IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225035674.html

Polycentricity or monocentricity? A multi-scale assessment of how urban agglomeration structures influence carbon emission performance in China

Author

Listed:
  • Zeng, Peng
  • Liang, Liying

Abstract

Global warming driven by persistent carbon emissions has made emission reduction a pressing research focus. Understanding how urban agglomeration spatial structures influence carbon emission performance is essential for developing effective mitigation strategies. This study examines 13 urban agglomerations in China from 2006 to 2020. A two-dimensional framework is proposed to capture spatial structure, encompassing both morphological and functional dimensions. A Geographically and Temporally Weighted Regression model is employed at both the city and county scales. The results indicate that: (1) Spatial morphological structures show a general trend toward monocentricity at both scales. (2) Functional structures become increasingly monocentric patterns at the city scale while remaining relatively stable at the county scale. (3) Carbon emission performance has improved overall, yet interregional disparities have widened. (4) The influence of spatial structure on carbon emission performance shows significant structure type and scale-dependent characteristics. At the city scale, the polycentricity of spatial morphological and functional structures is unstable in its direction of influence and lacks consistency. In contrast, at the county scale, morphological polycentricity continues to inhibit carbon emission performance, while functional polycentricity continues to promote performance enhancement. (5) Most control variables significantly affect carbon performance at city and county scales, but these effects' direction, strength, and temporal pattern vary across scales. These results provide new insights into the spatial mechanisms underlying low-carbon development and offer a theoretical basis for designing targeted emission reduction strategies tailored to varying spatial structures and governance contexts.

Suggested Citation

  • Zeng, Peng & Liang, Liying, 2025. "Polycentricity or monocentricity? A multi-scale assessment of how urban agglomeration structures influence carbon emission performance in China," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035674
    DOI: 10.1016/j.energy.2025.137925
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225035674
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137925?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Alex Anas & Richard Arnott & Kenneth A. Small, 1998. "Urban Spatial Structure," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1426-1464, September.
    2. He, Xiaoping & Yu, Yuxuan & Jiang, Shuo, 2023. "City centrality, population density and energy efficiency," Energy Economics, Elsevier, vol. 117(C).
    3. Meng, Conghui & Du, Xiaoyun & Zhu, Mengcheng & Ren, Yitian & Fang, Kai, 2023. "The static and dynamic carbon emission efficiency of transport industry in China," Energy, Elsevier, vol. 274(C).
    4. Martijn Burger & Evert Meijers, 2012. "Form Follows Function? Linking Morphological and Functional Polycentricity," Urban Studies, Urban Studies Journal Limited, vol. 49(5), pages 1127-1149, April.
    5. Yanyan Wu & Jiadong Yuan, 2022. "Is There a Regulation in the Expansion of Urban Spatial Structure? Empirical Study from the Main Urban Area in Zhengzhou, China," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    6. Yubo Zhao & Gui Zhang & Hongwei Zhao & Anirban Chakraborti, 2021. "Spatial Network Structures of Urban Agglomeration Based on the Improved Gravity Model: A Case Study in China’s Two Urban Agglomerations," Complexity, Hindawi, vol. 2021, pages 1-17, February.
    7. P. J. Taylor & D. M. Evans & K. Pain, 2008. "Application of the Interlocking Network Model to Mega-City-Regions: Measuring Polycentricity Within and Beyond City-Regions," Regional Studies, Taylor & Francis Journals, vol. 42(8), pages 1079-1093.
    8. Xinhua Zhu & Qianli Wang & Peifeng Zhang & Yunjiang Yu & Lingling Xie, 2021. "Optimizing the spatial structure of urban agglomeration: based on social network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 683-705, April.
    9. Zhou, Anhua & Xin, Ling & Li, Jun, 2022. "Assessing the impact of the carbon market on the improvement of China's energy and carbon emission performance," Energy, Elsevier, vol. 258(C).
    10. Glaeser, Edward L., 2008. "Cities, Agglomeration, and Spatial Equilibrium," OUP Catalogue, Oxford University Press, number 9780199290444.
    11. Zhang, Songlin & Miao, Xuaner & Zheng, Haoqing & Chen, Weihong & Wang, Huafeng, 2024. "Spatial functional division in urban agglomerations and carbon emission intensity: New evidence from 19 urban agglomerations in China," Energy, Elsevier, vol. 300(C).
    12. Teng, Xiangyu & Liu, Fan-peng & Chiu, Yung-ho, 2021. "The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model," Energy, Elsevier, vol. 216(C).
    13. Yuan Zhang & Zhen Yu & Juan Zhang, 2021. "Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    14. Yongjun Li & Wenhui Hou & Weiwei Zhu & Feng Li & Liang Liang, 2021. "Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs," Annals of Operations Research, Springer, vol. 304(1), pages 233-261, September.
    15. Xu, Le & Fan, Meiting & Yang, Lili & Shao, Shuai, 2021. "Heterogeneous green innovations and carbon emission performance: Evidence at China's city level," Energy Economics, Elsevier, vol. 99(C).
    16. Yang, Fei & Liu, Kang & Wu, Lei & Ren, Yi & Liang, Tian, 2025. "Urban geometry and energy efficiency: Evidence from 282 cities in China," Energy, Elsevier, vol. 319(C).
    17. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).
    18. Zhang, Hao & Song, Yan & Zhang, Ming & Duan, Ye, 2024. "Land use efficiency and energy transition in Chinese cities: A cluster-frontier super-efficiency SBM-based analytical approach," Energy, Elsevier, vol. 304(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    2. Antti Vasanen, 2012. "Functional Polycentricity: Examining Metropolitan Spatial Structure through the Connectivity of Urban Sub-centres," Urban Studies, Urban Studies Journal Limited, vol. 49(16), pages 3627-3644, December.
    3. Antti Vasanen, 2013. "Spatial Integration and Functional Balance in Polycentric Urban Systems: A Multi-Scalar Approach," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 104(4), pages 410-425, September.
    4. Ilenia Epifani & Rosella Nicolini, 2013. "On The Population Density Distribution Across Space: A Probabilistic Approach," Journal of Regional Science, Wiley Blackwell, vol. 53(3), pages 481-510, August.
    5. Hashem Dadashpoor & Neda Malekzadeh & Sadegh Saeidishirvan, 2023. "A typology of metropolitan spatial structure: a systematic review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(12), pages 13667-13693, December.
    6. Yang Xiao & Yao Wang & Siyu Miao & Xinyi Niu, 2021. "Assessing polycentric urban development in Shanghai, China, with detailed passive mobile phone data," Environment and Planning B, , vol. 48(9), pages 2656-2674, November.
    7. Tianren Yang & Ying Jin & Longxu Yan & Pei Pei, 2019. "Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai," Environment and Planning B, , vol. 46(7), pages 1264-1280, September.
    8. Dan He & Zhijing Sun & Peng Gao, 2019. "Development of Economic Integration in the Central Yangtze River Megaregion from the Perspective of Urban Network Evolution," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    9. Paolo VENERI & David BURGALASSI, 2011. "Spatial Structure and Productivity in Italian NUTS-3 Regions," Working Papers 364, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    11. Juan Zhu & Xinyi Niu & Yao Wang, 2024. "Polycentric Urban Spatial Structure Identification Based on Morphological and Functional Dimensions: Evidence from Three Chinese Cities," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    12. Gubins, Sergejs & Verhoef, Erik T., 2014. "Dynamic bottleneck congestion and residential land use in the monocentric city," Journal of Urban Economics, Elsevier, vol. 80(C), pages 51-61.
    13. Martijn J. Burger & Evert J. Meijers & Frank G. Oort, 2014. "Multiple Perspectives on Functional Coherence: Heterogeneity and Multiplexity in the Randstad," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 105(4), pages 444-464, September.
    14. Cats, Oded & Wang, Qian & Zhao, Yu, 2015. "Identification and classification of public transport activity centres in Stockholm using passenger flows data," Journal of Transport Geography, Elsevier, vol. 48(C), pages 10-22.
    15. Jiang, Hongli & Hu, Wenjie & Hou, Yan & Jiang, Pengcheng & Lu, Na, 2025. "Green fiscal policies, economic growth target constraints and carbon emission efficiency: Evidence from DID and double machine learning," Energy, Elsevier, vol. 339(C).
    16. Mao, Yumeng & Li, Xuemei & Jiao, Dehan & Zhao, Xiaolei, 2024. "Characterizing the spatial correlation network structure and impact mechanism of carbon emission efficiency: Evidence from China's transportation sector," Energy, Elsevier, vol. 313(C).
    17. Ivan Muñiz & Miguel-Àngel García-López, 2012. "Chaos and order in the contemporary city. The impact of urban spatial structure on population density and commuting distance in Barcelona, 1986-2001," Working Papers wpdea1207, Department of Applied Economics at Universitat Autonoma of Barcelona.
    18. Pan Jiang & Mengyue Li & Yuting Zhao & Xiujuan Gong & Ruifeng Jin & Yuhan Zhang & Xue Li & Liang Liu, 2022. "Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    19. Hui Cheng & David Shaw, 2021. "POLYCENTRIC DEVELOPMENT AND THE FORMATION OF EDGE URBAN AREAS IN CHINA'S MEGA CITY REGIONS: Case Study of Nansha, Guangzhou," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 45(6), pages 1009-1027, November.
    20. Michael Wurm & Jan Goebel & Gert G Wagner & Matthias Weigand & Stefan Dech & Hannes Taubenböck, 2021. "Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception," Environment and Planning B, , vol. 48(2), pages 265-279, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225035674. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.