A directional distance function approach to regional environmental–economic assessments. Macpherson, A. J., Principe, P. P., & Smith, E. R. Ecological Economics, 69(10):1918–1925, August, 2010. ![A directional distance function approach to regional environmental–economic assessments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Numerous difficulties await those creating regional-scale environmental assessments, from data having inconsistent spatial or temporal scales to poorly-understood environmental processes and indicators. Including socioeconomic variables further complicates assessments. While statistical or process-based regional environmental assessment models may be computationally or financially expensive, we propose a simple nonparametric outcomes-based approach using a directional distance function from the efficiency and productivity analysis literature. The regional environmental–economic directional distance function characterizes the relative efficiency of geographic units in combining multiple inputs to produce multiple desirable and undesirable socioeconomic and environmental outputs. This function makes no assumptions about the functional relationships among variables, but by quantifying the extent to which desirable outputs can be expanded and inputs and undesirable outputs contracted, the function can help decisionmakers identify the most important broad-scale management and restoration opportunities across a heterogeneous region. A case study involving 134 watersheds in the Mid-Atlantic region of the USA indicates that, depending on which outputs are specified as desirable in the models, 25%–33% of the watersheds are efficient in producing desirable outputs while minimizing inputs and undesirable outputs. Models including socioeconomic indicators exhibit increased watershed efficiency compared to models using only environmental indicators. Efficiency levels appear to be correlated with ecoregions.
@article{macpherson_directional_2010,
title = {A directional distance function approach to regional environmental–economic assessments},
volume = {69},
issn = {0921-8009},
url = {https://www.sciencedirect.com/science/article/pii/S0921800910001552},
doi = {10.1016/j.ecolecon.2010.04.012},
abstract = {Numerous difficulties await those creating regional-scale environmental assessments, from data having inconsistent spatial or temporal scales to poorly-understood environmental processes and indicators. Including socioeconomic variables further complicates assessments. While statistical or process-based regional environmental assessment models may be computationally or financially expensive, we propose a simple nonparametric outcomes-based approach using a directional distance function from the efficiency and productivity analysis literature. The regional environmental–economic directional distance function characterizes the relative efficiency of geographic units in combining multiple inputs to produce multiple desirable and undesirable socioeconomic and environmental outputs. This function makes no assumptions about the functional relationships among variables, but by quantifying the extent to which desirable outputs can be expanded and inputs and undesirable outputs contracted, the function can help decisionmakers identify the most important broad-scale management and restoration opportunities across a heterogeneous region. A case study involving 134 watersheds in the Mid-Atlantic region of the USA indicates that, depending on which outputs are specified as desirable in the models, 25\%–33\% of the watersheds are efficient in producing desirable outputs while minimizing inputs and undesirable outputs. Models including socioeconomic indicators exhibit increased watershed efficiency compared to models using only environmental indicators. Efficiency levels appear to be correlated with ecoregions.},
language = {en},
number = {10},
urldate = {2023-07-04},
journal = {Ecological Economics},
author = {Macpherson, Alexander J. and Principe, Peter P. and Smith, Elizabeth R.},
month = aug,
year = {2010},
keywords = {Terrestrial Ecoregions (CEC 1997)},
pages = {1918--1925},
}
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