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Author:

Li, Ming-Yang (Li, Ming-Yang.) | Gao, Feng (Gao, Feng.) | Sun, Bo-Xue (Sun, Bo-Xue.) | Nie, Zuo-Ren (Nie, Zuo-Ren.)

Indexed by:

EI Scopus

Abstract:

According to the guidance of the national carbon peak target, the historical data of greenhouse gas (GHG) emissions from primary aluminum production were calculated and analyzed in this study. A multi-factor analysis model of GHG emissions from primary aluminum was established by forecasting the potential for improvement of electrolytic aluminum production technology, power structure adjustment and recycled aluminum production in China for the next 15 years. The GHG emission reduction and carbon peaking pathways of primary aluminum production in China were explored under three scenarios including the baseline scenario (BAU), the low improvement efficiency scenario (LIR) and the high improvement efficiency scenario (HIR).The results show that the GHG emission potential of 1 ton primary aluminum production in China illustrated a decreasing trend over the years. The potential of the advanced technology application of aluminum electrolysis, the change of electricity consumption structure and increasing the proportion of recycled aluminum for the GHG emissions reduction of 1 ton aluminum were forecasted. The GHG emissions of China primary aluminum production would peak in 2030 with the BAU scenario, while in 2027 with the LIR scenario and in 2021 with the HIR scenario, respectively. The variation range of aluminum output in the three scenarios was then given as the limiting condition to ensure the peak. © 2022, China Science Publishing & Media Ltd. All right reserved.

Keyword:

Recycling Emission control Gas emissions Electrolysis Carbon Aluminum Greenhouse gases Efficiency

Author Community:

  • [ 1 ] [Li, Ming-Yang]National Engineering Laboratory for Industrial Big-data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Gao, Feng]National Engineering Laboratory for Industrial Big-data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Sun, Bo-Xue]National Engineering Laboratory for Industrial Big-data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Nie, Zuo-Ren]National Engineering Laboratory for Industrial Big-data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing; 100124, China

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Source :

Chinese Journal of Nonferrous Metals

ISSN: 1004-0609

Year: 2022

Issue: 1

Volume: 32

Page: 148-158

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 11

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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