Energy consumption in China's ICT sectors: From the embodied energy perspective

被引:26
作者
Shi, Jianglan [1 ,4 ]
Li, Chao [2 ,3 ]
Li, Huajiao [5 ,6 ,7 ]
机构
[1] Hebei Univ, Sch Management, Baoding 071002, Peoples R China
[2] Hebei Univ, Coll Qual & Tech Supervis, Baoding 071002, Peoples R China
[3] Natl & Local Joint Engn Res Ctr Metrol Instrument, Baoding 071002, Peoples R China
[4] Hebei Univ, Sch Management, Lab Digital Econ & Management, Baoding 071002, Peoples R China
[5] China Univ Geosci, Sch Econ & Management, Beijing 100083, Peoples R China
[6] Minist Nat Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100083, Peoples R China
[7] China Ctr Int Econ Exchanges, Beijing 100050, Peoples R China
基金
中国国家自然科学基金;
关键词
ICT; Embodied energy; China; Input-output model; Structural path analysis; STRUCTURAL PATH-ANALYSIS; LIFE-CYCLE ASSESSMENT; CRITICAL TRANSMISSION SECTORS; SUPPLY-CHAIN; EMISSIONS; INPUT; INFORMATION; FOOTPRINT; TRANSFERS; INDUSTRY;
D O I
10.1016/j.rser.2022.112313
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Understanding the energy consumption associated with information and communication technology (ICT) is essential for the sustainable development of a digital economy. Previous studies have mainly focused on the impact of ICT on direct energy consumption, and few have studied the embodied energy of ICT sectors. This paper develops an embodied energy analysis framework by integrating the input-output model and structural path analysis to examine the embodied energy of China's ICT sectors. It not only identifies key consumer sectors, final demand and primary sources, but also tracks critical supply chain paths. The results show that the embodied energy of ICT sectors in 2018 was 216.22 million tons of standard coal equivalent, which is nearly three times as much as their direct energy. Communication Equipment was the largest consumer with a share of 30.2%. Export, fixed capital formation and urban consumption were the main final demand, accounting for 57.1%, 25.4% and 12.8%, respectively. It is worth noting that 75.1% of ICT sectors' embodied energy indirectly originated from non-ICT sectors such as Smelting of Metals. This finding indicates that ICT sectors indirectly consume more energy from their upstream sectors, which can explain why the embodied energy of ICT sectors is much greater than their direct energy. In addition, critical supply chain paths that transfer more embodied energy, such as "Smelting of Metals-*Electronic Components-*Export", are extracted. Finally, some suggestions are proposed from production, consumption and path-based perspectives, which can provide new insights into the sustainable development of ICT sectors and the digital economy.
引用
收藏
页数:8
相关论文
共 45 条
  • [1] [Anonymous], 2008, Statistical Papers. Series M
  • [2] Mapping inter-industrial CO2 flows within China
    Bai, Hongtao
    Feng, Xiangyu
    Hou, Huimin
    He, Gang
    Dong, Yan
    Xu, He
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 93 : 400 - 408
  • [3] Exploring the role of ICT on household behavioural energy efficiency to mitigate global warming
    Bastida, Leire
    Cohen, Jed J.
    Kollmann, Andrea
    Moya, Ana
    Reichl, Johannes
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2019, 103 : 455 - 462
  • [4] Life-Cycle Energy Demand and Global Warming Potential of Computational Logic
    Boyd, Sarah B.
    Horvath, Arpad
    Dornfeld, David
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2009, 43 (19) : 7303 - 7309
  • [5] Life cycle assessment of sugar industry: A review
    Chauhan, Manish Kumar
    Varun
    Chaudhary, Sachin
    Kumar, Suneel
    Samar
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2011, 15 (07) : 3445 - 3453
  • [6] EMBODIED ENERGY AND ECONOMIC VALUATION
    COSTANZA, R
    [J]. SCIENCE, 1980, 210 (4475) : 1219 - 1224
  • [7] STRUCTURAL PATH-ANALYSIS AND MULTIPLIER DECOMPOSITION WITHIN A SOCIAL ACCOUNTING MATRIX FRAMEWORK
    DEFOURNY, J
    THORBECKE, E
    [J]. ECONOMIC JOURNAL, 1984, 94 (373) : 111 - &
  • [8] Which renewable energy consumption is more efficient by fuzzy EDAS method based on PESTLE dimensions?
    Demirtas, Ozgur
    Derindag, Omer Faruk
    Zarali, Fulya
    Ocal, Oguz
    Aslan, Alper
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (27) : 36274 - 36287
  • [9] Driving Factors of Serbian Competitiveness - Digital Economy and ICT
    Domazet, Ivana
    Zubovic, Jovan
    Lazic, Milena
    [J]. STRATEGIC MANAGEMENT, 2018, 23 (01): : 20 - 28
  • [10] European Commission Joint Research Centres, 2020, 2020 PREDICT DAT