Environmental decision‐making using Bayesian networks: creating an environmental report card

Sandra Johnson, Murray Logan, David Fox, John Kirkwood, Uthpala Pinto and Kerrie Mengersen

Applied Stochastic Models in Business and Industry, 2017, vol. 33, issue 4, 335-347

Abstract: Environmental report cards are popular mechanisms for summarising the overall status of an environmental system of interest. This paper describes the development of such a report card in the context of a study for Gladstone Harbour in Queensland, Australia. The harbour is within the World Heritage‐protected Great Barrier Reef and is the location of major industrial development, hence the interest in developing a way of reporting its health in a statistically valid, transparent and sustainable manner. A Bayesian network (BN) approach was used because of its ability to aggregate and integrate different sources of information, provide probabilistic estimates of interest and update these estimates in a natural manner as new information becomes available. BN modelling is an iterative process, and in the context of environmental reporting, this is appealing as model development can be initiated while quantitative knowledge is still under development, and subsequently refined as more knowledge becomes available. Moreover, the BN model helps build the maturity of the quantitative information needed and helps target investment in monitoring and/or process modelling activities to inform the approach taken. The model is able to incorporate spatial and temporal information and may be structured in such a way that new indicators of relevance to the underlying environmental gradient being monitored may replace less informative indicators or be added to the model with minimal effort. The model described here focuses on the environmental component, but has the capacity to also incorporate social, cultural and economic components of the Gladstone Harbour Report Card. Copyright © 2016 John Wiley & Sons, Ltd.

Date: 2017
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https://doi.org/10.1002/asmb.2190

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:33:y:2017:i:4:p:335-347

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