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Accounting For Uncertainty In Determining The Economic Value Of Green Infrastructure Investments

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Green Infrastructure (GI) practices attempt to mitigate the impacts of urbanization through water harvesting, infiltration, evapotranspiration, and reuse on a small-scale. Proponents of GI programs for some time have justified their case based on the potential advantages in cost-effectiveness and wider economic benefits compared to conventional ‘grey’ infrastructure. Today, a growing acceptance of GI as an integral part of an overall stormwater management plans has elevated the need to ensure that public programs, especially if they are intended to meet regulatory requirements (e.g. Combined Sewer Overflow (CSO) Long Term Control Plan) meet the objectives.

Uncertainties in GI performance, adoption rates, costs, and benefits can have significant implications on whether and where a GI program makes sense for municipalities, water utilities, and watershed managers. The issues are particularly important for programs that include some cost sharing because higher levels of public-supported costs reduce the balance to be paid by private entities and lead to higher rates of adoption.

This paper discusses the integration of engineering, economics, and risk analyses in a model for assisting in GI program decision making. Specifically, the paper proposes a methodology for quantifying this uncertainty, allowing decision making to precede with better information and at a confidence the GI program is structured and targeted most effectively. Methods for quantifying uncertainty, producing output (such as the graphic below) and interpretation are discussed along with several applications.

Document Type: Research Article


Publication date: 2012-01-01

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  • Proceedings of the Water Environment Federation is an archive of papers published in the proceedings of the annual Water Environment Federation® Technical Exhibition and Conference (WEFTEC® ) and specialty conferences held since the year 2000. These proceedings are not peer reviewed.

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