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A directional distance function approach to regional environmental-economic assessments

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  • Macpherson, Alexander J.
  • Principe, Peter P.
  • Smith, Elizabeth R.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Ecological Economics.

    Volume (Year): 69 (2010)
    Issue (Month): 10 (August)
    Pages: 1918-1925

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    Handle: RePEc:eee:ecolec:v:69:y:2010:i:10:p:1918-1925

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    Web page: http://www.elsevier.com/locate/ecolecon

    Related research

    Keywords: Environmental assessment Production analysis Efficiency Nonparametric modeling Mid-Atlantic;

    References

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    1. Lozano, Sebastián & Gutiérrez, Ester, 2008. "Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions," Ecological Economics, Elsevier, vol. 66(4), pages 687-699, July.
    2. Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
    3. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    4. Fare, Rolf & Grosskopf, Shawna & Pasurka, Carl Jr., 2007. "Pollution abatement activities and traditional productivity," Ecological Economics, Elsevier, vol. 62(3-4), pages 673-682, May.
    5. Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
    6. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    7. Paul J. Ferraro, 2004. "Targeting Conservation Investments in Heterogeneous Landscapes: A Distance-Function Approach and Application to Watershed Management," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 905-918.
    8. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    9. Picazo-Tadeo, Andres J. & Reig-Martinez, Ernest & Hernandez-Sancho, Francesc, 2005. "Directional distance functions and environmental regulation," Resource and Energy Economics, Elsevier, vol. 27(2), pages 131-142, June.
    10. Banker, Rajiv D. & Zheng, Zhiqiang (Eric) & Natarajan, Ram, 2010. "DEA-based hypothesis tests for comparing two groups of decision making units," European Journal of Operational Research, Elsevier, vol. 206(1), pages 231-238, October.
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    Cited by:
    1. Picazo-Tadeo, Andrés J. & Beltrán-Esteve, Mercedes & Gómez-Limón, José A., 2012. "Assessing eco-efficiency with directional distance functions," European Journal of Operational Research, Elsevier, vol. 220(3), pages 798-809.
    2. H. K. Millington & J. E. Lovell & C. A. K. Lovell, 2013. "Using Fieldwork, GIS and DEA to Guide Management of Urban Stream Health," CEPA Working Papers Series WP072013, School of Economics, University of Queensland, Australia.
    3. Kabata, Tshepelayi, 2011. "The US Agriculture Greenhouse Emissions and Environmental Performance," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103427, Agricultural and Applied Economics Association.
    4. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    5. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.
    6. Andersson, Christian & Månsson, Jonas & Sund, Krister, 2014. "Technical efficiency of Swedish employment offices," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 57-64.
    7. Halkos, George & Tzeremes, Nickolaos, 2012. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from the UK regions," MPRA Paper 38147, University Library of Munich, Germany.

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