We introduce new specifications and estimation procedures of traditional distance functions that allow researchers to undertake environmental efficiency and productivity studies within a parametric stochastic framework. Relying on a translog distance function specification that treats the outputs' vector asymmetrically by allowing equi proportional desirable outputs expansion and undesirable outputs contraction, we discuss the relevant properties that characterize the environmental hyperbolic distance function, and show that it can be easily implemented using conventional econometric techniques based on maximum likelihood estimation. We illustrate our model estimating technical efficiency scores for a panel of U.S. electricity generating units that produce electricity and SO2 emissions as byproduct. (C) 2009 Elsevier B.V. All rights reserved.
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Anhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R ChinaAnhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R China
Song, Malin
Wang, Shuhong
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Nankai Univ, Sch Econ, Tianjin 300071, Peoples R ChinaAnhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R China
Wang, Shuhong
Liu, Wei
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East China Univ Polit Sci & Law, Sch Business, Shanghai 201620, Peoples R ChinaAnhui Univ Finance & Econ, Sch Stat & Appl Math, Bengbu 233030, Peoples R China