Application of Malmquist productivity index with carry-overs in power industry

Amani, Naser; Valami, Hadi Bagherzadeh; Ebrahimnejad, Ali

doi:10.1016/j.aej.2017.11.007

Cited by 15 publications

(5 citation statements)

References 65 publications

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“…Li and Liu (2019) analyzed the economic growth clusters in the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei regions in China through an inefficiency model and found that resources and environment were the main factors leading to the GTFP inefficiency [41]. Amani et al (2019) considered the use of relaxation-based measurement (SBM) model transformation index in variable return on Scale (VRS) to develop MPI [42]. Balezentis et al (2020) used the environmental Luenberger-Hicks-Moorsteen productivity index based on the distance function of input and output directions to measure the green economic growth of the agricultural sector in selected European countries [43].…”

Section: Introductionmentioning

confidence: 99%

Regional Differences and Convergence of Inter-Provincial Green Total Factor Productivity in China under Technological Heterogeneity

Huang

Yin

Guo

et al. 2022

IJERPH

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Green development is an effective way to reconcile the main contradictions between resources, environment, and regional development. Green total factor productivity (GTFP) is an important index to measure green development; an undesirable output-oriented SBM-DEA model and GML model can be used to calculate GTFP. China’s 30 provinces (municipalities and autonomous regions) are divided into three groups: eastern, central, and western. The common frontier function and group frontier function are established, respectively, to deeply explore the temporal and spatial evolution characteristics and center of gravity shift of inter-provincial green total factor productivity (GTFP) in China, and test the convergence under group frontier, to compare the convergence problems under different regions. This study aims to point out the differences in economic growth in different regions of China, foster regional coordination and orderly progress, promote China’s green development process, and improve the high-quality economic development level. According to the results, the efficiency of green development is more reasonable under the frontier groups. The average TGR in the eastern region was 0.993, indicating that it reached 99.3% of the meta-frontier green development efficiency technology. The inter-provincial GTFP in China gradually increased, with an average value of 1.043, which means China’s green development and ecological civilization construction have achieved remarkable results and the three regions showed significant differences. Judging from the shift path of the spatial center of gravity, the spatial distribution pattern of inter-provincial GTFP in China tends to be concentrated and stable as a whole. Moreover, σ convergence only exists in the western region, while absolute β convergence and conditional β convergence exist in eastern, central, and western regions, indicating that the GTFP of different regions will converge to their stable states over time. The results provide a basis for improving the efficiency of institutional allocation of environmental resources, implementing regional differentiated environmental regulation policies, and increasing the value creation of factor resources, which is of great significance for realizing the high-quality economic development in which resources, environment, and economy are coordinated in China.

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Section: Introductionmentioning

confidence: 99%

Regional Differences and Convergence of Inter-Provincial Green Total Factor Productivity in China under Technological Heterogeneity

Huang

Yin

Guo

et al. 2022

IJERPH

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“…MPI enables the computation of input–output ratio without a production function 53 . This lack of a need for a production function or price information distinguishes it from some productivity indexes and has resulted in MPI being used frequently in dynamic performance change analyses 54 . The formulation of the MPI is presented below 55 …”

Section: Methodsmentioning

confidence: 99%

Investigating the energy security performance, productivity, and economic growth for the EU

Gökgöz

Yalçın

2023

Env Prog and Sustain Energy

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Energy is one of the key elements of economies. The maintenance of daily economic activities would not be possible without assurance of energy security, which is also a critical contributor to sustainable energy. The European Union (EU) strives to protect the energy security of its members in order to prevent supply problems or crises. The main purpose of our analysis is to evaluate the energy security of EU countries by analyzing their performance, productivity, and relationship with economic growth. Using hybrid and comparative MCDM and Malmquist productivity approaches, we assess the static and dynamic energy security performance of the EU countries in the period from 2014 to 2018. We use IDOCRIW‐weighted SAW, MARCOS, and CODAS techniques for static performance measurement and the Malmquist productivity approach for dynamic performance evaluation. Considering both static and dynamic performance among developed EU countries, Denmark is found to be superior. Further, the Malmquist productivity index results indicate a slight improvement for developed countries and deterioration for developing countries. According to the panel data analysis, energy security is a factor that leads to economic growth. Consequently, energy efficiency initiatives such as efficiency action plans, hybrid and electric vehicles, and energy storage advancements play crucial roles in enhancing the EU's energy security.

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“…In this way, an organization's productivity can be easily compared to the other periods or the other organizations. The MPI can also provide support for managers to make decisions and policies to promote the performance of the DMUs (Amani et al, 2018). Therefore, MPI is the most adequate method to compare productivity of the DMUs.…”

Section: Malmquist Productivity Index (Mpi)mentioning

confidence: 99%

A DEA-based comprehensive benchmarking approach for implementing continuous improvement

Yoo

Lee

2022

BPMJ

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PurposeThe purpose of this study is to propose a comprehensive benchmarking approach to help subsidiaries of a company to implement continuous improvement (CI).Design/methodology/approachThe proposed approach classifies subsidiaries of a company into the three stages of CI. After that, the proposed approach employs data envelopment analysis (DEA)-super slacks-based measure (SBM) model and Malmquist Productivity Index (MPI) to evaluate the operational efficiency of the subsidiaries and selects benchmarking targets and sets benchmarking goals based on the operational efficiency for benchmarking of input and output criteria. Then, the proposed approach suggests a four-step benchmarking process for benchmarking of detailed efficiency. Finally, the proposed approach makes the subsidiaries achieve CI by implementing the input and output benchmarking and the efficiency benchmarking.FindingsThe results show that the proposed approach can help subsidiaries of a company to implement a staged benchmarking which considers input and output criteria and Malmquist productivity and efficiency comprehensively.Originality/valueGenerally, benchmarking is implemented in many aspects. However, due to the restriction of a company’s resource level, a practical and staged benchmarking is preferred. This study proposes a comprehensive approach to benchmark systematically and gradually, and provides a more reasonable benchmarking process for implementing CI.

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Application of Malmquist productivity index with carry-overs in power industry

Cited by 15 publications

References 65 publications

Regional Differences and Convergence of Inter-Provincial Green Total Factor Productivity in China under Technological Heterogeneity

Regional Differences and Convergence of Inter-Provincial Green Total Factor Productivity in China under Technological Heterogeneity

Investigating the energy security performance, productivity, and economic growth for the EU

A DEA-based comprehensive benchmarking approach for implementing continuous improvement

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