Big Data, Small Data: Bayesian Networks in Environmental Policy Analysis in Canada’s Energy Sector
Abstract
Expanding oil and gas development in northwestern Canada is changing the boreal forest and creating tensions with local aboriginal populations, in part because caribou abundance is declining. I present two case studies where Bayesian networks are being used to understand the dynamics of this complex system and to influence environmental policy in the province of British Columbia. In the first case, a causal network based on an emerging consensus of the interaction between landscape change and caribou population dynamics was developed and populated based on a meta-analysis of existing research as well as expert opinion. The model generated insights despite limited data and led to significant policy recommendations. In the second case, habitat selection by caribou was determined by contrasting observed movement data collected using GPS satellite technology with simulated movement data of “unselective” caribou. In this instance, a large dataset was available to parameterize the model and output identified the relative importance of natural features and industrial infrastructure in generating observed behavior. Maps of current and ideal habitat conditions developed from the model identified the locations and expected benefits of potential habitat restoration activities.
About the Presenter
Steve Wilson has more than 25 years’ experience working at technical and professional levels in strategic and operational planning for public and private-sector clients. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia.