山西省农业领域碳排放影响因素及碳达峰预测研究  

Study on influencing factors and carbon peak prediction of agricultural carbon emissions in Shanxi Province

作　　者：葛晓华 朱宇恩[2] 张铭昊 李华[2] 马建超[4] 张淑玲 张西珠 Ge Xiaohua;Zhu Yuen;Zhang Minghao;Li Hua;Ma Jianchao;Zhang Shuling;Zhang Xizhu(Department of Environmental and Safety Engineering,Taiyuan Institute of Technology,Taiyuan 030008,China;School of Environmental&Resource Sciences,Shanxi University,Taiyuan 030031,China;Department of Electronic Engineering,Taiyuan Institute of Technology,Taiyuan 030008,China;College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原工业学院环境与安全工程系,山西太原030008 [2]山西大学环境与资源学院,山西太原030031 [3]太原工业学院电子工程系,山西太原030008 [4]太原理工大学矿业工程学院,山西太原0300024

出　　处：《山西农业大学学报(自然科学版)》2025年第2期128-140,共13页Journal of Shanxi Agricultural University(Natural Science Edition)

基　　金：山西省回国留学人员科研资助项目(2023-173);山西省留学人员科研资助项目(20230041);山西省基础研究计划资助项目(202303021221057;202103021224016)。

摘　　要：[目的]农业是重要的温室气体排放源之一,本文探究山西省农业领域温室气体排放特征、预测碳达峰时间和峰值,并综合分析碳减排潜力,提出碳减排策略。[方法]首先按照《2006年IPCC国家温室气体清单指南》和《省级温室气体清单编制指南》核算山西省2005-2022年农业领域温室气体排放量,运用对数平均迪氏指数法(LMDI模型)对山西省农业领域碳排放进行影响因素分解,选择农业生产效率、农业经济水平、农业人口、农业化肥施用量和肉类总产量5个指标,构建基准、经济发展、低碳和低速4种碳排放情景,并采用STIRPA模型对2023-2040年农业领域温室气体排放量进行预测。[结果]山西省2005-2022年农业领域碳排放总体呈波动变化趋势,其中2020-2022年碳排放增长较快,2022年碳排放达到1087.49万吨当量;碳生产效率效应是主要的碳减排影响因素,与2005年相比,累计减少碳排放1450.40万吨当量,减排贡献率达到1037.75%;农业经济水平效应是主要的促增效应,与2005年相比,累计增加碳排放1728.59万吨当量,减排贡献率为-1236.80%。低碳发展情景是各指标最优的达峰情景,达峰时间在2030年左右,达峰碳排放量为1261.73万吨当量。[结论]山西农业领域未来碳排放仍有较大不确定性,低碳发展是实现碳减排的最优路径,建议在优先发展低碳技术、优化化肥施用结构、提升农民低碳意识等方面制定相关政策措施,推进山西农业低碳绿色稳定发展。[Objective]Agriculture is a significant source of greenhouse gas emissions.This study aimed to understand the char-acteristics of agricultural greenhouse gas emissions in Shanxi Province,predict the carbon emission peak time and peak value,and comprehensively analyze carbon emission reduction potential,and propose carbon reduction strategies.[Methods]Based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories and the Provincial Greenhouse Gas Inventory Compilation Guidelines,the agricultural greenhouse gas emissions in Shanxi Province from 2005 to 2022 were calculated.The logarithmic mean Divisia index(LMDI)model was employed to decompose the influencing factors of agricultural carbon emissions in Shanxi Province.Five indicators were selected:agricultural production efficiency,agricultural economic level,agricultural pop-ulation,agricultural fertilizer application amount,and total meat production.Four carbon emission scenarios:baseline,eco-nomic development,low carbon,and low speed,were constructed,and the STIRPA model was used to forecast the agricultur-al greenhouse gas emissions from 2023 to 2040.[Results]From 2005 to 2022,agricultural carbon emissions in Shanxi Province showed a fluctuating trend,with rapid growth from 2020 to 2022,reaching 10874900 tons of CO_(2) equivalent in 2022.Carbon production efficiency was the primary factor in carbon emission reduction,cumulatively reducing emissions by 14504 million tons of CO_(2) equivalent compared to 2005,with a reduction contribution rate of 1037.75%.The agricultural economic level was the main driving factor for emission increases,cumulatively increasing emissions by 172859 million tons of CO_(2) equivalent compared to 2005,with a contribution rate of-1236.80%.The low carbon development scenario was the optimal peaking sce-nario,with the carbon peak expected around 2030 and a peak carbon emission of 12617300 tons of CO_(2) equivalent.[Conclu-sion]Future agricultural carbon emissions in Shanxi Province remain highly uncertain.Low-carbon developmen

关 键 词：农业 碳达峰 LMDI模型 STIRPAT模型 

分 类 号：S-01[农业科学] X24[环境科学与工程—环境科学] X321