51.60 57.02 49.10 47.78 49.40 52.29 18.50 25.73

Region Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

Early rice 6.55 5.10 2.38 0 0 0 0 0 0

Late rice 18.5 21.00 7.60 0 0 0 0 0 0

In-season rice 25.73 22.05 7.25 6.83 12.51 6.83 0 7.35 10.50

The CH4 emission coefficient of paddy field comes from some researchers (Wang et al. 1998; Min and Hu 2012).

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Table 4 The carbon emission coefficient of major livestock1) Carbon resources Dairy cow Buffalo

Enteric fermentation CH4 kg/head/year 61 55

Cattle

47

Horse Donkey Mule Camel Pig Goat Sheep

18 10 10 46 1 5 5

1)

Manure management CH4 N2O kg/head/year kg/head/year 18 1 2 1.34 1 1.39 1.64 0.9 0.90 1.92 4 0.17 0.15

1.39 1.39 1.39 1.39 0.53 0.33 0.33

Source: IPCC 2007

Decomposition of carbon emissions factors This research will apply LMDI decomposition method to decompose China’s agricultural carbon emissions. The LMDI model can make the model more convincing, which can eliminate the residual term, thus overcome the shortcoming like existence of residuals or the improper residual decomposition caused by using other methods. Meanwhile, the aggregation of sub-sectors effect and the total effect is consistent in LMDI model, that is, the sum of different sub-sector effect and the role of the various departments in the total effect on the overall level are equal, which is very useful in multilevel analysis. Following the framework of LMDI analysis, based on the existing literature (Wang et al. 2005; Liu et al. 2007; Tian et al. 2011b), and combined with the actual situation of agricultural carbon emissions, the total carbon emissions can be expressed by the following basic formula. C=

C PGDP AGDP × × × AL PGDP AGDP AL

C PGDP AGDP ; CI = ; SI = EI= PGDP AGDP AL

(2)

In above eq. (2), C, PGDP, AGDP, and AL, separately represent agricultural carbon emissions, farming-animal husbandry gross output value, agricultural gross output value, and employment labor of agricultural industry. EI, CI and SI represent efficiency factor of agricultural production means, agricultural structural factor and agricultural economic level factor. Since various types of carbon source is closely related to planting industry and animal husbandry, the agricultural carbon emissions we

studied in this paper is the actual carbon emissions from farming and livestock production. Meanwhile, there are great inconsistencies in the departments of agriculture production and scale quantitative method, in order to make comparison convenient, we uniform output as a comparative amount. LMDI model uses two methods, namely “product decomposition” and “plus decomposition”, to make decomposition. The final decomposition results of both methods are consistent. For the model in eq. (2), the total carbon emissions of base period and T period were set as C0 and Ct and subscript tot represents the total change. Using plus decomposition to decompose the difference into: ∆Ctot=Ct-C0 The expression of contribution value of each decomposition factor is as follows: ∆EI =∑

C t -C 0 EI t ln ln C t -ln C 0 EI 0

∆CI = ∑

C t- C 0 CI t ln ln C t - ln C 0 CI 0

∆SI =∑

C t -C 0 SI t ln ln C t -ln C 0 SI 0

∆AL= ∑

C t- C 0 ALt ln ln C t - ln C 0 AL0

The total effect is: ∆Ctot=Ct-C0=∆EI+∆CI+∆SI+∆AL

Data sources and processing The data in this study come from China Statistical Yearbook 2011 (NBS of PRC 2011), China Rural Statistical Yearbook 2011 (RSEID of NBS of PRC 2011) and China Agricultural Statistical Report 2010 (MOA of PRC 2011), in which, fertilizers, pesticides, plastic sheeting, diesel oil, rice planting area, irrigation area, crop seeded area, crop yields are in actual situation; the amount of cattle, horse, donkey, mule, camel, pig, goat, and sheep have been corrected based on the actual situation. Taking into account that the agricultural production value which was calculated in actual price couldn’t be compared longitudinally, we served 1995 as the benchmark year of price by using comparable price of GDP. And Table 5 is basic statistical analysis of carbon sources of different regions in China, including mean, standard deviation (SD), max and min.

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Table 5 Basic statistical analysis of carbon sources of different regions in China1) Carbon sources Agricultural materials

Soil

Paddy field

Livestock breeding

Fertilizers Pesticides Plastic sheeting Diesel oil Irrigation Paddy rice Spring wheat Winter wheat Soybeans Corn Vegtable Other dryland crops Early rice Late rice In-season rice Dairy cow Buffalo Cattle Horse Donkey Mule Camel Pig Goat Sheep

Unit ×104 t ×104 t ×104 t ×104 t ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×103 ha ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads ×104 heads

Mean 179.41 5.67 7.01 65.26 1 946.70 995.78 121.79 902.06 283.86 1 048.39 612.90 1 500.94 526.89 518.73 637.60 224.63 45.81 85.09 26.05 25.59 10.80 6.40 1 498.71 458.19 534.00

SD 146.85 4.63 6.42 64.21 1 479.85 1 124.04 174.78 1 319.19 653.83 1 189.40 488.08 1 038.12 534.18 564.85 753.11 188.15 69.68 119.66 31.67 37.06 16.08 5.96 1 321.40 512.75 857.86

Max 655.20 16.49 32.30 298.50 5 081.00 4 030.50 566.20 5 280.00 3 547.90 4 368.37 1 770.79 3 465.88 1 401.10 1 525.50 2 768.80 666.20 292.50 504.70 99.40 112.10 67.10 12.90 5 157.90 1 794.90 3 569.00

Min 4.70 0.10 0.09 3.10 201.00 0.30 0.60 0.90 0.10 4.22 21.33 31.71 1.20 0.70 0.30 5.80 0.20 0.10 0.10 0.10 0.10 0.90 29.60 4.70 0.20

N2) 31 31 31 31 31 30 14 25 30 31 31 31 11 12 28 30 31 29 26 25 25 4 31 31 26

1)

Source: The use of fertilizers, pesticides, plastic sheeting, diesel oil as well as the amount of every livestock come from China Statistical Yearbook 2011 (NBS of PRC 2011), the planting area of all kinds of crop, irrigated area come from China Agricultural Statistical Report 2010 (MOA of PRC 2011). 2) N, the number of provinces.

RESULTS Structure and time series variation characteristics of agricultural carbon emissions in China According to the formula gave above, we estimated China’s agricultural carbon emissions from 1995 to 2010 shown in Table 6. The results showed that the amount of China’s agricultural carbon emissions in 2010 was 291.169 million t, increased by 16.69% compared to 249.5239 million t in 1995, and the average annual growth rate is 1.03%. Among them, carbon emissions caused by agricultural material inputs, paddy fields, soil, enteric fermentation and manure management were 97.814, 64.1446, 21.7097, 51.028 and 56.4721 million t, respectively, accounting for 33.59, 22.03, 7.46, 17.53, and 19.39% of the total amount in turn. Although “up and downs” characteristics existed in this period, China’s agricultural carbon emissions overall present the signs of cyclical rise. The total amount of carbon emissions were dropped in 1997, 2000 and 2007, but rebounded quickly in the following year, then begun to rise again and last for several years.

The growth rate reached the maximum of 4.32% in 2004, due to the government issuing the No. 1 central document of “benefiting-farmers” after a lapse of many years, which led to full recovery of the agricultural production, especially in the farming production sector. Carbon emissions caused by the agricultural production increase rapidly along with increase of agricultural materials investment as well as recovery of the scale of rice cultivation. Agricultural material inputs was the largest source of agricultural greenhouse gas emissions, which account for 33.59% of the China’s total agricultural carbon emissions (in 2010, the same as below). China’s agricultural material carbon emissions had been showing a clear upward trend since 1995, increased from 61.8705 to 97.8141 million t in 2010 with an average annual growth rate of 3.10%. Paddy field was the second largest carbon source, which generated about 22.03% of the total agricultural greenhouse gas emissions of China. Over the past 15 yr, the overall carbon emissions caused by the rice cultivation rising at a slow speed, increased from 63.2025 million t in 1995 to 64.1446 million t in 2010 with an average annual growth rate of 0.10%, which was affected by change of the rice planting area. © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

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Table 6 Agricultural carbon emissions in China from 1995 to 2010 (×106 t)1) Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 1)

Agricultural material inputs 61.8705 65.1862 68.5078 70.6326 72.1745 73.0357 75.1533 76.6822 78.0205 82.3679 84.9587 87.5261 90.8257 92.3379 95.0169 97.8141

Paddy field

Soil

63.2025 66.0580 66.7445 66.4425 66.4425 63.9402 61.6786 61.3152 58.3643 62.4675 63.5570 63.9873 62.9400 63.5073 63.9895 64.1446

17.1890 17.8638 18.1193 18.7722 19.1455 19.2394 19.8936 19.8413 19.7280 19.7114 20.0970 19.9007 20.3330 20.7220 21.2115 21.7097

Enteric fermentation 55.6393 57.8453 52.2801 54.4929 55.1670 55.9948 55.6773 56.7031 58.6666 60.5375 60.3465 59.6379 52.9498 50.1700 50.6670 51.0286

Manure management

Total

Growth (%)

51.6225 51.6247 52.4272 54.9614 55.9395 55.2114 55.2027 55.5605 56.2319 57.6382 58.2918 56.8211 55.6335 56.0359 56.7002 56.4721

249.5239 258.5780 258.0790 265.3015 268.8690 267.4216 267.6055 270.1023 271.0114 282.7225 287.2511 287.8730 282.6820 282.7731 287.5852 291.1691

3.63 -0.19 2.80 1.34 -0.54 0.07 0.93 0.34 4.32 1.60 0.22 -1.80 0.03 1.70 1.25

In order to facilitate the analysis, we unified replaced CH4 and N2O with standard C, according to IPCC (2007), the greenhouse effect caused by 1 t CH4 or N2O is equivalent to the greenhouse effect produced by 25 t CO2 (about 6.8182 t C) and 298 t CO2 (81.2727 t C). The same as in Table 7.

Manure management of livestock lead to the 19.39 % of China’s agricultural carbon emissions, although existed “up and downs” characteristic between those years, the overall upward trend is quite obvious, which was from 51.6225 million t in 1995 to 56.4721 million t in 2010 with an average annual growth rate of 0.60%, thus showing the variation characteristic of three-phase, namely “up-down-up”. It was closely related with changes in cows and pigs rearing. Enteric fermentation of livestock caused 17.53% of China’s agricultural carbon emissions. Because of influencing by some factors, such as animal diseases, internal adjustment of the industrial structure, the carbon emissions caused by enteric fermentation was in larger fluctuations over the years and the variation characteristics was less obvious, but overall was in a slight downward trend, decreased from 55.6393 million t in 1995 to 51.0286 million t in 2010 with an average annual decrement rate of 0.58%. Cattle population has sharply declined in recent years with the widespread use of agricultural machinery, objectively reducing carbon emissions from livestock enteric fermentation. The amount of carbon emissions caused by destructing suffered soil surface was the least, accounting for only 7.46% of agricultural greenhouse gas emissions in China, which however overall was in a obvious upward trend from 17.189 in 1995 to 21.7097 million t in 2010 with an average annual growth rate of 1.57%, it was mainly affected by the change of sown area as well as adjustment of agricultural structure.

Regional comparative analysis of China’s agricultural carbon emissions In order to establish corresponding carbon reduction idea scientifically, we must understand and analyze the characteristics of agricultural carbon emissions in different provinces and cities. For this purpose, the amount of agricultural carbon emissions of China’s 31 provinces and cities in 2010 was estimated, shown in Table 7. From the ranking of China’s 31 provinces and cities agricultural carbon emissions in 2010, the top 10 provinces were Henan (21.9038 million t), Hunan (19.6914 million t), Sichuan (18.6365 million t), Shandong (17.1729 million t), Jiangsu (16.5894 million t), Hubei (16.298 million t), Anhui (15.215 million t), Jiangxi (13.4893 million t), Hebei (13.1971 million t) and Guangxi (12.8539 million t), which account for 56.68% of the total agricultural carbon emissions in China. The provinces and cities ranked the last 10 were Beijing (0.6586 million t), Tianjin (0.9254 million t), Shanghai (1.0441 million t), Ningxia (1.6823 million t), Hainan (2.5452 million t), Qinghai (3.4046 million t), Shanxi (3.8026 million t), Tibet (4.1504 million t), Chongqing (4.9538 million t) and Shaanxi (5.4819 million t), which account for only 9.84% of the China’s total agricultural carbon emissions. In those, the total agricultural carbon emissions of Henan (rank the first) is 31.26 times more than Beijing (the last one) in 2010, which shows that the amount of agricultural carbon emissions have great difference in different provinces and cities. In terms of © 2014, CAAS. All rights reserved. Published by Elsevier Ltd.

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Table 7 Agricultural carbon emissions of China’s 31 provinces and cities in 2010 Region

Beijing

Agricultural Paddy field Soil Enteric fermentation Manure management material inputs Emissions Proportion Emissions Proportion Emissions Proportion Emissions Proportion Emissions Proportion (%) (×106 t) (%) (×106 t) (%) (×106 t) (%) (×106 t) (%) (×106 t) 0.2919

44.32

0.0003

0.04

0.0670

10.18

0.1147

17.42

0.1847

28.04

Total

Carbon emissions intensity (kg)/ ×104 CNY agricultural output value

0.6586

200.78

Tianjin

0.5015

54.20

0.0122

1.32

0.0875

9.45

0.1335

14.43

0.1906

20.60

0.9254

291.62

Hebei

6.9050

52.32

0.0833

0.63

1.5706

11.90

2.1890

16.59

2.4492

18.56

13.1971

306.24

Shanxi

1.8321

48.18

0.0005

0.01

0.6124

16.11

0.6407

16.85

0.7169

18.85

3.8026

362.9

Inner Mongolia

3.2144

26.95

0.0561

0.47

0.8876

7.44

4.5087

37.80

3.2611

27.34

11.9279

Liaoning

3.0830

39.41

0.4268

5.46

0.6564

8.39

1.6909

21.62

1.9650

25.12

7.8220

647 251.79

Jilin

2.9422

40.58

0.2558

3.53

0.8154

11.25

1.7692

24.40

1.4677

20.24

7.2502

391.84

Hei Longjiang

4.4387

37.77

1.5688

13.35

1.3249

11.28

2.3504

20.00

2.0680

17.60

11.7507

463.3

Shanghai

0.3911

37.46

0.3985

38.17

0.0646

6.19

0.0483

4.62

0.1415

13.56

1.0441

363.76

Jiangsu

5.6143

33.84

8.1554

49.16

1.0209

6.15

0.4000

2.41

1.3987

8.43

16.5894

386.06

Zhejiang

2.9562

39.70

3.0466

40.92

0.3111

4.18

0.1938

2.60

0.9384

12.60

7.4461

342.69

Anhui

5.1994

34.17

6.7739

44.52

1.0702

7.03

0.8013

5.27

1.3702

9.01

15.2150

514.81

Fujian

2.4175

38.62

2.1486

34.32

0.3073

4.91

0.3722

5.95

1.0142

16.20

6.2598

271.33

Jiangxi

2.6348

19.53

7.9896

59.23

0.3698

2.74

1.0561

7.83

1.4389

10.67

13.4893

709.74

Shandong

9.1700

53.40

0.1836

1.07

1.9931

11.61

2.6128

15.21

3.2134

18.71

17.1729

258.2

Henan

9.2389

42.18

0.7643

3.49

2.3596

10.77

4.4695

20.41

5.0714

23.15

21.9038

381.99

Hubei

5.1424

31.55

6.5443

40.15

0.9266

5.69

1.4291

8.77

2.2555

13.84

16.2980

465.39

Hunan

4.0379

20.51

9.4299

47.89

0.7419

3.77

1.9503

9.90

3.5312

17.93

19.6914

519.91

Guangdong

3.8037

32.38

4.5242

38.52

0.5692

4.85

0.9710

8.27

1.8781

15.99

11.7463

312.83

Guangxi

3.3745

26.25

4.5845

35.67

0.6694

5.21

1.9139

14.89

2.3116

17.98

12.8539

472.4

Hainan

0.9211

36.19

0.7480

29.39

0.1052

4.13

0.3496

13.74

0.4214

16.56

2.5452

309.9

Chongqing

1.3945

28.15

1.1998

24.22

0.4475

9.03

0.6078

12.27

1.3042

26.33

4.9538

485.13

Sichuang

4.0491

21.73

3.5146

18.86

1.2081

6.48

4.4381

23.81

5.4265

29.12

18.6365

456.57

Guizhou

1.3684

19.04

1.0461

14.56

0.6308

8.78

2.1665

30.15

1.9737

27.47

7.1856

720.13

Yunnan

3.1354

28.85

0.4925

4.53

0.8344

7.68

3.0915

28.45

3.3144

30.50

10.8682

Tibet

0.1331

3.21

0.0005

0.01

0.0268

0.65

2.6783

64.53

1.3117

31.60

4.1504

600.28

Shaanxi

2.7741

50.60

0.1037

1.89

0.6859

12.51

0.8606

15.70

1.0576

19.29

5.4819

Gansu

2.1372

32.86

0.0027

0.04

0.5591

8.60

2.1797

33.51

1.6251

24.99

6.5038

615.3

Qinghai

0.2083

6.12

0.0000

0.00

0.0508

1.49

2.0381

59.86

1.1073

32.52

3.4046

1 691.16

4 118.6 329.03

Ningxia

0.6688

39.76

0.0417

2.48

0.1511

8.98

0.4911

29.19

0.3296

19.59

1.6823

549.88

Xinjiang

3.8361

44.01

0.0479

0.55

0.5844

6.70

2.5135

28.83

1.7352

19.91

8.7170

472.16

regional distribution, traditional agricultural provinces, especially major grain-producing provinces, were the major sources of China’s agricultural carbon emissions, and the carbon emissions of 9 major grain-producing provinces rank top 10 in China. Currently, the major agricultural province still mainly adopt the traditional development mode, namely adhere to the principle of “high input, high-yield”, together with quite single industrial structure, which lead to a large amount of agricultural carbon emissions.

Based on the difference in proportion constitute of carbon emission, 31 provinces and cities were divided into five types: (1) agricultural materials dominant regions, where agricultural carbon emissions were mainly from agricultural material inputs, mainly distributed in northeast, north, and northwest China, including Beijing, Tianjin, Hebei, Shanxi, Liaoning, Jilin, Heilongjiang, Shandong, Henan, Shaanxi, Xinjiang and Ningxia. The main way of agricultural land use was non-irrigated farmland; (2) paddy field dominant regions, where

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agricultural carbon emissions generated mainly from paddy field, distributed in the central and eastern China, including Jiangsu, Anhui, Jiangxi, Hunan and Hubei. In those 5 provinces, rice is the main food crop; (3) livestock enteric fermentation dominant regions, where agricultural carbon emissions mainly come from enteric fermentation of livestock, including Qinghai and Tibet, animal husbandry took the absolute position in their agricultural structure; (4) compound factors oriented regions, where agricultural carbon emissions mainly from two aspects, and the difference in absolute proportion between two share was less than 10%, including Shanghai, Zhejiang, Fujian, Guangdong, Guangxi, Hainan, and Guizhou, mainly distributed in East, South and Southwest China. These provinces and cities took planting industry as the main choice with reasonable collocation between food crops and cash crops; (5) balanced regions, where agricultural carbon emissions were derived from three or more aspects, including Inner Mongolia, Chongqing, Sichuan, Yunnan, and Gansu in a balanced way, mainly distributed in the northwest and southwest of China. Intensity of carbon emissions can objectively reflect every region’s agricultural carbon emissions degree and conveniently make horizontal comparison among different regions. The research result shows that the difference in intensity of agricultural carbon emissions among China’s 31 provinces and cities was very obvious, which present the characteristics of “west high, east low”, namely “western>central>eastern”. The intensity of agricultural carbon emissions of Tibet was the highest, up to 4 118.60 kg per 10 000 CNY agricultural output value; while Beijing was the lowest area, where per 10 000 CNY agricultural output value only generated 200.78 kg carbon emissions, less than Tibet’s 1/20. Based on the difference in absolute amount, three levels were divided: (1) In the first level, the regions, where more than 500 kg agricultural carbon emissions generated per 10 000 CNY agricultural output value, were mainly distributed in the western and several central parts of China, including Tibet, Qinghai, Guizhou, Jiangxi, Inner Mongolia, Gansu, Yunnan, Ningxia, Hunan and Anhui. (2) In the second level, the regions, where 350 to 500 kg agricultural carbon emissions generated per 10 000 CNY agricultural output value, were mainly distributed in the central, western and several eastern parts of Chi-

TIAN Yun et al.

na, including Chongqing, Guangxi, Xinjiang, Hubei, Heilongjiang, Sichuan, Jilin, Jiangsu, Henan, Shanghai and Shanxi. (3) In the third level, the regions, where less than 350 kg agricultural carbon emissions generated per 10 000 CNY agricultural output value, including Zhejiang, Shaanxi, Guangdong, Hainan, Hebei, Tianjin, Fujian, Shandong, Liaoning, and Beijing, all of these regions were distributed in the eastern of China except for Shaanxi. It is closely related to China’s social and economic development and agricultural modernization. The agricultural output efficiency is higher in eastern and central areas, while in western region dominated are based mainly on extensive farming, production level and use of agricultural materials are both low, resulting in higher agricultural carbon intensity than that of eastern and central regions.

Driving factors analysis of agricultural carbon emissions in China Based on LMDI model and the original data, combined with China’s agricultural carbon emissions over the years, we calculated the factors decomposition results of China’s agricultural carbon emissions, shown in Table 8. Efficiency factor, structure factor and labor factor inhibited China’s agricultural carbon emissions in various degrees. Compared with 1995, three major factors cumulatively achieved 96.47% (240.7264 million t) carbon emission reduction from 1996 to 2010, in which efficiency factor cumulatively cut 65.78% (164.134 million t) carbon emissions, indicating that when other factors remain unchanged, enhancement of the agricultural production efficiency had made China’s agricultural carbon decreased with an average rate of 10.9423 million t per year; labor factor cumulatively cut 27.51% (68.6393 million t) carbon emissions, indicated that the change of agricultural labor had made China’s agricultural carbon emissions with an average annual decrement of 4.576 million t if other factors keep unchanged; structure factor cumulatively cut 3.19% (7.9531 million t) carbon emissions, which indicated that continuous optimization of agricultural structure had promoted China’s agricultural carbon emissions with an average annual decrement of 0.5302 million t if other factors held constant.

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Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Table 8 Factors decomposition results of agricultural carbon emissions in China (×106 t)1) Year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total 1)

Efficiency factor -12.8691 -16.5958 -7.5086 -7.9411 -9.8299 -11.8314 -9.8578 -7.3805 -9.5308 -11.0365 -14.0460 -14.4251 -15.2313 -8.0712 -7.9788 -164.1340

Structure factor -0.8985 -0.6560 -0.5164 -0.6489 -1.1010 1.0093 -0.6144 -2.3219 1.3389 -0.1328 -0.5086 -1.5769 -0.4212 0.0581 -0.9631 -7.9531

Economy factor 27.9493 16.6045 12.7285 7.7076 7.4335 8.3767 11.1910 13.8474 30.6179 27.2867 28.3841 21.8247 23.2732 22.8425 22.3040 282.3717

Labor factor -5.1276 0.1483 2.5190 4.4499 2.0500 2.6293 1.7779 -3.2360 -10.7149 -11.5889 -13.2076 -11.0137 -7.5296 -10.0173 -9.7783 -68.6393

Total effect 9.0541 -0.4990 7.2225 3.5675 -1.4474 0.1839 2.4968 0.9091 11.7111 4.5286 0.6219 -5.1910 0.0911 4.8121 3.5839 41.6452

Source: The original data of agricultural gross output value, employment labor of agricultural industry, which factor decomposition needed in this table, were derived from China Statistical Yearbook 2011 (NBS of PRC 2011), and the amount of agricultural carbon emissions came from the front calculation.

The rapid promotion of the agricultural economy level led to China’s agricultural carbon emissions constantly increasing. Compared with 1995, the economy factor cumulatively increased by 113.16% (282.3717 million t) carbon emission from 1996 to 2010, that is, if other factors remain the same, the growing of agricultural economy had resulted in the increasing of China’s agricultural carbon emissions with an average rate of 18.8248 million t per year. However, it is clear that steadily pushing forward the development of agriculture and increasing farmers’ income will continue to be the China’s basic strategy for quite a period due to the basis status of agriculture for the national economy. Therefore, the mode for giving up the agricultural growth in order to promote agricultural carbon emission reduction will not be implemented. It is not difficult to predict that the agricultural economy factor will continue to be the dominant factor leading to increasing China’s agricultural carbon emissions in short-term, and transforming the mode of agricultural production is the key to achieve agricultural carbon emission reduction.

DISCUSSION Compared with the previous study, this article was deepened in following three aspects: Firstly, research

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vision got further expand, it was no longer limited to the single perspective research of agricultural carbon emissions, but transferred to grasp comprehensively carbon emissions of the entire agricultural production system. Secondly, the index of agricultural carbon source were more detailed and with wide range coverage, not only include both planting industry and stockbreeding, but also tend to be more comprehensive in the selection of secondary index. Thirdly, on the selection of research area, taking China as the object of empirical study, researched spatial-temporal pattern and driving factors of its agricultural carbon emissions, provided important literature supporting for the government about establishing reduction policies. From the analysis, economy factor is the critical factor leading to the increase in agricultural carbon emissions. However, to achieve the overall goals of agricultural carbon emissions reduction cannot sacrifice agricultural development. Otherwise, a certain area implements emission reduction targets by reducing local agricultural production, may inevitably lead agricultural production transfer to areas with higher emission intensity. Eventually, such situation will lead to failure of national agricultural GHG reduction goal. In order to reduce agricultural carbon emissions, some measures can be taken as follows. Firstly, we reduce consumption per unit agricultural output by improving the utilization efficiency of agricultural materials. Secondly, adjusting and optimizing agricultural industry structure constantly, tilting appropriately to forestry, fisheries and other low-carbon industries under the premise of guaranteeing food security. Thirdly, strengthen investment in low-carbon agriculture and its legislation. Of course, due to limitation of available data, the research needs to be carried out further. Firstly, although we strived to build a perfect index system of agricultural carbon emissions, it still need further refinement. Secondly, lack in-depth analysis for phase characteristics of China’s agricultural carbon emissions as well as why exist regions differences. These research topics call for the author to make further study in the future.

CONCLUSION The following conclusions can be drawn:

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TIAN Yun et al.

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(1) The total amount of China’s agricultural carbon emissions was 291.1691 million t (equal to 1.06762 billion t of carbon dioxide) in 2010, which increased by 16.69% compared to the 249.5239 million t in 1995, with an annual growth rate of 2.2%. Among them, the amount of carbon emission caused by agricultural material inputs, paddy fields, soil, enteric fermentation, and manure management are respectively 97.8141, 64.1446, 21.7097, 51.0286, and 56.4721 million t, which account for 33.59, 22.03, 7.46, 17.53, and 19.39% of the total agricultural carbon emissions. Seen from different stages, although there were fluctuations, it overall presented the signs of cyclical rise. (2) The regional difference is very obvious: the amount of agricultural carbon emissions from top 10 provinces take 56.68%, while 9.84% from last 10 provinces and cities. The carbon emissions of Henan ranked the first which was up to 21.9038 million t, or 32.26 times as many as Beijing produced. Beijing ranked the last and only produced 0.6586 million t. Based on the difference in proportion constitute of agricultural carbon emissions, 31 provinces and cities were divided into five types, namely agricultural materials dominant, paddy field oriented, enteric fermentation oriented, composite factors oriented, and balanced type. Intensity of agricultural carbon emissions in west China was the highest, followed by the region of central China, the east zone was the lowest. Tibet was the highest zone with intensity of agricultural carbon emission as high as 4 118.60 kg per 10 000 CNY agricultural output value, while Beijing was the lowest zone with the intensity of agricultural carbon emission as low as 200.78 kg per 10 000 CNY agricultural output value. (3) Efficiency factor, labor factor and industry structure factor had strong inhibitory effect to China’s agricultural carbon emissions and achieved carbon emission reduction, of which the percentage were 65.78, 27.51 and 3.19 compared with 1995, respectively. While economy factor play an important role in promoting agricultural carbon emission, which increased carbon emission by 113.16%.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (71273105) and the Fundamental Research Funds for the Central Universities, China (2013YB12). Any errors and all views expressed remain our own.

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