Keywords
bayesian networks, catchment management, cost-benefit analysis, environmental values, integrated assessment modelling, non-market valuation
Start Date
1-7-2010 12:00 AM
Abstract
Catchment natural resource management (NRM) involves complex decisions that affect a wide variety of values, issues and stakeholders. Designing efficient NRM policies requires assessments of the environmental impacts, and costs and benefits of management interventions in an integrated manner. However, despite the need for integrated assessment (IA), there are few comprehensive frameworks that integrate biophysical models with economic valuation. Cost-benefit analysis (CBA) is a framework that can support efficient NRM by assessing and comparing the total social costs and benefits of management interventions. However, the environmental modelling that has underpinned CBA has typically been poor, reducing the credibility of the framework to assist in the formulation of policy efficiencies. IA provides an approach to integrate the several dimensions of catchment NRM by considering multiple issues and knowledge from various disciplines and stakeholders. In this paper, we demonstrate how IA can be used to consistently integrate economic information with environmental data in a systematic framework to guide economically efficient decision making. We develop a Bayesian Network (BN) model that can be used as a decision support tool to evaluate the welfare impacts of NRM actions.
An Integrated Assessment approach to linking biophysical modelling and economic valuation tools
Catchment natural resource management (NRM) involves complex decisions that affect a wide variety of values, issues and stakeholders. Designing efficient NRM policies requires assessments of the environmental impacts, and costs and benefits of management interventions in an integrated manner. However, despite the need for integrated assessment (IA), there are few comprehensive frameworks that integrate biophysical models with economic valuation. Cost-benefit analysis (CBA) is a framework that can support efficient NRM by assessing and comparing the total social costs and benefits of management interventions. However, the environmental modelling that has underpinned CBA has typically been poor, reducing the credibility of the framework to assist in the formulation of policy efficiencies. IA provides an approach to integrate the several dimensions of catchment NRM by considering multiple issues and knowledge from various disciplines and stakeholders. In this paper, we demonstrate how IA can be used to consistently integrate economic information with environmental data in a systematic framework to guide economically efficient decision making. We develop a Bayesian Network (BN) model that can be used as a decision support tool to evaluate the welfare impacts of NRM actions.
