This study applies a directional distance function(DDF)data envelopment analysis(DEA)model to measure the environmental efficiency of 12 U.S.airlines 2013–2016 by considering flight delay and greenhouse gas(GHG)emiss...This study applies a directional distance function(DDF)data envelopment analysis(DEA)model to measure the environmental efficiency of 12 U.S.airlines 2013–2016 by considering flight delay and greenhouse gas(GHG)emissions as joint undesirable outputs.First,the environmental efficiency of airlines is compared using the CCR DEA(without flight delay)and DDF DEA(with flight delay).We find that several airlines experienced substantial changes in environmental efficiency scores when flight delay is considered.Secondly,a tobit regression is used to explore whether the environmental factors of fleet age,ownership type,freight traffic,market share,and carrier type affect airlines’environmental efficiency.The results demonstrate that all of these factors significantly influence airline performance.展开更多
The directional distance function(DDF)framework has been widely used to estimate the marginal abatement cost(MAC)of CO_(2)emissions to support decision-making in environmental sustainability and climate change issues....The directional distance function(DDF)framework has been widely used to estimate the marginal abatement cost(MAC)of CO_(2)emissions to support decision-making in environmental sustainability and climate change issues.In the use of DDF,an important task is mapping evaluated entities towards a realistic production technology frontier.This study develops a new nonparametric approach for estimating the MAC of CO_(2)emissions.The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages.First,it avoids the arbitrariness in mapping directions.Second,it captures the heterogeneity in optimization paths across different decision-making units(DMUs).Third,it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle.We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level.The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO_(2)emissions when CO_(2)emissions are unregulated.Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs.展开更多
文摘This study applies a directional distance function(DDF)data envelopment analysis(DEA)model to measure the environmental efficiency of 12 U.S.airlines 2013–2016 by considering flight delay and greenhouse gas(GHG)emissions as joint undesirable outputs.First,the environmental efficiency of airlines is compared using the CCR DEA(without flight delay)and DDF DEA(with flight delay).We find that several airlines experienced substantial changes in environmental efficiency scores when flight delay is considered.Secondly,a tobit regression is used to explore whether the environmental factors of fleet age,ownership type,freight traffic,market share,and carrier type affect airlines’environmental efficiency.The results demonstrate that all of these factors significantly influence airline performance.
基金support provided by the National Natural Science Foundation of China(nos.71804066&71625005)。
文摘The directional distance function(DDF)framework has been widely used to estimate the marginal abatement cost(MAC)of CO_(2)emissions to support decision-making in environmental sustainability and climate change issues.In the use of DDF,an important task is mapping evaluated entities towards a realistic production technology frontier.This study develops a new nonparametric approach for estimating the MAC of CO_(2)emissions.The approach incorporates the optimal endogenous direction into an enhanced environmental production technology and has three advantages.First,it avoids the arbitrariness in mapping directions.Second,it captures the heterogeneity in optimization paths across different decision-making units(DMUs).Third,it generates more reliable benchmarks for estimating MAC by constructing an environmental technology frontier that is consistent with the material balance principle.We apply the approach to study China's thermal power industry and find clear heterogeneity in MACs and optimization paths at the province level.The results on the optimal endogenous directions show that the DMUs prefer to increase both desirable output and CO_(2)emissions when CO_(2)emissions are unregulated.Comparisons with other approaches reveal that arbitrarily mapping exogenous directions and technology representations are likely to generate distorted and unrealistic MACs.
