中国城市能源消费碳排放影响因素的时空异质性
基于构建的合成DMSP/OLS夜间灯光数据集，模拟了2005—2019年中国286个城市能源消费碳排放，并利用MGWR模型从时空异质性视角对其影响因素进行解析。结果表明：（1）MGWR模型更适合于分析中国城市碳排放影响因素的空间异质性。（2）总体上，经济发展与能源强度对中国城市能源消费碳排放具有促进作用，产业升级和人口密度主要表现为抑制作用，而外商投资、人口规模及绿色创新则呈现互异性影响模式。（3）具体地，各因素影响效果都具有较强的时空异质性。经济发展的正效应由东到西、由南到北依次增强；能源强度呈现出以中部地区城市为中心向周围辐散式递减的正效应；产业升级负向影响高值区主要集中在江浙沪等区域，而...
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| Published in | 世界地理研究 Vol. 33; no. 8; pp. 102 - 116 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国地理学会
15.08.2024
安徽建筑大学经济与管理学院,合肥 230022 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1004-9479 |
| DOI | 10.3969/j.issn.1004-9479.2024.08.20222252 |
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| Abstract | 基于构建的合成DMSP/OLS夜间灯光数据集，模拟了2005—2019年中国286个城市能源消费碳排放，并利用MGWR模型从时空异质性视角对其影响因素进行解析。结果表明：（1）MGWR模型更适合于分析中国城市碳排放影响因素的空间异质性。（2）总体上，经济发展与能源强度对中国城市能源消费碳排放具有促进作用，产业升级和人口密度主要表现为抑制作用，而外商投资、人口规模及绿色创新则呈现互异性影响模式。（3）具体地，各因素影响效果都具有较强的时空异质性。经济发展的正效应由东到西、由南到北依次增强；能源强度呈现出以中部地区城市为中心向周围辐散式递减的正效应；产业升级负向影响高值区主要集中在江浙沪等区域，而低值区则位于广西、贵州、云南及海南等省份；在东北地区城市，人口密度的负向影响强度偏低；外商投资负向影响呈由西到东增强趋势；人口规模影响模式由互异性影响转变为正向影响，正向影响由东北向西南地区梯度递减；绿色创新影响模式由负向影响转变为互异性影响，正向影响区域主要位于长三角地区。 |
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| AbstractList | 基于构建的合成DMSP/OLS夜间灯光数据集，模拟了2005—2019年中国286个城市能源消费碳排放，并利用MGWR模型从时空异质性视角对其影响因素进行解析。结果表明：（1）MGWR模型更适合于分析中国城市碳排放影响因素的空间异质性。（2）总体上，经济发展与能源强度对中国城市能源消费碳排放具有促进作用，产业升级和人口密度主要表现为抑制作用，而外商投资、人口规模及绿色创新则呈现互异性影响模式。（3）具体地，各因素影响效果都具有较强的时空异质性。经济发展的正效应由东到西、由南到北依次增强；能源强度呈现出以中部地区城市为中心向周围辐散式递减的正效应；产业升级负向影响高值区主要集中在江浙沪等区域，而低值区则位于广西、贵州、云南及海南等省份；在东北地区城市，人口密度的负向影响强度偏低；外商投资负向影响呈由西到东增强趋势；人口规模影响模式由互异性影响转变为正向影响，正向影响由东北向西南地区梯度递减；绿色创新影响模式由负向影响转变为互异性影响，正向影响区域主要位于长三角地区。 基于构建的合成DMSP/OLS夜间灯光数据集,模拟了 2005-2019年中国286个城市能源消费碳排放,并利用MGWR模型从时空异质性视角对其影响因素进行解析.结果表明:①MGWR模型更适合于分析中国城市碳排放影响因素的空间异质性.②总体上,经济发展与能源强度对中国城市能源消费碳排放具有促进作用,产业升级和人口密度主要表现为抑制作用,而外商投资、人口规模及绿色创新则呈现互异性影响模式.③具体地,各因素影响效果都具有较强的时空异质性.经济发展的正效应由东到西、由南到北依次增强;能源强度呈现出以中部地区城市为中心向周围辐散式递减的正效应;产业升级负向影响高值区主要集中在江浙沪等区域,而低值区则位于广西、贵州、云南及海南等省份;在东北地区城市,人口密度的负向影响强度偏低;外商投资负向影响呈由西到东增强趋势;人口规模影响模式由互异性影响转变为正向影响,正向影响由东北向西南地区梯度递减;绿色创新影响模式由负向影响转变为互异性影响,正向影响区域主要位于长三角地区. |
| Abstract_FL | Based on the constructed synthetic DMSP/OLS nighttime lighting dataset,carbon emissions from energy consumption in 286 cities in China from 2005 to 2019 were simulated and their influencing factors were analyzed from the perspective of spatial and temporal hetero-geneity using the MGWR model.The results show that:①The MGWR model is more suitable for analyzing the spatial heterogeneity of factors influencing carbon emissions in Chinese cities.②In general,economic development and energy intensity facilitate carbon emissions from ener-gy consumption in Chinese cities.Industrial upgrading and population density mainly show in-hibiting effects,while foreign investment,population size,and green innovation show a heteroge-neous impact model.③specifically,the effects of each factor have strong spatial and temporal heterogeneity.The positive effect of economic development increases from east to west and from south to north;the energy intensity shows a positive impact with the central cities as the center and decreases in a radial pattern;the high-value area of the negative impact of industrial upgrading is mainly in the areas of Jiangsu,Zhejiang,and Shanghai,while the low-value area is in the provinces of Guangxi,Guizhou,Yunnan,and Hainan;the negative impact of population density is low in the cities of the northeast;the negative impact of foreign investment tends to in-crease from west to east;the impact pattern of population size changes from heterogeneous to positive,with the positive impact decreasing from the northeast to the southwest;the impact pat-tern of green innovation changes from negative to heterogeneous,with the positive impact area mainly in the cities of the Yangtze River Delta. |
| Author | 洪剑涛 李化夫 王素凤 |
| AuthorAffiliation | 安徽建筑大学经济与管理学院,合肥 230022 |
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| Author_FL | LI Huafu WANG Sufeng HONG Jiantao |
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| Author_xml | – sequence: 1 fullname: 王素凤 organization: 安徽建筑大学经济与管理学院,合肥230022 – sequence: 2 fullname: 洪剑涛 organization: 安徽建筑大学经济与管理学院,合肥230022 – sequence: 3 fullname: 李化夫 organization: 安徽建筑大学经济与管理学院,合肥230022 |
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| Keywords | MGWR 夜间灯光数据 城市 碳排放 时空异质性 影响因素 influencing factors nighttime lighting data spa-tial and temporal heterogeneity cities carbon emissions |
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| Snippet | 基于构建的合成DMSP/OLS夜间灯光数据集，模拟了2005—2019年中国286个城市能源消费碳排放，并利用MGWR模型从时空异质性视角对其影响因素进行解析。结果表明：（1）MGWR模型... 基于构建的合成DMSP/OLS夜间灯光数据集,模拟了 2005-2019年中国286个城市能源消费碳排放,并利用MGWR模型从时空异质性视角对其影响因素进行解析.结果表明:①MGWR模型更适合... |
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