The situation, dynamic evolution, and prediction of carbon emissions in the planting industry of Sichuan Province
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摘要:
加强种植业碳排放测算, 可为推进种植业绿色低碳转型提供重要依据。本文针对种植业从投入到产出的全过程, 基于农地利用碳排放、稻田CH4排放和农地N2O排放3类主要来源, 运用碳排放系数法测算2010—2021年四川省种植业碳排放量, 从时序特征和地区差异揭示四川省种植业碳排放特征, 采用核密度分析法剖析四川省种植业碳排放动态演进趋势, 运用灰色预测模型预测2022—2030年四川省种植业碳排放量和碳排放强度。结果表明: 1)四川省种植业碳排放量在2010—2016年间呈波动上升趋势, 2016年以后呈波动下降趋势, 其碳排放强度在2010—2021年间持续降低, 四川省种植业碳排放以农地利用和稻田CH4碳排放为主, 2021年较2010年农地利用碳排放和稻田CH4排放比重均略有减少, 而农地N2O排放比重有所上升。2)四川省各市(自治州)种植业碳排放量和强度差异明显, 2021年碳排放量最高的南充市比最低的甘孜高24.69倍, 碳排放强度最高的巴中市比最低的甘孜高2.8倍。3)四川省五大区域种植业碳排放动态演进呈差异化特征, 成都平原、川南、川东北、攀西和川西北五大区域种植业碳排放强度总体均呈下降趋势, 但下降速度和变化幅度各异, 总体上五大区域内碳排放强度的差距在逐步缩小。4)四川省种植业碳排放量和碳排放强度预计保持稳步下降态势, 估计到2025年和2030年四川省种植业碳排放量将分别减少约68.72万t和137.84万t, 碳排放强度将分别减少约0.13 t·万元−1和0.26 t·万元−1。基于此, 四川省种植业碳减排应主要关注源于化肥投入和稻田CH4碳排放, 因此需因地制宜采取差异化的减排措施, 强化农业科技创新和推广, 提升四川省种植业绿色低碳整体发展水平。
Abstract:Strengthening the calculation of carbon emissions in the planting industry will provide support for advancing green and low-carbon transformations. Focusing on the entire process of the planting industry, from input to output, this study utilized carbon emission coefficients to estimate the carbon emissions of the planting industry in Sichuan Province from 2010 to 2021. This estimation includes carbon emissions from three main sources: agricultural land use, rice field CH4 emissions, and agricultural land N2O emissions. This study aims to reveal the temporal characteristics and regional differences in carbon emissions from the planting industry in Sichuan Province. Furthermore, kernel density analysis was employed to analyze the dynamic evolution trend of carbon emissions from the planting industry of Sichuan Province. In addition, a gray prediction model was utilized to predict carbon emissions and intensity from the planting industry of Sichuan Province from 2022 to 2030. The results showed that: 1) carbon emissions from the planting industry in Sichuan Province showed a fluctuating increasing trend from 2010 to 2016 and a fluctuating decresing trend from 2016 to 2021, while carbon emission intensity continued to decrease from 2010 to 2021. Agricultural land use and rice field CH4 emissions were the primary contributors to carbon emissions from the planting industry. There was a decrease in the proportion of carbon emissions from agricultural land use and rice field CH4 in 2021 compared to 2010, whereas the proportions of agricultural land N2O emissions increased. 2) Significant differences exist in carbon emissions and intensity among the cities (prefectures) in Sichuan Province. In 2021, carbon emissions from the city (Nanchong) with the highest value was 24.69 times higher than that from the prefecture (Ganzi) with the lowest value, and carbon emission intensity of the city (Bazhong) with the highest value was 2.8 times higher than that of the prefecture (Ganzi) with the lowest value. 3) The dynamic evolution of carbon emissions in the planting industry across the five regions of Sichuan Province displayed different characteristics. The carbon emission intensity in the Chengdu Plain, south Sichuan, northeast Sichuan, Panzhihua-Xichang, and northwest Sichuan showed a decreasing trend, albeit with varying rates and extents of decline. Overall, the disparity in carbon emission intensity between these five regions gradually narrowed. 4) The projected trend suggests a steady decrease in both carbon emissions and intensity in the planting industry of Sichuan Province. It was estimated that by 2025 and 2030, the carbon emissions in the planting industry of Sichuan Province will decrease by approximately 68.72×104 t and 137.84×104 t, respectively, while the carbon emission intensity will decrease by 0.13 t·(104 ¥)–1 and 0.26 t·(104¥)–1. Based on these results, this study suggests that carbon emissions reduction in the planting industry of Sichuan Province mainly reduced the carbon emissions caused by chemical fertilizer input and rice field CH4. Tailored reduction measures need to be adopted according to local conditions, and agricultural scientific and technological innovation and promotion should be strengthened to improve green and low-carbon development in the planting industry of Sichuan Province.
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图 1 2010—2021年四川省、成都平原、川南、川东北、攀西和川西北种植业碳排放强度动态演进趋势
Figure 1. Dynamic evolution trend of carbon emission intensity of planting industry in Sichuan Province, Chengdu Plain, South Sichuan, Northeast Sichuan, Panzhihua-Xichang and Northwest Sichuan from 2010 to 2021
表 1 农地利用不同碳源碳排放系数
Table 1 Carbon emission coefficients of different carbon sources of agricultural land use
碳源
Carbon source碳排放系数
Carbon emission coefficient数据选取
Data selection氮肥
Nitrogen fertilizer0.42 t(C)·t−1 氮肥折纯施用量
Nitrogen fertilizer application amount converted to pure nitrogen (t)磷肥
Phosphorus fertilizer0.44 t(C)·t−1 磷肥折纯施用量
Phosphorus fertilizer application amount converted to pure phosphorus (t)钾肥
Potassium fertilizer0.18 t(C)·t−1 钾肥折纯施用量
Potassium fertilizer application amount converted to pure potassium (t)复合肥
Compound fertilizer0.48 t(C)·t−1 复合肥折纯施用量
Compound fertilizer application amount converted to pure nitrogen/phosphorus/potassium fertilizer (t)农药
Pesticides3.39 t(C)·t−1 农药施用量
Pesticide consumption (t)农膜
Agricultural plastic film6.2 t(C)·t−1 农膜使用量
Consumption of agricultural plastic film (t)农用柴油
Agricultural diesel0.59 t(C)·t−1 农用柴油消耗量
Consumption of agricultural diesel (t)农业灌溉
Agricultural irrigation0.025 t(C)·hm−2 有效灌溉面积
Effective irrigated area (hm2)土地翻耕
Soil ploughing0.003 t(C)·hm−2 农作物播种面积
Crop sown area (hm2)资料来源于中国生命周期数据库、Ecoinvent数据库、政府间气候变化专门委员会(IPCC) 、Dubey等[31]和伍芬琳等[32]。These data are derived from China Life Cycle Database, Ecoinvent Database, Intergovernmental Panel on Climate Change (IPCC), Dubey et al.[31], and Wu Fenlin et al.[32]. 
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表 2 各类作物参数
Table 2 Various crop parameters
% 作物类型
Crop type干重比
Dry weight ratio经济系数
Economic coefficient根冠比
Root shoot ratio秸秆/根茬含氮率
Nitrogen content ratio of straw/stubble水稻 Rice 85.50 48.90 12.50 0.75 小麦 Wheat 87.00 43.40 16.60 0.52 玉米 Maize 86.00 43.80 17.00 0.58 大豆 Soybean 86.00 42.50 13.00 1.81 薯类 Tubers 45.00 66.70 5.00 1.10 油菜 Rapeseed 82.00 27.10 15.00 0.55 蔬菜 Vegetables 15.00 83.00 25.00 0.80 数据来源于《省级温室气体清单编制指南(试行)》。Data are derived from the Provincial Greenhouse Gas Inventories Compilation Guide (Trial).
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表 3 2010—2021年四川省种植业碳排放总量、结构及强度
Table 3 The amount, structure and intensity of carbon emissions from planting industry in Sichuan Province from 2010 to 2021
年份
Year农地利用碳排放
Carbon emissions from
agricultural land use稻田CH4碳排放
CH4 emissions from
rice field农地N2O碳排放
N2O emissions from
agricultural land种植业
碳排放
总量
Emissions from
planting industry
(×104 t)种植业
碳排放
强度
Emission intensity of
planting industry
[t·(104 ¥)−1]排放量
Emissions
(×104 t)比重
Ratio
(%)排放强度
Emission intensity
[t·(104 ¥)−1]排放量
Emissions
(×104 t)比重
Ratio
(%)排放强度
Emission intensity
[t·(104 ¥)−1]排放量
Emissions
(×104 t)比重
Ratio
(%)排放强度
Emission intensity
[t·(104 ¥)−1]2010 838.88 35.75 0.41 932.39 39.74 0.45 575.22 24.51 0.28 2346.49 1.14 2011 865.26 36.44 0.40 921.15 38.79 0.42 588.35 24.77 0.27 2374.76 1.09 2012 881.03 36.78 0.39 914.80 38.19 0.40 599.26 25.02 0.26 2395.09 1.05 2013 880.88 36.79 0.37 903.24 37.72 0.38 610.31 25.49 0.26 2394.43 1.01 2014 888.94 37.00 0.36 897.07 37.33 0.37 617.05 25.68 0.25 2403.06 0.98 2015 897.02 37.12 0.35 890.58 36.85 0.34 628.92 26.03 0.24 2416.52 0.93 2016 896.29 36.82 0.33 888.35 36.49 0.33 649.78 26.69 0.24 2434.42 0.90 2017 879.9 36.42 0.31 888.78 36.79 0.31 647.03 26.78 0.23 2415.71 0.85 2018 839.41 35.25 0.28 888.35 37.30 0.30 653.79 27.45 0.22 2381.55 0.80 2019 823.67 34.73 0.26 886.45 37.37 0.28 661.77 27.90 0.21 2371.89 0.75 2020 792.25 33.70 0.24 884.71 37.64 0.27 673.79 28.66 0.20 2350.75 0.71 2021 782.65 33.18 0.23 888.82 37.69 0.26 687.04 29.13 0.20 2358.51 0.68 累计增幅
Cumulative growth rate (%)−6.70 −43.90 −4.67 −42.22 19.44 −28.57 0.51 −40.35 平均增速
Average growth rate (%)−0.63 −5.12 −0.43 −4.86 1.63 −3.01 0.05 −4.59
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表 4 2010—2021年四川省各市州种植业碳排放量
Table 4 Carbon emissions from planting industry in each city (prefecture) in Sichuan Province from 2010 to 2021
地区 Region 种植业碳排放量 Carbon emissions from planting industry (×104 t) 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 成都 Chengdu 214.97 210.99 205.16 200.56 197.79 195.69 209.36 202.87 203.15 198.97 195.62 195.64 自贡 Zigong 90.61 92.24 93.81 95.24 96.85 97.85 95.85 97.39 95.99 95.78 94.63 95.42 攀枝花 Panzhihua 25.41 24.88 25.23 25.12 25.49 25.72 25.94 25.99 26.01 25.67 24.98 24.93 泸州 Luzhou 132.08 133.45 134.31 135.83 136.96 136.74 134.93 137.35 136.98 135.64 134.88 135.06 德阳 Deyang 155.46 155.76 156.33 154.15 154.80 155.28 155.07 154.93 150.56 148.39 146.89 147.66 绵阳 Mianyang 164.78 167.56 169.30 169.28 169.68 170.83 172.77 170.78 167.52 165.67 163.42 163.78 广元 Guangyuan 93.96 105.07 110.39 112.88 115.20 116.68 116.49 117.69 102.46 101.51 98.71 99.30 遂宁 Suining 92.52 91.41 90.90 90.71 90.64 90.57 90.22 88.17 87.81 86.11 85.86 86.09 内江 Neijiang 108.70 110.33 112.16 113.69 114.80 116.44 116.63 117.70 116.74 116.42 115.98 116.18 乐山 Leshan 93.10 93.69 94.56 92.45 92.70 93.22 94.89 93.91 92.16 91.47 92.06 92.54 南充 Nanchong 204.52 207.13 209.03 206.73 206.84 208.59 212.04 210.16 209.56 208.95 208.16 210.15 眉山 Meishan 124.96 125.71 125.10 124.22 123.83 123.16 124.08 119.19 114.08 111.88 109.85 110.04 宜宾 Yibin 142.81 144.93 145.71 146.61 147.42 149.32 151.78 151.66 151.87 150.05 150.53 152.29 广安 Guang’an 121.25 122.66 124.35 125.36 126.29 127.80 132.28 128.54 128.15 127.76 127.17 128.25 达州 Dazhou 203.30 205.83 208.25 210.30 210.96 212.33 214.18 212.93 209.98 208.87 205.68 206.79 雅安 Ya’an 31.69 30.76 30.58 30.00 29.61 29.00 30.27 28.38 28.30 27.90 27.17 27.18 巴中 Bazhong 116.43 118.02 120.39 121.19 120.36 122.15 120.47 121.08 120.72 119.37 118.78 119.27 资阳 Ziyang 96.16 97.43 98.38 98.78 98.91 99.09 88.10 88.48 88.58 88.79 88.35 91.15 阿坝 Aba 11.04 11.17 11.68 12.48 12.84 13.58 14.24 12.60 12.95 12.93 12.57 12.59 甘孜 Ganzi 6.10 6.41 6.65 6.83 6.95 7.22 7.99 7.79 8.26 8.41 8.25 8.18 凉山 Liangshan 116.70 119.38 122.83 122.02 124.24 125.45 127.82 127.80 130.74 141.94 141.04 141.52
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表 5 2010—2021年四川省各市州种植业碳排放强度
Table 5 Carbon emission intensity of planting industry in each city (prefecture) in Sichuan Province from 2010 to 2021
地区 Region 种植业碳排放强度 Carbon emission intensity of planting industry [t·(104 ¥)−1] 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 成都 Chengdu 0.91 0.83 0.77 0.70 0.66 0.61 0.62 0.57 0.54 0.50 0.48 0.46 自贡 Zigong 1.35 1.32 1.28 1.24 1.21 1.14 1.07 1.03 0.97 0.91 0.85 0.81 攀枝花 Panzhihua 1.31 1.21 1.17 1.11 1.06 1.02 0.97 0.90 0.86 0.80 0.71 0.65 泸州 Luzhou 1.54 1.53 1.45 1.40 1.35 1.29 1.22 1.18 1.13 1.06 1.01 0.97 德阳 Deyang 1.35 1.28 1.24 1.18 1.14 1.11 1.07 1.00 0.93 0.86 0.81 0.77 绵阳 Mianyang 1.22 1.17 1.13 1.10 1.06 1.02 0.99 0.93 0.87 0.81 0.76 0.73 广元 Guangyuan 1.56 1.64 1.58 1.54 1.49 1.43 1.35 1.30 1.08 1.00 0.93 0.90 遂宁 Suining 1.11 1.07 1.05 1.03 1.00 0.94 0.90 0.84 0.80 0.74 0.70 0.67 内江 Neijiang 1.21 1.13 1.09 1.05 1.01 0.97 0.93 0.88 0.84 0.79 0.76 0.72 乐山 Leshan 1.22 1.16 1.14 1.08 1.04 0.99 0.97 0.92 0.86 0.80 0.77 0.73 南充 Nanchong 1.40 1.35 1.31 1.24 1.19 1.15 1.11 1.04 0.99 0.93 0.88 0.84 眉山 Meishan 1.58 1.51 1.44 1.37 1.31 1.23 1.17 1.07 0.98 0.91 0.85 0.82 宜宾 Yibin 1.36 1.33 1.27 1.23 1.20 1.16 1.13 1.09 1.05 0.98 0.93 0.88 广安 Guang’an 1.40 1.34 1.30 1.27 1.24 1.19 1.19 1.10 1.05 0.98 0.95 0.91 达州 Dazhou 1.30 1.28 1.24 1.21 1.17 1.12 1.09 1.03 0.98 0.92 0.86 0.82 雅安 Ya’an 0.82 0.75 0.72 0.69 0.66 0.62 0.62 0.55 0.53 0.49 0.45 0.43 巴中 Bazhong 1.74 1.70 1.68 1.64 1.58 1.53 1.44 1.39 1.32 1.24 1.18 1.13 资阳 Ziyang 0.87 0.82 0.79 0.76 0.74 0.70 0.59 0.56 0.54 0.51 0.49 0.47 阿坝 Aba 1.07 1.03 0.98 1.01 0.96 0.96 0.97 0.83 0.81 0.78 0.73 0.70 甘孜 Ganzi 0.50 0.47 0.45 0.41 0.39 0.38 0.40 0.36 0.36 0.35 0.33 0.31 凉山 Liangshan 0.84 0.81 0.78 0.74 0.72 0.69 0.68 0.64 0.63 0.65 0.62 0.58
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表 6 四川省和各市(自治州)种植业碳排放量及碳排放强度预测值
Table 6 Predicted values of carbon emissions and intensity of planting industry in each city (prefecture) in Sichuan Province
地区 Region 碳排放量 Carbon emissions (×104 t) 碳排放强度 Carbon emission intensity [t·(104 ¥)−1] 2022 2023 2024 2025 2026 2027 2028 2029 2030 2022 2023 2024 2025 2026 2027 2028 2029 2030 四川 Sichuan 2332.30 2318.04 2303.87 2289.79 2275.80 2261.89 2248.07 2234.33 2220.67 0.64 0.61 0.58 0.55 0.52 0.49 0.46 0.44 0.42 成都 Chengdu 192.74 190.63 188.53 186.47 184.42 182.40 180.40 178.42 176.46 0.44 0.41 0.39 0.37 0.35 0.34 0.32 0.30 0.29 自贡 Zigong 94.26 93.74 93.22 92.70 92.19 91.68 91.17 90.67 90.16 0.76 0.72 0.67 0.63 0.60 0.56 0.53 0.50 0.47 攀枝花 Panzhihua 24.59 24.29 23.99 23.70 23.41 23.12 22.84 22.56 22.29 0.60 0.56 0.51 0.47 0.43 0.40 0.37 0.34 0.31 泸州 Luzhou 133.99 133.33 132.68 132.03 131.38 130.74 130.10 129.46 128.82 0.91 0.87 0.82 0.78 0.74 0.70 0.67 0.63 0.60 德阳 Deyang 144.26 142.50 140.76 139.05 137.35 135.68 134.03 132.40 130.78 0.71 0.67 0.62 0.58 0.55 0.51 0.48 0.45 0.42 绵阳 Mianyang 160.85 159.10 157.37 155.66 153.96 152.29 150.63 148.99 147.37 0.68 0.64 0.60 0.57 0.53 0.50 0.47 0.44 0.42 广元 Guangyuan 91.98 88.36 84.89 81.55 78.34 75.26 72.30 69.46 66.72 0.78 0.71 0.65 0.59 0.54 0.49 0.44 0.40 0.37 遂宁 Suining 84.99 84.39 83.80 83.21 82.63 82.05 81.47 80.90 80.33 0.63 0.59 0.56 0.53 0.50 0.47 0.44 0.42 0.39 内江 Neijiang 115.47 115.09 114.72 114.34 113.97 113.60 113.23 112.86 112.49 0.68 0.64 0.61 0.58 0.55 0.52 0.50 0.47 0.45 乐山 Leshan 91.57 91.29 91.00 90.72 90.44 90.16 89.88 89.60 89.32 0.68 0.64 0.61 0.57 0.54 0.51 0.48 0.45 0.43 南充 Nanchong 208.97 208.82 208.68 208.54 208.40 208.26 208.12 207.98 207.83 0.80 0.75 0.72 0.68 0.64 0.61 0.58 0.55 0.52 眉山 Meishan 106.34 104.22 102.14 100.10 98.11 96.15 94.24 92.36 90.52 0.75 0.70 0.66 0.61 0.57 0.53 0.50 0.47 0.44 宜宾 Yibin 151.25 151.24 151.23 151.22 151.21 151.20 151.20 151.19 151.18 0.84 0.79 0.75 0.71 0.67 0.64 0.61 0.57 0.54 广安 Guang’an 127.51 127.36 127.20 127.05 126.90 126.74 126.59 126.44 126.29 0.86 0.82 0.78 0.74 0.71 0.68 0.64 0.61 0.58 达州 Dazhou 203.91 202.29 200.69 199.10 197.52 195.95 194.40 192.86 191.33 0.77 0.72 0.68 0.64 0.60 0.57 0.54 0.50 0.48 雅安 Ya’an 26.75 26.42 26.08 25.76 25.43 25.12 24.80 24.49 24.18 0.40 0.37 0.35 0.32 0.30 0.28 0.26 0.25 0.23 巴中 Bazhong 118.19 117.64 117.10 116.56 116.02 115.48 114.95 114.42 113.89 1.06 1.00 0.95 0.90 0.86 0.81 0.77 0.73 0.69 资阳 Ziyang 90.62 91.15 91.67 92.20 92.74 93.27 93.81 94.35 94.90 0.45 0.43 0.41 0.39 0.38 0.36 0.34 0.33 0.31 阿坝 Aba 12.61 12.57 12.53 12.49 12.45 12.41 12.37 12.33 12.29 0.67 0.64 0.62 0.59 0.56 0.54 0.52 0.49 0.47 甘孜 Ganzi 8.41 8.48 8.56 8.64 8.72 8.80 8.88 8.96 9.04 0.31 0.29 0.28 0.27 0.26 0.25 0.24 0.23 0.23 凉山 Liangshan 148.13 152.23 156.43 160.76 165.20 169.77 174.46 179.28 184.23 0.59 0.57 0.56 0.55 0.54 0.53 0.52 0.50 0.49
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[1] 刘杨, 刘鸿斌. 山东省农业碳排放特征、影响因素及达峰分析[J]. 中国生态农业学报(中英文), 2022, 30(4): 558−569 LIU Y, LIU H B. Characteristics, influence factors, and prediction of agricultural carbon emissions in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 558−569
[2] 赵敏娟. 兼顾农业现代化和低碳发展有多难[N]. 经济日报, 2021-11-03 ZHAO M J. How difficult is it to balance agricultural modernization and low-carbon development[N]. Economic Daily, 2021-11-03
[3] WEST T O, MARLAND G. Net carbon flux from agriculture: carbon emissions, carbon sequestration, crop yield, and land-use change[J]. Biogeochemistry, 2003, 63(1): 73−83 doi: 10.1023/A:1023394024790
[4] JOHNSON J M F, FRANZLUEBBERS A J, WEYERS S L, et al. Agricultural opportunities to mitigate greenhouse gas emissions[J]. Environmental Pollution, 2007, 150(1): 107−124 doi: 10.1016/j.envpol.2007.06.030
[5] NORSE D. Low carbon agriculture: objectives and policy pathways[J]. Environmental Development, 2012, 1(1): 25−39 doi: 10.1016/j.envdev.2011.12.004
[6] GARNIER J, LE NOË J, MARESCAUX A, et al. Long-term changes in greenhouse gas emissions from French agriculture and livestock (1852-2014): from traditional agriculture to conventional intensive systems[J]. Science of the Total Environment, 2019, 660: 1486−1501 doi: 10.1016/j.scitotenv.2019.01.048
[7] BENNETZEN E H, SMITH P, PORTER J R. Decoupling of greenhouse gas emissions from global agricultural production: 1970−2050[J]. Global Change Biology, 2016, 22(2): 763−781 doi: 10.1111/gcb.13120
[8] YADAV D, WANG J Y. Modelling carbon dioxide emissions from agricultural soils in Canada[J]. Environmental Pollution, 2017, 230: 1040−1049 doi: 10.1016/j.envpol.2017.07.066
[9] AL-MANSOUR F, JEJCIC V. A model calculation of the carbon footprint of agricultural products: the case of Slovenia[J]. Energy, 2017, 136: 7−15 doi: 10.1016/j.energy.2016.10.099
[10] SHABANI E, HAYATI B, PISHBAHAR E, et al. A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model[J]. Journal of Cleaner Production, 2021, 279: 123708 doi: 10.1016/j.jclepro.2020.123708
[11] 戴小文, 杨雨欣. 2007—2016年中国省域种植业碳排放测算、驱动效应与时空特征[J]. 四川农业大学学报, 2020, 38(2): 241−250 DAI X W, YANG Y X. Calculation, driving effect and temporal and spatial characteristics of carbon emissions in China’s provincial planting industry from 2007 to 2016[J]. Journal of Sichuan Agricultural University, 2020, 38(2): 241−250
[12] 伍国勇, 陈莹, 孙小钧. 中国种植业碳补偿率区域差异、动态演进及收敛性分析[J]. 中国生态农业学报(中英文), 2021, 29(10): 1774−1785 WU G Y, CHEN Y, SUN X J. Regional differences, dynamic evolution, and convergence of the carbon compensation rate of planting industry in China[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1774−1785
[13] 李远玲, 王金龙, 杨伶. 基于县域尺度的湖南省农业碳排放时空特征分析[J]. 中国农业资源与区划, 2022, 43(4): 75−84 LI Y L, WANG J L, YANG L. Study on temporal and apatial characteristics of agricultural carbon emissions in Hunan Province at county scale[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(4): 75−84
[14] 邓维忠, 许中坚. 湖南省农业碳排放特征及碳达峰分析[J]. 中国生态农业学报(中英文), 2024, 32(2): 206−217 DENG W Z, XU Z J. Characteristics of agricultural carbon emissions and carbon peak analysis in Hunan Province[J]. Chinese Journal of Eco-Agriculture, 2024, 32(2): 206−217
[15] 丁宝根, 赵玉, 邓俊红. 中国种植业碳排放的测度、脱钩特征及驱动因素研究[J]. 中国农业资源与区划, 2022, 43(5): 1−11 DING B G, ZHAO Y, DENG J H. Calculation, decoupling effects and driving factors of carbon emission from planting industry in China[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(5): 1−11
[16] 甄伟, 秦全德, 匡耀求, 等. 广东省种植业能源消费温室气体排放影响因素分析[J]. 科技管理研究, 2017, 37(7): 78−85 ZHEN W, QIN Q D, KUANG Y Q, et al. Factor analysis of energy consumption related greenhouse gas emissions from crop production system in Guangdong Province[J]. Science and Technology Management Research, 2017, 37(7): 78−85
[17] 宋宏恩, 姜常宜, 李树超, 等. 山东省种植业碳排放度量分析[J]. 中国农学通报, 2023, 39(8): 43−50 SONG H E, JIANG C Y, LI S C, et al. Measurement and analysis of carbon emissions of Shandong planting industry[J]. Chinese Agricultural Science Bulletin, 2023, 39(8): 43−50
[18] 吴金凤, 王秀红. 青岛市种植业主要投入要素碳排放及其强度分析[J]. 中国农学通报, 2014, 30(36): 288−294 WU J F, WANG X H. Analysis of carbon emissions and carbon intensity of main input factors of crop production in Qingdao City[J]. Chinese Agricultural Science Bulletin, 2014, 30(36): 288−294
[19] 谢永浩, 刘争. 县域尺度下河南省种植业碳排放空间溢出效应及公平性研究[J]. 地域研究与开发, 2022, 41(5): 159−164, 172 XIE Y H, LIU Z. Study on spatial spillover effect and equity of planting carbon emission in Henan Province at county scale[J]. Areal Research and Development, 2022, 41(5): 159−164, 172
[20] 郭旋, 张良茂, 胡荣桂, 等. 华中地区种植业生产碳排放驱动因素分析[J]. 长江流域资源与环境, 2016, 25(5): 695−701 GUO X, ZHANG L M, HU R G, et al. Influencing factor decomposition of planting carbon emission in central China[J]. Resources and Environment in the Yangtze Basin, 2016, 25(5): 695−701
[21] 丁宝根, 杨树旺, 赵玉. 长江经济带种植业碳排放时空特征及驱动因素研究[J]. 生态与农村环境学报, 2019, 35(10): 1252−1258 DING B G, YANG S W, ZHAO Y. Study on spatial-temporal characteristics and driving factors of carbon emission from planting industry in the Yangtze River Economic Belt[J]. Journal of Ecology and Rural Environment, 2019, 35(10): 1252−1258
[22] 黄祖辉, 米松华. 农业碳足迹研究−以浙江省为例[J]. 农业经济问题, 2011, 32(11): 40−47, 111 HUANG Z H, MI S H. Agricultural sector carbon footprint accounting: a case of Zhejiang, China[J]. Issues in Agricultural Economy, 2011, 32(11): 40−47, 111
[23] 杜江, 罗珺, 王锐, 等. 粮食主产区种植业碳功能测算与时空变化规律研究[J]. 生态与农村环境学报, 2019, 35(10): 1242−1251 DU J, LUO J, WANG R, et al. The estimation and analysis of spatio-temporal change patterns of carbon functions in major grain producing area[J]. Journal of Ecology and Rural Environment, 2019, 35(10): 1242−1251
[24] 胡永浩, 张昆扬, 胡南燕, 等. 中国农业碳排放测算研究综述[J]. 中国生态农业学报(中英文), 2023, 31(2): 163−176 HU Y H, ZHANG K Y, HU N Y, et al. Review on measurement of agricultural carbon emission in China[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 163−176
[25] 刘华军, 鲍振, 杨骞. 中国农业碳排放的地区差距及其分布动态演进−基于Dagum基尼系数分解与非参数估计方法的实证研究[J]. 农业技术经济, 2013(3): 72−81 LIU H J, BAO Z, YANG Q. Regional gap and dynamic evolution of agricultural carbon emissions in China: an empirical study based on Dagum Gini coefficient decomposition and nonparametric estimation method[J]. Journal of Agrotechnical Economics, 2013(3): 72−81
[26] 胡婉玲, 张金鑫, 王红玲. 中国种植业碳排放时空分异研究[J]. 统计与决策, 2020, 36(15): 92−95 HU W L, ZHANG J X, WANG H L. Spatial and temporal differentiation of carbon emissions from planting in China[J]. Statistics & Decision, 2020, 36(15): 92−95
[27] 黄杰, 孙自敏. 中国种植业碳生产率的区域差异及分布动态演进[J]. 农业技术经济, 2022(7): 109−127 HUANG J, SUN Z M. Regional differences and dynamic evolution of carbon productivity of China’s planting industry[J]. Journal of Agrotechnical Economics, 2022(7): 109−127
[28] 陈炜, 殷田园, 李红兵. 1997—2015年中国种植业碳排放时空特征及与农业发展的关系[J]. 干旱区资源与环境, 2019, 33(2): 37−44 CHEN W, YIN T Y, LI H B. Spatiotemporal distribution characteristics of carbon emission from plant industry and the relationship with agriculture development in China from 1997 to 2015[J]. Journal of Arid Land Resources and Environment, 2019, 33(2): 37−44
[29] 汤钧惠. 种植业产业集聚对碳排放的影响研究[D]. 贵阳: 贵州大学, 2023: 6−7 TANG J H. Research on the impact of planting industry agglomeration on carbon emissions[D]. Guiyang: Guizhou University, 2023: 6−7
[30] 田云, 张俊飚, 丰军辉, 等. 中国种植业碳排放与其产业发展关系的研究[J]. 长江流域资源与环境, 2014, 23(6): 781−791 TIAN Y, ZHANG J B, FENG J H, et al. Relationship between planting industry carbon emissions and its industry development in China[J]. Resources and Environment in the Yangtze Basin, 2014, 23(6): 781−791
[31] DUBEY A, LAL R. Carbon footprint and sustainability of agricultural production systems in Punjab, India, and Ohio, USA[J]. Journal of Crop Improvement, 2009, 23(4): 332−350 doi: 10.1080/15427520902969906
[32] 伍芬琳, 李琳, 张海林, 等. 保护性耕作对农田生态系统净碳释放量的影响[J]. 生态学杂志, 2007, 26(12): 2035−2039 WU F L, LI L, ZHANG H L, et al. Effects of conservation tillage on net carbon flux from farmland ecosystems[J]. Chinese Journal of Ecology, 2007, 26(12): 2035−2039
[33] 闵继胜, 胡浩. 中国农业生产温室气体排放量的测算[J]. 中国人口·资源与环境, 2012, 22(7): 21−27 MIN J S, HU H. Calculation of greenhouse gases emission from agricultural production in China[J]. China Population, Resources and Environment, 2012, 22(7): 21−27
[34] 卢奕亨, 田云, 周丽丽. 四川省农业碳排放时空演变特征及其影响因素研究[J]. 中国农业资源与区划, 2023, 44(11): 1−14 LU Y H, TIAN Y, ZHOU L L. Spatial-temporal evolution and influencing factors of agricultural carbon emissions in Sichuan Province[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2023, 44(11): 1−14
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