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Carbon footprints of rice production in five typical rice districts in China
Acta Ecologica Sinica 33 (2013) 227–232
Contents lists available at SciVerse ScienceDirect
Acta Ecologica Sinica journal homepage: www.elsevier.com/locate/chnaes
Carbon footprints of rice production in five typical rice districts in China Xiaoming Xu a,⇑, Bo Zhang b, Yong Liu a, Yanni Xue a, Binsheng Di c a
Institute of Loess Plateau, Shanxi University, Taiyuan, Shanxi 030006, China Weinan Gaoxin High School, Weinan, Shaanxi 714000, China c Lanxian High School, Lanxian, Shanxi 033500, China b
a r t i c l e
i n f o
Article history: Received 7 May 2012 Revised 26 October 2012 Accepted 30 January 2013
Keywords: Rice Carbon footprint Life cycle assessment Energy consumption Rice districts of China
a b s t r a c t Five provinces located in the five main rice-growing regions in China were selected as study areas, which were Jiangsu, Heilongjiang, Sichuan, Guangdong and Hunan province respectively in the middle and lower reaches of the Yangtze River, northern, southwest, southern and central rice districts. Carbon footprints of rice production in these five provinces were calculated through the life cycle assessment method using governmental statistical data, industrial standards and relevant technical data separately. Material and energy consumptions were estimated, key stages of energy consumptions and carbon emissions were identified as well. Moreover, improving measurements had been suggested correspondingly. The results indicated that: the energy consumptions of rice production in these five provinces ranked as following (high to low): Guangdong, Heilongjiang, Hunan, Sichuan and Jiangsu. The carbon footprints of rice production were 2504.20 kg carbon dioxide equation per ton rice (kgCO2-eq./t) (Guangdong province), 2326.47 kgCO2-eq./t (Hunan province), 1889.97 kgCO2-eq./t (Heilongjiang province), 1538.90 kgCO2eq./t (Sichuan province) and 1344.92 kgCO2-eq./t (Jiangsu province) respectively. Reducing the quantities of urea and using the intermittent irrigation method could decrease energy consumption as well as carbon footprint. Ó 2013 Ecological Society of China. Published by Elsevier B.V. All rights reserved.
1. Introduction Greenhouse gases (GHGs) emission is one of the most important issues in global warming research. Carbon footprint (CF) is introduced to indicate the influence of GHGs emission of human activities or products [1]. This conception provides a comprehensive way to measure the global warming effects on human activities and products. CFs of crop productions is an attractive and important issue, not only because of the importance of crops, but also because of the huge carbon sequestration potential in farmland soils. Rice has the largest yield among all food crops in China. Perennial rice acreage accounts for about 30% of the total food crops area, and it produces about 40% of the total grain output. Previous studies had illustrated that rice planting would promote the accumulation of paddy field soil organic carbon [2]. However, at the same time, the process of rice cultivation would stimulate the emission of some GHGs such as CH4 and N2O [3,4]. Rice planting has both positive and negative effects to global warming. There are five major rice-growing regions in China: the middle and lower reaches of the Yangtze River, northern, southwest, southern and central rice districts. In this study, five provinces ⇑ Corresponding author. Tel.: +86 351 7010700. E-mail address: [email protected] (X. Xu).
located in these five regions were selected as the study areas, which were Jiangsu province in the middle and lower reaches of the Yangtze River, Heilongjiang province in northern rice districts, Sichuan province in southwest rice districts, Guangdong province in southern rice districts and Hunan province in central rice districts. CFs of rice production in these five provinces were calculated by using the Life Cycle Assessment (LCA) method. The LCA method is first introduced in the late 1960s when CocaCola Company analyzed its beverage packaging [5]. According to ISO14040:2006, LCA addresses the environmental aspects and potential environmental throughout a product’s whole life cycle [6]. LCA was characterized for its systemic consideration in the effect of products or activities to the environment. In LCA, the environmental impact was categorized into land use, energy consumption (EC), global warming, acidification, eutrophication, human toxicity, etc. Essentially, CF was the effects of global warming in LCA. Because of the comprehensive and synthetic traits, the method of LCA has been widely used in the CF research. A complete LCA include four standard phases, which were the goal and scope definition phase, the inventory analysis phase, the impact assessment phase and interpretation phase, respectively. The purposes of this study were to (i) calculating the total ECs and CFs of rice production in selected five provinces; (ii) find out the key stages of EC and CF; and (iii) suggest methods on decreasing EC and CF during rice production.
1872-2032/$ - see front matter Ó 2013 Ecological Society of China. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chnaes.2013.05.010
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X. Xu et al. / Acta Ecologica Sinica 33 (2013) 227–232
2. Materials and methods The data sources of this study include governmental statistics, technological standards, and relevant research results. Average rice yields per unit area of the five provinces were obtained from China Statistical Yearbook [7], as well as the chemical fertilizers and pesticides application amount per unit area. EC of railway and highway transportation was calculated using data from China Statistical Yearbook [7]. From the percentage of diesel/electric locomotive in freight ton-kilometers (transported amount times transported distance) and average diesel oil/electricity consumption of diesel/electric locomotive, comprehensive diesel oil and electricity consumption per ton per kilometer could be calculated. Then, combined with the freight ton-kilometers data and transported amount of freights, integrated railway transportation EC per ton of different freights such as fertilizers–pesticides, minerals and coals could be calculated. Average highway transportation EC was achieved by the same method. Finally, based on the average transported distances of railway and highway, the integrated transportation EC was achieved (Table 1). ECs of rice planting were collected from related research results. Energy and material consumptions of urea production were from Liu’s research [7,8], energy and material consumptions of P2O5were from Gong’s result [9], and energy and material consumptions of K2O were from Chen’s data [10]. Emission coefficients of GHGs in the five provinces were cited from the Chinese GHGs emissions inventory report. And the soil carbon sequestration (i.e. net CO2 flux) rates were from domestic research [11–16]. Average water consumptions were gathered from the technology instructions of rice irrigation. 3. Results
Starting boundary
Energy consumption
Raw material stage
Mineral exploitation
Agricultural materials stage
Energy consumption
CO 2 emission
Fertilizer & pesticides production Harvested crop
GHGs emissions
Straw
Rice planting
Planting stage
Energy consumption of farming and irrigation
Returning
SOC variation
Termination boundary Fig. 1. Carbon footprint framework of rice production.
3.3. Impact assessment The impact assessment aimed at evaluating the magnitude and significance of the potential environmental impacts qualitatively or quantitatively. In this paper, the impact category was global warming. Global warming potential (GWP100) were used to illustrate the results. According to the 4th assessment report of Intergovernmental Panel on Climate Change (IPCC), GWP100 in our research could be estimated by the following formula:
3.1. Goal and scope definition
GWP100 ¼ CO2 þ 25CH4 þ 298N2 O
The goal and scope of this study was to evaluate the GHGs emission per ton rice production throughout the whole life cycle, calculate the CFs of rice production in typical rice districts, recognize the major impact factor, and finally suggest efficient methods to decrease EC, resource consumption and CF. Previous research indicated that the whole life cycle of rice production which was divided into three stages started by the mineral exploration and ended by products and GHGs emissions (Fig. 1) [17,18]. We estimated the material inputs per ton rice according to the statistical data (Table 2). During the calculation, CF and EC introduced by equipment and facilities using in fertilizer and pesticides producing were not included.
3.3.1. Energy consumption Calorific values of coal and diesel oil were set as 29 MJ/k and 42.6 MJ/kg respectively. Total ECs were shown in Table 8 and Fig. 2. Therefore, EC in agricultural material stage counted for a major portion of total EC in each province (Fig. 2). Among five provinces, ECs in Guangdong and Heilongjiang were higher than others (Table 8), this may due to larger fertilizer consumptions. Water irrigating was the main source of EC in planting stage. And the water consumption in Guangdong was the highest according to governmental standard water consumption of rice irrigation.
3.2. Inventory analysis Regular LCA includes several environmental impacts such as global warming, acidification, eutrophication, and human toxicity. In this research, because we just aim to calculate the CF which was only referred the global warming impact of LCA. And the LCA inventory in this section just presented material consumptions in five provinces (Tables 3–7).
3.3.2. Carbon footprint Carbon emission coefficients of different stages were cited from relevant research results [19–22]. The results were presented in kgCO2 equivalence (abbreviated in kgCO2-eq.) (Table 9). In contrast to the EC results, CF was mostly generated in planting stage, in which GHGs such as CH4 and N2O were emitted from soil during rice planting. CFs of rice production per ton in Guangdong and Hunan were higher, which was mainly because there were more GHGs emitted (citation). 3.4. Interpretation
Table 1 Integrated transportation energy consumption.
Coal Mineral Fertilizer and pesticide
ð1Þ
Diesel oil (kg/t)
Electricity (kWh/t)
2.58 2.63 3.30
3.13 3.44 7.11
Interpretation aimed to recognize the main factors and key stages in the whole life cycle, and to provide proposals correspondingly. As previously mentioned, EC in agricultural material stage was the largest. Urea production was the most important stage as it
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X. Xu et al. / Acta Ecologica Sinica 33 (2013) 227–232 Table 2 Materials inputs of rice production. Area 2
Per unit yield (t/hm ) Urea (kg) P2O5 (kg) K2O (kg) Water (t) Diesel oil (kg) Pesticide (kg)
Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
6.22 22.25 12.54 20.36 837.14 10.77 3.38
6.66 48.21 11.79 12.85 933.15 21.82 1.26
8.09 24.39 5.43 20.57 598.13 8.77 0.81
7.54 23.71 4.69 25.62 631.34 6.04 0.89
5.43 42.68 14.92 12.45 1039.80 26.78 3.71
Table 3 Material consumptions of rice production of Hunan province. Coal (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Diesel oil (kg)
Electric (kWh)
2.05 2.05 34.49 18.08 26.47 1.71 80.74
82.79
10.78 0.90 11.68 22.92 14.56 40.72 0.31 0.42 78.92 0.57 0.57 91.17
0.70 0.70
0.19 0.19 10.77 10.77 11.67
Water (t)
0.40 0.05 0.06 0.01 0.53 837.14 837.14 837.67
Other minerals (kg)
60.28 128.40
188.68
188.68
Assuming all urea was produced by using coal. Assuming P2O5 content of phosphate ore was 30%. Assuming KCl content of potassium ore was 25%.
Table 4 Resource consumptions of rice production of Heilongjiang province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Coal (kg)
Diesel oil (kg)
1.88
56.67 81.01
1.88 74.73 16.99 16.70 0.63
0.64 0.64
137.68
109.06
137.68
110.93
0.24 0.24 21.82 21.82 22.71
Coal (kg)
Diesel oil (kg)
Electric (kWh)
Water (t)
9.87 0.81 10.68 49.66 13.69 25.69 0.11 0.53 89.68 0.64 0.64 101.00
0.96 933.15 933.15 934.11
Electric (kWh)
Water (t)
0.87 0.05 0.04 0.01
Table 5 Resource consumptions of rice production of Jiangsu province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O Production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
1.74
26.11 129.69 0.00
1.74 37.80 7.83 26.74 0.41
155.80
72.77
155.80
74.51
consumed more energy than others. In this study, EC of urea production was calculated from coal-based producing process. However, 80% of urea was produced from coal currently, the rest was produced from either gases or oil, which have lower ECs than coal, thus, the EC estimated here is higher than actual use. But this approach would not influence the predominate status of EC in urea production. Therefore, decreasing the amount of urea application,
0.60 0.60
0.17 0.17 8.77 8.77 9.53
9.14 0.76 9.91 25.12 6.31 41.13 0.07 0.36 72.99 0.41 0.41 83.31
0.44 0.02 0.06 0.00 0.53 598.13 598.13 598.65
enhancing urea utilization efficiency and improving the technology of the urea production could lower total EC effectively. The CF results illustrated that CFs were mainly generated in the planting stage. And the main reason was the emission of CH4 and N2O. The amount of N2O emission was directly determined by the urea application amount, so reducing the amount of urea application and improving urea utilization efficiency would not just
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Table 6 Resource consumptions of rice production of Sichuan province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O Production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Coal (kg)
Diesel oil (kg)
1.99
Electric (kWh)
79.25
0.18 0.18 6.04 6.04 6.91
10.45 0.88 11.33 24.42 5.44 51.24 0.08 0.39 81.57 0.43 0.43 93.33
Coal(kg)
Diesel oil(kg)
Electric (kWh)
22.54 161.55 0.00
1.99 36.75 6.76 33.30 0.45
184.09
77.26
184.09
0.68 0.68
Water (t)
0.43 0.02 0.08 0.00 0.53 631.34 631.34 631.86
Table 7 Resource consumptions of rice production of Guangdong province. Other minerals(kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
1.95
71.70 78.53 0.00
1.95 66.16 21.50 16.19 1.87
150.23
105.72
150.23
107.66
0.67 0.67
0.24 0.24 26.78 26.78 27.69
10.24 0.85 11.09 43.96 17.32 24.91 0.34 0.52 87.05 0.71 0.71 98.84
Water(t)
0.77 0.06 0.04 0.02 0.88 1039.80 1039.80 1040.68
Table 8 Energy consumptions of rice production in the whole life-cycle. Stages
Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops Planting Planting stage Sum
Total energy consuming(MJ) Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
98.18 33.26 131.44 1082.58 576.63 914.26 50.57 9.73 2633.77 460.85 460.85 3226.06
89.91 30.35 120.26 2345.83 542.11 576.82 18.81 12.31 3495.88 931.97 931.97 4548.11
83.29 28.20 111.49 1186.64 249.76 923.41 12.11 8.51 2380.43 374.88 374.88 2866.80
95.24 32.27 127.50 1153.64 215.58 1150.30 13.34 9.12 2541.98 259.02 259.02 2928.50
93.27 31.50 124.77 2076.85 685.85 559.14 55.48 12.26 3389.58 1143.48 1143.48 4657.83
lower the ECs, but also cut down CFs. Besides, as previous studies showed, paddy fields in intermittent flooding conditions emitted less CH4 than those in a water logging situation [23,24]. So alternating dry and flood irrigating method would lessen CFs, as well as lower ECs since less water would be used. 4. Discussion This paper used a unified value of EC per mass in fertilizer producing process in the five provinces. That means more fertilizer implied more energy consumed. Average rice yield per unit acreage in Guangdong was lower comparatively, so fertilizer application amount per unit yield was higher correspondingly. Also, single cropping planting system in Heilongjiang caused higher fertilizer application amount per unit yield. Besides, average evaporation was the highest in Guangdong while average precipitation was
the lowest in Heilongjiang. Thus, both two provinces desired more irrigation water. Conversely, because of the largest average yield and lower irrigating water amount needed in Jiangsu, EC of this province was the lowest. All of above factors demonstrated the consumption rank of the five provinces. CFs were mostly introduced from cultural and irrigating machines and GHGs emissions in planting stage. So more water irrigated and higher GHGs emission coefficients would lead to lager CF. More irrigation water amount in Guangdong and higher CH4 emission coefficients in Hunan resulted in higher CFs in these two areas. Contrarily, lower GHGs emission in Jiangsu could explain why CF in Jiangsu was the lowest. As reported, the coefficients were influenced by soil properties, rice varieties, fertilization, water management and cropping system. Comparing to Wang’s result of high-yield rice in Taihu region in Jiangsu province, our EC results were higher. Differences were
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X. Xu et al. / Acta Ecologica Sinica 33 (2013) 227–232
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Hunan
Heilongjiang
Raw material stage
Jiangsu
Sichuan
Agricultural material stage
Guangdong
Planting stage
Fig. 2. Energy consumption composition of different stages in the rice production life cycle.
Table 9 Carbon footprint of rice production lifecycle (kgCO2-eq./t). Stages
Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Greenhouse gases and soil carbon sequestration Planting stage Sum
13.82 2.95 16.77 108.71 58.83 101.38 4.72 0.94 274.58 796.49 1238.62 2035.11 2326.47
12.66 2.69 15.35 235.55 55.31 63.96 1.76 1.19 357.77 919.06 597.79 1516.85 1889.97
11.73 2.50 14.23 119.16 25.48 102.39 1.13 0.82 248.98 572.49 509.22 1081.71 1344.92
13.41 2.86 16.27 115.84 22.00 127.55 1.25 0.88 267.52 594.07 661.04 1255.12 1538.90
13.13 2.79 15.92 208.54 69.98 62.00 5.18 1.19 346.89 1031.94 1109.44 2141.38 2504.20
mainly existed in agricultural material stage and planting stage. Rice yield in their research area was higher than in our study. This could cause lower EC per unit yield. Moreover, the CF result was 3768.1kgCO2-eq., which was significantly higher than our results [17]. Liang’s evaluation in Hunan province showed that ECs of rice production under regular management and improved management were 3679.3 MJ and 2596.96 MJ. The results were slightly higher than our results of Hunan. The CF results were 481.27 kgCO2-eq. and 370.2 kgCO2-eq., which were extremely lower than our results [25]. Li studied the rice production LCA of Hunan, Guangxi and Jiangsu. Their EC results were significantly higher than our results, as well as CFs, which was due to the higher estimation of urea producing [18]. Xu’s results of western Jilin were lower than ours in both EC and CF, which could be attributed to the underestimated GHGs coefficients [16]. Guo’s CF result in Taiwan was lower. This was mainly because N2O was not considered in that study [26]. The EC and CF in Vercelli (Italy) were higher than ours [27] and the CF of Indian rice production was lower than ours [28]. The main reason for these differences comes from the diversity of the definition of life cycle boundaries. Above comparisons indicated that differences among scope definitions, fertilizer application amounts, ECs and GHGs emission coefficients caused huge gaps among CF and EC results. In order to improve the research accuracy, this paper used the Chinese GHGs emissions inventory data which was normally treated as authorized data in related researches.
5. Conclusion The ECs of rice production in five typical rice production provinces of China were 4657.83 MJ/t (MJ per ton rice) (Guangdong province), 4548.11 MJ/t (Heilongjiang province), 3226.06 MJ/t (Hu-
nan province), 2928.50 MJ/t (Sichuan province) and 2866.80 MJ/t (Jiangsu province). Urea production consumed major portion of total ECs. The CFs of rice productions in these five provinces were 2504.20 kgCO2-eq./t (kg carbon dioxide equation per ton rice) (Guangdong province), 2326.47 kgCO2-eq./t (Hunan province), 1889.97 kgCO2-eq./t (Heilongjiang province), 1538.90 kgCO2-eq./t (Sichuan province) and 1344.92 kgCO2-eq./t (Jiangsu province) respectively. GHGs emission was the largest CF generating process. Because parameter settings of the calculation boundaries, material/energy consumptions and amount of greenhouse gases vary from different researches, the results of the carbon footprints were highly uncertain. Different estimations of emission coefficients of greenhouse gas were the most important reason for the difference of carbon footprint results. Reducing the quantities of urea could not only lower the EC, but also decrease the amount of N2O; and the intermittent irrigation system could save water and energy as well as decrease the amount of CH4 emission. Accuracy and comparability of CF results among different researches could be improved remarkably by using industry standards, technical specifications, governmental statistical data and related authoritative research results.
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[5] M. Hu, Z.L. Wei, Y.F. Xu, LCA: a new kind of method of environmental impact assessment, Environment Herald 4 (1997) 23–25. [6] International Standards Organization (ISO), Environmental management-life cycle assessment-principles and framework, (21.04.12) [7] National Bureau of Statistics of China. China Statistical Yearbook, 2011
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Contents lists available at SciVerse ScienceDirect
Acta Ecologica Sinica journal homepage: www.elsevier.com/locate/chnaes
Carbon footprints of rice production in five typical rice districts in China Xiaoming Xu a,⇑, Bo Zhang b, Yong Liu a, Yanni Xue a, Binsheng Di c a
Institute of Loess Plateau, Shanxi University, Taiyuan, Shanxi 030006, China Weinan Gaoxin High School, Weinan, Shaanxi 714000, China c Lanxian High School, Lanxian, Shanxi 033500, China b
a r t i c l e
i n f o
Article history: Received 7 May 2012 Revised 26 October 2012 Accepted 30 January 2013
Keywords: Rice Carbon footprint Life cycle assessment Energy consumption Rice districts of China
a b s t r a c t Five provinces located in the five main rice-growing regions in China were selected as study areas, which were Jiangsu, Heilongjiang, Sichuan, Guangdong and Hunan province respectively in the middle and lower reaches of the Yangtze River, northern, southwest, southern and central rice districts. Carbon footprints of rice production in these five provinces were calculated through the life cycle assessment method using governmental statistical data, industrial standards and relevant technical data separately. Material and energy consumptions were estimated, key stages of energy consumptions and carbon emissions were identified as well. Moreover, improving measurements had been suggested correspondingly. The results indicated that: the energy consumptions of rice production in these five provinces ranked as following (high to low): Guangdong, Heilongjiang, Hunan, Sichuan and Jiangsu. The carbon footprints of rice production were 2504.20 kg carbon dioxide equation per ton rice (kgCO2-eq./t) (Guangdong province), 2326.47 kgCO2-eq./t (Hunan province), 1889.97 kgCO2-eq./t (Heilongjiang province), 1538.90 kgCO2eq./t (Sichuan province) and 1344.92 kgCO2-eq./t (Jiangsu province) respectively. Reducing the quantities of urea and using the intermittent irrigation method could decrease energy consumption as well as carbon footprint. Ó 2013 Ecological Society of China. Published by Elsevier B.V. All rights reserved.
1. Introduction Greenhouse gases (GHGs) emission is one of the most important issues in global warming research. Carbon footprint (CF) is introduced to indicate the influence of GHGs emission of human activities or products [1]. This conception provides a comprehensive way to measure the global warming effects on human activities and products. CFs of crop productions is an attractive and important issue, not only because of the importance of crops, but also because of the huge carbon sequestration potential in farmland soils. Rice has the largest yield among all food crops in China. Perennial rice acreage accounts for about 30% of the total food crops area, and it produces about 40% of the total grain output. Previous studies had illustrated that rice planting would promote the accumulation of paddy field soil organic carbon [2]. However, at the same time, the process of rice cultivation would stimulate the emission of some GHGs such as CH4 and N2O [3,4]. Rice planting has both positive and negative effects to global warming. There are five major rice-growing regions in China: the middle and lower reaches of the Yangtze River, northern, southwest, southern and central rice districts. In this study, five provinces ⇑ Corresponding author. Tel.: +86 351 7010700. E-mail address: [email protected] (X. Xu).
located in these five regions were selected as the study areas, which were Jiangsu province in the middle and lower reaches of the Yangtze River, Heilongjiang province in northern rice districts, Sichuan province in southwest rice districts, Guangdong province in southern rice districts and Hunan province in central rice districts. CFs of rice production in these five provinces were calculated by using the Life Cycle Assessment (LCA) method. The LCA method is first introduced in the late 1960s when CocaCola Company analyzed its beverage packaging [5]. According to ISO14040:2006, LCA addresses the environmental aspects and potential environmental throughout a product’s whole life cycle [6]. LCA was characterized for its systemic consideration in the effect of products or activities to the environment. In LCA, the environmental impact was categorized into land use, energy consumption (EC), global warming, acidification, eutrophication, human toxicity, etc. Essentially, CF was the effects of global warming in LCA. Because of the comprehensive and synthetic traits, the method of LCA has been widely used in the CF research. A complete LCA include four standard phases, which were the goal and scope definition phase, the inventory analysis phase, the impact assessment phase and interpretation phase, respectively. The purposes of this study were to (i) calculating the total ECs and CFs of rice production in selected five provinces; (ii) find out the key stages of EC and CF; and (iii) suggest methods on decreasing EC and CF during rice production.
1872-2032/$ - see front matter Ó 2013 Ecological Society of China. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chnaes.2013.05.010
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2. Materials and methods The data sources of this study include governmental statistics, technological standards, and relevant research results. Average rice yields per unit area of the five provinces were obtained from China Statistical Yearbook [7], as well as the chemical fertilizers and pesticides application amount per unit area. EC of railway and highway transportation was calculated using data from China Statistical Yearbook [7]. From the percentage of diesel/electric locomotive in freight ton-kilometers (transported amount times transported distance) and average diesel oil/electricity consumption of diesel/electric locomotive, comprehensive diesel oil and electricity consumption per ton per kilometer could be calculated. Then, combined with the freight ton-kilometers data and transported amount of freights, integrated railway transportation EC per ton of different freights such as fertilizers–pesticides, minerals and coals could be calculated. Average highway transportation EC was achieved by the same method. Finally, based on the average transported distances of railway and highway, the integrated transportation EC was achieved (Table 1). ECs of rice planting were collected from related research results. Energy and material consumptions of urea production were from Liu’s research [7,8], energy and material consumptions of P2O5were from Gong’s result [9], and energy and material consumptions of K2O were from Chen’s data [10]. Emission coefficients of GHGs in the five provinces were cited from the Chinese GHGs emissions inventory report. And the soil carbon sequestration (i.e. net CO2 flux) rates were from domestic research [11–16]. Average water consumptions were gathered from the technology instructions of rice irrigation. 3. Results
Starting boundary
Energy consumption
Raw material stage
Mineral exploitation
Agricultural materials stage
Energy consumption
CO 2 emission
Fertilizer & pesticides production Harvested crop
GHGs emissions
Straw
Rice planting
Planting stage
Energy consumption of farming and irrigation
Returning
SOC variation
Termination boundary Fig. 1. Carbon footprint framework of rice production.
3.3. Impact assessment The impact assessment aimed at evaluating the magnitude and significance of the potential environmental impacts qualitatively or quantitatively. In this paper, the impact category was global warming. Global warming potential (GWP100) were used to illustrate the results. According to the 4th assessment report of Intergovernmental Panel on Climate Change (IPCC), GWP100 in our research could be estimated by the following formula:
3.1. Goal and scope definition
GWP100 ¼ CO2 þ 25CH4 þ 298N2 O
The goal and scope of this study was to evaluate the GHGs emission per ton rice production throughout the whole life cycle, calculate the CFs of rice production in typical rice districts, recognize the major impact factor, and finally suggest efficient methods to decrease EC, resource consumption and CF. Previous research indicated that the whole life cycle of rice production which was divided into three stages started by the mineral exploration and ended by products and GHGs emissions (Fig. 1) [17,18]. We estimated the material inputs per ton rice according to the statistical data (Table 2). During the calculation, CF and EC introduced by equipment and facilities using in fertilizer and pesticides producing were not included.
3.3.1. Energy consumption Calorific values of coal and diesel oil were set as 29 MJ/k and 42.6 MJ/kg respectively. Total ECs were shown in Table 8 and Fig. 2. Therefore, EC in agricultural material stage counted for a major portion of total EC in each province (Fig. 2). Among five provinces, ECs in Guangdong and Heilongjiang were higher than others (Table 8), this may due to larger fertilizer consumptions. Water irrigating was the main source of EC in planting stage. And the water consumption in Guangdong was the highest according to governmental standard water consumption of rice irrigation.
3.2. Inventory analysis Regular LCA includes several environmental impacts such as global warming, acidification, eutrophication, and human toxicity. In this research, because we just aim to calculate the CF which was only referred the global warming impact of LCA. And the LCA inventory in this section just presented material consumptions in five provinces (Tables 3–7).
3.3.2. Carbon footprint Carbon emission coefficients of different stages were cited from relevant research results [19–22]. The results were presented in kgCO2 equivalence (abbreviated in kgCO2-eq.) (Table 9). In contrast to the EC results, CF was mostly generated in planting stage, in which GHGs such as CH4 and N2O were emitted from soil during rice planting. CFs of rice production per ton in Guangdong and Hunan were higher, which was mainly because there were more GHGs emitted (citation). 3.4. Interpretation
Table 1 Integrated transportation energy consumption.
Coal Mineral Fertilizer and pesticide
ð1Þ
Diesel oil (kg/t)
Electricity (kWh/t)
2.58 2.63 3.30
3.13 3.44 7.11
Interpretation aimed to recognize the main factors and key stages in the whole life cycle, and to provide proposals correspondingly. As previously mentioned, EC in agricultural material stage was the largest. Urea production was the most important stage as it
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X. Xu et al. / Acta Ecologica Sinica 33 (2013) 227–232 Table 2 Materials inputs of rice production. Area 2
Per unit yield (t/hm ) Urea (kg) P2O5 (kg) K2O (kg) Water (t) Diesel oil (kg) Pesticide (kg)
Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
6.22 22.25 12.54 20.36 837.14 10.77 3.38
6.66 48.21 11.79 12.85 933.15 21.82 1.26
8.09 24.39 5.43 20.57 598.13 8.77 0.81
7.54 23.71 4.69 25.62 631.34 6.04 0.89
5.43 42.68 14.92 12.45 1039.80 26.78 3.71
Table 3 Material consumptions of rice production of Hunan province. Coal (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Diesel oil (kg)
Electric (kWh)
2.05 2.05 34.49 18.08 26.47 1.71 80.74
82.79
10.78 0.90 11.68 22.92 14.56 40.72 0.31 0.42 78.92 0.57 0.57 91.17
0.70 0.70
0.19 0.19 10.77 10.77 11.67
Water (t)
0.40 0.05 0.06 0.01 0.53 837.14 837.14 837.67
Other minerals (kg)
60.28 128.40
188.68
188.68
Assuming all urea was produced by using coal. Assuming P2O5 content of phosphate ore was 30%. Assuming KCl content of potassium ore was 25%.
Table 4 Resource consumptions of rice production of Heilongjiang province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Coal (kg)
Diesel oil (kg)
1.88
56.67 81.01
1.88 74.73 16.99 16.70 0.63
0.64 0.64
137.68
109.06
137.68
110.93
0.24 0.24 21.82 21.82 22.71
Coal (kg)
Diesel oil (kg)
Electric (kWh)
Water (t)
9.87 0.81 10.68 49.66 13.69 25.69 0.11 0.53 89.68 0.64 0.64 101.00
0.96 933.15 933.15 934.11
Electric (kWh)
Water (t)
0.87 0.05 0.04 0.01
Table 5 Resource consumptions of rice production of Jiangsu province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O Production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
1.74
26.11 129.69 0.00
1.74 37.80 7.83 26.74 0.41
155.80
72.77
155.80
74.51
consumed more energy than others. In this study, EC of urea production was calculated from coal-based producing process. However, 80% of urea was produced from coal currently, the rest was produced from either gases or oil, which have lower ECs than coal, thus, the EC estimated here is higher than actual use. But this approach would not influence the predominate status of EC in urea production. Therefore, decreasing the amount of urea application,
0.60 0.60
0.17 0.17 8.77 8.77 9.53
9.14 0.76 9.91 25.12 6.31 41.13 0.07 0.36 72.99 0.41 0.41 83.31
0.44 0.02 0.06 0.00 0.53 598.13 598.13 598.65
enhancing urea utilization efficiency and improving the technology of the urea production could lower total EC effectively. The CF results illustrated that CFs were mainly generated in the planting stage. And the main reason was the emission of CH4 and N2O. The amount of N2O emission was directly determined by the urea application amount, so reducing the amount of urea application and improving urea utilization efficiency would not just
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Table 6 Resource consumptions of rice production of Sichuan province. Other minerals (kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O Production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
Coal (kg)
Diesel oil (kg)
1.99
Electric (kWh)
79.25
0.18 0.18 6.04 6.04 6.91
10.45 0.88 11.33 24.42 5.44 51.24 0.08 0.39 81.57 0.43 0.43 93.33
Coal(kg)
Diesel oil(kg)
Electric (kWh)
22.54 161.55 0.00
1.99 36.75 6.76 33.30 0.45
184.09
77.26
184.09
0.68 0.68
Water (t)
0.43 0.02 0.08 0.00 0.53 631.34 631.34 631.86
Table 7 Resource consumptions of rice production of Guangdong province. Other minerals(kg) Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Planting stage Sum
1.95
71.70 78.53 0.00
1.95 66.16 21.50 16.19 1.87
150.23
105.72
150.23
107.66
0.67 0.67
0.24 0.24 26.78 26.78 27.69
10.24 0.85 11.09 43.96 17.32 24.91 0.34 0.52 87.05 0.71 0.71 98.84
Water(t)
0.77 0.06 0.04 0.02 0.88 1039.80 1039.80 1040.68
Table 8 Energy consumptions of rice production in the whole life-cycle. Stages
Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops Planting Planting stage Sum
Total energy consuming(MJ) Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
98.18 33.26 131.44 1082.58 576.63 914.26 50.57 9.73 2633.77 460.85 460.85 3226.06
89.91 30.35 120.26 2345.83 542.11 576.82 18.81 12.31 3495.88 931.97 931.97 4548.11
83.29 28.20 111.49 1186.64 249.76 923.41 12.11 8.51 2380.43 374.88 374.88 2866.80
95.24 32.27 127.50 1153.64 215.58 1150.30 13.34 9.12 2541.98 259.02 259.02 2928.50
93.27 31.50 124.77 2076.85 685.85 559.14 55.48 12.26 3389.58 1143.48 1143.48 4657.83
lower the ECs, but also cut down CFs. Besides, as previous studies showed, paddy fields in intermittent flooding conditions emitted less CH4 than those in a water logging situation [23,24]. So alternating dry and flood irrigating method would lessen CFs, as well as lower ECs since less water would be used. 4. Discussion This paper used a unified value of EC per mass in fertilizer producing process in the five provinces. That means more fertilizer implied more energy consumed. Average rice yield per unit acreage in Guangdong was lower comparatively, so fertilizer application amount per unit yield was higher correspondingly. Also, single cropping planting system in Heilongjiang caused higher fertilizer application amount per unit yield. Besides, average evaporation was the highest in Guangdong while average precipitation was
the lowest in Heilongjiang. Thus, both two provinces desired more irrigation water. Conversely, because of the largest average yield and lower irrigating water amount needed in Jiangsu, EC of this province was the lowest. All of above factors demonstrated the consumption rank of the five provinces. CFs were mostly introduced from cultural and irrigating machines and GHGs emissions in planting stage. So more water irrigated and higher GHGs emission coefficients would lead to lager CF. More irrigation water amount in Guangdong and higher CH4 emission coefficients in Hunan resulted in higher CFs in these two areas. Contrarily, lower GHGs emission in Jiangsu could explain why CF in Jiangsu was the lowest. As reported, the coefficients were influenced by soil properties, rice varieties, fertilization, water management and cropping system. Comparing to Wang’s result of high-yield rice in Taihu region in Jiangsu province, our EC results were higher. Differences were
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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Hunan
Heilongjiang
Raw material stage
Jiangsu
Sichuan
Agricultural material stage
Guangdong
Planting stage
Fig. 2. Energy consumption composition of different stages in the rice production life cycle.
Table 9 Carbon footprint of rice production lifecycle (kgCO2-eq./t). Stages
Hunan
Heilongjiang
Jiangsu
Sichuan
Guangdong
Mineral exploitation Mineral transportation Raw material stage Urea production P2O5 production K2O production Pesticide production Agricultural materials transportation Agricultural material stage Crops planting Greenhouse gases and soil carbon sequestration Planting stage Sum
13.82 2.95 16.77 108.71 58.83 101.38 4.72 0.94 274.58 796.49 1238.62 2035.11 2326.47
12.66 2.69 15.35 235.55 55.31 63.96 1.76 1.19 357.77 919.06 597.79 1516.85 1889.97
11.73 2.50 14.23 119.16 25.48 102.39 1.13 0.82 248.98 572.49 509.22 1081.71 1344.92
13.41 2.86 16.27 115.84 22.00 127.55 1.25 0.88 267.52 594.07 661.04 1255.12 1538.90
13.13 2.79 15.92 208.54 69.98 62.00 5.18 1.19 346.89 1031.94 1109.44 2141.38 2504.20
mainly existed in agricultural material stage and planting stage. Rice yield in their research area was higher than in our study. This could cause lower EC per unit yield. Moreover, the CF result was 3768.1kgCO2-eq., which was significantly higher than our results [17]. Liang’s evaluation in Hunan province showed that ECs of rice production under regular management and improved management were 3679.3 MJ and 2596.96 MJ. The results were slightly higher than our results of Hunan. The CF results were 481.27 kgCO2-eq. and 370.2 kgCO2-eq., which were extremely lower than our results [25]. Li studied the rice production LCA of Hunan, Guangxi and Jiangsu. Their EC results were significantly higher than our results, as well as CFs, which was due to the higher estimation of urea producing [18]. Xu’s results of western Jilin were lower than ours in both EC and CF, which could be attributed to the underestimated GHGs coefficients [16]. Guo’s CF result in Taiwan was lower. This was mainly because N2O was not considered in that study [26]. The EC and CF in Vercelli (Italy) were higher than ours [27] and the CF of Indian rice production was lower than ours [28]. The main reason for these differences comes from the diversity of the definition of life cycle boundaries. Above comparisons indicated that differences among scope definitions, fertilizer application amounts, ECs and GHGs emission coefficients caused huge gaps among CF and EC results. In order to improve the research accuracy, this paper used the Chinese GHGs emissions inventory data which was normally treated as authorized data in related researches.
5. Conclusion The ECs of rice production in five typical rice production provinces of China were 4657.83 MJ/t (MJ per ton rice) (Guangdong province), 4548.11 MJ/t (Heilongjiang province), 3226.06 MJ/t (Hu-
nan province), 2928.50 MJ/t (Sichuan province) and 2866.80 MJ/t (Jiangsu province). Urea production consumed major portion of total ECs. The CFs of rice productions in these five provinces were 2504.20 kgCO2-eq./t (kg carbon dioxide equation per ton rice) (Guangdong province), 2326.47 kgCO2-eq./t (Hunan province), 1889.97 kgCO2-eq./t (Heilongjiang province), 1538.90 kgCO2-eq./t (Sichuan province) and 1344.92 kgCO2-eq./t (Jiangsu province) respectively. GHGs emission was the largest CF generating process. Because parameter settings of the calculation boundaries, material/energy consumptions and amount of greenhouse gases vary from different researches, the results of the carbon footprints were highly uncertain. Different estimations of emission coefficients of greenhouse gas were the most important reason for the difference of carbon footprint results. Reducing the quantities of urea could not only lower the EC, but also decrease the amount of N2O; and the intermittent irrigation system could save water and energy as well as decrease the amount of CH4 emission. Accuracy and comparability of CF results among different researches could be improved remarkably by using industry standards, technical specifications, governmental statistical data and related authoritative research results.
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