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Tracking Carbon Flows from Coal Mines to Electricity Users in China Using an Ensemble Model.

Kaiyun LiuKun WangShuxiao WangShu-Xiao WangJiming Hao
Published in: Environmental science & technology (2023)
Accurately tracking carbon flows is crucial for preventing carbon leakage and allocating responsibility for reducing CO 2 eq emissions. In this study, we developed an ensemble model to effectively track carbon flows within China's power system. Our approach integrates coal quality tests, individual power plant datasets, a dynamic material-energy flow analysis model, and an extended version of an interconnected power grid model that incorporates transmission and distribution (T&D) losses. Our results not only provide accurate quantification of unit-based CO 2 eq emissions based on coal quality data but also enable the assessment of emissions attributed to T&D losses and emission shifts resulting from interprovincial coal and electricity trade. Remarkably, for CO 2 eq emissions from coal-fired units, the disparity between the guideline and our study can be as high as [-95%, 287%]. We identify Guangdong, Hebei, Jiangsu, and Zhejiang provinces as the major importers of both coal and electricity, responsible for transferring nearly half of their user-based emissions to coal and power bases. Significantly, T&D losses, often overlooked, contribute to 15-20% of provincial emissions at the user side. Our findings emphasize the necessity of up-to-date life cycle emissions and spatial carbon shifts in effectively allocating emission reduction responsibilities from the national level to provinces.
Keyphrases
  • life cycle
  • particulate matter
  • heavy metals
  • municipal solid waste
  • high resolution
  • convolutional neural network
  • mass spectrometry
  • neural network