Crop Production Pushes up Greenhouse Gases Emissions in China: Evidence from Carbon Footprint Analysis Based on National Statistics Data
Abstract
:1. Introduction
2. Methods
2.1. Research Boundary
2.2. Carbon Footprint Calculation
2.2.1. CFinputs Estimation
2.2.2. CFon-field Estimation
2.3. CF Indices
2.4. Total GHGs Emission Estimation of Cropping System in China
2.5. Scenario Analysis
2.5.1. Scenario A: Population Change
2.5.2. Scenario B: Improved Crops Structure
2.5.3. Scenario C: Reduced Fertilizer Input
2.5.4. Scenario D: Integrated Livestock-Cropping Approach
2.5.5. Scenario E: Increase Biodiversity
2.6. Raw Data Sources
3. Results
3.1. Total GHG Emissions of Cropping System in China
3.2. CFs of Different Crops in China During 2000–2016
3.2.1. Grain Crops
3.2.2. Oil Crops
3.2.3. Sugar Crops
3.2.4. Other Cash Crops
3.3. Contributions Analysis
3.4. Scenario Analysis
4. Discussion
4.1. Historical Dynamics of GHG Emissions from the Cropping System in China
4.2. Comparison of Crops’ CFs in Different Nations
4.3. Future Estimation of GHGs from Cropping System in China
4.4. Mitigation Measures
4.5. Limitations in the Study
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Agricultural Inputs | Units | Coefficient | Reference |
---|---|---|---|
N fertilizer (N) | kg CO2-eq kg−1 | 2.12 | Chen et al. [24] |
P fertilizer (P2O5) | kg CO2-eq kg−1 | 0.64 | Chen et al. [24] |
K fertilizer (K2O) | kg CO2-eq kg−1 | 0.18 | Chen et al. [24] |
Compound fertilizer | kg CO2-eq kg−1 | 1.77 | Huang et al. [19] |
Animal manure | kg CO2-eq kg−1 | 0.19 | Zhang et al. [13] |
Diesel 1 | kg CO2-eq kg−1 | 3.93 | Huang et al. [25] |
Pesticide | kg CO2-eq kg−1 | 13.7 | Huang et al. [19] |
Electricity | kg CO2 kwh−1 | 1.23 | Huang et al. [19] |
Agricultural film | kg CO2-eq kg−1 | 22.70 | Huang et al. [19] |
Order | Scenarios | Description |
---|---|---|
A | Population change | Total population in China would be 1.44 billion in 2020 [28]. |
B | Improved crops structure | Rice 30.0 million ha, wheat 24.0 million ha, maize, 33.3 million ha, soybean 9.3 million ha, peanut 4.7 million ha, oilseed rape 6.7 million ha, sugarcane 1.4 million ha, sugar-beet 20.0 thousand ha, cotton 3.3 million ha, tobacco 858.3 thousand ha, data unavailable for mulberry [12,29]. |
C | Reduced fertilizer input | Use efficiency of mineral fertilizer reaches to more than 40% [30]. |
D | Integrated livestock-cropping approach | Nutrient returning rate of animal manure increases by 10% [30]. |
E | Increase biodiversity | Utilization rate of pesticides increases by about 5% [30]. |
Crop Category | 2000 | 2005 | 2010 | 2016 | Change 1 | Proportion 2 | |
---|---|---|---|---|---|---|---|
2000 | 2016 | ||||||
Grain crop | 216.29 | 202.29 | 242.87 | 277.01 | 28.07% | 79.41% | 83.32% |
Oil crop | 25.07 | 24.40 | 25.00 | 25.18 | 0.43% | 9.20% | 7.57% |
Sugar crop | 5.80 | 6.03 | 7.86 | 6.63 | 14.27% | 2.13% | 1.99% |
Other cash crop | 25.21 | 21.71 | 21.54 | 23.67 | −6.11% | 9.25% | 7.12% |
Total | 272.37 | 254.44 | 297.27 | 332.48 | 20.07% | 100.00% | 100.00% |
Scenario | Grain Crop | Oil Crop | Sugar Crop | Other Cash Crop | Total |
---|---|---|---|---|---|
A | 288.47 | 26.22 | 6.90 | 24.64 | 346.19 |
B | 246.19 | 27.15 | 6.02 | 22.54 | 301.90 |
C | 257.73 | 23.49 | 6.09 | 22.67 | 309.98 |
D | 275.68 | 25.03 | 6.61 | 23.58 | 330.90 |
E | 275.92 | 25.03 | 6.60 | 23.56 | 331.11 |
A×B | 256.34 | 28.27 | 6.27 | 23.47 | 314.34 |
A×C | 268.35 | 24.46 | 6.34 | 23.60 | 322.76 |
A×D | 287.05 | 26.06 | 6.88 | 24.55 | 344.54 |
A×E | 287.29 | 26.06 | 6.87 | 24.53 | 344.75 |
A×B×C | 255.29 | 28.10 | 6.23 | 23.36 | 312.99 |
A×B×D | 255.12 | 28.10 | 6.25 | 23.39 | 312.86 |
A×B×E | 238.57 | 26.39 | 5.75 | 22.45 | 293.15 |
A×B×C×D×E | 236.47 | 26.08 | 5.70 | 22.26 | 290.51 |
2016 | 277.01 | 25.18 | 6.63 | 23.67 | 332.48 |
Crops | Value | Unit | Country | References |
---|---|---|---|---|
Wheat | 2.34 | t CO2-eq ha−1 | Italy | [44] |
1.06 | t CO2-eq ha−1 | Uruguay | [45] | |
1.03 | t CO2-eq ha−1 | India | [46] | |
2.66 | t CO2-eq ha−1 | China | This study 1 | |
Maize | 5.41 | t CO2-eq ha−1 | Argentine | [17] |
2.32 | t CO2-eq ha−1 | China | This study 1 | |
Rice | 3.04 | t CO2-eq ha−1 | China | This study 1 |
Peanut | 1.06 | t CO2-eq ha−1 | India | [47] |
3.93 | t CO2-eq ha−1 | China | [43] | |
1.85 | t CO2-eq ha−1 | China | This study 1 | |
Soybean | 2.18 | t CO2-eq ha−1 | Argentine | [17] |
0.22 | t CO2-eq ha−1 | China | [42] | |
0.73 | t CO2-eq ha−1 | China | This study 1 | |
Oilseed rape | 1.42 | t CO2-eq ha−1 | Italy | [49] |
1.45 | t CO2-eq ha−1 | China | This study 1 | |
Sugarcane | 3.44 | t CO2-eq ha−1 | Mexico | [48] |
4.27 | t CO2-eq ha−1 | China | This study 1 | |
Sugar-beet | 2.97 | t CO2-eq ha−1 | China | This study 1 |
Cotton | 1.30 | t CO2-eq ha−1 | India | [47] |
3.27 | t CO2-eq ha−1 | China | [22] | |
4.60 | t CO2-eq ha−1 | China | [41] | |
4.27 | t CO2-eq ha−1 | China | This study 1 | |
Tobacco | 4.72 | t CO2-eq ha−1 | China | This study 1 |
Mulberry | 4.62 | t CO2-eq ha−1 | China | This study 1 |
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Wang, X.; Chen, Y.; Chen, X.; He, R.; Guan, Y.; Gu, Y.; Chen, Y. Crop Production Pushes up Greenhouse Gases Emissions in China: Evidence from Carbon Footprint Analysis Based on National Statistics Data. Sustainability 2019, 11, 4931. https://doi.org/10.3390/su11184931
Wang X, Chen Y, Chen X, He R, Guan Y, Gu Y, Chen Y. Crop Production Pushes up Greenhouse Gases Emissions in China: Evidence from Carbon Footprint Analysis Based on National Statistics Data. Sustainability. 2019; 11(18):4931. https://doi.org/10.3390/su11184931
Chicago/Turabian StyleWang, Xiaolong, Yun Chen, Xiaowei Chen, Rongrong He, Yueshan Guan, Yawen Gu, and Yong Chen. 2019. "Crop Production Pushes up Greenhouse Gases Emissions in China: Evidence from Carbon Footprint Analysis Based on National Statistics Data" Sustainability 11, no. 18: 4931. https://doi.org/10.3390/su11184931