Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index

Fernández, David Soto; Pozo, Carlos; Folgado, Rubén; Jiménez, Laureano; Guillén‐Gosálbez, Gonzalo

doi:10.1016/j.apenergy.2017.12.008

Cited by 54 publications

(26 citation statements)

References 52 publications

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“…In order to overcome the previous limitations, Data Envelopment Analysis (DEA) represents an attractive tool for performance assessment and, focusing on the last 10 years, there are growing number of studies adopted DEA in energy efficiency analysis. Thanks to its ability to handle multiple inputs and outputs, DEA models have been used to evaluate energy and environmental performance of complex systems such as chemical processes [21], industrial gases facilities [22], service sector [23], and including water [24] and wastewater treatment facilities [25]. Although DEA has great potential for energy efficiency evaluation of WWTPs, it is hardly extensible at international level as a standard tool.…”

Section: Literature Reviewmentioning

confidence: 99%

ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants

et al. 2019

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This paper describes the first methodology specifically tailored to estimate energy efficiency at wastewater treatment plants (WWTPs). Inspired by the cycle of continuous improvement, the method i) precisely defines the concept of energy efficiency in WWTPs, ii) proposes systematic and comparable ways to measure it, and iii) allows benchmarking and diagnosing energy hotspots. The methodology delivers an aggregated measure of the WWTP energy efficiency defined as the Water Treatment Energy Index, a single energy label that uses universally known illustrations enabling wide communication of standardized information on the WWTP energy status. The accuracy, reproducibility and generality of the methodology were validated by a widespread energy benchmarking method, and a case study is presented to show its capabilities. By promoting dialogue towards the

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Section: Literature Reviewmentioning

confidence: 99%

ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants

et al. 2019

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“…Within the total-factor framework, two main methods are used to evaluate energy efficiency: one is stochastic frontier analysis (SFA) approach (Liu et al 2016), and another one is DEA method that is a nonparametric method (Charnes et al 1981). When evaluating energy efficiency, people need to consider artificial parameters are required in SFA approach, while the assumed functions are not needed in DEA approach (Zhang and Bin 2013;Fernández et al 2018). The DEA method can overcome the impact on evaluation results due to subjective factors in the SFA method, and therefore the estimation accuracy can be improved.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, DEA is a static evaluation method, so evaluations based on it can provide only relative levels of energy efficiency across DMUs but cannot show increases or decreases in energy efficiency between periods. Hence, effective practical recommendations and guidance cannot be proposed based on such methods (Wu et al 2017;Fernández et al 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

The total-factor energy productivity growth of China’s construction industry: evidence from the regional level

et al. 2018

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This study uses the total-factor energy productivity change index (TFEPCH) to investigate the changes in energy productivity of construction industry for 30 provincial regions in China from 2006 to 2015, adopting the improved Luenberger productivity index combined with the directional distance function. In addition to traditional economic output indicator, this study introduces building floor space under construction as a physical output indicator for energy productivity evaluation. The TFEPCH was decomposed into energy technical efficiency change and energy technical progress shift. Results indicate that, first, energy productivity of China's construction industry decreased by 7.1% annually during 2006-2015. Energy technical regress, rather than energy technical efficiency, contributed most to the overall decline in energy productivity of China's construction industry. Second, energy productivity in the central region of China decreased dramatically, by a cumulative sum of approximately 77.1%, since 2006, while energy productivity in the eastern and western regions decreased by over 54.3 and 65.3%, respectively. Only two of the 30 provinces considered-Hebei and Shandong-improved their energy productivity during 2006-2015. The findings presented here provide a basis for decision-making and references for administrative departments to set differentiated energy efficiency goals and develop relevant measures. Additionally, the findings are highly significant for energy and resource allocation of Chinese construction industry in different regions. Keywords China Á Total-factor energy productivity Á Construction industry Á Energy technical efficiency change Á Energy technical change

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“…The third stream works on dynamic efficiency assessment of the considered industrial systems. Fernández et al [25] applied DEA and the Malmquist index to assess the productivity and energy efficiency of industrial gases facilities. Sueyoshi et al [26] applied DEA window analysis to assess the performance of US coal-fired power plants.…”

Section: Introductionmentioning

confidence: 99%

Industrial Efficiency Evaluation in China: A Nonparametric Production-Frontier Approach

2019

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An industrial system has positive and negative strategies to adapt to environmental regulations, which can be defined as natural disposability and managerial disposability. Meanwhile, the operational process of an industrial system can be divided into regular production activities and pollutant control activities. Within this, industrial system’s technical efficiency (TE) can be decomposed into economic efficiency (ECE) and environmental efficiency (ENE). On the basis of natural disposability and managerial disposability, this paper proposes static and dynamic data envelopment analysis (DEA) models to evaluate the efficiencies of industrial systems. Based on the proposed approach, TE, ECE, ENE, and Malmqusit productivity index (MPI) values were obtained simultaneously. The MPI values were further separated into the effects of static efficiency change and technical change. The proposed method was applied to assess the technical efficiencies of Chinese regional industrial systems between 2011 and 2015. Key findings are that (1) the low ENE is the main source of technical inefficiency; (2) the average static TE and ENE under natural disposability are both lower than those under managerial disposability; (3) the static efficiency change and technical change of TE are similar to those of ENE; and (4) the technical change has a significant impact on the changes in TE.

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Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index

Cited by 54 publications

References 52 publications

ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants

ENERWATER – A standard method for assessing and improving the energy efficiency of wastewater treatment plants

The total-factor energy productivity growth of China’s construction industry: evidence from the regional level

Industrial Efficiency Evaluation in China: A Nonparametric Production-Frontier Approach

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