Efficiency and Productivity of China's Thermal Power Generation

Lam, Pun-Lee; Shiu, Alice

doi:10.1023/b:reio.0000031347.79588.f3

Cited by 73 publications

(41 citation statements)

References 28 publications

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“…In the second state, they identify the determinants of the efficiency using DEA-Tobit model. [32] shows that the average efficiency score of Chinese thermal power generation plants decreases from 93.7% in 1995 to 91.3% in 1998, but then increases to 93.5% in 2000. And the TFP growth between 1995 and 2000 was 2.1% per year on average.…”

Section: Literature Review On Efficiency Estimation In Electricity Inmentioning

confidence: 95%

Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function

Tian

Zou

2015

Applied Energy

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Section: Literature Review On Efficiency Estimation In Electricity Inmentioning

confidence: 95%

Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function

Tian

Zou

2015

Applied Energy

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“…To model the production process of natural gas-fired electricity generation, a basic production model in line with other applications in the literature (e.g. Lam and Shiu, 2004;Färe et al, 1985) is used. Capital (C), labor (L), and natural gas (F) are used as inputs to produce energy (E) in the form of heat and electricity as sole output.…”

Section: Data Sources Key Variables and Descriptive Statisticsmentioning

confidence: 99%

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

Seifert

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Scale characteristics are key properties of production functions that determine optimal firm sizes, and have considerable policy implications for sectors undergoing restructuring. However, estimates of scale characteristics typically vary with the assumptions of the underlying empirical model. This paper derives estimators of scale efficiency and scale elasticity for semi-parametric stochastic nonsmooth envelopment of data (StoNED) that are based on few assumptions and rely neither on a functional form nor on distributional assumptions, but satisfy basic microeconomic properties. The estimators are applied to a unique sample covering 124 natural gas-fired power plants operating in Germany in 2011. Terms of use: Documents inResults indicate that on average plants operate under constant to slightly decreasing returns-to-scale, and scale inefficiency is found to be overall rather low. However, considerable improvement potential exists due to technical inefficiency. The results allow the strong fragmentation of gas-fired electricity generation in Germany, but emphasize the importance of using best practices on plant level.JEL-Codes: D24,C14,O13,L94

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“…Lam and Shiu used Data Envelopment Analysis (DEA) to study the increasing ratio of total-factor energy efficiency from 1995 to 2000. The effect of technical efficiency was analyzed emphatically [1]. Nelson and Wohar.…”

Section: Literature Reviewsmentioning

confidence: 99%

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Liu

Yang

2017

Energies

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Abstract:Under the background of a new round of power market reform, realizing the goals of energy saving and emission reduction, reducing the coal consumption and ensuring the sustainable development are the key issues for thermal power industry. With the biggest economy and energy consumption scales in the world, China should promote the energy efficiency of thermal power industry to solve these problems. Therefore, from multiple perspectives, the factors influential to the energy efficiency of thermal power industry were identified. Based on the economic, social and environmental factors, a combination model with Data Envelopment Analysis (DEA) and Malmquist index was constructed to evaluate the total-factor energy efficiency (TFEE) in thermal power industry. With the empirical studies from national and provincial levels, the TFEE index can be factorized into the technical efficiency index (TECH), the technical progress index (TPCH), the pure efficiency index (PECH) and the scale efficiency index (SECH). The analysis showed that the TFEE was mainly determined by TECH and PECH. Meanwhile, by panel data regression model, unit coal consumption, talents and government supervision were selected as important indexes to have positive effects on TFEE in thermal power industry. In addition, the negative indexes, such as energy price and installed capacity, were also analyzed to control their undesired effects. Finally, considering the analysis results, measures for improving energy efficiency of thermal power industry were discussed widely, such as strengthening technology research and design (R&D), enforcing pollutant and emission reduction, distributing capital and labor rationally and improving the government supervision. Relative study results and suggestions can provide references for Chinese government and enterprises to enhance the energy efficiency level.

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Efficiency and Productivity of China's Thermal Power Generation

Cited by 73 publications

References 28 publications

Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function

Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function

Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

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