Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach

Fei, Rilong; Lin, Boqiang

doi:10.1016/j.techfore.2016.05.012

Cited by 151 publications

(62 citation statements)

References 58 publications

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“…As shown in Figure 2, the technical efficiency under the metafrontier was lower than the technical efficiency under the group frontier in all of the sub-technologies. These are similar results to those found in previous studies [9,11,39,52]. This result is due to the fact that the metafrontier is composed of all government-sponsored biotechnology R&D project data, whereas the group frontier is composed of R&D project data belonging to an individual sub-biotechnology.…”

Section: Resultssupporting

confidence: 90%

Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach

Park

Shin

2018

Sustainability

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Government R&D investments are steadily increasing with the perception that R&D plays an important role in technological innovation and sustainable economic growth. In particular, because biotechnology is recognized as one of the next growth engines, the Korean government has recently increased their investment in biotechnology R&D. However, careful analysis of the efficiency of government-sponsored R&D projects is still lacking. In this paper, we measured the technical efficiency and technology gap ratio to investigate the efficiency of Korean government-sponsored R&D projects of 16 sub-biotechnologies from 2007 to 2013 using a metafrontier Data Envelopment Analysis approach. There was no improvement in overall efficiency between 2007 and 2013. Biochip development technology has been the most efficient sub-biotechnology field and the least efficient fields have been biotechnology product safety and efficacy assessment technology. Medical science and engineering is the closest to optimal production technology among sub-biotechnologies. The efficiency of universities and government-funded research institutes is high, while the efficiency of companies is relatively low. The results suggest that the government should improve the R&D planning process and establish a customized R&D investment strategy that considers the characteristics of technologies and the seven organization types of R&D conductors to increase R&D efficiency.

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Section: Resultssupporting

confidence: 90%

Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach

Park

Shin

2018

Sustainability

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“…Chen et al [23] measure China's industrial green development. Fei & Lin [24] assess agricultural energy efficiency in China's agricultural sector. However, a substantial part of previous studies have employed the cross-sectional data to assess energy and environmental performance, rather than panel data which includes a time dimension.…”

Section: Literature Reviewmentioning

confidence: 99%

Green Economy Performance and Green Productivity Growth in China’s Cities: Measures and Policy Implication

Lin

2016

Sustainability

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Abstract:Resource depletion and environmental degradation have become serious challenges for China's sustainable development. This paper constructs indicators to assess China's green economy performance and green productivity growth, in which economic expansion, resource conservation and environmental protection need to be incorporated simultaneously. For this purpose, we combine non-radial directional distance function and meta-frontier Malmquist productivity to develop the indicators. The methodology also allows for the decomposition of driving forces of China's green economy. Moreover, the dataset employed in this paper allows for the evaluation of 275 cities in China during the period 2003-2012. The main findings are as follows. First, most of China's cities did not perform efficiently in terms of the green economy, with an average score of only 0.233. Second, the growth rate of green productivity is slower than real GDP, and the green productivity growth in China is only moderate. Third, innovation is the main driving force of China's green productivity growth, but the central region lags behind when it comes to green innovation. Fourth, artificial local protectionism and transport limitations impede the progress of cities that perform ineffectively in the green economy. Based on our empirical findings, we provide policy implications and suggestions for enhancing China's green economy performance and productivity growth.

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“…According to O'Donnell et al [26] and Fei and Lin [33], we define the metatechnology ratio index (MTRI) as:…”

Section: Total-factor Energy Efficiency Under the Metafrontier Frameworkmentioning

confidence: 99%

“…To avoid confusion, we prefer MTR to TGR in this paper as O'Donnell et al have done. Actually, Fei and Lin [33] have redefined TGR to be one minus MTR to follow the intuition of "technology gap". Differently, we next provide an alternative terminology of MTR in order to emphasize its technical efficiency nature.…”

Section: Total-factor Energy Efficiency Under the Metafrontier Frameworkmentioning

confidence: 99%

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

2017

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Under the concept of metafrontier, technology gap ratio is alternatively interpreted as potential energy efficiency. Combined with Malmquist index framework and Shephard energy distance function, we then develop a metafrontier Malmquist energy productivity index to analyze the total-factor energy productivity growth with four specific components: groupfrontier efficiency change index, groupfrontier technological change index, efficiency catch-up index and technological catch-up index. Methodologically, a newly developed two-step stochastic metafrontier analysis is applied to address the potentially biased estimation problems in the previous mixed approach. This analytical framework is used to evaluate the energy productivity growth of China's 35 sub-industries in industrial sector from 2001 to 2015. The main empirical results show that: (1) In terms of cumulative metafrontier Malmquist energy productivity growth, China's overall industry has witnessed a 25% growth and a U-shaped growing trend bottoming out in 2006; meanwhile, 19 sub-industries have suffered an energy productivity loss and the remaining 16 sub-industries have experienced an energy productivity gain. (2) From the technology heterogeneity perspective, light industry outperforms heavy industry in metafrontier Malmquist energy productivity growth, while the intra-group and inter-group energy productivity develops roughly in balance for overall industry. (3) The change of metafrontier Malmquist energy productivity is mainly driven by technological change components rather than efficiency change components. On average, groupfrontier technological change makes the biggest contribution to energy productivity growth, followed by technological catch-up, then efficiency catch-up, and groupfrontier efficiency change is last. (4) The metafrontier Malmquist energy productivity growth has shown a significant convergence in heavy industry and light industry, as well as overall industry.

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Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach

Cited by 151 publications

References 58 publications

Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach

Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach

Green Economy Performance and Green Productivity Growth in China’s Cities: Measures and Policy Implication

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

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