Third generation algae biofuels in Italy by 2030: A scenario analysis using Bayesian networks

被引:44
|
作者
Gambelli, Danilo [1 ]
Alberti, Francesca [1 ]
Solfanelli, Francesco [1 ]
Vairo, Daniela [1 ]
Zanoli, Raffaele [1 ]
机构
[1] Univ Politecn Marche, Via Brecce Bianche, Ancona, Italy
关键词
Advanced biofuels; Microalgae; Scenario analysis; Bayesian networks; Climate change; Sustainability; BIODIESEL PRODUCTION; ENVIRONMENTAL IMPACTS; EXPERT ELICITATION; MICROALGAE; POLICY; OIL; TECHNOLOGY; EVOLUTION; BALANCE; EUROPE;
D O I
10.1016/j.enpol.2017.01.013
中图分类号
F [经济];
学科分类号
02 ;
摘要
We have analysed the potential for biofuels from microalgae in the Italian biofuels context. This scenario analysis considers alternative pathways for the adoption of biofuels from microalgae by the year 2030. The scenarios were developed using a probabilistic approach based on Bayesian networks, through a structured process for elicitation of expert knowledge. We have identified the most and least favourable scenarios in terms of the expected likelihood for the development of the market of biofuels from microalgae, through which we have focussed on the contribution of economic and policy aspects in the development of the sector. A detailed analysis of the contribution of each variable in the context of the scenarios is also provided. These data represent a starting point for the evaluation of different policy options for the future biofuel market in Italy. The best scenario shows a 75% probability that biofuels from microalgae will exceed 20% of the biofuel market by 2030. This is conditional on the improvement and development of the technological changes and environmental policies, and of the markets for bioenergy and novel foods derived from microalgae.
引用
收藏
页码:165 / 178
页数:14
相关论文
共 50 条
  • [11] Financial analysis using Bayesian networks
    Gemela, J
    APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, 2001, 17 (01) : 57 - 67
  • [12] Temporal reasoning for scenario recognition in video-surveillance using Bayesian networks
    Ziani, A.
    Motamed, C.
    Noyer, J. -C.
    IET COMPUTER VISION, 2008, 2 (02) : 99 - 107
  • [13] Preliminary study of acidic hydrolysis in third generation bioethanol production using green algae
    Yheni, M.
    Theofany, H. C.
    Aditiya, H. B.
    Sepwin, N. S.
    4TH ANNUAL APPLIED SCIENCE AND ENGINEERING CONFERENCE, 2019, 2019, 1402
  • [14] GNetIc - Using Bayesian Decision Networks for Iconic Gesture Generation
    Bergmann, Kirsten
    Kopp, Stefan
    INTELLIGENT VIRTUAL AGENTS, PROCEEDINGS, 2009, 5773 : 76 - 89
  • [15] Scenario Generation for Market Risk Models Using Generative Neural Networks
    Flaig, Solveig
    Junike, Gero
    RISKS, 2022, 10 (11)
  • [16] Scenario Generation for Wind Power Using Improved Generative Adversarial Networks
    Jiang, Congmei
    Mao, Yongfang
    Chai, Yi
    Yu, Mingbiao
    Tao, Songbing
    IEEE ACCESS, 2018, 6 : 62193 - 62203
  • [17] Generation of incomplete test-data using Bayesian networks
    Francois, Olivier
    Leray, Philippe
    2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, 2007, : 2390 - 2395
  • [18] Arctic shipping accident scenario analysis using Bayesian Network approach
    Afenyo, Mawuli
    Khan, Faisal
    Veitch, Brian
    Yang, Ming
    OCEAN ENGINEERING, 2017, 133 : 224 - 230
  • [19] Using Bayesian Networks for Cyber Security Analysis
    Xie, Peng
    Li, Jason H.
    Ou, Xinming
    Liu, Peng
    Levy, Renato
    2010 IEEE-IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS DSN, 2010, : 211 - 220
  • [20] Population viability analysis using Bayesian networks
    Penman, Trent D.
    McColl-Gausden, Sarah C.
    Marcot, Bruce G.
    Ababei, Dan A.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2022, 147