Yang Jieqiong, Luo Panzhu, Li Langping
College of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China.
College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Math Biosci Eng. 2022 Sep 6;19(12):13093-13113. doi: 10.3934/mbe.2022612.
Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO emission reduction strategy. The main research conclusions are summarized as follows: 1) During the period from 1996 to 2020, China's agricultural CO emission showed two stages, a rapid growth stage (1996-2015) and a rapid decline stage (2016-2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996-2000), expansive negative decoupling (END) (2001-2005), WD (2006-2015) and strong decoupling (SD) (2016-2020), respectively.
基于数学模型，深入分析农业碳排放与经济发展之间的相互关系日益成为一个备受争议的热门话题。通过应用对数平均迪氏指数（LMDI）和Tapio脱钩这两种数学模型，本研究旨在探讨1996年至2020年中国农业碳排放的驱动因素和脱钩趋势。首先，选用政府间气候变化专门委员会（IPCC）方法估算1996年至2020年的农业碳排放，并采用LMDI模型将农业碳排放的驱动因素分解为经济发展、碳排放强度、结构和劳动力效应这四个农业因素。然后，运用Tapio脱钩模型分析农业经济发展与碳排放之间的脱钩状态和发展趋势。最后，本文提出了一些政策建议，以制定可行的农业碳排放减排策略。主要研究结论总结如下：1）1996年至2020年期间，中国农业碳排放呈现两个阶段，即快速增长阶段（1996 - 2015年）和快速下降阶段（2016 - 2020年）。2）农业经济发展是农业碳排放增加的首要驱动因素，而农业劳动力因素和农业生产效率因素起到两个关键的抑制作用。3）1996年至2020年，总体而言，中国农业部门碳排放与经济发展呈现弱脱钩（WD）状态。各时间段对应的脱钩状态分别为强负脱钩（SND）（1996 - 2000年）、扩张负脱钩（END）（2001 - 2005年）、WD（2006 - 2015年）和强脱钩（SD）（2016 - 2020年）。
