Stepwise Improvement for Environmental Performance of Transportation Industry in China: A DEA Approach Based on Closest Targets

Jiawei Li,Qingbo Huang,Yan Li,Taseer Muhammad

doi:10.1155/2021/6620823

Jiawei Li, Qingbo Huang + Show 2 more

Open Access

https://doi.org/10.1155/2021/6620823

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Abstract

Transportation is regarded as an industry with high energy consumption and high CO2 emissions. Little attention has been paid to the environmental performance improvement of China’s transportation industry, especially in a stepwise improvement way. In this study, we first apply the closest targets DEA method to evaluate the environmental performance in the transportation industry of 30 provincial-level regions in China’s mainland from 2010 to 2017. Then, we incorporate the closest targets and context-dependent DEA model and thus conform a stepwise projection path for each inefficient province to improve environmental performance with less effort by the way of identifying a sequence of intermediate closest targets. The empirical study shows that the environmental performance of the transportation industry obtained from the closest targets model is greater than that obtained from the SBM model for each province. Among the three areas, the eastern area performs the best in environmental performance followed by the central region and western region. Shanghai has the best environmental performance. Additionally, compared with conventional DEA models, the proposed stepwise improvement method can generate easier and closer achieved targets for the inefficient provinces. Hainan, Yunnan, and Xinjiang provinces have the lowest environmental performance, which need four steps to achieve efficiency.

Highlights

With the benefit from the “reform and opening up” launched in 1978, China’s economy has rapidly improved
data envelopment analysis (DEA) has been applied as an analytical technique in the fields of agriculture, banking, transportation, supply chain, and others [17]. us, we suggest the use of the DEA methodology as the main tool to measure the environmental performance of the transportation industry in China’s mainland
Some scholars have paid attention to the environmental performance of the transportation industry focusing one region; for example, Tian et al [32] utilized an improved super-efficiency Slacks-Based Measure (SBM)-DEA model to measure the sustainable development of the transportation industry in Shaanxi province

Summary

Introduction

With the benefit from the “reform and opening up” launched in 1978, China’s economy has rapidly improved. Prior research applying DEA approaches usually yields a “furthest” target or benchmark for any inefficient DMU Under such circumstances, it is difficult for the inefficient DMU to achieve efficiency along the direction determined by its “furthest” target or benchmark in a single step because of the large difference in the inputs and/or outputs between it and the targets. It is difficult for the inefficient DMU to achieve efficiency along the direction determined by its “furthest” target or benchmark in a single step because of the large difference in the inputs and/or outputs between it and the targets To avoid this problem, one effective way is to find the closest targets for the inefficient DMU. E following section reviews the literature on environmental performance evaluation of the transportation industry based on DEA methods and the closest targets approaches in DEA.

Literature Review

Preliminaries

Stepwise Improvement Based on Closest Targets in DEA

Findings

Empirical Study