首页|安徽省能源消费碳排放时空分布特征分析

安徽省能源消费碳排放时空分布特征分析

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  • NSTL
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碳排放是气候变暖最主要的问题.为全面了解近些年安徽省能源消费碳排放的时空变化情况,应用IPCC碳排放计算法计算2011年至2020年安徽省及其16个城市的碳排放量.运用GIS绘制2011年、2015年以及2020年安徽省16个城市碳排放的空间分布图,并研究GDP、PD以及第二第三产业产值与碳排放之间的关系.结果表明:安徽省各市在2011年至2020年10年中能源消费碳排放逐年上升.从城市碳排放总量看,合肥市最高,碳排放从2011年的0.16亿t到2020年的0.37亿t;最少的是黄山市,从2011年的0.019亿t到2020年的0.043亿t.从人均碳排放来看,芜湖市的人均碳排放量最高,2021年的0.029百万t/人到2020年的0.094百万t/人;亳州市的人均碳排放量最低,2011年的0.006百万t/人到2020年的0.015百万t/人.GDP的决定系数是0.791 3,人口密度的决定系数为0.073 3,第二第三产业的产值决定系数为0.799 5.所以经济发展和第二产业、第三产业具有显著的影响,人口密度分布几乎没有影响.
Analysis of Spatiotemporal Distribution Characteristics of Carbon Emissions from Energy Consumption in Anhui Province
Carbon emissions are the biggest problem with climate change.In order to comprehensively understand the temporal and spatial changes of carbon emissions from energy consumption in Anhui Province in recent years,the IPCC carbon emissions calculation method was used to calculate the carbon emissions of Anhui Province and its 16 cities from 2011 to 2020.GIS was used to draw the spatial distribution map of carbon emissions in 16 cities in Anhui Province in 2011,2015 and 2020,and to study the relationship between GDP,PD and the output value of the secondary and tertiary industries and carbon emissions.The results showed that carbon emissions from energy consumption increased year by year from 2011 to 2020.In terms of total urban carbon emissions,Hefei is the highest,with carbon emissions from 16 million tons in 2011 to 37 million tons in 2020.The least is Huangshan City,which saw its share rise from 0.019 million tons in 2011 to 0.04.3 million tons in 2020.In terms of per capita carbon emissions,Wuhu City has the highest per capita carbon emissions,from 0.029 million tons/person in 2021 to 0.094 million tons/person in 2020.The city of Haozhou had the lowest per capita carbon emissions,from 0.006 million tons per person in 2011 to 0.015 million tons per person in 2020.The coefficient of determination of GDP is 0.791 3,that of population density is 0.073 3,and that of output value of the secondary and tertiary industries is 0.799 5.Therefore,economic development and secondary and tertiary industries have a significant impact,while population density distribution has almost no impact.

carbon emissions from energy consumptionspatial and temporal distribution characteristics IPCC carbon emissions calculation methodAnhui Province

梁玉海、杜明霞、潘佳盼、宋国明、王杰、傅妍芳

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安徽睿晟环境科技有限公司,安徽合肥 230000

合肥学院生物食品与环境学院,安徽合肥 230000

池州学院材料与环境工程学院,安徽池州 247000

安徽省池州生态环境监测中心,安徽池州 247000

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能源消费碳排放 时空分布特征 IPCC碳排放计算方法 安徽省

高校优秀青年人才支持计划一般项目国家级大学生创新创业计划训练项目安徽省大学生创新创业计划训练项目

gxyq2021032202311306037S202211306192

2024

山东化工
山东省化工研究院 山东省化工信息中心

山东化工

影响因子：0.249
ISSN：1008-021X
年,卷(期)：2024.53(14)