IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v383y2025ics030626192500056x.html
   My bibliography  Save this article

Assessing urban carbon health in China's three largest urban agglomerations: Carbon emissions, energy-carbon emission efficiency and carbon sinks

Author

Listed:
  • Cai, Angzu
  • Guo, Ru
  • Zhang, Yuhao
  • Wang, Leyi
  • Lin, Ruimin
  • Wu, Haoran
  • Huang, Runyao
  • Zhang, Jing
  • Wu, Jiang

Abstract

Energy consumption is the most important source of carbon emissions in China. Research on carbon emissions is of great significance for energy transition and regional sustainable development. Urban agglomerations (UAs) in China face significant challenges, including uneven development, rising carbon emissions, and encroachment on ecological green spaces. This study introduces a novel index, Carbon Health Index (CHI), to comprehensively evaluate the interrelated dynamics within the carbon emission intensity-energy carbon emission efficiency‑carbon sink (IES) system. The term “health” in this context reflects the balance and sustainability of the carbon system, where lower carbon emission intensity (negative impact), higher energy carbon emission efficiency (positive impact), and enhanced carbon sink capacity (positive impact) indicate a more sustainable and environmentally balanced system. Taking the three largest UAs in China as a case, this study explores the interrelationships within the carbon system. The study employed a coupling index and geostatistical methods to calculate the CHI and its spatial autocorrelation over the two-decade period. Machine learning methods were then used to assess the impact of driving factors on CHI. Key findings indicate that during the study period, the carbon emission intensity of the three UAs decreased by an average of 66.11 % compared to 2000. Energy carbon emission efficiency improved, with an average growth rate of 1.42 %, while carbon sink intensity remained relatively stable. The CHI exhibited a positive growth trend, with an average annual increase of 8.22 %. Spatial analysis revealed a pattern of higher CHI values on the peripheries and lower values at the core of these UAs. The primary positive contributors to CHI were GDP and ecological carbon sink land, whereas energy consumption was a significant negative contributor. This study concludes that optimizing energy structure, enhancing ecological carbon sequestration capacity, and strengthening regional collaborative governance are essential strategies for decoupling regional carbon emissions from economic growth and improving the CHI.

Suggested Citation

  • Cai, Angzu & Guo, Ru & Zhang, Yuhao & Wang, Leyi & Lin, Ruimin & Wu, Haoran & Huang, Runyao & Zhang, Jing & Wu, Jiang, 2025. "Assessing urban carbon health in China's three largest urban agglomerations: Carbon emissions, energy-carbon emission efficiency and carbon sinks," Applied Energy, Elsevier, vol. 383(C).
  • Handle: RePEc:eee:appene:v:383:y:2025:i:c:s030626192500056x
    DOI: 10.1016/j.apenergy.2025.125326
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192500056X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.125326?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. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    2. Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).
    3. Zhao, Na & Wang, Keqing & Yuan, Yongna, 2023. "Toward the carbon neutrality: Forest carbon sinks and its spatial spillover effect in China," Ecological Economics, Elsevier, vol. 209(C).
    4. Xing, Peixue & Wang, Yanan & Ye, Tao & Sun, Ying & Li, Qiao & Li, Xiaoyan & Li, Meng & Chen, Wei, 2024. "Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity," Energy Economics, Elsevier, vol. 129(C).
    5. Jing Meng & Jingwen Huo & Zengkai Zhang & Yu Liu & Zhifu Mi & Dabo Guan & Kuishuang Feng, 2023. "The narrowing gap in developed and developing country emission intensities reduces global trade’s carbon leakage," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    6. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    7. Wen-Yong Guo & Josep M. Serra-Diaz & Wolf L. Eiserhardt & Brian S. Maitner & Cory Merow & Cyrille Violle & Matthew J. Pound & Miao Sun & Ferry Slik & Anne Blach-Overgaard & Brian J. Enquist & Jens-Chr, 2023. "Climate change and land use threaten global hotspots of phylogenetic endemism for trees," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Huang, Yong & Elahi, Ehsan & You, Jiansheng & Sheng, Yuhua & Li, Jinwei & Meng, Anchan, 2024. "Land use policy implications of demographic shifts: Analyzing the impact of aging rural populations on agricultural carbon emissions in China," Land Use Policy, Elsevier, vol. 147(C).
    9. Sun, Lu & Liu, Wenjing & Li, Zhaoling & Cai, Bofeng & Fujii, Minoru & Luo, Xiao & Chen, Wei & Geng, Yong & Fujita, Tsuyoshi & Le, Yiping, 2021. "Spatial and structural characteristics of CO2 emissions in East Asian megacities and its indication for low-carbon city development," Applied Energy, Elsevier, vol. 284(C).
    10. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    11. Li, Lei & Ma, Shaojun & Zheng, Yilin & Xiao, Xinyue, 2022. "Integrated regional development: Comparison of urban agglomeration policies in China," Land Use Policy, Elsevier, vol. 114(C).
    12. Qiao, Renlu & Liu, Xiaochang & Gao, Shuo & Liang, Diling & GesangYangji, Gesang & Xia, Li & Zhou, Shiqi & Ao, Xiang & Jiang, Qingrui & Wu, Zhiqiang, 2024. "Industrialization, urbanization, and innovation: Nonlinear drivers of carbon emissions in Chinese cities," Applied Energy, Elsevier, vol. 358(C).
    13. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Li, Na & Liu, Feng, 2022. "Spatiotemporal characteristics and influencing factors of renewable energy production in China: A spatial econometric analysis," Energy Economics, Elsevier, vol. 116(C).
    14. Ma, Jianhong & Wang, Ning & Chen, Zihao & Wang, Libo & Xiong, Qiyang & Chen, Peilin & Zhang, Hongxia & Zheng, Ying & Chen, Zhan-Ming, 2024. "Accounting and decomposition of China's CO2 emissions 1981–2021," Applied Energy, Elsevier, vol. 375(C).
    15. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2020. "Adjusting energy consumption structure to achieve China's CO2 emissions peak," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    16. Cheng, Hao & Wu, Boyu & Jiang, Xiaokun, 2024. "Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities," Applied Energy, Elsevier, vol. 371(C).
    17. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    18. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    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. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Cai, Angzu & Wang, Leyi & Zhang, Yuhao & Wu, Haoran & Zhang, Huai & Guo, Ru & Wu, Jiang, 2025. "Uncovering the multiple socio-economic driving factors of carbon emissions in nine urban agglomerations of China based on machine learning," Energy, Elsevier, vol. 319(C).
    3. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    4. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    5. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    6. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    7. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    8. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    9. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    10. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    11. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    12. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    13. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    14. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    15. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    16. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    17. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    18. Sylwester Bejger, 2024. "Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market," Energies, MDPI, vol. 17(16), pages 1-17, August.
    19. Karim Zkik & Anass Sebbar & Oumaima Fadi & Sachin Kamble & Amine Belhadi, 2024. "Securing blockchain-based crowdfunding platforms: an integrated graph neural networks and machine learning approach," Electronic Commerce Research, Springer, vol. 24(1), pages 497-533, March.
    20. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.

    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:appene:v:383:y:2025:i:c:s030626192500056x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.