A data envelopment analysis of the efficiency of China’s thermal power generation

Lam, Pun Lee; Shiu, Alice

doi:10.1016/s0957-1787(02)00036-x

Cited by 128 publications

(50 citation statements)

References 26 publications

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“…All eastern provinces except Hainan witnessed higher than 0.90 in terms of TFCP on average. This means that in general the provinces in the eastern area are more efficient than those in other areas, which is the same with the finding of Lam and Shiu (2001) [26]. In order to further investigate the difference of TFCP among provinces, Estimated Results and Discussion of MCPI Table 3 presents the results of MCPI in China's regional thermal power generation sector, which describes the dynamic change of carbon emission performance.…”

Section: Model Estimation Resultssupporting

confidence: 71%

“…Referring to existing studies [26][27][28], the efficiency of fuel utilization (FE), the research and development (R&D) expenditure (RD), the investment-based regulations (IR) and fee-based regulations (FR), are selected as the main influential variables. As a result, it is assumed that the stochastic term u it is expressed as …where λ i (i = 0, 1, …, 4) represents the vector of unknown parameters to be estimated, here λ i (i = 1, …, 4) are assumed to be negative; w it follows truncated normal distribution.…”

Section: Sfa Model Specificationmentioning

confidence: 99%

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A Parametric Malmquist Analysis of Carbon Emission Performance and Its Dynamic Variation in China’s Thermal Power Generation

Jiang¹

2017

Pol. J. Environ. Stud.

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IntroductionIn response to the challenges arising from global warming and climate change, the Chinese government has made a great effort to control CO 2 emissions in terms of both intensity and amount. In 2015 China promised to lower its carbon intensity by 60-65% by 2030 from the 2005 level and peak its carbon emissions by approximately 2030. The power industry is of vital importance for the accomplishment of these carbon emission reduction targets. As the largest CO 2 emitter among all industries, it consumes approximately 50% of China's coal and emits more than 40% of China's CO 2 from fossil fuel combustion [1]. Given the coal-dominant energy structure in China, the situation of relying on coal-fired power plants to satisfy the growing demand for electricity can hardly be changed in the near future. Under this circumstance, it is crucial for China's thermal power industry to promote its CO 2 emission performance, not only for achieving the Pol. J. Environ. Stud. Vol. 26, No. 6 (2017), 2801-2809 AbstractThis paper employs a parametric Malmquist index to measure carbon emission performance and its dynamic variation of China's thermal power generation sector from 2003 to 2013. The parametric stochastic frontier analysis (SFA) is applied to estimate the total factor carbon emission performance (TFCP), while the Malmquist carbon emission performance index (MCPI) is used to measure the dynamic change. The main findings are as follows:1. The average value of TFCP shows an increasing trend but varies among regions. In addition, the investment-based regulations and the efficiency of fuel utilization have positive effects on TFCP, while the effects of R&D expenditure and fee-based regulations are insignificant. 2. The MCPI of China's provincial thermal power industry grew by an average of 3.1% annually and was mainly driven by the efficiency change component. 3. According to the average levels of TFCP and MCPI, the 30 provinces are divided into four categories, which provide a scientific basis for policymakers to implement regional-oriented strategies for the improvement of both TFCP and MCPI.

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Section: Model Estimation Resultssupporting

confidence: 71%

Section: Sfa Model Specificationmentioning

confidence: 99%

A Parametric Malmquist Analysis of Carbon Emission Performance and Its Dynamic Variation in China’s Thermal Power Generation

Jiang¹

2017

Pol. J. Environ. Stud.

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“…In the first stage, a conventional Data Envelopment Analysis (DEA) model was used to evaluate the water service operator efficiency, while in the second stage the influence of environmental factors on efficiency was investigated implementing a bootstrapped DEA procedure [42]. Since its introduction by Charnes et al [43], Data Envelopment Analysis as a non-parametric technique has been extensively used for measuring efficiency in the public utilities sector to identify and design policy measures (see, for instance [44][45][46][47][48][49][50].…”

Section: Methodsmentioning

confidence: 99%

Are Public-Private Partnerships a Source of Greater Efficiency in Water Supply? Results of a Non-Parametric Performance Analysis Relating to the Italian Industry

Storto

2013

Water

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This article reports the outcome of a performance study of the water service provision industry in Italy. The study evaluates the efficiency of 21 "private or public-private" equity and 32 "public" equity water service operators and investigates controlling factors. In particular, the influence that the operator typology and service management nature -private vs. public -has on efficiency is assessed. The study employed a two-stage Data Envelopment Analysis methodology. In the first stage, the operational efficiency of water supply operators is calculated by implementing a conventional BCC DEA model, that uses both physical infrastructure and financial input and output variables to explore economies of scale. In the second stage, bootstrapped DEA and Tobit regression are performed to estimate the influence that a number of environmental factors have on water supplier efficiency. The results show that the integrated water provision industry in Italy is characterized by operational inefficiencies of service operators, and scale and agglomeration economies may have a not negligible effect on efficiency. In addition, the operator typology and its geographical location affect efficiency.

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“…There have been several efficiency frontier analysis methods reported in the literature (for instance only about power plants Cook & Green, 2005;Golany, Yaakov, & Rybak, 1994;Goto & Tsutsui, 1997;Knittel, 2002;Lam & Shiu, 2001;Olatfubi & Dismukes, 2000;Park & Lesourd, 2000;Sanhueza et al, 2004;Sueyoshi & Goto, 2001). But, the assumptions made for each of these methods are restrictive.…”

Section: Problem Formulationmentioning

confidence: 99%

An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Azadeh

Saberi

Anvari

2011

Computers & Industrial Engineering

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A data envelopment analysis of the efficiency of China’s thermal power generation

Cited by 128 publications

References 26 publications

A Parametric Malmquist Analysis of Carbon Emission Performance and Its Dynamic Variation in China’s Thermal Power Generation

A Parametric Malmquist Analysis of Carbon Emission Performance and Its Dynamic Variation in China’s Thermal Power Generation

Are Public-Private Partnerships a Source of Greater Efficiency in Water Supply? Results of a Non-Parametric Performance Analysis Relating to the Italian Industry

An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

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