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Shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions at US electric utilities: a random-coefficient, random-directional-vector directional output distance function approach

Guohua Feng (), Chuan Wang () and Apostolos Serletis ()

Empirical Economics, 2018, vol. 54, issue 1, 231-258

Abstract: Abstract We estimate the shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions of electric utilities in the US over the period from 2001 to 2014, using a random-coefficient, random-directional-vector directional output distance function (DODF) model. The main feature of this model is that both its coefficients and directional vector are allowed to vary across firms, thus allowing different firms to have different production technologies and to follow different growth paths. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed-coefficient DODF model. Our results obtained from this model suggest that the average annual shadow price of $$\hbox {CO}_{2}$$ CO 2 emissions ranges from $61.62 to $105.72 (in 2001 dollars) with an average of $83.12. The results also suggest that the firm-specific average shadow price differs significantly across electric utilities. In addition, our estimates of the shadow price of $$\hbox {CO}_{2}$$ CO 2 emissions show an upward trend for both the sample electric utilities as a whole and the majority of the individual sample electric utilities.

Keywords: Shadow price of $$\hbox {CO}_{2}$$ CO 2 emissions; Directional output distance function; Bayesian estimation; Electric utilities (search for similar items in EconPapers)
JEL-codes: D24 C11 Q54 (search for similar items in EconPapers)
Date: 2018
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