Laura Kaikkonen1,2, Tuuli Parviainen1,2, Mika Rahikainen3, Laura Uusitalo4, Annukka Lehikoinen1,2,5. 1. Ecosystems and Environment Research Programme, University of Helsinki, Helsinki, Finland. 2. Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, Finland. 3. Bioeconomy Statistics, Natural Resource Institute Finland, Helsinki, Finland. 4. Programme for Environmental Information, Finnish Environment Institute, Helsinki, Finland. 5. Kotka Maritime Research Centre, Kotka, Finland.
Abstract
Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78.
Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78.
Authors: Andrew K Ringsmuth; Ilona M Otto; Bart van den Hurk; Glada Lahn; Christopher P O Reyer; Timothy R Carter; Piotr Magnuszewski; Irene Monasterolo; Jeroen C J H Aerts; Magnus Benzie; Emanuele Campiglio; Stefan Fronzek; Franziska Gaupp; Lukasz Jarzabek; Richard J T Klein; Hanne Knaepen; Reinhard Mechler; Jaroslav Mysiak; Jana Sillmann; Dana Stuparu; Chris West Journal: Clim Risk Manag Date: 2022-01-11
Authors: Laura Kaikkonen; Inari Helle; Kirsi Kostamo; Sakari Kuikka; Anna Törnroos; Henrik Nygård; Riikka Venesjärvi; Laura Uusitalo Journal: Environ Sci Technol Date: 2021-06-21 Impact factor: 9.028