Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry

Abstract

This study utilizes the Data Envelopment Efficiency (DEA) model to assess input-output efficiency from two perspectives. First, not considering the distribution of interval data, we introduce an adjusted parameter to transform interval data to determination data. Second, by contrast, we take into account the distribution characteristics of interval data and test the DEA model with interval data based on linear uniform distribution and normal distribution with uncertainty. Based on the normal distribution DEA evaluation model, this paper aims to evaluate the input-output performance of the manufacturing industry with the constraint of environmental pollution in the Yangtze River Delta (YRD) region, China. Research has shown that the optimal solution of the normal distribution model is better than that of linear distribution. Therefore, it is imperative to adopt an appropriate method to evaluate the energy and environmental efficiency of this region.

Exploring Driving Forces of Sustainable Development of China’s New Energy Vehicle Industry: An Analysis from the Perspective of an Innovation Ecosystem

“…As a typical emerging industry, the rise of the NEV industry is due to new technological breakthroughs in the fields of energy batteries, motors, electric controls, and so on. Recent studies suggest that technological innovation remains the bottleneck factor affecting sustainable development of the NEV industry [22,23]. Although China has been the largest NEV production base and the largest consumer market in the world since 2015, it lacks competitive core technologies [24].…”

Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends

“…First, this review paper found there are various models of DEA have been used in previous studies. The important of DEA models were non-radial DEA (Wang et al [142]; Bian et al [66]), bootstrap DEA (Duan et al [120]), CCR and BCC models (Shi et al [80]; Mousavi-Avval et al [82]; Khoshnevisan et al [83]), DEA window analysis (Vlontzos and Pardalos [102]; He et al [115]), DEA frontier (Jan et al [78]; Lins et al [61]), VRS (Wang and Wei [81]; Zhou et al [97]), DDF (Vlontzos et al [154]; Wang et al [152]), DEA-Malmquist (Martínez and Piña [145]; Huang et al [137]; Wang and Feng [76]), SBM-DEA (Guo et al [108]; Chu et al [109]), DEA-MBP model (Welch and Barnum [72]), network DEA (Wu et al [67]; Yan et al [121]), stochastic DEA (Vaninsky [126]), stochastic network DEA (Chen et al [127]), SFA (Li and Lin [118]; ), radial stochastic DEA (Zha et al [132]), fuzzy dynamic network-DEA (Olfat et al [138]), CRTS and VRTS (Sueyoshi and Yuan [139]), DEA-DA (Chen et al [141]), fuzzy network SBM model (Shermeh et al [147]), Interval DEA-CCR (Gong and Chen [155]) and SE-DEA (Liu et al [159]). In addition, the results found that one previous review study classifies and review the recent DEA models under the methodological aspect, application schemes, efficiency measure, inputs, outputs.…”

Haze Influencing Factors: A Data Envelopment Analysis Approach

“…In this paper, the stochastic data envelopment analysis model is applied to resolve this problem. When compared with traditional DEA, chance constrained stochastic DEA has two advantages: admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint [41,42]. We study the coupling effect of multiple factors in the effort to identify the influencing factors of PM 2.5 pollution in Jiangsu province.…”