Over the past decade, Bayesian models have been increasingly applied in environmental modelling particularly to support decision-making, policymaking and environmental risk assessment. Bayesian modelling is a method to analyze data and estimate parameters based on Bayes’ theorem and uses probability to solve statistical problems. Among the Bayesian models are Bayesian networks, which are probabilistic graphical models which have become popular to practitioners mainly due to their flexibility, transparency and ability to incorporate uncertainty. Moreover, Bayesian algorithms are used in data mining to generate key patterns from databases.
The Earth is facing unprecedented environmental problems such as pollution, biodiversity loss, climate change, habitat degradation, natural resource depletion, waste disposal and public health issues. The need to sustainably manage the environment is of paramount importance. In this respect, Bayesian models are valuable tools to support sustainable development and management of habitats and ecosystems.
Despite the number of Bayesian models applied in environmental modeling, their application is still limited in various environmental domains such as disease control, climate change adaptation, fate, and transport of pollutants, biodiversity conservation, sustainable use of natural resources, water-energy-food-ecosystems nexus, ecosystem services, cost-benefit analyses of mitigation and restoration actions, the assessment of the cost-effectiveness of nature-based solutions and disaster risk reduction measures. The economic, social and environmental aspects are also rarely integrated into these models. Furthermore, studies seldom investigate the acceptance and use of Bayesian models by stakeholders in environmental management. This Research Topic aims to fill these missing gaps in the literature.
We invite experts to contribute papers covering aspects of the application of Bayesian modeling in environmental management. We are interested in original research, systematic reviews, reviews, mini-reviews, perspectives, and policy briefs. Although contributions on all related topics are welcome, those addressing the following themes are preferred:
• The application of Bayesian models as learning tools for environmental management.
• Overcoming data scarcity and weaknesses of Bayesian models.
• The application of Bayesian models in:
-cost-benefit assessment of environmental measures
-water-food-energy-ecosystem nexus
-disease control through the use of environmental measures
-ecosystem services
-environmental risk assessment
-fate and transport of emerging pollutants
-green infrastructure
-invasive species distribution and control
-environment – human health decision support
-climate risk analysis and mitigation
• The integrated assessment of environmental measures incorporating social and economic indicators.
• Bayesian models on biodiversity and endangered species conservation.
• Assessments of environmental management strategies, policies, and governance.
Over the past decade, Bayesian models have been increasingly applied in environmental modelling particularly to support decision-making, policymaking and environmental risk assessment. Bayesian modelling is a method to analyze data and estimate parameters based on Bayes’ theorem and uses probability to solve statistical problems. Among the Bayesian models are Bayesian networks, which are probabilistic graphical models which have become popular to practitioners mainly due to their flexibility, transparency and ability to incorporate uncertainty. Moreover, Bayesian algorithms are used in data mining to generate key patterns from databases.
The Earth is facing unprecedented environmental problems such as pollution, biodiversity loss, climate change, habitat degradation, natural resource depletion, waste disposal and public health issues. The need to sustainably manage the environment is of paramount importance. In this respect, Bayesian models are valuable tools to support sustainable development and management of habitats and ecosystems.
Despite the number of Bayesian models applied in environmental modeling, their application is still limited in various environmental domains such as disease control, climate change adaptation, fate, and transport of pollutants, biodiversity conservation, sustainable use of natural resources, water-energy-food-ecosystems nexus, ecosystem services, cost-benefit analyses of mitigation and restoration actions, the assessment of the cost-effectiveness of nature-based solutions and disaster risk reduction measures. The economic, social and environmental aspects are also rarely integrated into these models. Furthermore, studies seldom investigate the acceptance and use of Bayesian models by stakeholders in environmental management. This Research Topic aims to fill these missing gaps in the literature.
We invite experts to contribute papers covering aspects of the application of Bayesian modeling in environmental management. We are interested in original research, systematic reviews, reviews, mini-reviews, perspectives, and policy briefs. Although contributions on all related topics are welcome, those addressing the following themes are preferred:
• The application of Bayesian models as learning tools for environmental management.
• Overcoming data scarcity and weaknesses of Bayesian models.
• The application of Bayesian models in:
-cost-benefit assessment of environmental measures
-water-food-energy-ecosystem nexus
-disease control through the use of environmental measures
-ecosystem services
-environmental risk assessment
-fate and transport of emerging pollutants
-green infrastructure
-invasive species distribution and control
-environment – human health decision support
-climate risk analysis and mitigation
• The integrated assessment of environmental measures incorporating social and economic indicators.
• Bayesian models on biodiversity and endangered species conservation.
• Assessments of environmental management strategies, policies, and governance.
