A directional distance function approach to regional environmental-economic assessments
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Léopold Simar & Paul W. Wilson, 1998.
"Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models,"
INFORMS, vol. 44(1), pages 49-61, January.
- SIMAR, LÃ©opold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- 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.
- Kumar, Surender, 2006. "Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index," Ecological Economics, Elsevier, vol. 56(2), pages 280-293, February.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Färe, Rolf & Grosskopf, Shawna & Pasurka, Carl A., 2007. "Environmental production functions and environmental directional distance functions," Energy, Elsevier, vol. 32(7), pages 1055-1066.
When requesting a correction, please mention this item's handle: RePEc:eee:ecolec:v:69:y:2010:i:10:p:1918-1925. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.