摘要
利用 IPCC 经典碳排放计算理论, 基于农资投入、农田利用及畜禽养殖 3 类主要碳源, 测算了山东省2000—2020 年农业碳排放量, 采用 LMDI 模型开展影响因素分析, 并运用灰色预测模型 GM(1, 1) 预测 2021—2045年碳排放量。结果表明: 2020 年山东省农业碳排放量为 1.58×10^(7)t, 农业碳排放强度为 0.205 t·(10^(4)￥)^(-1)。2000—2020 年山东省农业碳排放总量呈先上升后波动下降趋势, 农业碳排放强度逐年降低。农业碳排放源类贡献率由高到低依次为农资投入、畜禽养殖和农田土壤利用。2000—2020 年 16 地市农业碳排放量及排放强度均呈现一定的区域差异, 且有扩大趋势, 菏泽农业碳排放量和平均碳排放强度均居首位。农业生产效率、农业产业结构、地区产业结构、劳动力因素对碳减排起到一定作用, 地区经济发展水平和城镇化率因素为农业碳排放量增加的主要因素。预测结果表明, 山东省农业碳排放量在 2030 年前已达到峰值, 济南、青岛等 9 市农业碳排放量在 2030 年前已达峰,枣庄、东营等 7 市在 2030 年前未达峰, 并针对山东省农业碳排放特征及影响因素提出减排建议。
The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report indicated that human-induced climate change has already affected many weather and climate extremes in every region across the globe. Greenhouse gases (GHG) produced via the process of agricultural production constitute a large proportion of the total GHG emissions from worldwide production activities. Therefore, estimation of agricultural GHG emissions, analysis of the influencing factors, and prediction of the peak are important Based on the classical IPCC carbon emission calculation theory, agricultural carbon emissions were estimated for Shandong Province from 2000 to 2020 by using agricultural material input, livestock and poultry breeding, and agricultural soil utilization. The influence factor decomposition was conducted based on Logarithmic Mean Divisia Index (LMDI), and the agricultural carbon emissions from2021 to 2045 were predicted by using the grey model GM (1, 1). Results showed that the total agricultural carbon emissions in Shandong Province in 2020 were 1.58×10^(7)t and the intensity of carbon emissions was 0.205 t·(10^(4)￥)^(-1). Carbon emissions tended to increase from 2000 to 2006 and then decrease from 2007 to 2020;however, the intensity of carbon emissions decreased at an annual rate of 3.8%. The source structure of agricultural carbon emissions was ranked, with agricultural material input, livestock and poultry breeding, and crop farming accounting for 49.6%, 38.5%, and 11.9%, respectively. Carbon emissions and intensities showed regional differences between the 16 cities and tended to increase. Carbon emissions and the intensity of carbon emissions in Heze were higher than those of other cities. The LMDI decomposition results showed that agricultural production efficiency, agricultural industrial structure, regional industrial structure, and rural population were emission reduction factors, whereas regional economic development level and urbanization were emission growth factors. The prediction results showed that agricultural carbon emission of Shandong Province would reach its peak before 2030, and carbon emissions of cities, such as Jinan, Qingdao, Zibo, Weifang, Jining, Tai’an Weihai, Rizhao, and Liaocheng, would also reach their peaks before 2030. However, the prediction result showed that the agricultural carbon emissions in Zaozhuang, Dongying, Yantai, Linyi, Dezhou, Binzhou, and Heze did not reach their peaks before 2030Therefore, suggestions for agricultural carbon emission reduction in Shandong Province were put forward.
作者
刘杨
刘鸿斌
LIU Yang;LIU Hongbin(Jinan Ecological and Environmental Monitoring Center,Shandong Province,Jinan 250101,China;Shandong Lushang Commercial Architectural Design Co.Ltd,Jinan 250100,China)
出处
《中国生态农业学报（中英文）》
CAS
CSCD
北大核心
2022年第4期558-569,共12页
Chinese Journal of Eco-Agriculture
关键词
农业碳排放
碳达峰
碳排放强度
碳排放源
碳减排
Agricultural carbon emissions
Carbon peak
Carbon emission intensity
Carbon emission resources
Carbon emission reduciton
