Endogenizing long-term material and energy demand in response to power capacity changes by model soft-linking: application to Japan

被引:0
|
作者
Ju, Yiyi [1 ,2 ]
Cao, Tao [3 ]
Firdaus, Nur [4 ]
Li, Baixin [5 ]
机构
[1] Waseda Univ, Waseda Inst Adv Study, Tokyo 1690051, Japan
[2] Int Inst Appl Syst Anal IIASA, A-2361 Laxenburg, Austria
[3] Univ Tokyo, Grad Sch Arts & Sci, Tokyo 1538902, Japan
[4] Kyoto Univ, Grad Sch Global Environm Studies, Kyoto 6068501, Japan
[5] Waseda Univ, Grad Sch Econ, Tokyo 1698050, Japan
基金
日本学术振兴会;
关键词
Decarbonization transition; Endogenous material demand; Integrated Assessment Model; Life Cycle Assessment; Soft-linking; INTEGRATED ASSESSMENT;
D O I
10.1007/s12667-025-00724-9
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Japan's decarbonization transition towards carbon neutrality by 2050 will be more dependent on the long-term development of renewables. However, the renewable power generation technologies themselves are highly material- and energy-intensive. We estimated such materials and energy demands in response to power capacity changes. Our main results show that: (1) achieving a 100% reduction of GHG emissions requires enormous and urgent investment during 2020-2030; (2) the largest gap of material demands would show in 2020-2030, especially for cement-related products, petrochemical products, cables, wood products, and steel products, but with different degrees of dispersion; (3) the largest gap of industrial energy demands would show later in 2030-2040 as a result of early investment (inter-period iterations). Increasing material efficiency and benefiting more and earlier from the increasingly low-carbon energy supply would be the key to Japan's industrial decarbonization.
引用
收藏
页数:20
相关论文
共 50 条
  • [21] A long-term capacity investment and operational energy planning model with power-to-X and flexibility technologies
    Feijoo, F.
    Pfeifer, A.
    Herc, L.
    Groppi, D.
    Duic, N.
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 167
  • [22] Long-Term Energy and Peak Power Demand Forecasting Based on Sequential-XGBoost
    Zhang, Tingze
    Zhang, Xinan
    Rubasinghe, Osaka
    Liu, Yulin
    Chow, Yau Hing
    Iu, Herbert H. C.
    Fernando, Tyrone
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2024, 39 (02) : 3088 - 3104
  • [23] Development and utilization of an energy and mass flow optimization model for long-term capacity planning in power supply facilities
    Göbelt, M
    Fichtner, W
    Wietschel, M
    Rentz, O
    OPERATIONS RESEARCH PROCEEDINGS 1999, 2000, : 536 - 541
  • [24] Long-Term Forecast of Sierra Leone's Energy Supply and Demand (2019-2040): A LEAP Model Application for Sustainable Power Generation System
    Conteh, Foday
    Furukakoi, Masahiro
    Rangarajan, Shriram Srinivasarangan
    Collins, Edward Randolph
    Conteh, Michael A. A.
    Rashwan, Ahmed
    Senjyu, Tomonobu
    SUSTAINABILITY, 2023, 15 (15)
  • [25] A novel model for the prediction of long-term building energy demand: LSTM with Attention layer
    Gao, Yuan
    Fang, Chengkuan
    Ruan, Yingjun
    SUSTAINABLE BUILT ENVIRONMENT CONFERENCE 2019 TOKYO (SBE19TOKYO) - BUILT ENVIRONMENT IN AN ERA OF CLIMATE CHANGE: HOW CAN CITIES AND BUILDINGS ADAPT?, 2019, 294
  • [26] Energy Model for Long-Term Scenarios in Power Sector under Energy Transition Laws
    Hernandez-Luna, Gabriela
    Romero, Rosenberg J.
    Rodriguez-Martinez, Antonio
    Maria Ponce-Ortega, Jose
    Cerezo Roman, Jesus
    Toledo Vazquez, Guadalupe Diocelina
    PROCESSES, 2019, 7 (10)
  • [27] Study on the Medium and Long-term Power Demand Probabilistic Forecasting and Its Application in Beijing City
    Niu Yantao
    Huang Guohe
    Yang Yongping
    Lin Qianguo
    PROCEEDINGS OF THE 7TH EURO-ASIA CONFERENCE ON ENVIRONMENT AND CSR: TECHNOLOGICAL INNOVATION AND MANAGEMENT SCIENCE SESSION, PT I, 2011, : 106 - +
  • [28] Long-term Power Consumption Demand Prediction: a comparison of Energy associated and Bayesian modeling approach
    Rodriguez Rivero, Cristian
    Sauchelli, Victor
    Daniel Patino, Hector
    Antonio Pucheta, Julian
    Laboret, Sergio
    2015 LATIN AMERICA CONGRESS ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2015,
  • [29] An Analysis of Long-Term Impacts of Demand Response on Investments in Thermal Power Plants and Generation Adequacy
    Zimmermann, Florian
    Bublitz, Andreas
    Keles, Dogan
    Dehler, Joris
    Fichtner, Wolf
    2016 13TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), 2016,
  • [30] An optimally combined forecast model for long-term power demand based on improved grey and SVM model
    School of Economic and Management, North China Electric Power University, Beijing 102206, China
    Song, X.-H. (sxh_bj@126.com), 1803, Central South University of Technology (43):