Equitable deep decarbonization: A framework to facilitate energy justice-based multidisciplinary modeling

被引:13
|
作者
Spurlock, C. Anna [1 ]
Elmallah, Salma [2 ]
Reames, Tony G. [3 ,4 ]
机构
[1] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd,Mailstop 90R2002, Berkeley, CA 94720 USA
[2] Lawrence Berkeley Natl Lab, 1 Cyclotron Rd,Mailstop 90R4000, Berkeley, CA 94720 USA
[3] United Stated Dept Energy, Off Energy Justice Policy & Anal, 1000 Independence Ave SW, Washington, DC 20585 USA
[4] United Stated Dept Energy, Off Econ Impact & Divers, 1000 Independence Ave SW, Washington, DC 20585 USA
关键词
Energy justice; Deep decarbonization; Energy transitions; Restorative justice; Climate change; Distributional analysis;
D O I
10.1016/j.erss.2022.102808
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Persistent, systemic harms have resulted in inequalities in wealth distribution, energy insecurity, infrastructure reliability, heat island exposure, and preexisting health conditions, all of which have exacerbated climate change driven damages. Efforts to decarbonize our energy system to address the climate crisis must seize the opportunity to reduce inequality. Doing so requires a multidisciplinary approach to assess the tradeoffs between alternative decarbonization pathways. In this Perspective we introduce an Equitable Deep Decarbonization Framework for mapping the tenets of energy justice to the practice of large-scale deep decarbonization pathways modeling designed to facilitate this multidisciplinary effort. We provide discussion of key considerations for each step of the framework to enable modeling that accounts for adaptation co-benefits associated with systematic climate risks to vulnerable communities.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Point Cloud Information Modeling: Deep Learning Based Automated Information Modeling Framework for Point Cloud Data
    Park, Jisoo
    Cho, Yong K.
    JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2022, 148 (02)
  • [42] A framework for modeling and estimating the energy dissipation of VLIW-based embedded systems
    Benini, L
    Bruni, D
    Chinosi, M
    Silvano, C
    Zaccaria, V
    Zafalon, R
    DESIGN AUTOMATION FOR EMBEDDED SYSTEMS, 2002, 7 (03) : 183 - 203
  • [43] Automated Energy Modeling Framework for Microcontroller-Based Edge Computing Nodes
    Lange, Emanuel Oscar
    Jose, Jiby Mariya
    Benedict, Shajulin
    Gerndt, Michael
    ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2022, PT I, 2023, 1797 : 422 - 437
  • [44] An ontology-based framework for automatic building energy modeling with thermal zoning
    Wu, Zhaoji
    Cheng, Jack C. P.
    Wang, Zhe
    Kwok, Helen H. L.
    ENERGY AND BUILDINGS, 2023, 296
  • [45] A Framework for Modeling and Estimating the Energy Dissipation of VLIW-Based Embedded Systems
    L. Benini
    D. Bruni
    M. Chinosi
    C. Silvano
    V. Zaccaria
    R. Zafalon
    Design Automation for Embedded Systems, 2002, 7 : 183 - 203
  • [46] Deep Learning based Modeling for Cutting Energy Consumed in CNC turning process
    Xiao, Qinge
    Li, Congbo
    Kou, Yang
    Tang, Ying
    Du, Yanbin
    2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 1398 - 1403
  • [47] A module-based simulation framework to facilitate the modeling of Quantum Key Distribution system post-processing functionalities
    Engle, Ryan D.
    Hodson, Douglas D.
    Mailloux, Logan O.
    Grimaila, Michael R.
    McLaughlin, Colin, V
    Baumgartner, Gerald
    JOURNAL OF DEFENSE MODELING AND SIMULATION-APPLICATIONS METHODOLOGY TECHNOLOGY-JDMS, 2019, 16 (01): : 45 - 56
  • [48] Energy management for a hybrid electric vehicle based on prioritized deep reinforcement learning framework
    Du, Guodong
    Zou, Yuan
    Zhang, Xudong
    Guo, Lingxiong
    Guo, Ningyuan
    ENERGY, 2022, 241
  • [49] A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling
    Gong, Jibing
    Zhang, Xinghao
    Li, Qing
    Wang, Cheng
    Song, Yaxi
    Zhao, Zhiyong
    Wang, Shuli
    APPLIED SCIENCES-BASEL, 2021, 11 (16):
  • [50] Spatiotemporal grid-based crash prediction—application of a transparent deep hybrid modeling framework
    Mohammad Tamim Kashifi
    Ibrahim Yousif Al-Sghan
    Syed Masiur Rahman
    Hassan Musaed Al-Ahmadi
    Neural Computing and Applications, 2022, 34 : 20655 - 20669