1. 中国矿业大学 公共管理学院,江苏,徐州,221116
2. 中国资源型城市转型发展与乡村振兴研究中心,江苏,徐州,221116
3. 中国矿业大学 矿区土地利用与生态安全治理研究中心,江苏,徐州,221116
4. 中国矿业大学 环境与测绘学院,江苏,徐州,221116
纸质出版:2023
移动端阅览
徐玥, 王辉, 韩秋凤. 徐州市农业碳排放时空特征与脱钩效应[J]. 水土保持通报, 2023,43(5):395-403.
Xu Yue, Wang Hui, Han Qiufeng. Spatial-temporal Characteristics and Decoupling Effects of Agricultural Carbon Emissions at Xuzhou City[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 395-403.
徐玥, 王辉, 韩秋凤. 徐州市农业碳排放时空特征与脱钩效应[J]. 水土保持通报, 2023,43(5):395-403. DOI: 10.13961/j.cnki.stbctb.20230526.004.
Xu Yue, Wang Hui, Han Qiufeng. Spatial-temporal Characteristics and Decoupling Effects of Agricultural Carbon Emissions at Xuzhou City[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 395-403. DOI: 10.13961/j.cnki.stbctb.20230526.004.
[目的
]
分析徐州市时空尺度下农业碳排放时空特征与脱钩效应,为江苏省徐州市及其周边资源型城市未来农业的绿色高质可持续发展以及农业经济的良性增长提供理论与数据参考。[方法
]
采用排放系数法测算2000—2020年时空尺度下徐州市农业碳排放总量、强度和结构,而后基于Tapio脱钩模型分析其与农业经济发展间的脱钩关系。[结果
]
①徐州市农业碳排放变化趋势总体呈“快速上升—波动上升—快速下降”3阶段,从2000年的1.61×10
6
t先增后减至2020年的1.69×10
6
t,形象地表现为“M”形。对农业碳排放贡献率大小依次是耕地利用(46.44%)、作物种植(31.90%)和牲畜养殖(21.66%),化肥是最主要碳源。②徐州市各区(县、市)农业碳排放量差异明显,经历了由升到降的长期演化进程,空间层面上总体呈现出“中部高,周边低”的分布格局,邳州市最为突出; ③徐州市农业碳排放与农业经济发展总体经历了“弱脱钩—强负脱钩—扩张负脱钩—强脱钩”的变化历程,且“十三五”以来主要表现为强脱钩。[结论
]
徐州市农业碳排放随着低碳减排理念的不断深入而日趋合理,农业经济发展也取得了一定成效。
[Objective] The spatial-temporal characteristics and decoupling effects of agricultural carbon emissions at Xuzhou City
Jiangsu Province were analyzed in order to provide a theoretical basis and data reference for green
high-quality sustainable development of agriculture and the benign growth of the agricultural economy in Xuzhou City
and its surrounding resource-based cities in the future. [Methods] The total amount
intensity
and structure of agricultural carbon emissions at Xuzhou City from 2000 to 2020 were measured using the emission coefficient method
and their decoupling relationship with agricultural economic development was determine based on the Tapio decoupling model. [Results] ① The overall trend of agricultural carbon emissions at Xuzhou City could be described as M-shaped (rapid rise-fluctuating rise-rapid decline) from 1.61×106 t in 2000 to 1.69×106 t in 2020. The contributions of factors affecting agricultural carbon emissions followed the order of arable land use (46.44%)
crop cultivation (31.90%) and livestock breeding (21.66%)
with chemical fertilizers being the most important carbon source; ② Agricultural carbon emission at Xuzhou City varied significantly among districts (counties and cities) and had undergone a long-term evolutionary process from rising to falling
with a spatial distribution pattern of “higher in the middle and lower in the surrounding areas”
with Pizhou City being the most prominent; ③ Xuzhou City’s agricultural carbon emissions and agricultural economic development have generally undergone a process of “weak decoupling—strong negative decoupling—expansion of negative decoupling—strong decoupling”
and has mainly manifested as strong decoupling since the 13th Five-Year Plan. [Conclusion] Xuzhou City’s agricultural carbon emissions are becoming more and more reasonable as the concept of low-carbon emission reduction continues to deepen. Agricultural economic development has also achieved clear and convincing effects.
周一凡,李彬,张润清.县域尺度下河北省农业碳排放时空演变与影响因素研究[J].中国生态农业学报(中英文),2022,30(4):570-581.
林斌,徐孟,汪笑溪.中国农业碳减排政策、研究现状及展望[J].中国生态农业学报(中英文),2022,30(4):500-515.
Tian Shiqi, Wang Shijie, Bai Xiaoyong, et al. Global patterns and changes of carbon emissions from land use during 1992—2015[J]. Environmental Science and Ecotechnology, 2021,7:100108.
张俊飚,何可.“双碳”目标下的农业低碳发展研究: 现状、误区与前瞻[J].农业经济问题,2022,43(9):35-46.
范紫月,齐晓波,曾麟岚,等.中国农业系统近40年温室气体排放核算[J].生态学报,2022,42(23):9470-9482.
常青,蔡为民,谷秀兰,等.河南省农业碳排放时空分异、影响因素及趋势预测[J].水土保持通报,2023,43(1):367-377.
田云,尹忞昊.中国农业碳排放再测算: 基本现状、动态演进及空间溢出效应[J].中国农村经济,2022(3):104-127.
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.
徐玥,王辉,韩秋凤,等.我国耕地碳排放时空特征与影响因素[J].江苏农业科学,2022,50(16):218-226.
Cui Yu, Khan S U, Sauer J, et al. Exploring the spatiotemporal heterogeneity and influencing factors of agricultural carbon footprint and carbon footprint intensity: embodying carbon sink effect [J]. Science of the Total Environment, 2022,846:157507.
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.
陈胜涛,张开华,张岳武.农业碳排放绩效的测量与脱钩效应[J].统计与决策,2021,37(22):85-88.
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.
丁宝根,杨树旺,赵玉,等.中国耕地资源利用的碳排放时空特征及脱钩效应研究[J].中国土地科学,2019,33(12):45-54.
吴昊玥,黄瀚蛟,陈文宽.中国粮食主产区耕地利用碳排放与粮食生产脱钩效应研究[J].地理与地理信息科学,2021,37(6):85-91.
段华平,张悦,赵建波,等.中国农田生态系统的碳足迹分析[J].水土保持学报,2011,25(5):203-208.
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.
田云,吴海涛.产业结构视角下的中国粮食主产区农业碳排放公平性研究[J].农业技术经济,2020(1):45-55.
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):78-90.
徐玥,王辉,韩秋凤.县级尺度下耕地碳收支区域差异与公平性: 以江苏省为例[J].河北农业大学学报(社会科学版),2023,25(1):62-73.
吴昊玥,孟越,黄瀚蛟,等.中国耕地利用净碳汇与农业生产的时空耦合特征[J].水土保持学报,2022,36(5):360-368.
吴昊玥,周蕾,何艳秋,等.中国种植业碳排放达峰进程初判及脱钩分析[J/OL].中国生态农业学报(中英文),2023,31(0):1-12.http://kns.cnki.net/kcms/detail/13.1432.S.20230213.0846.001.html.
0
浏览量
855
下载量
2
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621