Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model

Lu, Li; Zhang, Jijun; Yang, Fan; Zhang, Yongjian

doi:10.1016/j.suscom.2020.100397

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…This study selects an undesirable output model (Shyu and Chiang, 2012;Lu et al, 2020). The undesirable output model includes desirable output and undesirable output, which can effectively reduce the influence on raw data changes and subjective factors.…”

Section: First Stage: Undesirable-outputs Modelmentioning

confidence: 99%

Analyzing the Effect of Local Government Competition on Green Total Factor Productivity From the Market Segmentation Perspective in China—Evidence From a Three-Stage DEA Model

Tang

Fang-ming

2021

Front. Environ. Sci.

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Under both Chinese-style fiscal decentralization (vertical competition) and promotion tournament systems (horizontal competition), the economic development system used by the government determines whether local government competition significantly influences green total factor productivity (GTFP). Moreover, market segmentation, an important strategic tool for local government competition, will significantly impact GTFP because of the implied changes in production efficiency and blocked factor flows. This study applies GMM and the mediation effect model to explore the relationship between local government competition and GTFP from the market segmentation perspective using statistical data from 30 provinces from 2006 to 2017 in China. Overall, our results demonstrate that local government competition significantly inhibits GTFP promotion. Local government competition also has a negative impact on GTFP by promoting market segmentation. As a mediating variable, the market segmentation coefficient was statistically significant. Considering regional heterogeneity, in the eastern region, local government competition has no significant inhibitory effect on GTFP. Moreover, market segmentation has no intermediary effect. In the central and western regions, GTFP remains significantly inhibited by local government competition, and the mediation effect of market segmentation is significant. Finally, our empirical results are robust.

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Section: First Stage: Undesirable-outputs Modelmentioning

confidence: 99%

Analyzing the Effect of Local Government Competition on Green Total Factor Productivity From the Market Segmentation Perspective in China—Evidence From a Three-Stage DEA Model

Tang

Fang-ming

2021

Front. Environ. Sci.

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“…They studied the factors that affect the efficiency in the industry. (Lu et al, 2020) used A three-stage data envelopment analysis (DEA) model to evaluate the total factor productivity of 50 listed Chinese petroleum companies from 2009 to 2018. The study showed that the average annual growth rate of total factor productivity of these companies as 9.05 %, and its efficiency change index and scale efficiency change index were the main driving force for the growth of total factor productivity.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Relative Efficiency for Listed Manufacturing Firms in Jordan Using Data Envelopment Analysis

Al-Durgham¹,

Adeinat²

2021

IJEFI

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The purpose of this study is to measure and compare the efficiencies for 35 manufacturing firms listed in Amman Stock exchange (ASE) in Jordan over the period 2009-2017. A panel data was collected for the firms over the 9 years, the data was collected from the annual reports of the firms. The data envelopment analysis (DEA) was used to measure an average efficiency score for each firm, the DEA was also used to find a panel data for efficiency scores, since the data was available for a short period of 9 years the bootstrap technique was used to estimate a confidence interval for the efficiency score for each firm. The linear transformation form of Cobb-Douglas production function with two inputs (capital and labor) and one output (production) was used in DEA. The study revealed that among the 35 firms only 4 firms were efficient, and the rank for the firms' efficiency were also obtained.

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“…For example, Abbot [26] studied multifactor productivity in the Australian electric utility industry, Ramos-Real et al [27] analysed productivity changes in Brazilian electric utilities, and Liu et al [11] studied technical efficiency and productivity in Taiwanese energy companies. Specifically, country-level studies tend to be conducted in China (e.g., Song et al [28], who measured productivity in the Chinese thermal power industry, Lu et al [12], who analysed and predicted total factor productivity for Chinese petroleum companies, and Zhang et al [29], who studied multifactor productivity in the Chinese coal industry).…”

Section: Conceptual Background With a Literature Reviewmentioning

confidence: 99%

“…For example, Zhang et al [29] used the super-slack-based measure (Super-SBM) with the MPI to evaluate the total factor productivity of 25 Chinese coal companies. Lu et al [12] combined three-stage DEA with time series neural networks to evaluate and predict the total factor productivity of 50 Chinese petroleum companies. Finally, Song et al [28] used DEA and the Malmquist-Luenberger index to evaluate the productivity of the Chinese thermal industry.…”

Section: Conceptual Background With a Literature Reviewmentioning

confidence: 99%

“…Additionally, it leads to an increase in multifactor productivity (hereafter referred to as productivity), which is commonly defined as the ratio between aggregate outputs and aggregate inputs used in the production process. In the last decade, there have been few studies on the efficiency and productivity of the energy industry worldwide [10][11][12]. However, to the best of our knowledge, there have been no studies analysing productivity changes in the European energy industry.…”

Section: Introductionmentioning

confidence: 99%

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Analysing the Pattern of Productivity Change in the European Energy Industry

Borozan

Starčević

2021

Sustainability

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For an industry to succeed in a competitive market, it should continuously take care of not only its stakeholders but also its technical efficiency and productivity. In this paper, data envelopment analysis was combined with Malmquist productivity analysis to investigate the pattern of multifactor productivity changes in the European energy industry over the period from 2005–2016. The results showed that the whole industry was technically inefficient and had large potential for improvement. A slight average increase in productivity that was observed over the studied period proved to be sensitive to the financial and economic situation and equally sensitive to technological and efficiency advances. As for efficiency gains, they reflected the nature of the energy industry, implying that they were due to scale efficiencies rather than human resource improvements. Although technological innovation and the optimal scale of production increased productivity, the slow pace at which this occurred and the negative outlook highlighted by the observed trends call for more serious consideration of the future productivity deployment of the European energy industry, particularly in the context of its decarbonisation, diversification, and modernisation.

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Evaluation and prediction on total factor productivity of Chinese petroleum companies via three-stage DEA model and time series neural network model

Cited by 8 publications

References 25 publications

Analyzing the Effect of Local Government Competition on Green Total Factor Productivity From the Market Segmentation Perspective in China—Evidence From a Three-Stage DEA Model

Analyzing the Effect of Local Government Competition on Green Total Factor Productivity From the Market Segmentation Perspective in China—Evidence From a Three-Stage DEA Model

Assessing the Relative Efficiency for Listed Manufacturing Firms in Jordan Using Data Envelopment Analysis

Analysing the Pattern of Productivity Change in the European Energy Industry

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