资源科学 ›› 2019, Vol. 41 ›› Issue (8): 1450-1461.doi: 10.18402/resci.2019.08.06

• 碳排放 • 上一篇    下一篇

基于水-土要素匹配视角的农业碳排放时空分异及影响因素——以长江经济带为例

王若梅1,马海良2(),王锦3   

  1. 1.河海大学商学院,南京 211100
    2.河海大学低碳经济研究所,常州 213022
    3.安徽财经大学国际经济贸易学院,蚌埠 233030
  • 收稿日期:2018-09-27 修回日期:2019-05-29 出版日期:2019-08-28 发布日期:2019-08-21
  • 通讯作者: 马海良
  • 作者简介:王若梅,女,安徽淮南市人,博士生,研究方向为资源经济。E-mail: wrmeihh@126.com
  • 基金资助:
    国家自然科学基金项目(41301620);中央高校业务基金项目(2019B22814);河海大学常州校区研究生科技创新项目(XZX/15B001-06)

Spatial and temporal differences of agricultural carbon emissions and impact factors of the Yangtze River Economic Belt based on a water-land perspective

Ruomei WANG1,Hailiang MA2(),Jin WANG3   

  1. 1.Business School, Hohai University, Nanjing 211100, China
    2.Institute of Low Carbon Economy, Hohai University, Changzhou 213002, China
    3.School of International Trade and Economics, Anhui University of Finance and Economics, Bengbu 233030, China
  • Received:2018-09-27 Revised:2019-05-29 Online:2019-08-28 Published:2019-08-21
  • Contact: Hailiang MA

摘要:

长江经济带水稻和马铃薯等传统农产品的生产规模约占全国50%以上,其农业碳排放直接影响长江经济带生态优先和绿色发展战略的实施。运用系统的观点综合考虑水、土、能、碳的相互作用,并以此为基础考察长江经济带农业碳排放的时空分异及影响因素。首先对长江经济带2009—2016年农业碳排放进行测算,并计算水土匹配情况,借助Kaya恒等式和完全分解方法LMDI加法形式,探讨农业碳排放各影响因素的贡献值情况,主要研究结果如下:①2009—2016年长江经济带整体农业碳排放呈阶段式上升趋势,从2009年1208.7398万t上升为2016年1407.2846万t,增长率达到16.43%,其中2016年湖南、江苏、浙江和湖北农业碳排放占比合计达59.32%;②农业碳排放强度、农业水土资源因素和人均耕地面积对农业碳排放表现为抑制因素,而农业水资源的经济产出和人口因素则表现为促成因素。③从分省市层面看,各因素对农业碳排放的影响不尽相同:农业水土资源因素,对湖南、四川、贵州和云南起促进作用,而对上海、江苏、浙江、安徽、江西和湖北起抑制作用;且农业水土资源匹配度越高的省市区,农业水土资源因素对农业碳排放的抑制效果相对越好。据此提出,制定低碳农业发展策略,要综合考虑与农业发展密切相关的水、土、能要素,将农业低碳发展与节水节能、保护性开发利用耕地等相结合等建议。

关键词: 农业碳排放, 水土资源匹配度, 时空分异, LMDI完全分解模型, 长江经济带

Abstract:

The production of traditional agricultural products of the Yangtze River Economic Belt, such as rice and potatoes, accounts for more than 50% of the national total in China. The agricultural carbon emissions directly affect the green development of the region. From a system’s point of view, the interactions of water, land, energy, and carbon was comprehensively considered, and based on this, spatial and temporal differences of agricultural carbon emissions in the Yangtze River Economic Belt and its impact factors were studied. (1) At the meso-level, agricultural carbon emissions of the Yangtze River Economic Belt from 2009 to 2016 were calculated, and the matching degree of water and land was calculated to lay the foundation for the introduction of water and land elements into Kaya’s identity. (2) With the help of logarithmic mean divisia index method, contribution values of impact factors of agricultural carbon emissions were discussed. The results show that: (1) From 2009 to 2016, the overall agricultural carbon emissions of the Yangtze River Economic Belt showed a staged upward trend, with a growth rate of 16.43% from 1208.7398×10 4 t in 2009 to 1407.2846×10 4 t in 2016, and the proportion of agricultural carbon emissions in Hunan, Jiangsu, Zhejiang, and Hubei provinces reached 59.32% in 2016; (2) For the Yangtze River Economic Belt as a whole, agricultural carbon emissions intensity, agricultural land and water use ratio, and per capita arable land area are inhibitory factors for agricultural carbon emissions, while economic output of agricultural water resources and population factors are contributing factors; (3) At the provincial level of the Yangtze River Economic Belt, the effects of impact factors on agricultural carbon emissions are different, especially the agricultural land and water use ratio, which plays a promoting role in Hunan, Sichuan, Guizhou, and Yunnan provinces, but a inhibiting role in Shanghai and Jiangsu, Zhejiang, Anhui, Jiangxi, and Hubei provinces and Municipality. Provinces with high matching degree of water and land (MDWL) have relatively higher inhibiting effect on agricultural carbon emissions compared with provinces with low MDWL. Based on this, it is proposed that water, land, and energy elements should be comprehensively considered to formulate low-carbon agriculture development strategies and combine low-carbon development of agriculture with water-saving, energy-saving, and conservation and utilization of cultivated land.

Key words: agricultural carbon emissions, matching degree of water and land, spatial-temporal differentiation, logarithmic mean divisia index, Yangtze River Economic Belt