Evaluating air quality in China based on daily data: Application of integer data envelopment analysis

Zhou, Zhixiang; Guo, Xiumei; Wu, Huaqing; Yu, Jie

doi:10.1016/j.jclepro.2018.06.180

Cited by 59 publications

(33 citation statements)

References 34 publications

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“…The Evaluated efficiency model has two forms: a parametric method and a non-parametric method. The types of Non-parametric methods include the DEA model, and includes the integer DEA model [33], super-Efficiency DEA model [34,35], cross efficiency DEA model [36], multi-stage DEA model [37,38], undesired SBM model [39], etc. The Parametric model is Stochastic Frontier Analysis (SFA).…”

Section: Evaluated Model and Methods Of Regional Ecological Efficiencymentioning

confidence: 99%

Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover

Huang

et al. 2020

Sustainability

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A regional coordinated development strategy is an important measure that is often used to implement sustainable development in China. However, many obstacles greatly limit the realization of regional ecological coordinated sustainable development. In this paper, ecological efficiency is utilized as an important indicator of sustainable development, and the network analysis method is used to explore the spatial correlation relationship of regional ecological coordinated sustainable development. This paper calculates the ecological efficiency of each region using the Window slacks-based measure (Window-SBM) model, formulates the spatial network of regional ecological efficiency spillover through the vector auto-regressive (VAR) Granger causality model, and analyzes the spatial spillover relationship and influencing factors of regional ecological efficiency by using the social network analysis method. It is found that the spillover network of ecological efficiency in each region presents a typical core-edge structure. In addition, there is an obvious hierarchical structure among blocks with different directions and functions. Industrial structure, economic development, and geographical proximity have a positive impact on the spatial spillover of regional ecological efficiency, while environmental regulation has a negative impact. Finally, relevant policy suggestions are put forward.

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Section: Evaluated Model and Methods Of Regional Ecological Efficiencymentioning

confidence: 99%

Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover

Huang

et al. 2020

Sustainability

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“…In model (1), and are random variables, and are intensity vectors corresponding to sub-stage 1 and sub-stage 2. Constraints and represent the weak Following the practice in Zhou et al [44] and Bi et al [47], the present paper ensures that the intermediate outputs used in sub-stage 2 are not greater than the output in sub-stage1.…”

Section: Proposed Stochasticmentioning

confidence: 99%

“…However, the environmental efficiency in China's industry section cannot match its size. As China's industrialization deepens, the problem of air pollution is gradually increasing, which has caused serious health problems in the whole country [1]. China has become the country with the most serious air pollution all around the world based on the results in "2016 Yale environmental performance index report".…”

Section: Introductionmentioning

confidence: 99%

“…Sueyoshi and Yuan [24] believe incorporate PM 2.5 and PM 10 as undesirable outputs to scaling air pollution based on DEA models. Zhou et al [1] use the composite indicator, namely the air quality index (AQI), as the only undesirable output to calculate air pollution based on DEA models. Taking industrial waste gas emission as undesirable output, Yang and Li [25] evaluate the environmental efficiency of industrial waste gas control of 39 industrial sectors in China.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Stochastic Two-Stage DEA Model for Evaluating Industrial Production and Waste Gas Treatment Systems

2020

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In recent decades, the high-speed development in China has caused serious air pollution in China. The present paper proposes a stochastic data envelopment analysis (DEA) model based on a general two-stage structure with comprehensively considering the randomness in both desirable and undesirable outputs to calculate the environmental efficiency of the industry system. The new proposed model is more applicable to practical system, and is applied to evaluate the performance of production and waste gas treatment in the industrial sector for China's regions along the "One Belt and One Road" in 2015. The results show that about half of the regions along "One Belt and One Road" in China are inefficient, where the performance on waste gas treatment is significantly worse than that of industrial production. Further, the managers should take different strategies for efficiency improvement in different areas because of the obvious differences in efficiency scores, in which the regions in the southeast area should pay more attention to improving waste gas treatment efficiency while that in the northwest area need to focus on industrial production efficiency.

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“…One of the deficiencies is that previous DEA models ignore proportionality of benchmarks and inefficient DMUs and they present idealistic improvement solutions (Yousefi, Shabanpour, Farzipoor Saen, & Faramarzi, 2014;Yousefi, Shabanpour, Fisher, & Farzipoor Saen, 2016). In fact, these models project the improvement path regardless of proportion between benchmark and inefficient DMUs (Qingyuan, Jie, & Malin, 2018;Rashidi, Shabani, & Farzipoor Saen, 2015;Shokri Kahi, Yousefi, Shabanpour, & Farzipoor Saen, 2017;Zhou, Guo, Wu, & Yu, 2018). To resolve this deficiency, Yousefi, Shabanpour, and Farzipoor Saen (2015) presented an improvement solution by incorporating goal programming into DEA.…”

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confidence: 99%

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

et al. 2019

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To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.

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Evaluating air quality in China based on daily data: Application of integer data envelopment analysis

Cited by 59 publications

References 34 publications

Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover

Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover

A Novel Stochastic Two-Stage DEA Model for Evaluating Industrial Production and Waste Gas Treatment Systems

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

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