IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v348y2023ics0306261923008097.html
   My bibliography  Save this article

Carbon emissions accounting and prediction in urban agglomerations from multiple perspectives of production, consumption and income

Author

Listed:
  • Yue, Wencong
  • Li, Yangqing
  • Su, Meirong
  • Chen, Qionghong
  • Rong, Qiangqiang

Abstract

Urban agglomerations (UAs) play a momentous role in carbon reduction. The prerequisites for achieving carbon reduction goals in UAs are accounting and predicting their carbon emissions. When considering carbon reduction goals in China, it is crucial to pay attention to the joint influence of carbon emissions and economic benefits. Hence, in this study, an improved multi-regional input–output (MRIO) approach was established to quantify and predict carbon emissions for the UA, incorporating a biproportional scaling method (RAS) and Latin hypercube sampling (LHS). Specifically, a) the carbon emissions of UAs were quantified using the MRIO model from the perspectives of production, consumption and income; b) the carbon flows between cities in UAs were identified based on final demand and primary inputs, and c) the features of UAs’ carbon emissions in the future were predicted using RAS and LHS. To verify the effectiveness of the approach, a case study of a typical UA region in China [i.e., the Pearl River Delta (PRD)] was proposed. The results showed that the contribution of sectors to carbon emissions could be identified from multiple perspectives, and carbon flows can help regions coordinate emissions reductions. The majority of future carbon emissions would be generated from the areas of population and economic agglomeration (i.e., Guangzhou and Shenzhen), although the growth trend of carbon emissions of those would keep lower. The policy of carbon reduction should be urgently carried out in locations with high carbon emissions growth rates (e.g., Zhaoqing and Zhuhai). To improve the ability for carbon reduction in the PRD, cooperation in multiple cities to promote energy efficiency is advocated. The government should also increase technical support for carbon reduction and consider the balanced development of the economy, population, and resources in the PRD.

Suggested Citation

  • Yue, Wencong & Li, Yangqing & Su, Meirong & Chen, Qionghong & Rong, Qiangqiang, 2023. "Carbon emissions accounting and prediction in urban agglomerations from multiple perspectives of production, consumption and income," Applied Energy, Elsevier, vol. 348(C).
    • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923008097
    DOI: 10.1016/j.apenergy.2023.121445
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923008097
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121445?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Fang, Kai & Tang, Yiqi & Zhang, Qifeng & Song, Junnian & Wen, Qi & Sun, Huaping & Ji, Chenyang & Xu, Anqi, 2019. "Will China peak its energy-related carbon emissions by 2030? Lessons from 30 Chinese provinces," Applied Energy, Elsevier, vol. 255(C).
    2. Su, Bin & Ang, B.W. & Li, Yingzhu, 2017. "Input-output and structural decomposition analysis of Singapore's carbon emissions," Energy Policy, Elsevier, vol. 105(C), pages 484-492.
    3. Li, Jizhe & Huang, Guohe & Liu, Lirong & Li, Yongping & Zhai, Mengyu & Xu, Xinli, 2021. "Projections of carbon metabolism in 2035 and implications for demand-side controls under various scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    4. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    5. Xia, Quanzhi & Han, Mengyao & Guan, Shihui & Wu, Xiaofang & Zhang, Bo, 2022. "Tracking embodied energy flows of China's megacities via multi-scale supply chains," Energy, Elsevier, vol. 260(C).
    6. Awais Mahmood & Viganda Varabuntoonvit & Jitti Mungkalasiri & Thapat Silalertruksa & Shabbir H. Gheewala, 2022. "A Tier-Wise Method for Evaluating Uncertainty in Life Cycle Assessment," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    7. Wang, Shaojian & Zeng, Jingyuan & Huang, Yongyuan & Shi, Chenyi & Zhan, Peiyu, 2018. "The effects of urbanization on CO2 emissions in the Pearl River Delta: A comprehensive assessment and panel data analysis," Applied Energy, Elsevier, vol. 228(C), pages 1693-1706.
    8. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    9. Wen, Wen & Feng, Cuiyang & Zhou, Hao & Zhang, Li & Wu, Xiaohui & Qi, Jianchuan & Yang, Xuechun & Liang, Yuhan, 2021. "Critical provincial transmission sectors for carbon dioxide emissions in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    10. Xiaowen Li & Yiming Tan & Desheng Xue, 2022. "From World Factory to Global City-Region: The Dynamics of Manufacturing in the Pearl River Delta and Its Spatial Pattern in the 21st Century," Land, MDPI, vol. 11(5), pages 1-19, April.
    11. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
    12. Wang, Yanan & Yin, Shiwen & Fang, Xiaoli & Chen, Wei, 2022. "Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China," Energy, Elsevier, vol. 241(C).
    13. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    14. Chen, Weiming & Lei, Yalin & Feng, Kuishuang & Wu, Sanmang & Li, Li, 2019. "Provincial emission accounting for CO2 mitigation in China: Insights from production, consumption and income perspectives," Applied Energy, Elsevier, vol. 255(C).
    15. Lv, J. & Li, Y.P. & Huang, G.H. & Ding, Y.K. & Li, X. & Li, Y., 2022. "Planning energy economy and eco-environment nexus system under uncertainty: A copula-based stochastic multi-level programming method," Applied Energy, Elsevier, vol. 312(C).
    16. Shao, Ling & Li, Yuan & Feng, Kuishuang & Meng, Jing & Shan, Yuli & Guan, Dabo, 2018. "Carbon emission imbalances and the structural paths of Chinese regions," Applied Energy, Elsevier, vol. 215(C), pages 396-404.
    17. Liu, Xiaoping & Ou, Jinpei & Chen, Yimin & Wang, Shaojian & Li, Xia & Jiao, Limin & Liu, Yaolin, 2019. "Scenario simulation of urban energy-related CO2 emissions by coupling the socioeconomic factors and spatial structures," Applied Energy, Elsevier, vol. 238(C), pages 1163-1178.
    18. Zhan-Ming Chen & Stephanie Ohshita & Manfred Lenzen & Thomas Wiedmann & Magnus Jiborn & Bin Chen & Leo Lester & Dabo Guan & Jing Meng & Shiyun Xu & Guoqian Chen & Xinye Zheng & JinJun Xue & Ahmed Alsa, 2018. "Consumption-based greenhouse gas emissions accounting with capital stock change highlights dynamics of fast-developing countries," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    19. Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Tan, Q., 2009. "Identification of optimal strategies for energy management systems planning under multiple uncertainties," Applied Energy, Elsevier, vol. 86(4), pages 480-495, April.
    20. Yafei Wang, 2017. "An industrial ecology virtual framework for policy making in China," Economic Systems Research, Taylor & Francis Journals, vol. 29(2), pages 252-274, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhai, Mengyu & Wu, Yufeng & Gu, Yifan & Liu, Lirong & Su, Shuai & Zang, Hongkuan, 2024. "A multidimensional factorial enviro-economic model: Approaches of retrospective decomposition and prospective projection for energy systems," Energy, Elsevier, vol. 287(C).

    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. Wang, Zhenyu & Meng, Jing & Zheng, Heran & Shao, Shuai & Wang, Daoping & Mi, Zhifu & Guan, Dabo, 2018. "Temporal change in India’s imbalance of carbon emissions embodied in international trade," Applied Energy, Elsevier, vol. 231(C), pages 914-925.
    2. Zhu, Qingyuan & Xu, Chengzhen & Pan, Yinghao & Wu, Jie, 2024. "Identifying critical transmission sectors, paths, and carbon communities for CO2 mitigation in global supply chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Guo, Xuepeng & Pang, Jun, 2023. "Analysis of provincial CO2 emission peaking in China: Insights from production and consumption," Applied Energy, Elsevier, vol. 331(C).
    4. Zhou, Xiaoyong & Zhou, Dequn & Wang, Qunwei & Su, Bin, 2020. "Who shapes China's carbon intensity and how? A demand-side decomposition analysis," Energy Economics, Elsevier, vol. 85(C).
    5. Li, Jia Shuo & Zhou, H.W. & Meng, Jing & Yang, Q. & Chen, B. & Zhang, Y.Y., 2018. "Carbon emissions and their drivers for a typical urban economy from multiple perspectives: A case analysis for Beijing city," Applied Energy, Elsevier, vol. 226(C), pages 1076-1086.
    6. Ghazala Aziz & Zouheir Mighri, 2022. "Carbon Dioxide Emissions and Forestry in China: A Spatial Panel Data Approach," Sustainability, MDPI, vol. 14(19), pages 1-40, October.
    7. Xue, Ruoyu & Wang, Shanshan & Long, Wenqi & Gao, Gengyu & Liu, Donghui & Zhang, Ruiqin, 2021. "Uncovering GHG emission characteristics of industrial parks in Central China via emission inventory and cluster analysis," Energy Policy, Elsevier, vol. 151(C).
    8. Cai, Bofeng & Lu, Jun & Wang, Jinnan & Dong, Huijuan & Liu, Xiaoman & Chen, Yang & Chen, Zhanming & Cong, Jianhui & Cui, Zhipeng & Dai, Chunyan & Fang, Kai & Feng, Tong & Guo, Jie & Li, Fen & Meng, Fa, 2019. "A benchmark city-level carbon dioxide emission inventory for China in 2005," Applied Energy, Elsevier, vol. 233, pages 659-673.
    9. Fang, Kai & Zhang, Qifeng & Long, Yin & Yoshida, Yoshikuni & Sun, Lu & Zhang, Haoran & Dou, Yi & Li, Shuai, 2019. "How can China achieve its Intended Nationally Determined Contributions by 2030? A multi-criteria allocation of China’s carbon emission allowance," Applied Energy, Elsevier, vol. 241(C), pages 380-389.
    10. Song, Weize & Zhang, Xiaoling & An, Kangxin & Yang, Tao & Li, Heng & Wang, Can, 2021. "Quantifying the spillover elasticities of urban built environment configurations on the adjacent traffic CO2 emissions in mainland China," Applied Energy, Elsevier, vol. 283(C).
    11. Hongkuan Zang & Lirong Zhang & Ye Xu & Wei Li, 2020. "Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    12. Ling Li & Ling Tang & Junrong Zhang, 2019. "Coupling Structural Decomposition Analysis and Sensitivity Analysis to Investigate CO 2 Emission Intensity in China," Energies, MDPI, vol. 12(12), pages 1-23, June.
    13. Chunli Jin & Qiaoqiao Zhu & Hui Sun, 2023. "Temporal and Spatial Divergence of Embodied Carbon Emissions Transfer and the Drivers—Evidence from China’s Domestic Trade," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    14. Linlin Xia & Jianfeng Wei & Ruwei Wang & Lei Chen & Yan Zhang & Zhifeng Yang, 2022. "Exploring Potential Ways to Reduce the Carbon Emission Gap in an Urban Metabolic System: A Network Perspective," IJERPH, MDPI, vol. 19(10), pages 1-23, May.
    15. Wang, Miao & Feng, Chao, 2018. "Using an extended logarithmic mean Divisia index approach to assess the roles of economic factors on industrial CO2 emissions of China," Energy Economics, Elsevier, vol. 76(C), pages 101-114.
    16. Zhangqi, Zhong & Zhuli, Chen & Lingyun, He, 2022. "Technological innovation, industrial structural change and carbon emission transferring via trade-------An agent-based modeling approach," Technovation, Elsevier, vol. 110(C).
    17. Xu, Zhongwen & Huang, Liqiao & Liao, Maolin & Xue, Jinjun & Yoshida, Yoshikuni & Long, Yin, 2022. "Quantifying consumption-based carbon emissions of major economic sectors in Japan considering the global value chain," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 330-341.
    18. Cai, Bofeng & Guo, Huanxiu & Ma, Zipeng & Wang, Zhixuan & Dhakal, Shobhakar & Cao, Libin, 2019. "Benchmarking carbon emissions efficiency in Chinese cities: A comparative study based on high-resolution gridded data," Applied Energy, Elsevier, vol. 242(C), pages 994-1009.
    19. 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).
    20. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).

    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:348:y:2023:i:c:s0306261923008097. 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.