Keywords

Bayesian Decision Networks; trade-offs; ecosystem services

Location

Session F3: Modeling with Stakeholders: Old Problems, New Solutions

Start Date

17-6-2014 2:00 PM

End Date

17-6-2014 3:20 PM

Description

Decisions in environmental management often have trade-offs attached: Either cost-benefit trade-offs, such as real costs and effectiveness of water treatment alternatives, or environmental trade-offs, such as environmental costs and benefits of land use alternatives which are more difficult to quantify. Vegetation managers in oasis towns of the Taklamakan desert in Xinjiang, NW China, were interested in a specific local environmental trade-off. They wanted to know which urban and peri- urban plant species were most effective in mitigating dust weather while needing the least irrigation. To assess this trade-off, we developed a Bayesian Network and two simple Bayesian Decision Networks (BDNs). BDNs use so-called decision and utility nodes to compare and rank management options according to their costs and benefits. In our research project, the BDNs perform a cost-benefit analysis by calculating the net benefits of 16 urban and peri-urban plant species according to their irrigation needs (= costs) and ecosystem services (= benefits). Our case study shows that BDNs can be easily adapted to specific needs of environmental planners and managers.

 
Jun 17th, 2:00 PM Jun 17th, 3:20 PM

Assessing environmental trade-offs with Bayesian Decision Networks - Comparing ecosystem services and irrigation needs of urban and peri-urban plant species in Xinjiang, NW China

Session F3: Modeling with Stakeholders: Old Problems, New Solutions

Decisions in environmental management often have trade-offs attached: Either cost-benefit trade-offs, such as real costs and effectiveness of water treatment alternatives, or environmental trade-offs, such as environmental costs and benefits of land use alternatives which are more difficult to quantify. Vegetation managers in oasis towns of the Taklamakan desert in Xinjiang, NW China, were interested in a specific local environmental trade-off. They wanted to know which urban and peri- urban plant species were most effective in mitigating dust weather while needing the least irrigation. To assess this trade-off, we developed a Bayesian Network and two simple Bayesian Decision Networks (BDNs). BDNs use so-called decision and utility nodes to compare and rank management options according to their costs and benefits. In our research project, the BDNs perform a cost-benefit analysis by calculating the net benefits of 16 urban and peri-urban plant species according to their irrigation needs (= costs) and ecosystem services (= benefits). Our case study shows that BDNs can be easily adapted to specific needs of environmental planners and managers.