中国农牧业碳排放时空变化及预测

doi:10.19741/j.issn.1673-4831.2018.0884

生态与农村环境学报 ›› 2019, Vol. 35 ›› Issue (10): 1232-1241.doi: 10.19741/j.issn.1673-4831.2018.0884

中国农牧业碳排放时空变化及预测

徐丽¹, 曲建升^1,2, 吴金甲¹, 韦沁¹, 白静¹, 李恒吉^1,2

1. 兰州大学资源环境学院, 甘肃兰州 730000;
2. 中国科学院兰州文献情报中心/全球变化研究信息中心, 甘肃兰州 730000

收稿日期:2018-12-28 出版日期:2019-10-25 发布日期:2019-10-23
通讯作者: 曲建升 E-mail:jsqu@lzb.ac.cn
作者简介:徐丽(1993-),女,山西忻州人,硕士生,主要从事温室气体排放评估与预测研究。E-mail:lxu16@lzu.edu.cn
基金资助:
国家重点研发计划（2016YFA0602803）

Spatial-Temporal Dynamics and Prediction of Carbon Emission From Agriculture and Animal Husbandry in China

XU Li¹, QU Jian-sheng^1,2, WU Jin-jia¹, WEI Qin¹, BAI Jing¹, LI Heng-ji^1,2

1. College of Resources and Environment, Lanzhou University, Lanzhou 730000, China;
2. Lanzhou Information Center/Global Change Research Information Center, Chinese Academy of Sciences, Lanzhou 730000, China

Received:2018-12-28 Online:2019-10-25 Published:2019-10-23

摘要/Abstract

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1.13×10⁸ t，年均增速提高。

关键词: 农牧业碳排放, 时空变化, 标准差优选组合模型, 预测

Abstract: 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.

Key words: agricultural and animal husbandry carbon emission, temporal and spatial variation, standard deviation optimal combination model, prediction

中图分类号:

徐丽, 曲建升, 吴金甲, 韦沁, 白静, 李恒吉. 中国农牧业碳排放时空变化及预测[J]. 生态与农村环境学报, 2019, 35(10): 1232-1241.

XU Li, QU Jian-sheng, WU Jin-jia, WEI Qin, BAI Jing, LI Heng-ji. Spatial-Temporal Dynamics and Prediction of Carbon Emission From Agriculture and Animal Husbandry in China[J]. Journal of Ecology and Rural Environment, 2019, 35(10): 1232-1241.

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中国农牧业碳排放时空变化及预测

Spatial-Temporal Dynamics and Prediction of Carbon Emission From Agriculture and Animal Husbandry in China

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