Possibility and pathways of China's nonferrous metals industry to achieve its carbon peak target before 2030: A new integrated dynamic forecasting model

被引:0
|
作者
Cao, Yue [1 ]
Guo, Lingling [1 ]
Qu, Ying [1 ]
Wang, Liang [2 ]
机构
[1] Dalian Univ Technol, Sch Econ & Management, 2 Ling Gong Rd, Dalian 116024, Peoples R China
[2] Hainan Univ, Int Business Sch, 58 Renmin Ave, Haikou 570228, Hainan, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon peaking; Carbon emission forecast; Carbon peaking path; Carbon reduction strategy for nonferrous metals industry; CO2 EMISSIONS PEAK; ENVIRONMENTAL PERFORMANCE; DECOMPOSITION ANALYSIS; ENERGY-CONSUMPTION; COMPOSITIONAL DATA; SCENARIO ANALYSIS; NEURAL-NETWORK; REDUCTION; IMPACT; GAS;
D O I
10.1016/j.energy.2024.132386
中图分类号
O414.1 [热力学];
学科分类号
摘要
The Chinese government has mandated that the nonferrous metals industry (NMI) achieve carbon peaking before 2030. Consequently, nonscientific mitigation measures, such as indiscriminate closure of production lines, have been implemented. An appropriate carbon peak pathway must be developed NMI. This study designs three carbon peaking pathways for two subsectors within the industrial chain of the NMI according to the latest industry policies and industry characteristics. Subsequently, a novel integrated dynamic assessment model is constructed in this study to evaluate the probability of carbon peaking under various pathways and choose an appropriate one. Results indicate that: (1) Under the baseline development pathway, the likelihood of meeting the target as anticipated is 61.6 % for the nonferrous metal mining and processing industry (MS) and 9.9 % for the nonferrous metal smelting and refining industry (SS). (2) The low-carbon scenario is the most suitable development path for both subsectors. Under this pathway, the likelihood of MS and SS reaching their targets on time increases to 100 % and 87.4 %, respectively. MS likely peaked in 2013, with a peak at 28 Mt. SS is projected to peak in carbon emissions by 2025, with the highest probability of peaking between 417.6 Mt and 424.5 Mt. If no fluctuations will takes place during the implementation of low-carbon policies, then the peak would occur at 423.9 Mt (3) In this pathway, the industrial value added and energy intensity have the greatest influence on the probability of missing the deadline. This study enriches prediction methods for CO2 emissions and offers guidance to the Chinese nonferrous metal industry for effectively achieving the peak carbon target.
引用
收藏
页数:16
相关论文
共 6 条
  • [1] Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China's Logistics Industry
    Chen, Ting
    Wang, Maochun
    SUSTAINABILITY, 2024, 16 (05)
  • [2] Analysis of China's Electricity Price and Electricity Burden of Basic Industries Under the Carbon Peak Target Before 2030
    Qiu, Yue
    Zhou, Suyang
    Gu, Wei
    Zhang, Xiao-Ping
    CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2024, 10 (02): : 481 - 491
  • [3] Allocation of provincial carbon emission allowances under China's 2030 carbon peak target: A dynamic multi-criteria decision analysis method
    Cheng, Yonglong
    Gu, Baihe
    Tan, Xianchun
    Yan, Hongshuo
    Sheng, Yuhui
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 837
  • [4] Will China's carbon intensity achieve its policy goals by 2030? Dynamic scenario analysis based on STIRPAT- PLS framework
    Xie, Pinjie
    Liao, Jie
    Pan, Xianyou
    Sun, Feihu
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 832
  • [5] Pathways for electric power industry to achieve carbon emissions peak and carbon neutrality based on LEAP model: A case study of state-owned power generation enterprise in China
    Cai, Liya
    Duan, Jinglin
    Lu, Xijia
    Luo, Ji
    Yi, Bowen
    Wang, Ya
    Jin, Dong
    Lu, Yanghui
    Qiu, Laiyi
    Chen, Shen
    Zhang, Hao
    Wang, Liao
    COMPUTERS & INDUSTRIAL ENGINEERING, 2022, 170
  • [6] Optimum low-carbon transformation pathways of China's iron and steel industry towards carbon neutrality based on a dynamic CGE model
    Liu, Xianmei
    Li, Jialin
    Bai, Caiquan
    Peng, Rui
    Chi, Yuanying
    Liu, Yuxiang
    ENERGY, 2024, 313