In the context of the era of carbon peaking and carbon neutrality,clarifying the emission patterns of non-CO_(2)Greenhouse Gas(GHG)from agricultural sources is of practical significance to China’s implementation of g...In the context of the era of carbon peaking and carbon neutrality,clarifying the emission patterns of non-CO_(2)Greenhouse Gas(GHG)from agricultural sources is of practical significance to China’s implementation of greenhouse gas emission reduction policies.The Intergovernmental Panel on Climate Change(IPCC)coefficient method was used to calculate non-CO_(2)GHG emissions from agricultural sources in 122 counties in Hunan Province,China,from 2010 to 2020,and the spatiotemporal evolution characteristics of emission intensity were analyzed.The Stochastic Impacts by Regression on Population,Affluence,and Technology(STRIPAT)model forecasted the prospective evolution of non-CO_(2)GHG emissions from agricultural sources at the county level under various scenarios from 2030 to 2050.The results demonstrated a general decline in non-CO_(2)GHG emissions from agricultural sources within the study area,with 75.41%of counties exhibiting a reduction in emissions.Geographically,emissions were higher in the Dongting Lake area and central Hengyang.The emission intensity per unit of agricultural added value and the intensity per unit of agricultural land area showed an overall downward trend.Spatially,the emission intensity per unit of farmland area in a few counties(cities,districts)in southern Hunan was still relatively high.By forecasting the non-CO_(2)GHG emissions from agricultural sources,the majority of counties(cities and districts)demonstrated a gradual decline in emissions,suggesting that agricultural production had the potential to reduce emissions in the future,while also facing certain pressure to reduce emissions.It is recommended that Hunan Province formulate agricultural carbon emission reduction policies that take regional development differences into account.This would provide a reference for future agricultural carbon emission reduction research in the whole country.展开更多
碳排放量、能源消费量、人口和经济增长存在着较为密切的关系,而作为清洁二次能源的电能,其消费使用量的多少影响着能源消费结构,进而影响着能源消费量。因此,电力消费强度和碳排放量之间存在着何种联系,是电力工业低碳之路需要考虑的...碳排放量、能源消费量、人口和经济增长存在着较为密切的关系,而作为清洁二次能源的电能,其消费使用量的多少影响着能源消费结构,进而影响着能源消费量。因此,电力消费强度和碳排放量之间存在着何种联系,是电力工业低碳之路需要考虑的问题。利用随机性环境影响评估模型(stochastic impacts by regression on population,affluence,and technology,STRIPAT),通过最小二乘回归方法测算碳排放量、人口、人均国民生产总值、电力消费量和能源消费量之间的碳排放影响系数。研究发现,人口、人均国民生产总值以及电力消费量和能源消费量之间的比值每发生1%的变化,将引起碳排放总量1.207%、0.901%以及?1.188%的变化,因此,在未来我国人口增长趋势放缓、国民经济保持7%以上较快发展的情况下,减少碳排放的途径应该从技术因素入手,通过提高电能占使用能源的比率、提高化石能源的使用效率和发展可再生能源来进行。展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42471241,41971219,41571168)Hunan Provincial Natural Science Foundation(No.2020JJ4372)。
文摘In the context of the era of carbon peaking and carbon neutrality,clarifying the emission patterns of non-CO_(2)Greenhouse Gas(GHG)from agricultural sources is of practical significance to China’s implementation of greenhouse gas emission reduction policies.The Intergovernmental Panel on Climate Change(IPCC)coefficient method was used to calculate non-CO_(2)GHG emissions from agricultural sources in 122 counties in Hunan Province,China,from 2010 to 2020,and the spatiotemporal evolution characteristics of emission intensity were analyzed.The Stochastic Impacts by Regression on Population,Affluence,and Technology(STRIPAT)model forecasted the prospective evolution of non-CO_(2)GHG emissions from agricultural sources at the county level under various scenarios from 2030 to 2050.The results demonstrated a general decline in non-CO_(2)GHG emissions from agricultural sources within the study area,with 75.41%of counties exhibiting a reduction in emissions.Geographically,emissions were higher in the Dongting Lake area and central Hengyang.The emission intensity per unit of agricultural added value and the intensity per unit of agricultural land area showed an overall downward trend.Spatially,the emission intensity per unit of farmland area in a few counties(cities,districts)in southern Hunan was still relatively high.By forecasting the non-CO_(2)GHG emissions from agricultural sources,the majority of counties(cities and districts)demonstrated a gradual decline in emissions,suggesting that agricultural production had the potential to reduce emissions in the future,while also facing certain pressure to reduce emissions.It is recommended that Hunan Province formulate agricultural carbon emission reduction policies that take regional development differences into account.This would provide a reference for future agricultural carbon emission reduction research in the whole country.
文摘碳排放量、能源消费量、人口和经济增长存在着较为密切的关系,而作为清洁二次能源的电能,其消费使用量的多少影响着能源消费结构,进而影响着能源消费量。因此,电力消费强度和碳排放量之间存在着何种联系,是电力工业低碳之路需要考虑的问题。利用随机性环境影响评估模型(stochastic impacts by regression on population,affluence,and technology,STRIPAT),通过最小二乘回归方法测算碳排放量、人口、人均国民生产总值、电力消费量和能源消费量之间的碳排放影响系数。研究发现,人口、人均国民生产总值以及电力消费量和能源消费量之间的比值每发生1%的变化,将引起碳排放总量1.207%、0.901%以及?1.188%的变化,因此,在未来我国人口增长趋势放缓、国民经济保持7%以上较快发展的情况下,减少碳排放的途径应该从技术因素入手,通过提高电能占使用能源的比率、提高化石能源的使用效率和发展可再生能源来进行。
