An integrated assessment approach to linking biophysical modelling and economic valuation

Abstract

Changes to land use and land management in Australian catchments have led to pressures on natural resources, and concerns over water quality and ecosystem health in catchment rivers and estuaries. To increase the efficiency of natural resource management (NRM) policies that address these concerns, decision makers require information about the environmental impacts, as well as the marginal costs and benefits associated with policy decisions. Including cost-benefits estimates in NRM policy assessment provides decision makers with economic information about the trade-offs between alternative NRM actions. There are, however, few studies that have assessed the complex environmental and economic trade-offs associated with changes in catchment NRM actions in a single modelling framework. This study uses an integrated assessment (IA) approach to develop a decision support model that incorporates environmental and economic dimensions of catchment NRM, for a case-study of the George catchment in Tasmania. Various (academic and non-academic) stakeholders were consulted during the model development process, to gain an understanding of the wide variety of values that may be impacted by NRM changes. Knowledge from different sources was integrated in a single framework using Bayesian network modelling techniques. The framework incorporates three major sub-models: 1. A physically based water quality model to predict the changes in sediment and nutrient loadings in the George rivers and estuary; 2. Expert opinion and Bayesian network modelling to predict the impacts of catchment NRM changes on three ecosystem attributes: riparian vegetation, rare species and estuary seagrass area; and 3. A choice experiment (CE) survey to estimate the non-market values associated with changes in George catchment ecosystem attributes. The CE study was not only aimed at assessing catchment non-market values, but also addressed methodological challenges associated with attribute level framing and cost anchoring in CE. Rather than coupling existing information and models, synchronous data collection and model development were used to ensure tailored information exchange between the different components. The IA approach to model development highlighted several challenges in synchronizing economic and scientific research. Frequent communication was required between the stakeholders involved in the project to construct a common framework for analysis. The selection of attributes that were relevant for scientists, policy makers, and CE survey respondents was a lengthy process. Agreeing on the level of modelling detail, and predicting attribute levels based on sound scientific information, also posed considerable challenges during the model development process.