RESEARCH ON SPATIO-TEMPORAL EVOLUTION OF CARBON ARRANGEMENT IN NORTH CHINA CITIES AND ITS INFLUENCING FACTORS
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摘要: 不断增加的城市能源消耗和CO2排放对区域减排政策构成了严峻挑战。基于2004—2020年的夜间灯光遥感模拟反演华北地区29个城市的碳排放数据,并采用空间自相关、空间Markov链从动态和静态方面对华北地区市域视角碳排放空间分布特征进行分析,以探讨城市间的聚集效应;同时为进一步明确碳排影响因素,基于时空地理加权回归模型,从经济、社会、环境和政策方面对影响城市碳排的相关因素进行定量识别,并探讨了其空间异质性,为差异化减排提供理论依据。结果表明:华北地区人均碳排放的增速在逐渐减小,同时城市间具有明显的聚集特征。各因素对不同时期的华北地区各城市碳排放的影响呈时空异质性,经济发展水平和产业结构是促进碳排放产生的强大推动力,政府政策对于碳排放的抑制作用最为明显,城镇化率和气候对碳排放的产生整体具有先促进后抑制的特征。
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关键词:
- 碳排放 /
- 华北地区 /
- 夜间灯光 /
- 空间Markov链 /
- 时空地理加权回归模型
Abstract: Increasing urban energy consumption and carbon dioxide emissions pose serious challenges to regional emission reduction policies. Based on the remote sensing simulation of night lights from 2004 to 2020, this study inverted the carbon emission data of 29 cities in North China, and used spatial autocorrelation and spatial Markov chain to analyze the spatial distribution characteristics of carbon emissions from the perspective of cities in North China from the dynamic and static aspects, to explore the agglomeration effect between cities; at the same time, in order to further clarify the factors affecting carbon emissions, based on the weighted regression model of time, space and geography, this study quantitatively identifies the relevant factors affecting urban carbon emissions from the aspects of the economy, society, environment and policy, and discusses the spatial heterogeneity can provide a theoretical basis for differentiated emission reduction. The results shows that the growth rate of per capita carbon emissions in North China is gradually decreasing, and there are obvious clustering characteristics between those cities. The influence of various factors on carbon emissions of cities in North China in different periods shows temporal and spatial heterogeneity. The level of economic development and industrial structure are strong driving forces to promote carbon emissions. Government policies have the most obvious inhibitory effect on carbon emissions. The urbanization rate and climate have the characteristics of first promoting and then inhibiting the production of carbon emissions. -
[1] ZHOU Z H, CAO L J, ZHAO K K, et al.Spatio-temporal effects of multi-dimensional urbanization on carbon emission efficiency analysis based on panel data of 283 cities in China[J].International Journal of Environmental Research and Public Health, 2021, 18(23):12712. [2] SHAHBAZ M, LOGANATHAN N, MUZAFFAR A T, et al.How urbanization affects CO2 emissions in Malaysia The application of STIRPAT model[J].Renewable and Sustainable Energy Reviews, 2016, 57:83-93. [3] 潘思羽, 张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程, 2023, 41(7):61-68, 85. [4] 朱海, 王立国, 贺焱, 等.多情景下中国省域旅游业碳达峰的时空特征研究[J].干旱区资源与环境, 2023, 37(1):169-176. [5] 苏泳娴, 陈修治, 叶玉瑶, 等.基于夜间灯光数据的中国能源消费碳排放特征及机理[J].地理学报, 2013, 68(11):1513-1526. [6] LIU S R, SHEN J W, LIU G F, et al.Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China:a socioeconomic density distribution approach based on remotely sensed nighttime light data[J].Computers, Environment and Urban Systems, 2022, 96:101847. [7] KARMELLOS M, KOPIDOU D, DIAKOULAKI D.A decomposition analysis of the driving factors of CO2 (carbon dioxide) emissions from the power sector in the European Union countries[J].Energy, 2016, 94:680-692. [8] 马景富.辽宁省碳排放的环境库兹涅茨曲线实证研究[J].节能, 2021, 40(5):58-62. [9] 程叶青, 王哲野, 张守志, 等.中国能源消费碳排放强度及其影响因素的空间计量[J].地理学报, 2013, 68(10):1418-1431. [10] SHI K F, CHEN Y, YU B L, et al.Modeling spatiote-mporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis[J].Applied Energy, 2016, 168:523-533. [11] 杨迪, 杨旭, 吴相利, 等.东北地区能源消费碳排放时空演变特征及其驱动机制[J].环境科学学报, 2018, 38(11):4554-4565. [12] 曹子阳, 吴志峰, 匡耀求, 等.DMSP/OLS夜间灯光影像中国区域的校正及应用[J].地球信息科学学报, 2015, 17(9):1092-1102. [13] SUN Y, ZHENG S, WU Y Z, et al.Spatiotemporal variations of city-level carbon emissions in China during 2000-2017 using nighttime light data[J].Remote Sensing, 2020, 12(18):2916. [14] 季涛.技术进步对中国碳排放的影响效应研究[D].兰州:兰州大学, 2022. [15] ZHAO J C, JI G X, YUE Y L, et al.Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets[J].Applied Energy, 2019, 235:612-624. [16] 王少剑, 苏泳娴, 赵亚博.中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素[J].地理学报, 2018, 73(3):414-428. [17] 韦彦汀, 李思佳, 张华.成渝城市群的碳排放时空演变特征及其影响因素分析[J].中国环境科学, 2022, 21(2):1-10. [18] 杜海波, 魏伟, 张学渊, 等.黄河流域能源消费碳排放时空格局演变及影响因素:基于DMSP/OLS与NPP/VIIRS夜间灯光数据[J].地理研究, 2021, 40(7):2051-2065. [19] HUANG B, WU B, BARRY M.Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J].International Journal of Geographical Information Science, 2010, 24(3):383-401. [20] 边小萌.绿色技术能力对区域碳排放的影响[D].北京:北京理工大学, 2018.
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