• 关键词：非CO₂温室气体  影响因素  峰值预测  时空特征

• 基金项目：国家自然科学基金（No.71573260）；国家水体污染控制与治理科技重大专项（No.2018ZX07301007）；中国人民大学2020年度拔尖创新人才培育计划

• 李阳
• 中国人民大学农业与农村发展学院, 北京 100872

• 陈敏鹏
• 中国人民大学农业与农村发展学院, 北京 100872

• 摘要：运用IPCC清单方法核算了中国各省(直辖市、自治区)农业源非二氧化碳(非CO₂)温室气体(GHG)的排放,基于Tapio弹性脱钩理论和情景预测法、STIRPAT模型和向量自回归模型(VAR)预测了其达峰时间和规模,并结合对数平均迪氏指数(LMDI)模型、STIRPAT模型和固定效应模型识别了中国农业非CO₂ GHG排放的影响因素.结果表明,高情景和中情景下中国农业非CO₂ GHG排放量整体呈上升趋势,到2050年仍未达峰;2018—2050年低情景下GHG排放量整体呈下降趋势,其中,低情景下已于2018年达峰,峰值为0.73×10⁹ t (以CO₂-eq计,下同);北京市、上海市、江苏省、浙江省、福建省、广东省、海南省、重庆市、四川省和青海省农业生产与其农业非CO₂ GHG排放呈强脱钩状态,其余21个省(直辖市、自治区)呈弱脱钩状态;除天津市和黑龙江省以外的29个省(直辖市、自治区),经济和人口为农业非CO₂ GHG排放的促进因素,效率和结构为其抑制因素.

• Abstract：This study applies IPCC inventory method to estimate the non-carbon dioxide (non-CO₂) greenhouse gases (GHG) emissions from agricultural sources in China at the provincial level from 1980 to 2018. It also predicts the peaking time and magnitude of non-CO₂ GHG emissions from agriculture in China by applying scenario method based on Tapio elastic decoupling theory, STIRPAT model and vector autoregressive model (VAR), and identifies the influencing factors by combing with the logarithmic mean Divisia index (LMDI) model, STIRPAT model and fixed effect model. Results show that high and middle emission scenarios of non-CO₂ GHG from agricututral source in China have an upward trend from 2018 to 2050, while low emission scenario has downward trend. Under the low emission scenario, non-CO₂ GHG emission from agriculture in China peaked in 2018 at 0.73×10⁹t. The non-CO₂ GHG emissions from agriculture in China have decoupled with agriculture development in Beijing, Shanghai, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan, Chongqing, Sichuan and Qinghai, and show a weak decoupling relationship with agricultural development in the other 21 provinces. In all provinces except Tianjin and Heilongjiang, the economic factor and population are the driving factors for the non-CO₂ GHG emission from agriculture, while the efficiency factor and structural factor are curbing the growth of emissions.

• Key words：non-CO₂ greenhouse gases  influencing factors  peak forecast  spatial and temporal characteristics

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