This paper applies the Bayesian approach to estimate the directional distance function (DDF) with the imposition of monotonicity and curvature, using national data covering 1970-2010. The inefficiency term is further specified as a function of several environmental variables. The salient feature of DDF is its ability to take undesirables into account. For the purpose of comparison we also estimate the output distance function. Evidence is found that the output distance function tends to overestimate the measure of technical efficiency and underestimate the rate of technical change. DDF that imposes monotonicity and curvature is found to be superior to the output distance function in terms of estimated efficiency scores and the rate of technical progress.