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Spatiotemporal evolutionary characteristics and influencing factors of carbon emissions in Central Plain urban agglomeration.

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Title: Spatiotemporal evolutionary characteristics and influencing factors of carbon emissions in Central Plain urban agglomeration.
Authors: Wei, Haitao1,2 (AUTHOR), Luo, Ziyi1 (AUTHOR), Guo, Hengliang1,2 (AUTHOR), Wang, Lingling3 (AUTHOR), Zhao, Shan1 (AUTHOR), Wang, Nan4 (AUTHOR), Cui, Jian5 (AUTHOR), Ma, Shuangliang6 (AUTHOR), Zhang, Dujuan2,7 (AUTHOR) duduzdj@zzu.edu.cn
Source: Environment, Development & Sustainability. Jun2026, Vol. 28 Issue 6, p13593-13622. 30p.
Subject Terms: *Carbon emissions, *Spatiotemporal processes, *Energy consumption, *Metropolitan areas, *Countries, *Sustainable development, *Carbon sequestration
Geographic Terms: China
Abstract: Comprehensively analyzing carbon emissions in the Central Plains Urban Agglomeration (CPUA) of China is an effective case study for promoting sustainable development and supporting China in achieving its carbon peak targets. This study applies an energy balance sheet downscaling method to estimate the carbon emissions of 30 cities in the CPUA from 2000 to 2021, examining trends in carbon emissions and land carbon sequestration. Key influencing factors of carbon emissions are identified using knowledge graph technology, and the spatiotemporal effects of these factors are analyzed using Geographically and Temporally Weighted Regression Geographically Weighted Regression and Multiscale Geographically Weighted Regression models. The study shows that carbon emissions in the CPUA increased from 452.639 million tons in 2000 to 1737.107 million tons in 2021, with a growth rate that declined from 24.18% to 3.06%. Fossil fuel consumption and cultivated land were major carbon sources, while forest land was a significant carbon sink. The spatial pattern of carbon emissions predominantly showed lower values in the south and higher values in the north, with significant clustering in high emission areas. Population size, per capita gross domestic product, technological progress, and energy consumption intensity had significant impacts on the urban agglomeration's carbon emissions. However, the impact was influenced by fluctuations driven by government policies, industrial and energy structures, and other factors. This study not only provides critical insights for China's low-carbon development but also offers valuable lessons for other developing countries facing similar challenges. Urban agglomeration planning should focus on optimizing energy and industrial structures, promoting green technology, and designing tailored carbon reduction policies to achieve sustainable and green development. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194093156
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Spatiotemporal evolutionary characteristics and influencing factors of carbon emissions in Central Plain urban agglomeration.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Wei%2C+Haitao%22">Wei, Haitao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Ziyi%22">Luo, Ziyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Hengliang%22">Guo, Hengliang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Lingling%22">Wang, Lingling</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Shan%22">Zhao, Shan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Nan%22">Wang, Nan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cui%2C+Jian%22">Cui, Jian</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Shuangliang%22">Ma, Shuangliang</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dujuan%22">Zhang, Dujuan</searchLink><relatesTo>2,7</relatesTo> (AUTHOR)<i> duduzdj@zzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jun2026, Vol. 28 Issue 6, p13593-13622. 30p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br />*<searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Metropolitan+areas%22">Metropolitan areas</searchLink><br />*<searchLink fieldCode="DE" term="%22Countries%22">Countries</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainable+development%22">Sustainable development</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+sequestration%22">Carbon sequestration</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Comprehensively analyzing carbon emissions in the Central Plains Urban Agglomeration (CPUA) of China is an effective case study for promoting sustainable development and supporting China in achieving its carbon peak targets. This study applies an energy balance sheet downscaling method to estimate the carbon emissions of 30 cities in the CPUA from 2000 to 2021, examining trends in carbon emissions and land carbon sequestration. Key influencing factors of carbon emissions are identified using knowledge graph technology, and the spatiotemporal effects of these factors are analyzed using Geographically and Temporally Weighted Regression Geographically Weighted Regression and Multiscale Geographically Weighted Regression models. The study shows that carbon emissions in the CPUA increased from 452.639 million tons in 2000 to 1737.107 million tons in 2021, with a growth rate that declined from 24.18% to 3.06%. Fossil fuel consumption and cultivated land were major carbon sources, while forest land was a significant carbon sink. The spatial pattern of carbon emissions predominantly showed lower values in the south and higher values in the north, with significant clustering in high emission areas. Population size, per capita gross domestic product, technological progress, and energy consumption intensity had significant impacts on the urban agglomeration's carbon emissions. However, the impact was influenced by fluctuations driven by government policies, industrial and energy structures, and other factors. This study not only provides critical insights for China's low-carbon development but also offers valuable lessons for other developing countries facing similar challenges. Urban agglomeration planning should focus on optimizing energy and industrial structures, promoting green technology, and designing tailored carbon reduction policies to achieve sustainable and green development. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10668-024-05490-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 30
        StartPage: 13593
    Subjects:
      – SubjectFull: Carbon emissions
        Type: general
      – SubjectFull: Spatiotemporal processes
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Metropolitan areas
        Type: general
      – SubjectFull: Countries
        Type: general
      – SubjectFull: Sustainable development
        Type: general
      – SubjectFull: Carbon sequestration
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Spatiotemporal evolutionary characteristics and influencing factors of carbon emissions in Central Plain urban agglomeration.
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          Name:
            NameFull: Wei, Haitao
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            NameFull: Luo, Ziyi
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            NameFull: Guo, Hengliang
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            NameFull: Wang, Lingling
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            NameFull: Zhao, Shan
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            NameFull: Wang, Nan
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            NameFull: Cui, Jian
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            NameFull: Ma, Shuangliang
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            NameFull: Zhang, Dujuan
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 1387585X
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            – TitleFull: Environment, Development & Sustainability
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