Literature DB >> 28750284

Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective.

Anna Sperotto1, José-Luis Molina2, Silvia Torresan1, Andrea Critto3, Antonio Marcomini1.   

Abstract

The evaluation and management of climate change impacts on natural and human systems required the adoption of a multi-risk perspective in which the effect of multiple stressors, processes and interconnections are simultaneously modelled. Despite Bayesian Networks (BNs) are popular integrated modelling tools to deal with uncertain and complex domains, their application in the context of climate change still represent a limited explored field. The paper, drawing on the review of existing applications in the field of environmental management, discusses the potential and limitation of applying BNs to improve current climate change risk assessment procedures. Main potentials include the advantage to consider multiple stressors and endpoints in the same framework, their flexibility in dealing and communicate with the uncertainty of climate projections and the opportunity to perform scenario analysis. Some limitations (i.e. representation of temporal and spatial dynamics, quantitative validation), however, should be overcome to boost BNs use in climate change impacts assessment and management.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Climate change; Integrated modelling; Multiple stressors; Probabilistic risk assessment; Uncertainty

Mesh:

Year:  2017        PMID: 28750284     DOI: 10.1016/j.jenvman.2017.07.044

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  5 in total

1.  A Decision Analysis Approach to Electronics Standard Development Informed by Life Cycle Assessment Using Influence Diagrams.

Authors:  Therese Garvey; David Meyer; Michael Gonzalez; Brian Dyson; John F Carriger
Journal:  J Clean Prod       Date:  2020-05-01       Impact factor: 9.297

2.  Identification of Factors Influencing Out-of-county Hospitalizations in the New Cooperative Medical Scheme.

Authors:  Wan-Rong Lu; Wen-Jie Wang; Chen Li; Huang-Guo Xiong; Yi-Lei Ma; Mi Luo; Hong-Yu Peng; Zong-Fu Mao; Ping Yin
Journal:  Curr Med Sci       Date:  2019-10-14

3.  Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors.

Authors:  Jinhua Pan; Zeping Ren; Wenhan Li; Zhen Wei; Huaxiang Rao; Hao Ren; Zhuang Zhang; Weimei Song; Yuling He; Chenglian Li; Xiaojuan Yang; LiMin Chen; Lixia Qiu
Journal:  Sci Rep       Date:  2018-02-28       Impact factor: 4.379

4.  Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification.

Authors:  Zhuang Zhang; Jie Zhang; Zhen Wei; Hao Ren; Weimei Song; Jinhua Pan; Jinchun Liu; Yanbo Zhang; Lixia Qiu
Journal:  Sci Rep       Date:  2019-04-18       Impact factor: 4.379

Review 5.  Integrating Public Health into Climate Change Policy and Planning: State of Practice Update.

Authors:  Mary Fox; Christopher Zuidema; Bridget Bauman; Thomas Burke; Mary Sheehan
Journal:  Int J Environ Res Public Health       Date:  2019-09-04       Impact factor: 3.390

  5 in total

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