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  • 2025年7月23日 星期三

干旱区地理 ›› 2025, Vol. 48 ›› Issue (5): 879-892.doi: 10.12118/j.issn.1000-6060.2024.329 cstr: 32274.14.ALG2024329

• 碳排放 • 上一篇    下一篇

甘肃省农业碳排放时空分异特征及影响因素

马海清1(), 陈强强1,2()   

  1. 1.甘肃农业大学财经学院,甘肃 兰州 730070
    2.甘肃省生态建设与环境保护研究中心,甘肃 兰州 730070
  • 收稿日期:2024-05-28 修回日期:2024-07-29 出版日期:2025-05-25 发布日期:2025-05-13
  • 通讯作者: 陈强强(1979-),男,硕士,教授,主要从事资源与环境经济研究. E-mail: jjglxy666@126.com
  • 作者简介:马海清(1993-),男,硕士研究生,主要从事资源与环境经济研究. E-mail: 18419286658@163.com
  • 基金资助:
    甘肃省人文社科项目(22ZZ81);甘肃省哲学社会科学规划项目(2022QN017);甘肃农业大学青年导师基金项目(GAU-QDFC-2022-20)

Spatial-temporal differentiation characteristics and influencing factors of agricultural carbon emissions in Gansu Province

MA Haiqing1(), CHEN Qiangqiang1,2()   

  1. 1. College of Finance and Economics, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2. Gansu Ecological Construction and Environmental Protection Research Center, Lanzhou 730070, Gansu, China
  • Received:2024-05-28 Revised:2024-07-29 Published:2025-05-25 Online:2025-05-13

摘要: 探明农业碳排放的时空特征和影响因素,是有效消除碳减排目标核算过程中的不确定性,精准施策以实现农业碳减排目标的客观要求。在测算2014—2022年甘肃省农业碳排放基础上,运用莫兰指数分析碳排放时空分异特征,应用核密度估计法分析农业碳排放动态演进趋势,构建地理加权回归(GWR)模型剖析农业碳排放影响因素。结果表明:(1) 2014—2022年甘肃省农业碳排放量总体趋于下降,畜牧养殖是主要排放源且其碳排放量呈增长趋势,区域农业碳排放量从大到小依次为:河西绿洲农业区>陇东陇中黄土高原旱作农业区>高寒牧区>陇南山地雨养农业区。(2) 农业碳排放总量空间集聚性弱且集中在河西绿洲农业区,其中酒泉和张掖2市为高-低集聚,金昌市处于低-高集聚,其余区域无明显集聚性。(3) 4个农业分区农业碳排放强度均呈递减态势,区域差异趋于缩小。(4) 人均农业生产总值、农业产业结构、总人口数对农业碳排放具有减排效应,而农村居民可支配收入、农业化肥施用量、农业机械总动力对农业碳排放具有增量效应。从而,提出通过改良畜禽品种,实施适度规模养殖和全产业链精准经营管理来加大畜牧业减排降碳力度,减少化肥、农膜等农用物资的使用,推广绿色技术应用,以实现农业碳减排目标。

关键词: 农业碳排放, 农业碳排放系数, 核密度估计, 地理加权回归, 甘肃省

Abstract:

Identifying the spatiotemporal characteristics and influencing factors of agricultural carbon emissions is essential for reducing uncertainty in carbon emission reduction target accounting and implementing accurate policies to achieve those targets. Based on agricultural carbon emission estimates in Gansu Province, China, from 2014 to 2022, this study employed the Moran’s I index to analyze the spatiotemporal differentiation characteristics of carbon emissions. The dynamic evolution trends were analyzed using the kernel density estimation method, and a geographically weighted regression model was constructed to identify the factors influencing agricultural carbon emissions. The results showed the following. (1) From 2014 to 2022, the agricultural carbon emissions in Gansu Province tended to decline. However, emissions from animal husbandry, identified as the primary source, exhibited an upward trend. The order of carbon emissions by region from highest to lowest was as follows: Hexi oasis agricultural area>Longdong-Longzhong Loess Plateau dry farming area>alpine pastoral area> Longnan Mountain rain-fed agricultural area. (2) The spatial agglomeration of total agricultural carbon emissions was weak and concentrated within the Hexi Oasis agricultural area. Notably, Jiuquan City and Zhangye City exhibited high-low agglomeration, while Jinchang City exhibited low-high agglomeration. Other regions exhibited no significant agglomeration patterns. (3) The carbon emission intensity across all four agricultural regions declined over the study period, and regional differences in the emission intensity gradually narrowed. (4) Per capita gross agricultural product, agricultural industrial structure, and total population demonstrated a significant role in reducing agricultural carbon emission. The disposable income of rural residents, amount of agricultural fertilizer used, and total power of agricultural machinery caused an increase in the carbon emissions. Finally, it was recommended to improve livestock and poultry varieties, implement moderate-scale breeding practices, and ensure accurate operation and management across the entire industrial chain to increase emission reduction efforts in animal husbandry. Furthermore, minimizing the use of agricultural materials such as fertilizers and plastic films, along with promoting the application of green technologies, is essential to achieving agricultural carbon reduction goals.

Key words: agricultural carbon emission, agricultural carbon emission coefficient, kernel density estimation, geographical weighted regression, Gansu Province