Estimation of the energy efficiency in Chinese provinces

Filippini, Massimo; Lin, Zhang

doi:10.1007/s12053-016-9425-z

Cited by 96 publications

(64 citation statements)

References 45 publications

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“…DEA differs from simple ratio analysis in that it contains multiple inputs and outputs and provides important additional information about where efficiency improvements can be achieved and by how much. DEA is recognized in the literature as a powerful method, which is more suitable for performance measurement activities than traditional econometric methods, such as regression analysis and simple ratio analysis [25][26][27][28][29], and it is also able to determine the operational and managerial efficiencies of companies and industries, etc. [30].…”

Section: Dea-malmquist Index Modelmentioning

confidence: 99%

Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company

Chang

2019

Sustainability

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In recent years, the bakery market has grown rapidly. Alongside its growth and fast change, it is very important to comprehend the productivity change of the bakery industry. Nowadays, effective management is more and more important to ensure the sustainable development of enterprises. Thus, productivity change of 22 self-owned stores of a famous bakery company (85 • C) from 2011 to 2016 was quantitatively analyzed and evaluated by adopting Malmquist index model in this study. Based on the Malmquist index model, the overall mean for total productivity change of 85 • C increased slightly from 2011 to 2016, and the productivity change was easily affected by technical progress. Moreover, the results also show that the north-district self-owned stores (which are located in subtropical climate) have the worst technical progress and total factor productivity change during 2011-2016 period by adopting the non-parametric Kruskal-Wallis and Dunn post-hoc test.

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Section: Dea-malmquist Index Modelmentioning

confidence: 99%

Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company

Chang

2019

Sustainability

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“…Lin and Wang [26] employed an ERF for multi-industries in China. Lin and Du [27], Lin and Long [28], and Shen and Lin [29] employed a SEDF for China's different regions, chemical industry, and sub-industries, respectively; whereas, Broadstock et al [30] and Filippini and Zhang [31] used an EDF for China's households and sub-industries, respectively.…”

Section: Introductionmentioning

confidence: 99%

Measuring underlying energy efficiency in the GCC countries using a newly constructed dataset

et al. 2019

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Energy consumption accounts for most of global anthropogenic greenhouse gas emissions. Managing the growth in energy demand is therefore a key part of climate change mitigation. In the Gulf Cooperation Council (GCC) countries, energy consumption has been growing rapidly. Between 2004 and 2014, final energy consumption grew at an average annual rate of 6.8% compared to a global average of 1.8%. Energy efficiency can help GCC countries manage their energy demand growth, but it is difficult to measure energy efficiency, so analysts often rely on indirect indicators such as energy intensity. Frontier analysis can be used to measure energy efficiency, but is data intensive. To undertake frontier analysis for the GCC countries it was therefore necessary to construct a new dataset, which was used for corrected ordinary least squares to estimate underlying energy efficiency between 2004 and 2014 for two key sectors in the GCC countries: residential electricity and road transport gasoline. The results suggest that underlying energy efficiency generally improved in the GCC region, in contrast to the trend of rising energy intensity that the region has been witnessing. The energy efficiency improvements may have been driven by global technical progress and tighter global fuel economy standards, of which the GCC countries were beneficiaries. With the provision of high quality, recent data with shorter lag times, frontier analysis could be used to provide prompt feedback on the impact of energy efficiency policies and programmes, leading to better outcomes.

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“…Lin and Yang [25,26] estimate the energy efficiency of the Chinese thermal power and steel industries. Filippini and Zhang [27] measure energy efficiency in multiple Chinese provinces. SFA has also been widely applied in productivity analyses.…”

Section: Introductionmentioning

confidence: 99%

Regional Determinants of Energy Efficiency: Residential Energy Demand in Japan

Otsuka

2018

Energies

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Abstract:Controlling the rapid growth of residential energy demand and enhancing energy efficiency are key policy issues in Japan. Thus, this study aims to estimate a residential energy demand function by conducting a stochastic frontier analysis and analyze the regional determinants of energy efficiency. The results indicate that population density and electrification rate foster energy efficiency and exert the same degree of impact. The study also highlights that population concentration has a nonlinear effect on energy efficiency. In other words, when combined with population concentration, the promotion of electrification policies can significantly contribute toward improving energy efficiency in the residential sector and consequently, have a positive effect on Japan's regional economy.

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Estimation of the energy efficiency in Chinese provinces

Cited by 96 publications

References 45 publications

Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company

Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company

Measuring underlying energy efficiency in the GCC countries using a newly constructed dataset

Regional Determinants of Energy Efficiency: Residential Energy Demand in Japan

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