中国农牧业碳排放时空变化及预测
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摘要
基于主要粮食作物、农资投入和牲畜数据,对中国31个省市自治区1997—2016年农牧业碳排放进行测算;采用变动指数、重心模型和标准差椭圆分析其时空变化特征;以趋势外推、灰色预测和差分整合移动平均自回归(ARIMA)模型为基础,利用标准差优选组合模型预测2017—2022年农牧业碳排放状况。结果表明:1997—2016年农业大环境向好,多省碳排放增加,重心向西北移动,主体区域在胡焕庸线右侧。但多省牧业碳排放降低,重心在河南境内摆动,主体区域扩大并向东南—西北扭转;农业碳排放高值区向东北三省和黄淮海转移,牧业碳排放高值区集中于传统区域和中部地区;组合模型预测优于单一模型,到2022年农业碳排放延续历史趋势但年均增速降低,牧业碳排放达到1. 13×10~8t,年均增速提高。
Based on the data of major grain crops,agricultural inputs and animal husbandry,agriculture and animal husbandry carbon emissions from 1997 to 2016 in 31 provinces were calculated. Their temporal and spatial variations were analyzed by means of changing index,barycenter model and the standard deviation ellipse. Based on trend extrapolation,grey prediction and ARIMA model as well as standard deviation optimal combination model were used to predict carbon emissions from agriculture and animal husbandry from 2017 to 2022. The results show that from 1997 to 2016,the agricultural environment improved,the carbon emission increased,the core moved toward northwest and the main area was on the right side of Hu Huanyong line. However,the animal husbandry carbon emissions in many provinces were reduced,and the core wiggled in Henan Province. The main area expanded and turned to the southeast-northwest. The high volume area of agricultural carbon emission transferred to the 3 northeastern provinces and north China plain,and the high volume area of animal husbandry carbon emission was concentrated in the traditional region and the central region. The combined model is better than the single model. By 2022,the agricultural carbon emissions will follow the historical trend but the annual growth rate will decrease. Animal husbandry carbon emission will reach 1. 13×108 t and the annual growth rate will increase.
Based on the data of major grain crops,agricultural inputs and animal husbandry,agriculture and animal husbandry carbon emissions from 1997 to 2016 in 31 provinces were calculated. Their temporal and spatial variations were analyzed by means of changing index,barycenter model and the standard deviation ellipse. Based on trend extrapolation,grey prediction and ARIMA model as well as standard deviation optimal combination model were used to predict carbon emissions from agriculture and animal husbandry from 2017 to 2022. The results show that from 1997 to 2016,the agricultural environment improved,the carbon emission increased,the core moved toward northwest and the main area was on the right side of Hu Huanyong line. However,the animal husbandry carbon emissions in many provinces were reduced,and the core wiggled in Henan Province. The main area expanded and turned to the southeast-northwest. The high volume area of agricultural carbon emission transferred to the 3 northeastern provinces and north China plain,and the high volume area of animal husbandry carbon emission was concentrated in the traditional region and the central region. The combined model is better than the single model. By 2022,the agricultural carbon emissions will follow the historical trend but the annual growth rate will decrease. Animal husbandry carbon emission will reach 1. 13×108 t and the annual growth rate will increase.
引文
[1]张军伟，张锦华，吴方卫．我国粮食生产的碳排放及减排路径分析[J]．统计与决策，2018,34(14):168-172．
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[18]张志启．我国能源消费总量预测建模分析[D]．包头:内蒙古科技大学，2012.[ZHANG Zhi-qi.Modeling Analysis of China's Total Energy Consumption Predictive[D].Baotou:Inner Mongolia University of Science and Technology,2012.]
[2]SMITH P,MARTINO D,CAI Z C,et al.Policy and Technological Constraints to Implementation of Greenhouse Gas Mitigation Options in Agriculture[J].Agriculture,Ecosystems&Environment,2007,118(1/2/3/4):6-28．
[3]XU L Q,BAI J J.Study on Characteristics and Reduction Countermeasures of the Agricultural Carbon Emission in China[J].Meteorological&Environmental Research,2013,4(11):58-62．
[4]韦沁，曲建升，白静，等．我国农业碳排放的影响因素和南北区域差异分析[J]．生态与农村环境学报，2018,34(4):318-325.[WEI Qin,QU Jian-sheng,BAI Jing,et al.Influencing Factors of Agricultural Carbon Emission and Regional Differences Between South and North in China[J].Journal of Ecology and Rural Environment,2018,34(4):318-325．]
[5]冉锦成，苏洋，胡金凤，等．新疆农业碳排放时空特征、峰值预测及影响因素研究[J]．中国农业资源与区划，2017,38(8):16-24.[RAN Jin-cheng,SU Yang,HU Jin-feng,et al.Temporal and Spatial Chatacteristics,Peak Value Forecast and Influencing Factors of Agricultural Carbon Emissions in Xinjiang[J].Chinese Journal of Agricultural Resources and Regional Planning,2017,38(8):16-24．]
[6]FRANK S,BEACH R,HAVLK P,et al.Structural Change as a Key Component for Agricultural Non-CO2Mitigation Efforts[J].Nature Communications,2018,9:1060.DOI:10.1038/s41467-018-03489-1．
[7]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):97-102.[ZHAO Yu.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．]
[8]黎孔清，陈俭军，马豆豆．基于STIRPAT和GM(1,1)模型的湖南省农地投入碳排放增长机理及趋势预测[J]．长江流域资源与环境，2018,27(2):345-352.[LI Kong-qing,CHEN Jian-jun,Ma Dou-dou.Growth Mechanism and Trend Forecast of Carbon E-mission From Farmland Inputs in Hunan Province Based in Stirpat and GM(1,1)Mode[J].Resources and Environment in the Yangtza Basin,2018,27(2):345-352．]
[9]田云，张俊飚，李波．中国农业碳排放研究:测算、时空比较及脱钩效应[J]．资源科学，2012,34(11):2097-2105.[TIANYun,ZHANG Jun-biao,LI Bo.Agricultural Carbon Emissions in China:Calculation,Spatial-Temporal Comparison and Decoupling Effects[J].Resources Science,2012,34(11):2097-2105．]
[10]李波．经济增长与农业碳排放关系的实证研究[J]．生态环境学报，2012,21(2):220-224.[Li Bo.Empirical Study on Relationship Between Economic Growth and Agricultural Carbon Emissions[J].Ecology and Environmental Sciences,2012,21(2):220-224．]
[11]姚成胜，钱双双，毛跃华，等．中国畜牧业碳排放量变化的影响因素分解及空间分异[J]．农业工程学报，2017,33(12):10-19.[YAO Cheng-sheng,QIAN Shuang-shuang,MAO Yue-hua,et al.Decomposition of Impacting Factors of Animal Husbandry Carbon Emissions Change and Its Spatial Differences in China[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(12):10-19．]
[12]何艳秋，陈柔，吴昊玥，等．中国农业碳排放空间格局及影响因素动态研究[J]．中国生态农业学报，2018,26(9):1269-1282.[HE Yan-qiu,CHEN Rou,WU Hao-yue,et al.Spatial Dynamics of Agricultural Carbon Emissions in China and the Related Driving Factors[J].Chinese Journal of Eco-Agriculture,2018,26(9):1269-1282．]
[13]韦沁．我国农牧业碳排放的时空差异和影响因素分析及预测研究[D]．兰州:兰州大学，2018.[WEI Qin.Research on SpatialTemporal Differences,Influence Factors and Prediction of Carbon Emission in Agriculture and Animal Husbandry in China[D].Lanzhou:Lanzhou University,2018．]
[14]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 Ji-sheng,HUHao.Calculation of Greenhouse Gases Emission From Agricultural Production in China[J].China Population,Resources and Environment,2012,22(7):21-27．]
[15]IPCC.Climate Change 2007:The Physical Science Basis[R]∥Contribution of Working GroupⅠto the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.New York,USA:Cambridge University Press,2007．
[16]苏洋，马惠兰，李凤．新疆农牧业碳排放及其与农业经济增长的脱钩关系研究[J]．干旱区地理，2014,37(5):1047-1054.[SU Yang,MA Hui-lan,LI Feng.Xinjiang Agriculture and Animal Husbandry Carbon Emissions and Its Decoupling Relationship With Agricultural Economic Growth[J].Arid Land Geography,2014,37(5):1047-1054．]
[17]唐洪松，苏洋，马惠兰，等．新疆畜牧业碳排放格局与公平性研究[J]．干旱区地理，2017,40(6):1338-1345.[TANG Hongsong,SU Yang,MA Hui-lan,et al.Spatial and Temporal Distribution and Equity of Carbon Emissions From Livestock in Xinjiang[J].Arid Land Geography,2017,40(6):1338-1345．]
[18]张志启．我国能源消费总量预测建模分析[D]．包头:内蒙古科技大学，2012.[ZHANG Zhi-qi.Modeling Analysis of China's Total Energy Consumption Predictive[D].Baotou:Inner Mongolia University of Science and Technology,2012.]