Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach

Abstract:In this study, we analyze the environmental efficiency performance and its determinants of 29 manufacturing industries in China from 2006 to 2011 by employing a two-stage DEA (data envelopment analysis)-Tobit model. For providing comparative and robust evidence, the 29 manufacturing industries are classified into three groups based on the pollution intensity. In the first stage, a SBM (slacks-based measure)-DEA model is applied to assess economic efficiency and environmental efficiency scores to illustrate the effects of the environmental factors, while taking into consideration the undesirable output. In the second stage, utilizing these calculated environmental efficiency scores as dependent variables, we employ a Tobit regression model to study the determinants of the environmental efficiency by selecting three independent variables including the industry scale, the openness degree and the energy structure. It turns out that the environmental factors have a positive effect on the lightly polluted industries and a negative effect on the moderately polluted industries, the heavily polluted industries, and the overall industries, while the openness degree, the industry scale, and the energy structure can be effective measures to improve environmental efficiency. Based on these findings, we propose policy measures to enhance the environmental efficiency of manufacturing in China.

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“…In SFA, a production function needs to be confirmed [22,23]. However, it cannot always be correct with respect to subjective perception [24].…”

Section: Introductionmentioning

confidence: 99%

Environmental Efficiency and Its Determinants for Manufacturing in China

2016

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“…Based on the dynamic cracking model, the relationship between the EEV, operating conditions, and nonoperational factors are quantified as Equations (23) to (27). Unlike the operating conditions, the nonoperational factors cannot be proactively adjusted to change the running status of the ethylene-cracking furnace.…”

Section: Least Squares Dynamic Modeling Of Ethylene-cracking Furnacementioning

confidence: 99%

A new energy efficiency evaluation solution for ethylene cracking furnace based on integrated TOPSIS‐DEA model

2019

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Ethylene-cracking furnace has the highest energy consumption of all the devices used in the ethylene industry. The accurate energy-efficiency evaluation of an ethylene-cracking furnace is therefore of great significance for energy saving and productivity gain in ethylene production. Traditionally, a data envelopment analysis (DEA) model has been used to evaluate the energy efficiency. However, ethylene-cracking furnace also involves a multi-index and multiworking-condition energy-efficiency evaluation problem that results from there being multiple material flows, multiple energy flows, and multiple production loads. Therefore, this paper proposes a new energy efficiency evaluation solution for an ethylene-cracking furnace that is based on the integrated technique for order preference by similarity to ideal solution (TOPSIS)-DEA model. First, from the perspective of material and energy flows, an energyefficiency vector (EEV) is designed and integrated into the DEA model to meet the demand for multi-index evaluation. Second, to eliminate interference of changes in working condition on energy efficiency, double virtual energyefficiency benchmarks (DVEEBs) are built using the relationship between EEV, operating conditions, and nonoperational factors, based on the established cracking model. Finally, the rearranged values of the EEV is solved by the TOPSIS method and then evaluated by the DEA model. The actual numerical results show that the proposed solution can support energyefficiency evaluation of an ethylene-cracking furnace subject to the multiple indexes and multiple working conditions. Specifically, the interference of changes in the working condition on the evaluation results can be reduced greatly which ensures the accuracy and objectivity of the energy-efficiency evaluation results.

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“…Undesirable Outputs Production Inputs [34] GDP SO 2 , COD, Nitrogen Labor, capital, energy, water [35] GDP CO 2 Labor, capital, energy [36] GDP CO 2 , SO 2 Labor, capital, coal, crude oil, natural gas [37] Industrial added value CO 2 Labor, capital, energy [38] GDP Labor, capital, energy [39] GDP Waste water, waste gas, solid waste Labor, capital, energy [40] Industrial added value CO 2 , SO 2 Labor, capital, energy [41] Industrial added value NO 2 Capital, electricity [26] GDP CO 2 Labor, capital, energy [42] GDP CO 2 , SO 2 Labor, capital, coal, electricity [4] GDP CO 2 Labor, capital, energy [43] GDP CO 2 Labor, capital, energy [44] GDP Solid waste Labor, capital, coal [45] GDP, primary secondary and tertiary industry PM 10 , SO 2 , NO 2 Coal, oil, gas, electricity, energy investment [46] Industrial added value Waste water, solid waste Labor, capital, coal [22] Industrial added value CO 2 Labor, capital, energy [47] GDP SO 2 , solid waste Labor, capital, energy [48] GDP CO 2 , SO 2 , COD Labor, capital, energy [49] GDP CO 2 , SO 2 , solid waste, industrial dust Labor, capital, energy [50] GDP CO 2 Labor, capital, energy [6] GDP CO 2 Labor, capital, energy [51] GDP CO 2 , SO 2 Labor, capital, energy [52] GDP SO 2 , waste water, solid waste Labor, capital, energy …”

Section: Authors Desirable Outputsmentioning

confidence: 99%

Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis

Zhang

Gao

2018

Sustainability

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There are two categories (i.e., radial and non-radial category) in conventional DEA (Data Envelopment Analysis). Recently, intermediate approach was put forward as a new third category. Intermediate approach is a newly proposed approach and there are quite limited related studies. This study contributes to the DEA studies by putting forward an analytical framework of combining intermediate approach and DEA window analysis along with the concepts of natural and managerial disposability. Such combination is quite meaningful and this new approach has three important features. To the best of our knowledge, such type of research has never been investigated by the existing studies. As an application, this approach is used to evaluate the performance of provinces in China from 2007 to 2014. Furthermore, this study develops a series of performance indices from different perspectives. This study identifies the three important findings. Firstly, eco-technology advancements can achieve economic prosperity and environmental protection simultaneously, and thus should become a new direction of climate policies. Secondly, considerable differences exist in a series of indices that evaluates the performance of various provinces and pollutants from different respective. Then, sufficient attention should be given to the provinces and the pollutants with poor performance. Finally, the Chinese government should promote efficiency improvement by "catching up" for provinces with poor performance in the short term. In addition, the central government should reduce regional disparity in order to promote the social sustainability in the long term.

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Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach

Cited by 246 publications

References 37 publications

Environmental Efficiency and Its Determinants for Manufacturing in China

Environmental Efficiency and Its Determinants for Manufacturing in China

A new energy efficiency evaluation solution for ethylene cracking furnace based on integrated TOPSIS‐DEA model

Social Sustainability Assessment across Provinces in China: An Analysis of Combining Intermediate Approach with Data Envelopment Analysis (DEA) Window Analysis

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