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A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from interconnected grids

Author

Listed:
  • Qu, Shen
  • Wang, Hongxia
  • Liang, Sai
  • Shapiro, Avi M.
  • Suh, Sanwong
  • Sheldon, Seth
  • Zik, Ory
  • Fang, Hong
  • Xu, Ming

Abstract

Estimating the embodied emissions in electricity has been a challenge due to the interconnectedness of electrical grids. Previous studies use a variety of methods that are often inaccurate or difficult to implement, lacking a standardized tool. We propose a method adapted from the economic input-output theory, which we term as “Quasi-Input-Output (QIO)” model, to evaluate embodied emissions in purchased electricity. The method takes into account the difference between the natures of trade in electricity and in goods and services, able to capture the effects of both direct and higher-order electricity transfers in the network. We use the Eurasian Continent grid network as a case, identifying regions where inter-grid electricity transfers, both direct and high-order, have sizable impacts on estimated emission factors of purchased electricity. Overall, while ignoring electricity trade can result in errors in embodied emissions estimation, directly adjusting for electricity trade (neglecting higher-order trade) tends to generate inaccuracies in the opposite direction. Our model can be potentially applied as a standard tool for the accounting of embodied emissions in purchased electricity in inter-grid electricity trade systems. It also provides a foundation for further applications of input-output theory in the analysis of demand-side drivers for environmental impacts of interconnected grids.

Suggested Citation

  • Qu, Shen & Wang, Hongxia & Liang, Sai & Shapiro, Avi M. & Suh, Sanwong & Sheldon, Seth & Zik, Ory & Fang, Hong & Xu, Ming, 2017. "A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from interconnected grids," Applied Energy, Elsevier, vol. 200(C), pages 249-259.
  • Handle: RePEc:eee:appene:v:200:y:2017:i:c:p:249-259
    DOI: 10.1016/j.apenergy.2017.05.046
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    Cited by:

    1. Yawen Han & Wanli Xing & Hongchang Hao & Xin Du & Chongyang Liu, 2022. "Interprovincial Metal and GHG Transfers Embodied in Electricity Transmission across China: Trends and Driving Factors," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    2. Zhang, Haoran & Li, Ruixiong & Cai, Xingrui & Zheng, Chaoyue & Liu, Laibao & Liu, Maodian & Zhang, Qianru & Lin, Huiming & Chen, Long & Wang, Xuejun, 2022. "Do electricity flows hamper regional economic–environmental equity?," Applied Energy, Elsevier, vol. 326(C).
    3. Wenbo Li & Ruyin Long & Linling Zhang & Zhengxia He & Feiyu Chen & Hong Chen, 2020. "Greenhouse Gas Emission Transfer of Inter-Provincial Electricity Trade in China," IJERPH, MDPI, vol. 17(22), pages 1-14, November.
    4. Zhou, Xi-Yin & Xu, Zhicheng & Zheng, Jialin & Zhou, Ya & Lei, Kun & Fu, Jiafeng & Khu, Soon-Thiam & Yang, Junfeng, 2023. "Internal spillover effect of carbon emission between transportation sectors and electricity generation sectors," Renewable Energy, Elsevier, vol. 208(C), pages 356-366.
    5. Zhang, Pengfei & Cai, Wenqiu & Yao, Mingtao & Wang, Zhiyou & Yang, Luzhen & Wei, Wendong, 2020. "Urban carbon emissions associated with electricity consumption in Beijing and the driving factors," Applied Energy, Elsevier, vol. 275(C).
    6. Haoran Zhang & Limin Jiao & Cai Li & Zhongci Deng & Zhen Wang & Qiqi Jia & Xihong Lian & Yaolin Liu & Yuanchao Hu, 2024. "Global environmental impacts of food system from regional shock: Russia-Ukraine war as an example," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    7. Zhou, Zhanhang & Zeng, Chen & Li, Keke & Yang, Yuemin & Zhao, Kuokuo & Wang, Zhen, 2024. "Decomposition of the decoupling between electricity CO2 emissions and economic growth: A production and consumption perspective," Energy, Elsevier, vol. 293(C).
    8. Zhu, Bangzhu & Su, Bin & Li, Yingzhu, 2018. "Input-output and structural decomposition analysis of India’s carbon emissions and intensity, 2007/08 – 2013/14," Applied Energy, Elsevier, vol. 230(C), pages 1545-1556.
    9. Dameng Hu & Yuanzhe Huang & Changbiao Zhong, 2021. "Does Environmental Information Disclosure Affect the Sustainable Development of Enterprises: The Role of Green Innovation," Sustainability, MDPI, vol. 13(19), pages 1-22, October.
    10. Bai, Muren & Li, Cunbin, 2024. "Research on the allocation scheme of carbon emission allowances for China's provincial power grids," Energy, Elsevier, vol. 299(C).
    11. Chen, Li & Wemhoff, Aaron P., 2021. "Predicting embodied carbon emissions from purchased electricity for United States counties," Applied Energy, Elsevier, vol. 292(C).
    12. Wang, Like & Fan, Yee Van & Jiang, Peng & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír, 2021. "Virtual water and CO2 emission footprints embodied in power trade: EU-27," Energy Policy, Elsevier, vol. 155(C).
    13. Yanfeng Li & Yongping Li & Guohe Huang & Rubing Zheng, 2022. "Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry," Energies, MDPI, vol. 15(10), pages 1-20, May.
    14. Kang, Zixuan & Ye, Zhongnan & Lam, Chor-Man & Hsu, Shu-Chien, 2023. "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving," Applied Energy, Elsevier, vol. 349(C).
    15. Hengjing He & Shangli Zhou & Leping Zhang & Wei Zhao & Xia Xiao, 2023. "Dynamic Accounting Model and Method for Carbon Emissions on the Power Grid Side," Energies, MDPI, vol. 16(13), pages 1-10, June.
    16. Han, Yawen & Du, Xin & Zhang, Hengming & Ni, Jinfeng & Fan, Fengyan, 2024. "Does smart home adoption reduce household electricity-related CO2 emissions? ——Evidence from Hangzhou city, China," Energy, Elsevier, vol. 289(C).

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