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Open Access Article

Advances in Resources and Environmental Science. 2023; 2: (3) ; 1-6 ; DOI: 10.12208/j.aes.20230011.

Analysis on the characteristics and influencing factors of agricultural carbon emission in Suining
遂宁市农业碳排放特征及影响因素分析

作者: 欧蓝蔓, 焦翠翠 *

四川轻化工大学经济学院 四川自贡

*通讯作者: 焦翠翠,单位:四川轻化工大学经济学院 四川自贡;

发布时间: 2023-09-08 总浏览量: 718

摘要

目的 通过研究遂宁市农业碳排放特征及影响因素为能够真正实现低碳农业提供科学参考。方法 本研究选取四川省遂宁市作为研究对象,基于排放系数法的碳排放模型测算了2010-2021年遂宁市农业生产的六类碳源的农业碳排放量,并结合迪氏对数指标分解法(Lograithmic Miviean Divisia Index,LMDI)对遂宁市农业碳排放的影响因素进行分解。结果 遂宁市农业碳排放量总体上呈缓慢下降的趋势,其中化肥和翻耕是主要贡献者;农业经济发展水平是引起遂宁市农业碳排放增加的主要驱动因素,而农业生产效率则是制约碳排放增加的关键因素。结论 遂宁市应从提高农用物资利用效率、引进科学农业技术及转变农业经济发展方式等方面促进低碳化农业发展。

关键词: 农业碳排放;遂宁市;LMDI模型

Abstract

Objective By studying the characteristics and influencing factors of agricultural carbon emissions in Suining, it can truly provide a scientific reference for the realization of low-carbon agriculture.
Methods In this study, Suining, Sichuan Province, was selected as the research object. Based on the carbon emission model of the emission coefficient method, the agricultural carbon emissions of the six types of carbon sources of agricultural production in Suining from 2010 to 2021 were calculated, and the Lograithmic Miviean Divisia Index (LMDI) was used to decompose the influencing factors of agricultural carbon emissions in Suining.
Results The agricultural carbon emissions in Suining showed a slow downward trend, among which chemical fertilizer and plowing were the main contributors. The level of agricultural economic development is the main driving factor for the increase of agricultural carbon emissions in Suining, and agricultural production efficiency is the key factor restricting the increase of carbon emissions.
Conclusion  s Suining should promote the development of low-carbon agriculture from the aspects of improving the utilization efficiency of agricultural materials, introducing scientific agricultural technology and changing the mode of agricultural economic development.

Key words: Agricultural carbon emissions; Suining; LMDI model

参考文献 References

[1] 自茂,朱丽霞,王会珍,等.赣南等原中央苏区特色产业协同创新机制与对策研究—以赣南地区为主[J].赣南师范大学学报,2017,38(5):86-90.

[2] 胡文娟.中国长期低碳发展战略与转型路径研究成果发布[J].可持续发展经济导刊,2020,19(10):12.

[3] 计志英,赖小锋,贾利军.家庭部门生活能源消费碳排放:测度与驱动因素研究[J].中国人口•资源与环境,2016,26(05): 64-72.

[4] 黎孔清,马豆豆,李义猛.基于STIRPAT模型的南京市农业碳排放驱动因素分析及趋势预测.科技管理研究,2018, 38(8):238-245.

[5] 李慧,李玮,姚西龙.基于GWR模型的农业碳排放影响因素时空分异研究.科技管理研究,2019,39(18):238-245.

[6] 张勇,黄淑玲,高杨.“十一五”期间淮北市农业碳排放趋势及影响因素研究[J].河北北方学院学,2013,29(5):42-46.

[7] 刘杨,刘鸿斌.山东省农业碳排放特征、影响因素及达峰分析.中国生态农业学报(中英文),2022,30(4):558-569.

[8] 田云,尹涨昊.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.中国农村经济,2022(3):104-127.

[9] 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解.中国人口•资源与环境,2011,21(8):80-86.

[10] 何艳秋,戴小文.中国农业碳排放驱动因素的时空特征研究[J].资源科学,2016,38(9):1780-1790. 

[11] 卢奕亨,田云,周丽丽.四川省农业碳排放时空演变特征及其影响因素研究[J/OL].中国农业资源与区划:1-14[2023-0724].http://gffiy28995338bdc041dahxn555xnvxunc6ocw.fffb.suse.cwkeji.cn:999/kcms/detail/11.3513.s.20230116.1840.008.html.

[12] 张喜花,陈秉谱,窦学诚等.西北地区农业碳排放效率及影响因素研究[J].科技与经济,2023,36(02):11-15.

[13] 贺亚亚,田云,张俊飚.湖北省农业碳排放时空比较及驱动因素分析[J].华中农业大学学报:社会科学版,2013(5): 79-85.

[14] Ang B W.Decomposition analysis for policy making in energy: which is the preferred method?[J].Energy Policy, 2004,32(9):1131-1139.

引用本文

欧蓝蔓, 焦翠翠, 遂宁市农业碳排放特征及影响因素分析[J]. 资源与环境科学进展, 2023; 2: (3) : 1-6.