东北三省农业碳排放时空分异特征及其关键驱动因素  

Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China

作　　者：钱凤魁[1,2,3] 王祥国 顾汉龙 王大鹏 李鹏飞 QIAN Fengkui;WANG Xiangguo;GU Hanlong;WANG Dapeng;LI Pengfei(College of Land and Environment,Shenyang Agriculture University,Shenyang 110161,China;Key Laboratory of Trinity Protection and Monitoring of Cultivated Land,Shenyang 110161,China;National Engineering Center for Efficient Utilization of Soil and Fertilizer Resources,Shenyang 110161,China;Liaoning Natural Resources Service Center,Shenyang 110011,China;Liaoning Urban&Rural Construction Planning Design Institute Co.,Ltd.,Shenyang 110000,China)

机构地区：[1]沈阳农业大学土地与环境学院,沈阳110161 [2]耕地立体保护与监测重点实验室,沈阳110161 [3]土肥资源高效利用国家工程研究中心,沈阳110161 [4]辽宁省自然资源事务服务中心,沈阳110011 [5]辽宁省城乡建设规划设计院有限责任公司,沈阳110000

出　　处：《中国生态农业学报（中英文）》2024年第1期30-40,共11页Chinese Journal of Eco-Agriculture

基　　金：国家自然科学基金项目(42077149,41671329)资助。

摘　　要：推动农业低碳发展是应对气候威胁和农业面源污染的有效途径。本文基于IPCC和农用物资投入数据核算2000—2019年东北三省农业碳排放,利用空间自相关等方法分析其时空分异特征,通过LMDI指数分解模型和地理探测器探究农业碳排放驱动因素及其交互作用关系。结果表明:1)东北三省2015年农业碳排放总量达到峰值,约为1759.66万t,较2000年(1048.19万t)增加67.88%,年均递增4.53%;研究期整体呈现“先上升后下降”态势,碳排放增量变动可划分为“波动上升期(2000—2009年)—过渡期(2010—2015年)—平稳下降期(2016—2019年)”3个阶段。化肥施用是主要碳源,占比75.12%。2)分解模型测算结果表明,农业生产效率、农业产业结构和农业劳动力规模对碳排放具有抑制作用,其碳减排比例分别为207.31%、21.56%、20.72%;农业经济发展水平对碳排放表现出较强的推动作用,实现349.59%的碳增量。3)相较于单因子来说,农业经济发展水平、农业生产效率与农业产业结构之间交互结果对农业碳排放的影响呈非线性增强特征,农业劳动力规模与其他因素叠加均呈现出双因子增强的作用效果。以上研究结果表明东北三省农业碳排放受周边地区影响且影响程度不断加强,同时碳排放关键驱动因素之间存在协同作用。本研究成果为推动农业低碳发展提供理论基础与政策依据。Climate change caused by increasing carbon dioxide emissions is one of the major challenges today.Promoting the development of low-carbon agriculture is an effective way to deal with climate threats and agricultural nonpoint source pollution.Accurately measuring the effect of agricultural carbon emissions and its spatial and temporal evolution characteristics is the basis for promoting the development of low-carbon of agriculture,and studying the key driving factors of agricultural carbon emissions and the trade-off and coordination relationship between driving factors is of great significance for formulating regional carbon emission reduction policies in Northeast China.Based on the input data of agricultural materials and the IPCC method,this study calculated the agricultural carbon emissions of three provinces in Northeast China from 2000 to 2019,used the spatial autocorrelation analysis method to clarify the spatial and temporal differentiation characteristics of agricultural carbon emissions,and explored the driving factors of agricultural carbon emissions and their interactions through the LMDI decomposition model and geographic detector.The results showed the following:1)The total carbon emissions of the three provinces in Northeast China showed a trend of increasing and then decreasing.The incremental changes in carbon emissions can be divided into three stages:the fluctuating rising period(2000–2009),transitional period(2010–2015),and steady decline period(2016–2019).In 2015,the total amount of agricultural carbon emissions reached a peak of 17.5966 million t,an increase of 67.88%compared to that in 2000,with an average annual increase of 4.53%.During the study period,all carbon sources showed different degrees of growth,and chemical fertilizer application was the main carbon source,accounting for 75.12%.2)The spatial distribution of the total carbon emissions in the three northeastern provinces had a significant spatial autocorrelation.The hotspots of carbon emissions were mainly distributed in th

关 键 词：农业碳排放 时空特征 驱动因素 LMDI模型 地理探测器 东北三省 

分 类 号：F301.21[经济管理—产业经济]