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

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  • 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
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    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. 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).
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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).
    16. 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).
    17. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    18. 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).
    19. 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).
    20. 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.
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