To achieve an environmental objective at least cost, decision makers must integrate information about spatially variable biophysical and economic conditions. Although the biophysical attributes that contribute to an environmental outcome are often known, the way in which these attributes interact to produce the outcome is often unknown. We introduce a nonparametric conservation targeting approach that relies on distance functions to cost‐efficiently allocate conservation funds. We apply the approach empirically to the case of conservation contracting for water‐quality objectives. The contract portfolios derived from the application have many desirable properties, including intuitive appeal and robust performance across plausible parametric scoring measures.
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Paul J. Ferraro (2004) studied this question.
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