Frame Work of Data Envelopment Analysis—A Model to Evaluate the Environmental Efficiency of China'S Industrial Sectors

Objective To evaluate the environmental and technical efficiencies of China＇s industrial sectors and provide appropriate advice for policy makers in the context of rapid economic growth and concurrent serious environmental damages caused by industrial pollutants. Methods A data of envelopment analysis （DEA） framework crediting both reduction of pollution outputs and expansion of good outputs was designed as a model to compute environmental efficiency of China＇s regional industrial systems. Results As shown by the geometric mean of environmental efficiency, if other inputs were made constant and good outputs were not to be improved, the air pollution outputs would have the potential to be decreased by about 60% in the whole China. Conclusion Both environmental and technical efficiencies have the potential to be greatly improved in China, which may provide some advice for policy-makers.

Evaluating the environmental efficiency of the U.S.airline industry using a directional distance function DEA approach

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.

Energy Efficiency and Emissions Reduction Potential of China's Industrial Sector

Based on the total-factor energy efficiency framework,this paper calculates China's industrial energy efficiency and CO2 emissions reduction potential from 2000 to 2009 by utilizing the directional distance function and data envelopment analysis.The empirical results show that:China's industrial overall energy efficiency is relatively lower while the emis-sions reduction potential is relatively greater,given the optimum production frontier.Significant indus-trial disparities of energy efficiency and emissions reduction potential exist.Energy efficiency and emis-sions reduction potential significantly show different tendencies of industrial dynamic variation.This paper suggests the Chinese government impose differential carbon taxes,flexibly utilize carbon market mecha-nism,strengthen energy-saving technological R&D,promote the utilization of renewable energy,and strengthen environmental supervision and regulation,so as to improve China's industrial energy efficiency and reduce CO2 emissions.

An Empirical Study on Environmental Regulatory Costs &Environmental TFP

This paper examines the cost of environmental regulation and the environmental total factor productivity （TFP） with directional distance function and the Malmquist-Luenberger （ALL） index respectively, using inputs and output data of 36 two-digit industries over the period 1998- 2010. It finds that Chinese industries incur a relatively high environmental regulatory cost and that China has paid a high price fulfilling its promise to emissions mitigation. A comparison between conventional and environmental TFP shows that the two indicators for all industries declined on average, but a hypothesis test reveals insignificant difference between the two. In addition, the rise in environmental TFP is mainly due to technological progress, which is consistent with findings of many researches; analysis demonstrates signs of absolute convergence of environmental TFP.

Operational and investment efficiency of investment trust companies:Do foreign firms outperform domestic firms?

This study examines the efficiency of investment trust companies(ITCs)from 2011 to 2020 using a meta-frontier two-stage network data envelopment analysis(DEA)based on the directional distance function(DDF).We improved the accuracy of the efficiency measurement and added a network-based ranking component to rank the topperforming entities.In the group-specific technology assessment,foreign ITCs excel in investment efficiency.Meanwhile,in the meta-technology assessment,domestic ITCs outperform foreign ITCs in terms of both investment and operational efficiencies.Group-specific technology efficiency scores were found to be lower than or equal to the meta-technology efficiency scores for both the operational and investment stages.Based on the network-based ranking approach,Yuan Ta,a domestic ITC that ranked fourth in the operational stage and first in the investment stage,can be used as a reliable benchmark.This study will enable practitioners to gain a better understanding of the performance of ITCs operating under heterogeneous technologies.

Re-measuring Productivity, Energy and Environmental Factors of China＇s New Industrialization

This paper identifies low-carbon, energy-efficient, and green economic growth as the primary goals of China＇s new industrialization and defines productivity under new industrialization which; unlike conventional productivity, simultaneously satisfies these goals. In contrast to conventional methods for measuring productivity, this paper employs a non-radial, non-angular SBM directional distance function to estimate new industrialization productivity of industrial sectors across 30 Chinese provinces, municipalities and autonomous regions between 1998 and 2008. Our research shows that conventional measurements of productivity overestimate industrial growth in China＇s central and western regions. On the whole, prospects for growth performance of China＇s industrial sectors are not optimistic. The degree of new industrialization is higher in eastern region than in central and western regions, where balancing industrial growth with resources and the environment is a daunting task. This paper＇s conclusions support ＂environmental Kuznets curve＂ and ＂Porter hypothesis＂ but reject ＂pollution asylum hypothesis ＂.

A nonparametric distance function approach with endogenous direction for estimating marginal abatement costs of CO_(2) emissions

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.

Dynamic changes and convergence of China's regional green productivity:A dynamic spatial econometric analysis

Low-carbon economic development is at the heart of the post-pandemic green recovery scheme worldwide.It requires economic recovery without compromising on the environment,implying a critical role that green productivity plays in achieving the carbon neutrality goal.Green productivity measures the quality of economic growth with consideration for energy consumption and environmental pollution.This study employs the slacks-based measure directional distance function(SBM-DDF)approach and the Malmquist-Luenberger(ML)index to calculate green productivity and its components of 30 provinces in China between 2001 and 2018.Using a spatial panel data model,we empirically analyzed the conditionalβ-convergence of China's green productivity.We found that overall,since 2001,China's green productivity has demonstrated a continuous upward trend.When taking into account spatial factors,China's green productivity demonstrates a significant conditionalβ-convergence.In terms of regional effects,the results indicate that the green productivity of the eastern and western regions demonstrates club convergence,implying a more balanced green economic development.Moreover,the convergence rate of China's green productivity increases with the addition of environmental regulation variable,and so the corresponding convergence time decreases.It indicates that environmental regulations help to facilitate the convergence of China's green productivity,narrowing the gap between the regional green economic development.The findings provide guideline for achieving a low-carbon development and carbon neutrality from a regional green productivity perspective.