An Integrated Assessment approach to linking biophysical modelling and economic valuation tools
AbstractNatural resource management (NRM) typically involves complex decision problems that affect a wide variety of stakeholder values. Efficient NRM that achieves the greatest environmental, social and financial net benefits, necessitates assessments of the environmental impacts, costs and benefits of investments in an integrated manner. Integrated assessment (IA) provides an approach to incorporate the several dimensions of catchment NRM by considering multiple issues and knowledge from various disciplines and stakeholders. Despite the need for IA, there are few studies that integrate biophysical modelling tools with economic valuation. In this paper, we demonstrate how economic non-market valuation tools can be used to support an IA of catchment NRM changes. We develop a Bayesian Network model that integrates a process-based water quality model, ecological assessments of native riparian vegetation, estimates of management costs and non- market (intangible) values of changes in riparian vegetation. The modelling approach illustrates how information from different sources can be integrated in one framework to evaluate the environmental and economic impacts of NRM actions, as well as the uncertainties associated with the estimated welfare effects. The estimation of marginal social costs and benefits enables a Cost-Benefit Analysis of alternative management intervention, providing more economic rationality to NRM decisions.
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Bibliographic InfoPaper provided by Environmental Economics Research Hub, Crawford School of Public Policy, The Australian National University in its series Environmental Economics Research Hub Research Reports with number 1053.
Date of creation: Mar 2010
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Bayesian Networks; Bio-economic modelling; Catchment Management; Cost-Benefit Analysis; Environmental values; Integrated Assessment and Modelling; Non-market valuation; Riparian Vegetation;
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- Kragt, Marit E., 2013. "Integrating biophysical and economic systems in a Bayesian Network Hydro-economic framework," Working Papers 153334, University of Western Australia, School of Agricultural and Resource Economics.
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