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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.

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.

Suggested Citation

  • Macpherson, Alexander J. & Principe, Peter P. & Smith, Elizabeth R., 2010. "A directional distance function approach to regional environmental-economic assessments," Ecological Economics, Elsevier, vol. 69(10), pages 1918-1925, August.
  • Handle: RePEc:eee:ecolec:v:69:y:2010:i:10:p:1918-1925
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    References listed on IDEAS

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    Cited by:

    1. A. Ruijs & M. Kortelainen & A. Wossink & C.J.E. Schulp & R. Alkemade & Paul Madden, 2012. "Opportunity cost estimation of ecosystem services," The School of Economics Discussion Paper Series 1222, Economics, The University of Manchester.
    2. Song, Malin & Zhang, Jie & Wang, Shuhong, 2015. "Review of the network environmental efficiencies of listed petroleum enterprises in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 65-71.
    3. Ke Wang & Yujiao Xian & Chia-Yen Lee & Yi-Ming Wei & Zhimin Huang, 2017. "On selecting directions for directional distance functions in a non-parametric framework: A review," CEEP-BIT Working Papers 99, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    4. 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.
    5. 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.
    6. Millington, H.K. & Lovell, J.E. & Lovell, C.A.K., 2015. "A framework for guiding the management of urban stream health," Ecological Economics, Elsevier, vol. 109(C), pages 222-233.
    7. Jianglong Li & Boqiang Lin, 2016. "Green Economy Performance and Green Productivity Growth in China’s Cities: Measures and Policy Implication," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-21, September.
    8. 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.
    9. Chen, Chih Cheng, 2017. "Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties," Omega, Elsevier, vol. 67(C), pages 60-80.
    10. Andersson, Christian & Månsson, Jonas & Sund, Krister, 2014. "Technical efficiency of Swedish employment offices," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 57-64.
    11. 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.
    12. repec:eee:ejores:v:262:y:2017:i:2:p:733-743 is not listed on IDEAS
    13. Pang, Rui-Zhi & Deng, Zhong-Qi & Hu, Jin-li, 2015. "Clean energy use and total-factor efficiencies: An international comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1158-1171.
    14. Alfredsson, Eva & Månsson, Jonas & Vikström, Peter, 2016. "Internalising external environmental effects in efficiency analysis," Economic Analysis and Policy, Elsevier, vol. 51(C), pages 22-31.
    15. 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.
    16. 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.

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