Characteristics, influence factors, and prediction of agricultural carbon emissions in Shandong Province
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摘要: 利用IPCC经典碳排放计算理论, 基于农资投入、农田利用及畜禽养殖3类主要碳源, 测算了山东省2000—2020年农业碳排放量, 采用LMDI模型开展影响因素分析, 并运用灰色预测模型GM(1, 1)预测2021—2045年碳排放量。结果表明: 2020年山东省农业碳排放量为1.58×107 t, 农业碳排放强度为0.205 t∙(104 ¥)−1。2000—2020年山东省农业碳排放总量呈先上升后波动下降趋势, 农业碳排放强度逐年降低。农业碳排放源类贡献率由高到低依次为农资投入、畜禽养殖和农田土壤利用。2000—2020年16地市农业碳排放量及排放强度均呈现一定的区域差异, 且有扩大趋势, 菏泽农业碳排放量和平均碳排放强度均居首位。农业生产效率、农业产业结构、地区产业结构、劳动力因素对碳减排起到一定作用, 地区经济发展水平和城镇化率因素为农业碳排放量增加的主要因素。预测结果表明, 山东省农业碳排放量在2030年前已达到峰值, 济南、青岛等9市农业碳排放量在2030年前已达峰, 枣庄、东营等7市在2030年前未达峰, 并针对山东省农业碳排放特征及影响因素提出减排建议。Abstract: The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report indicated that human-induced climate change has already affected many weather and climate extremes in every region across the globe. Greenhouse gases (GHG) produced via the process of agricultural production constitute a large proportion of the total GHG emissions from worldwide production activities. Therefore, estimation of agricultural GHG emissions, analysis of the influencing factors, and prediction of the peak are important. Based on the classical IPCC carbon emission calculation theory, agricultural carbon emissions were estimated for Shandong Province from 2000 to 2020 by using agricultural material input, livestock and poultry breeding, and agricultural soil utilization. The influence factor decomposition was conducted based on Logarithmic Mean Divisia Index (LMDI), and the agricultural carbon emissions from 2021 to 2045 were predicted by using the grey model GM (1, 1). Results showed that the total agricultural carbon emissions in Shandong Province in 2020 were 1.58×107 t and the intensity of carbon emissions was 0.205 t·(104 ¥)−1. Carbon emissions tended to increase from 2000 to 2006 and then decrease from 2007 to 2020; however, the intensity of carbon emissions decreased at an annual rate of 3.8%. The source structure of agricultural carbon emissions was ranked, with agricultural material input, livestock and poultry breeding, and crop farming accounting for 49.6%, 38.5%, and 11.9%, respectively. Carbon emissions and intensities showed regional differences between the 16 cities and tended to increase. Carbon emissions and the intensity of carbon emissions in Heze were higher than those of other cities. The LMDI decomposition results showed that agricultural production efficiency, agricultural industrial structure, regional industrial structure, and rural population were emission reduction factors, whereas regional economic development level and urbanization were emission growth factors. The prediction results showed that agricultural carbon emission of Shandong Province would reach its peak before 2030, and carbon emissions of cities, such as Jinan, Qingdao, Zibo, Weifang, Jining, Tai’an, Weihai, Rizhao, and Liaocheng, would also reach their peaks before 2030. However, the prediction result showed that the agricultural carbon emissions in Zaozhuang, Dongying, Yantai, Linyi, Dezhou, Binzhou, and Heze did not reach their peaks before 2030. Therefore, suggestions for agricultural carbon emission reduction in Shandong Province were put forward.
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图 1 2000—2020年山东省各地市累计碳排放量及平均碳排放强度
Figure 1. Total agricultural carbon emissions and average carbon emission intensity in cities of Shandong Province from 2000 to 2020
表 1 种植业碳排放源、碳排放系数及参考来源
Table 1. Carbon sources, carbon emission coefficients and reference sources for planting industry
农业物资投入Agricultural material input 农田土壤利用 Farmland utilization 源类名称
Carbon source碳排放系数
Carbon emission coefficient参考来源
Reference source源类名称
Carbon source碳排放系数
Carbon emission coefficient参考来源
Reference source化肥 Chemical fertilizer 0.8956 kg(C)∙kg−1 ORNL 水稻 Rice 210 kg(CH4)∙hm−2 [18] 农药 Pesticides 4.9341 kg(C)∙kg−1 ORNL 0.24 kg(N2O)∙hm−2 农膜 Plastic film 5.18 kg(C)∙kg−1 IREEA 冬小麦 Winter wheat 2.05 kg(N2O)∙hm−2 农用柴油 Agricultural diesel oil 0.5927 kg(C)∙kg−1 IPCC 大豆 Soybean 0.77 kg(N2O)∙hm−2 农业灌溉 Agricultural irrigation 266.48 kg(C)∙hm−2 [17] 玉米 Corn 2.532 kg(N2O)∙hm−2 棉花 Cotton 0.4804 kg(N2O)∙hm−2 蔬菜 Vegetables 4.21 kg(N2O)∙hm−2 ORNL: 美国橡树岭国家实验室; IREEA: 南京农业大学农业资源与生态环境研究所; IPCC: 政府间气候变化专门委员会。ORNL: Oak Ridge National Laboratory; IREEA: Institute of Resources, Ecosystem and Environment of Agriculture, Nanjing Agricultural University; IPCC: Intergovernmental Panel on Climate Change. 
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表 2 畜禽养殖碳排放源、CH4和N2O排放系数[19-20]
Table 2. Carbon sources, CH4 and N2O emission coefficients for livestock and poultry farming[19-20]
源类名称
Carbon source肠道发酵
Intestinal fermentation
[kg(CH4)∙(head∙a)−1]粪便管理 Fecal discharge kg(CH4)∙(head∙a)−1 kg(N2O)∙(head∙a)−1 牛 Cattle 47.00 1.00 1.39 羊 Sheep 5.00 0.16 0.86 猪 Pig 1.00 4.00 0.53 家禽 Poultry — 0.02 0.02
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表 3 2000—2020年山东省农业碳排放量情况
Table 3. Agricultural carbon emissions in Shandong Province from 2000 to 2020
年度
Year农资投入
Agricultural material input农田土壤利用
Farmland utilization畜禽养殖
Livestock and poultry breeding碳排放总量
Total amount碳排放强度
Carbon emission intensity排放量
Carbon emission
(×104 t)占比
Proportion
(%)排放量
Carbon emission
(×104 t)占比
Proportion
(%)排放量
Carbon emission
(×104 t)占比
Proportion
(%)总量
Amount
(×104 t)同比变化
Year-on-year
growth rate (%)排放强度
Emission intensity
[t∙(104 ¥)–1]同比变化
Year-on-year
growth rate (%)2000 786.5 50.5 220.8 14.2 551.6 35.4 1558.9 — 0.821 — 2001 813.5 51.0 204.5 12.8 576.8 36.2 1594.7 2.3 0.776 −5.5 2002 848.8 50.6 213.9 12.8 613.6 36.6 1676.3 5.1 0.791 2.0 2003 859.0 50.4 203.5 11.9 642.8 37.7 1705.4 1.7 0.702 −11.3 2004 875.7 50.2 193.4 11.1 676.7 38.8 1745.7 2.4 0.599 −14.6 2005 906.1 49.9 196.2 10.8 712.0 39.2 1814.3 3.9 0.574 −4.1 2006 943.6 50.4 195.5 10.4 732.3 39.1 1871.4 3.1 0.566 −1.5 2007 950.4 51.3 199.2 10.8 702.7 37.9 1852.3 −1.0 0.474 −16.2 2008 916.5 50.0 200.4 10.9 715.1 39.0 1832.1 −1.1 0.400 −15.6 2009 906.2 49.7 202.8 11.1 715.9 39.2 1824.9 −0.4 0.375 −6.3 2010 917.0 50.2 203.3 11.1 707.7 38.7 1828.0 0.2 0.339 −9.4 2011 913.1 50.6 204.8 11.3 686.5 38.0 1804.4 −1.3 0.304 −10.6 2012 910.5 50.4 205.9 11.4 691.3 38.2 1807.7 0.2 0.294 −3.3 2013 903.9 50.0 208.3 11.5 695.2 38.5 1807.4 0.0 0.268 −8.7 2014 889.6 49.4 211.4 11.7 699.2 38.8 1800.2 −0.4 0.256 −4.5 2015 880.7 49.2 213.0 11.9 698.2 39.0 1791.9 −0.5 0.247 −3.6 2016 870.3 48.9 211.8 11.9 699.4 39.3 1781.5 −0.6 0.254 3.1 2017 844.0 47.7 217.6 12.3 708.2 40.0 1769.8 −0.7 0.256 0.8 2018 810.9 46.6 217.4 12.5 711.2 40.9 1739.5 −1.7 0.245 −4.6 2019 773.8 47.7 214.5 13.2 634.7 39.1 1623.0 −6.7 0.222 −9.4 2020 751.1 47.4 214.2 13.5 618.1 39.0 1583.4 −2.4 0.205 −7.7
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表 4 2000—2020年山东省农业碳排放源排放量占比情况
Table 4. Ratios of different agricultural carbon sources emissions to total amount in Shandong Province from 2000 to 2020
年度
Year农资投入 Agricultural material input 农田土壤利用 Farmland utilization 畜禽养殖
Livestock and poultry breeding化肥
Chemical
fertilizer农药
Pesticides农膜
Plastic
sheeting农用柴油
Agricultural
diesel oil农业灌溉
Agricultural
irrigation水稻
Rice冬小麦
Winter
wheat大豆
Soybean玉米
Corn棉花
Cotton蔬菜
Vegetables猪
Pig牛
Cattle羊
Sheep家禽
Poultry2000 24.3 4.4 7.5 6.0 8.2 1.6 4.2 0.2 3.5 0.1 4.5 8.7 9.1 16.0 1.6 2001 24.1 4.5 8.4 6.0 8.1 1.6 3.7 0.2 3.2 0.2 4.0 8.9 9.4 16.2 1.7 2002 23.2 4.8 9.0 6.0 7.6 1.3 3.4 0.1 3.1 0.2 4.7 9.0 9.7 16.2 1.7 2003 22.7 4.9 9.3 6.0 7.4 1.0 3.0 0.1 2.9 0.2 4.7 9.3 10.0 16.6 1.8 2004 23.1 4.3 9.7 5.7 7.3 1.0 3.0 0.1 2.9 0.2 3.9 9.8 10.2 16.9 1.9 2005 23.1 4.2 9.5 6.1 7.0 1.0 3.0 0.1 3.1 0.2 3.5 9.9 10.2 17.0 2.1 2006 23.4 4.5 9.5 6.1 6.9 1.0 3.0 0.1 3.0 0.2 3.2 9.9 10.1 16.9 2.1 2007 24.2 4.4 9.5 6.2 7.0 1.0 3.2 0.1 3.2 0.2 3.1 8.4 10.4 17.1 2.0 2008 23.3 4.7 9.1 5.9 7.1 1.0 3.2 0.1 3.2 0.2 3.2 9.2 10.6 17.0 2.3 2009 23.2 4.6 8.9 5.8 7.2 1.1 3.2 0.1 3.3 0.2 3.3 9.8 10.4 16.6 2.3 2010 23.3 4.5 9.2 6.1 7.2 1.0 3.2 0.1 3.3 0.2 3.3 10.2 10.1 15.9 2.5 2011 23.5 4.5 9.1 6.1 7.4 1.0 3.3 0.1 3.4 0.2 3.4 10.3 9.8 15.3 2.7 2012 23.6 4.4 9.1 5.9 7.4 1.0 3.3 0.1 3.4 0.1 3.4 11.2 9.4 14.6 2.9 2013 23.4 4.3 9.1 5.7 7.4 1.0 3.4 0.1 3.5 0.1 3.5 11.8 9.4 14.4 2.9 2014 23.3 4.3 8.8 5.5 7.5 1.0 3.5 0.1 3.6 0.1 3.5 12.3 9.2 14.6 2.7 2015 23.2 4.2 8.7 5.5 7.6 0.9 3.5 0.0 3.6 0.1 3.6 12.2 9.1 14.8 2.8 2016 22.9 4.1 8.7 5.4 7.7 0.9 3.6 0.0 3.7 0.1 3.6 12.1 9.0 14.9 3.2 2017 22.3 3.9 8.4 5.3 7.8 0.9 3.8 0.0 4.7 0.0 2.8 12.4 9.0 15.3 3.3 2018 21.6 3.7 8.2 5.0 8.0 0.9 3.9 0.1 4.7 0.0 2.9 12.4 9.2 16.0 3.3 2019 21.8 3.7 8.5 5.0 8.6 1.0 4.1 0.1 4.9 0.0 3.1 8.3 9.6 17.4 3.8 2020 21.5 3.6 8.7 4.7 8.9 1.0 4.1 0.1 5.0 0.0 3.2 8.9 8.6 17.2 4.2 平均
Average23.1 4.3 8.9 5.7 7.6 1.1 3.5 0.1 3.6 0.1 3.5 10.2 9.7 16.1 2.6
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表 5 2000—2020年山东省各地市逐年农业碳排放量及其变异系数
Table 5. Agricultural carbon emissions and coefficients of variation in cities of Shandong Province from 2000 to 2020
年度
Year农业碳排放量 Agricultural carbon emission (×104 t) CV
(%)济南
Jinan青岛
Qingdao淄博
Zibo枣庄
Zaozhuang东营
Dongying烟台
Yantai潍坊
Weifang济宁
Jining泰安
Tai’an威海
Weihai日照
Rizhao临沂
Linyi德州
Dezhou聊城
Liaocheng滨州
Binzhou菏泽
Heze2000 107.3 115.7 37.5 48.2 40.8 84.5 184.2 158.7 77.0 41.8 53.7 142.5 122.5 131.1 74.6 155.8 48.5 2001 108.4 117.0 41.8 49.7 41.4 85.1 187.4 164.0 81.4 41.6 53.9 148.9 124.3 134.8 80.7 165.7 48.5 2002 113.7 120.6 43.0 53.1 45.0 87.4 193.0 181.0 87.1 43.2 54.1 155.2 143.1 152.7 83.6 186.1 49.8 2003 118.5 125.2 44.5 56.2 49.9 92.9 202.6 182.4 92.8 44.0 56.7 156.8 142.7 160.1 87.2 199.4 49.3 2004 124.8 126.3 45.6 59.8 55.1 93.8 211.4 197.4 100.9 46.2 60.8 162.9 158.1 158.4 89.3 212.2 49.5 2005 131.0 133.6 47.3 63.3 59.7 101.0 225.3 220.7 102.9 53.4 60.5 167.3 171.7 164.0 94.1 232.5 50.6 2006 134.2 134.3 43.3 68.2 65.2 109.8 226.5 214.4 101.2 54.1 62.6 173.6 172.7 159.9 97.0 247.9 50.3 2007 123.0 119.7 41.7 65.5 63.5 111.3 208.2 179.6 96.7 53.5 57.9 166.2 160.8 132.6 90.3 243.8 49.5 2008 116.6 104.8 41.7 64.6 62.0 114.4 193.6 171.5 93.2 53.6 52.4 172.0 167.9 132.8 93.6 242.8 49.9 2009 118.8 101.1 44.6 67.7 64.4 111.6 204.3 186.1 94.4 52.5 53.7 176.8 171.1 139.1 94.1 249.0 50.9 2010 122.8 101.0 45.2 69.5 65.8 111.3 209.9 192.5 96.4 50.6 60.4 183.3 172.8 145.0 97.7 254.8 51.0 2011 125.9 101.2 45.6 69.2 63.6 111.0 214.6 191.3 99.7 51.6 60.8 185.6 177.3 143.1 98.6 257.9 51.3 2012 127.3 100.2 45.9 70.8 64.6 107.6 217.6 187.9 102.7 52.8 60.1 185.8 181.8 141.9 98.2 261.1 51.6 2013 128.0 99.5 45.9 72.5 66.1 106.3 213.7 187.2 105.0 51.4 57.5 185.2 185.4 142.2 101.1 268.1 51.9 2014 127.4 98.4 44.0 73.7 61.8 105.9 209.6 174.6 107.5 49.4 59.0 179.3 184.9 144.0 101.7 270.6 52.0 2015 124.3 94.8 40.9 74.1 52.5 104.2 208.9 163.6 109.5 47.7 58.6 177.5 173.0 143.1 98.8 266.8 52.8 2016 120.0 93.0 39.8 72.9 48.6 103.9 209.0 155.0 107.1 47.1 58.1 175.4 157.3 139.9 94.0 253.1 52.3 2017 107.3 91.3 37.6 65.3 53.7 103.4 197.0 150.2 91.5 46.4 52.7 177.4 162.4 132.7 94.9 232.1 51.7 2018 96.3 90.9 37.2 60.9 55.4 103.1 189.1 148.9 83.7 45.3 48.8 171.7 165.8 121.0 91.4 220.3 51.9 2019 87.0 87.4 35.9 58.2 48.8 97.8 178.2 138.9 79.3 43.5 45.6 156.0 154.4 114.0 88.9 217.4 52.6 2020 76.2 83.4 34.6 50.8 50.7 91.0 168.1 124.7 68.1 40.4 38.3 155.7 135.2 110.6 81.1 184.1 51.7 CV: 变异系数。CV: coefficients of variation.
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表 6 2000—2020年山东省各地市逐年农业碳排放强度及其变异系数
Table 6. Agricultural carbon emission intensities and coefficients of variation in cities of Shandong Province from 2000 to 2020
年度
Year农业碳排放强度 Agricultural carbon emission intensity [t∙(104 ¥)−1] CV
(%)济南
Jinan青岛
Qingdao淄博
Zibo枣庄
Zaozhuang东营
Dongying烟台
Yantai潍坊
Weifang济宁
Jining泰安
Tai’an威海
Weihai日照
Rizhao临沂
Linyi德州
Dezhou聊城
Liaocheng滨州
Binzhou菏泽
Heze2000 0.633 0.639 0.542 0.742 1.025 0.574 0.714 0.861 0.686 0.812 0.948 0.781 0.786 0.860 0.786 0.954 17.9 2001 0.604 0.618 0.562 0.708 0.957 0.544 0.696 0.865 0.685 0.752 0.961 0.791 0.764 0.851 0.828 0.964 18.3 2002 0.614 0.632 0.559 0.702 1.021 0.560 0.722 0.891 0.711 0.756 0.905 0.791 0.851 0.925 0.871 1.033 19.4 2003 0.607 0.651 0.554 0.710 0.992 0.573 0.695 0.845 0.693 0.765 0.973 0.780 0.780 0.931 0.772 1.050 19.5 2004 0.566 0.596 0.501 0.629 0.949 0.482 0.637 0.738 0.646 0.717 0.867 0.676 0.758 0.793 0.701 0.949 20.0 2005 0.530 0.572 0.473 0.588 0.914 0.462 0.618 0.711 0.585 0.743 0.798 0.614 0.760 0.743 0.663 0.894 20.4 2006 0.507 0.551 0.409 0.579 0.879 0.444 0.592 0.665 0.544 0.673 0.771 0.593 0.717 0.681 0.646 0.887 21.5 2007 0.431 0.454 0.333 0.488 0.751 0.400 0.478 0.488 0.455 0.603 0.616 0.489 0.590 0.498 0.522 0.794 23.1 2008 0.347 0.350 0.310 0.387 0.659 0.354 0.380 0.381 0.367 0.470 0.499 0.442 0.487 0.400 0.462 0.736 26.3 2009 0.331 0.335 0.315 0.388 0.635 0.344 0.379 0.399 0.356 0.450 0.479 0.431 0.469 0.396 0.424 0.722 25.8 2010 0.289 0.280 0.266 0.342 0.557 0.285 0.354 0.350 0.312 0.383 0.457 0.418 0.429 0.372 0.385 0.692 28.9 2011 0.267 0.258 0.244 0.315 0.472 0.257 0.331 0.312 0.291 0.342 0.409 0.399 0.411 0.334 0.339 0.675 30.3 2012 0.254 0.246 0.233 0.308 0.465 0.242 0.309 0.292 0.276 0.347 0.395 0.384 0.396 0.314 0.321 0.648 30.9 2013 0.228 0.222 0.211 0.281 0.419 0.213 0.276 0.261 0.253 0.290 0.346 0.346 0.358 0.284 0.304 0.630 33.8 2014 0.220 0.211 0.194 0.275 0.375 0.201 0.258 0.230 0.250 0.259 0.331 0.321 0.340 0.266 0.294 0.615 35.0 2015 0.208 0.190 0.175 0.265 0.314 0.192 0.247 0.203 0.244 0.237 0.310 0.304 0.301 0.252 0.276 0.586 35.7 2016 0.195 0.183 0.161 0.246 0.284 0.180 0.236 0.182 0.230 0.224 0.296 0.286 0.262 0.231 0.246 0.534 34.8 2017 0.196 0.196 0.165 0.269 0.331 0.195 0.249 0.204 0.199 0.292 0.292 0.318 0.292 0.259 0.299 0.510 31.2 2018 0.172 0.185 0.158 0.240 0.326 0.186 0.234 0.197 0.176 0.285 0.256 0.292 0.301 0.230 0.288 0.465 31.2 2019 0.153 0.166 0.150 0.228 0.282 0.157 0.220 0.179 0.167 0.274 0.234 0.240 0.294 0.213 0.291 0.425 31.7 2020 0.128 0.151 0.137 0.190 0.272 0.138 0.199 0.152 0.137 0.247 0.192 0.221 0.243 0.196 0.248 0.334 29.5 CV: 变异系数。CV: coefficients of variation.
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表 7 2001—2020年山东省碳排放的影响因素
Table 7. Driving factors decomposition of agricultural carbon emission in Shandong Province from 2001 to 2020
年份
Year贡献值 Contribution value (×104 t) ΔCI ΔAI ΔIS ΔEDL ΔURB ΔP ΔC 2001 −89.0 18.7 −39.1 136.6 22.8 −14.2 35.8 2002 −59.6 21.4 −162.3 301.0 49.4 −32.6 117.4 2003 −255.6 18.6 −65.8 425.3 97.9 −73.9 146.5 2004 −519.7 31.4 −108.5 749.4 125.7 −91.5 186.8 2005 −600.8 32.8 −280.4 1059.9 177.2 −133.3 255.4 2006 −636.6 −26.5 −442.6 1360.6 196.1 −138.6 312.5 2007 −933.3 −12.3 −478.6 1648.7 248.3 −179.4 293.3 2008 −1215.3 −16.4 −502.0 1929.9 269.3 −192.5 273.1 2009 −1323.4 −20.5 −538.1 2061.1 266.9 −180.0 266.0 2010 −1491.7 −18.0 −604.7 2281.1 342.1 −239.6 269.1 2011 −1669.1 −30.9 −659.2 2493.2 364.8 −253.4 245.5 2012 −1727.3 −83.8 −706.8 2657.1 389.3 −279.7 248.8 2013 −1880.8 −86.2 −714.4 2814.6 418.3 −303.1 248.4 2014 −1953.9 −94.3 −751.7 2902.7 446.9 −308.5 241.3 2015 −2010.7 −94.4 −838.2 3025.5 566.9 −416.1 233.0 2016 −1954.4 −116.3 −975.2 3101.5 603.0 −435.8 222.6 2017 −1934.1 −152.4 −1076.2 3192.4 639.3 −458.1 210.9 2018 −1994.1 −148.3 −1113.5 3242.7 658.4 −464.5 180.6 2019 −2083.2 −141.4 −1119.6 3212.9 603.6 −408.2 64.1 2020 −2182.6 −135.5 −1080.3 3226.2 610.1 −413.4 24.5 合计 Total −26 515.2 −1054.3 −12 257.0 41 822.4 7096.4 −5016.5 4075.8 ΔCI、ΔAI、ΔIS、ΔEDL、ΔURB、ΔP分别表示农业生产效率、农业产业结构、地区产业结构、地区经济发展水平、城镇化水平和农村人口对农业碳排放在基期到t时间的变化量的贡献值。ΔC表示基期到t时间农业碳排放变化量。ΔCI, ΔAI, ΔIS, ΔEDL, ΔURB and ΔP respectively stand for the contribution values of agricultural production efficiency, agricultural structure, regional industry structure, regional economic development level, urbanization rate and rural population to carbon emission variation. ΔC stands for carbon emission variation during study period.
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表 8 2000—2020年山东省各地市碳排放的影响因素
Table 8. Driving factors decomposition of agricultural carbon emission in each city of Shandong Province from 2000 to 2020
城市 City 贡献值 Contribution value (×104 t) ΔCI ΔAI ΔIS ΔEDL ΔURB ΔP ΔC 济南 Jinan −1604.6 −63.5 −1097.0 2775.0 984.9 −810.2 184.6 青岛 Qingdao −1522.9 −64.4 −1827.5 3041.0 917.9 −726.0 −181.9 淄博 Zibo −586.3 −47.0 −407.7 1067.2 207.5 −207.5 26.1 枣庄 Zaohuang −805.4 −40.1 −536.9 1591.9 250.4 −196.9 263.0 东营 Dongying −636.1 −140.5 −369.1 1422.7 230.2 −251.7 255.5 烟台 Yantai −1308.2 −37.0 −1027.5 2698.2 598.4 −583.0 340.8 潍坊 Weifang −2469.2 −173.6 −2050.8 4981.3 1457.3 −1284.5 460.6 济宁 Jining −2744.8 −131.4 −1129.7 4185.5 842.5 −633.2 389.0 泰安 Tai’an −1238.8 −59.5 −779.6 2375.2 392.6 −359.0 330.9 威海 Weihai −622.5 −4.9 −383.3 1120.1 296.5 −340.1 65.6 日照 Rizhao −783.7 −4.9 −745.3 1528.2 322.1 −330.7 −14.3 临沂 Linyi −1877.0 −60.4 −1669.9 3931.4 923.8 −650.5 597.4 德州 Dezhou −1558.3 −230.3 −1394.4 3859.9 729.5 −578.4 828.0 聊城 Liaocheng −1991.0 −60.0 −1791.3 3879.9 747.2 −571.3 213.7 滨州 Binzhou −1061.0 −143.9 −991.3 2500.6 490.9 −464.3 331.0 菏泽 Heze −1241.8 −0.9 −3366.3 5770.0 1011.6 −574.2 1598.4 ΔCI、ΔAI、ΔIS、ΔEDL、ΔURB、ΔP分别表示农业生产效率、农业产业结构、地区产业结构、地区经济发展水平、城镇化水平和农村人口对农业碳排放在基期到t时间的变化量的贡献值。ΔC表示基期到t时间农业碳排放变化量。ΔCI, ΔAI, ΔIS, ΔEDL, ΔURB and ΔP respectively stand for the contribution values of agricultural production efficiency, agricultural structure, regional industry structure, regional economic development level, urbanization rate and rural population to carbon emission variation. ΔC stands for carbon emission variation during study period.
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表 9 2025年、2030年和2045年山东省及16地市农业碳排放量预测值
Table 9. Forecasted agricultural carbon emissions in Shandong Province and 16 cities in 2025, 2030 and 2045
×104 t 区域 Region 2025 2030 2045 区域 Region 2025 2030 2045 区域 Region 2025 2030 2045 山东省 Shandong 1742 1736 1715 烟台 Yantai 107 109 114 临沂 Linyi 180 183 193 济南 Jinan 100 95 82 潍坊 Weifang 191 187 176 德州 Dezhou 173 176 187 青岛 Qingdao 76 68 49 济宁 Jining 141 131 105 聊城 Liaocheng 118 111 93 淄博 Zibo 37 35 31 泰安 Tai’an 91 90 87 滨州 Binzhou 97 98 103 枣庄 Zaozhuang 70 72 78 威海 Weihai 47 46 45 菏泽 Heze 256 264 291 东营 Dongying 58 58 59 日照 Rizhao 49 47 41 － － － －
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[1] IPCC. Climate change 2021: the physical science basis[EB/OL]. IPCC, (2021-08-09) [2022-01-12]. https://www.ipcc.ch/report/ar6/wg1/ [2] 郎慧, 肖诗顺, 王艳. 四川省农业碳排放与经济增长的脱钩效应分析[J]. 山东农业大学学报: 社会科学版, 2019(2): 70−78LANG H, XIAO S S, WANG Y. Decoupling analysis of agricultural carbon emissions and economic development in Sichuan Province[J]. Journal of Shandong Agricultural University: Natural Science Edition, 2019(2): 70−78 [3] 郝小雨. 黑龙江省30年来农田生态系统碳源/汇强度及碳足迹变化[J]. 黑龙江农业科学, 2021(8): 97−104HAO X Y. Changes of carbon source/sink intensity and carbon footprint of farmland ecosystem in Heilongjiang Province[J]. Heilongjiang Agricultural Sciences, 2021(8): 97−104 [4] 程琳琳. 中国农业碳生产率时空分异: 机理与实证[D]. 武汉: 华中农业大学, 2018CHENG L L. Spatial and temporal differentiation of China’s agricultural carbon productivity mechanism and demonstration[D]. Wuhan: Huazhong Agricultural University, 2018 [5] 孙英. 山东省农业碳排放时空特征及其影响因素分析[D]. 兰州: 西北师范大学, 2018SUN Y. Spatial and temporal characteristics of agricultural carbon emission and its influencing factors in Shandong Province[D]. Lanzhou: Lanzhou University, 2018 [6] 吴昊玥, 黄瀚蛟, 何宇, 等. 中国农业碳排放效率测度、空间溢出与影响因素[J]. 中国生态农业学报(中英文), 2021, 29(10): 1762−1773WU H Y, HUANG H J, HE Y, et al. Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1762−1773 [7] 杨滨键, 孙红雨. 低碳绩效测度与动态效应研究−以山东省种植业为例[J]. 中国生态农业学报(中英文), 2021, 29(3): 581−589YANG B J, SUN H Y. Low carbon performance measurement and dynamic effects: A case study of the planting industry in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2021, 29(3): 581−589 [8] 张丽琼, 何婷婷. 1997—2018年中国农业碳排放的时空演进与脱钩效应−基于空间和分布动态法的实证研究[J]. 云南农业大学学报: 社会科学, 2022, 16(1): 78−90ZHANG L Q, HE T T. Spatio-temporal of agricultural carbon emission and decoupling in China during 1997−2018: an empirical research based on spatial and distribution dynamics method[J]. Journal of Yunnan Agricultural University: Social Science, 2022, 16(1): 78−90 [9] 伍国勇, 刘金丹, 杨丽莎. 中国农业碳排放强度动态演进及碳补偿潜力[J]. 中国人口·资源与环境, 2021, 31(10): 69−78WU G Y, LIU J D, YANG L S. Dynamic evolution of China’s agricultural carbon emission intensity and carbon offset potential[J]. China Population, Resources and Environment, 2021, 31(10): 69−78 [10] 蒋添诚, 胡纯, 王巧稚, 等. 湖北省农业碳排放时空特征及脱钩研究[J]. 环境污染与防治, 2021, 43(11): 1476−1480JIANG T C, HU C, WANG Q Z, et al. Research on spatial-temporal characteristics and decoupling of agricultural carbon emissions in Hubei[J]. Environmental Pollution & Control, 2021, 43(11): 1476−1480 [11] 李阳, 陈敏鹏. 中国省域农业源非CO2温室气体排放的影响因素分析与峰值预测[J]. 环境科学学报, 2021, 41(12): 5174−5189LI Y, CHEN M P. Analysis of influencing factors and peak forecast of non-CO2 greenhouse gas emissions from provincial agricultural sources in China[J]. Acta Scientiae Circumstantiae, 2021, 41(12): 5174−5189 [12] 朱通雅. 农业碳排放的驱动因素与退耦效应研究−以长江经济带为例[J]. 安徽农业科学, 2021, 49(24): 118−121 doi: 10.3969/j.issn.0517-6611.2021.24.027ZHU T Y. Research on driving factors and decoupling effects of agricultural carbon emissions — Taking the Yangtze River Economic Belt as an example[J]. Journal of Anhui Agricultural Sciences, 2021, 49(24): 118−121 doi: 10.3969/j.issn.0517-6611.2021.24.027 [13] 吕斯涵, 张小平. 山东省农业净碳汇时空演化特征分析[J]. 水土保持学报, 2019, 33(2): 227−234LYU S H, ZHANG X P. Spatial-temporal characteristics of agricultural net carbon sink in Shandong Province[J]. Journal of Soil and Water Conservation, 2019, 33(2): 227−234 [14] 王梁, 赵杰, 陈守越. 山东省农田生态系统碳源、碳汇及其碳足迹变化分析[J]. 中国农业大学学报, 2016, 21(7): 133−141 doi: 10.11841/j.issn.1007-4333.2016.07.17WANG L, ZHAO J, CHEN S Y. Analysis of ecosystem carbon sources/sinks and carbon footprint in farmland ecosystem of Shandong Province[J]. Journal of China Agricultural University, 2016, 21(7): 133−141 doi: 10.11841/j.issn.1007-4333.2016.07.17 [15] 田云, 张俊飚. 中国省级区域农业碳排放公平性研究[J]. 中国人口·资源与环境, 2013, 23(11): 36−44 doi: 10.3969/j.issn.1002-2104.2013.11.006TIAN Y, ZHANG J B. Fairness research of agricultural carbon emissions between provincial regions in China[J]. China Population, Resources and Environment, 2013, 23(11): 36−44 doi: 10.3969/j.issn.1002-2104.2013.11.006 [16] 张晓荟. 中国秸秆焚烧大气污染物高分辨率排放特征研究[D]. 南京: 南京大学, 2019ZHANG X H. High-resolution characteristics of air pollutant emissions from crop residue burning in China[D]. Nanjing: Nanjing University, 2019 [17] 许清涛, 李玉波, 杨淑杰. 吉林省农业现代化进程中碳排放测算与分解[J]. 中国农机化学报, 2018, 39(7): 103−109XU Q T, LI Y B, YANG S J. Measurement and decomposition of carbon emission by the process of agricultural modernization in Jilin Province[J]. Journal of Chinese Agricultural Mechanization, 2018, 39(7): 103−109 [18] 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 doi: 10.3969/j.issn.1002-2104.2012.07.004MIN 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 doi: 10.3969/j.issn.1002-2104.2012.07.004 [19] 冉锦成, 马惠兰, 苏洋. 西北五省农业碳排放测算及碳减排潜力研究[J]. 江西农业大学学报, 2017, 39(3): 623−632RAN J C, MA H L, SU Y. A study on agricultural carbon emission and carbon emission reduction potential in five provinces in Northwest China[J]. Acta Agriculturae Universitatis Jiangxiensis, 2017, 39(3): 623−632 [20] 胡向东, 王济民. 中国畜禽温室气体排放量估算[J]. 农业工程学报, 2010, 26(10): 247−252 doi: 10.3969/j.issn.1002-6819.2010.10.042HU X D, WANG J M. Estimation of livestock greenhouse gases discharge in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(10): 247−252 doi: 10.3969/j.issn.1002-6819.2010.10.042 [21] 章胜勇, 尹朝静, 贺亚亚, 等. 中国农业碳排放的空间分异与动态演进−基于空间和非参数估计方法的实证研究[J]. 中国环境科学, 2020, 40(3): 1356−1363 doi: 10.3969/j.issn.1000-6923.2020.03.046ZHANG S Y, YIN Z J, HE Y Y, et al. Spatial differentiation and dynamic evolution of agricultural carbon emission in China — Empirical research based on spatial and non-parametric estimation methods[J]. China Environmental Science, 2020, 40(3): 1356−1363 doi: 10.3969/j.issn.1000-6923.2020.03.046 [22] 张小平, 王龙飞. 甘肃省农业碳排放变化及影响因素分析[J]. 干旱区地理, 2014, 37(5): 1029−1035ZHANG X P, WANG L F. Variations and influential factors of agricultural carbon emissions in Gansu Province[J]. Arid Land Geography, 2014, 37(5): 1029−1035 [23] 胡婉玲, 张金鑫, 王红玲. 中国农业碳排放特征及影响因素研究[J]. 统计与决策, 2020(5): 56−61HU W L, ZHANG J X, WANG H L. Characteristics and influencing factors of agricultural carbon emission in China[J]. Statistics & Decision, 2020(5): 56−61 [24] 赵宇. 江苏省农业碳排放动态变化影响因素分析及趋势预测[J]. 中国农业资源与区划, 2018, 39(5): 97−102ZHAO Y. Influencing factors and trend prediction on dynamic change of agricultural carbon emissions in Jiangsu Province[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2018, 39(5): 97−102 [25] 旷爱萍, 胡超. 广西农业碳排放影响因素和趋势预测[J]. 西南林业大学学报: 社会科学, 2020, 4(2): 5−13KUANG A P, HU C. Influencing factors and trend forecast of agricultural carbon emission in Guangxi[J]. Journal of Southwest Forestry University: Social Science Edition, 2020, 4(2): 5−13 [26] 何慧爽, 付帮杰. 我国粮食主产区农业碳排放测度与减排压力研究[J]. 生态经济, 2019, 35(11): 99−104HE H S, FU B J. Measurement of agricultural carbon emission and pressure of emission reduction in main grain-producing areas of China[J]. Ecological Economy, 2019, 35(11): 99−104 [27] 田成诗, 陈雨. 中国省际农业碳排放测算及低碳化水平评价−基于衍生指标与TOPSIS法的运用[J]. 自然资源学报, 2021, 36(2): 359−410TIAN C S, CHEN Y. China’s provincial agricultural carbon emissions measurement and low carbonization level evaluation: based on the application of derivative indicators and TOPSIS[J]. Journal of Natural Resources, 2021, 36(2): 359−410 [28] 王先锋. 改革开放以来中国共产党的惠农政策研究[D]. 济南: 山东师范大学, 2012WANG X F. Since the reform and opening up agricultural policy of the Communist Party of China[D]. Jinan: Shandong Normal University, 2012 [29] 杨滨键, 田景仁, 孙红雨. 农业生产碳足迹影响因素研究−基于SVAR模型[J]. 生态经济, 2019, 35(8): 120−127YANG B J, TIAN J R, SUN H Y. Study on the factors affecting the carbon footprint of agricultural production based on the SVAR model[J]. Ecological Economy, 2019, 35(8): 120−127 [30] 李明杰. 山东省种植业-养殖业时空变化分析及其驱动力研究[D]. 青岛: 山东科技大学, 2019LI M J. Analysis of spatio-temporal changes and diving force research of crop farming and aquaculture in Shandong Province[D]. Qingdao: Shandong University of Science and Technology, 2019 [31] 李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分析[J]. 中国人口·资源与环境, 2011, 21(8): 80−86 doi: 10.3969/j.issn.1002-2104.2011.08.013LI B, ZHANG J B, LI H P. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China[J]. China Population, Resources and Environment, 2011, 21(8): 80−86 doi: 10.3969/j.issn.1002-2104.2011.08.013 [32] 曹俐, 王莹, 雷岁江. 山东省农业碳排放的时空特征与脱钩弹性研究[J]. 江苏农业科学, 2020, 48(17): 250−256CAO L, WANG Y, LEI S J. Study on spatial-temporal characteristics and decoupling elasticity of agricultural carbon emissions in Shandong Province[J]. Jiangsu Agricultural Sciences, 2020, 48(17): 250−256 [33] 黄锐, 周玉玺, 周霞. 山东省农业碳排放强度的时空特征与趋势演进[J]. 山东农业大学学报: 社会科学版, 2021, 23(2): 57−63HUANG R, ZHOU Y X, ZHOU X. Space and temporal characteristics and trend evolution of agricultural carbon emission intensity in Shandong Province[J]. Journal of Shandong Agricultural University: Social Science Edition, 2021, 23(2): 57−63 [34] 魏振香, 吴晓娟. 山东省农业低碳发展及驱动因素分析[J]. 河南科学, 2021, 39(11): 1878−1886 doi: 10.3969/j.issn.1004-3918.2021.11.024WEI Z X, WU X J. Low-carbon agricultural development and driving factors in Shandong Province[J]. Henan Science, 2021, 39(11): 1878−1886 doi: 10.3969/j.issn.1004-3918.2021.11.024 [35] 梁青青. 基于经典环境库茨涅茨曲线的我国农业碳排放拐点预测及区域比较[J]. 科技与经济, 2017, 30(177): 106−110LIANG Q Q. Prediction and regional comparison of China’s agriculture carbon emissions based on classical environmental Kuznets curves[J]. Science & Technology and Economy, 2017, 30(177): 106−110 [36] 付帮杰. 粮食主产区农业碳排放动态变化与减排潜力研究[D]. 郑州: 华北水利水电大学, 2020FU B J. Dynamic change of agricultural carbon emission and its emission reduction potential in major grain producing areas[D]. Zhengzhou: North China University of Water Resources and Electric Power, 2020 -
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