Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis

Sözen, Adnan; Alp, İhsan; Özdemir, A.

doi:10.1016/j.enpol.2010.06.005

Cited by 86 publications

(30 citation statements)

References 22 publications

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“…Numerous studies have applied the DEA method to investigate the efficiency of energy usage and pollution emissions (including CO 2 ). For example, Seiford & Zhu [16] evaluated the energy efficiency of 30 paper mills; Sözen et al [17] assessed the operational and environmental performance of thermal power plants in Turkey; Wei et al [18] estimated the CO 2 reduction potential of CO 2 for China's provinces; Zhang et al [19] computed the unified energy and carbon efficiency for 252 fossil fuel power plants in China; Honma & Hu [20] estimated total-factor energy efficiency scores for 47 regions across Japan; Yang et al [21] assess green development efficiency of 31 provinces in China; Chen [22] builds a dynamic indicator to measure the ecological economic transition in China using slacks-based DEA model. Chen et al [23] measure China's industrial green development.…”

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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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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“…The model is to maximize the efficiency of the target system with the constraints of the efficiencies of the other systems ≤1 [37]. The efficiency of the target system j 0 can be calculated by solving the programming problem as follows:

\begin{matrix} \max h_{normal0} = \frac{{true \sum}_{i = 1}^{p} v_{i} y_{i j_{0}}}{{true \sum}_{r = 1}^{m} u_{r} x_{r j_{normal0}}} \end{matrix}

subject to

\begin{matrix} \frac{{true \sum}_{i = 1}^{p} v_{i} y_{i j}}{{true \sum}_{r = 1}^{m} u_{r} x_{r j}} \leq normal1 (j = normal1,2, \dots, t) \\ u_{r} \geq ε r = normal1,2, \dots, m \\ v_{i} \geq ε i = normal1,2, \dots, p, \end{matrix}

where ε is a nonarchimedean construct.…”

Section: Sustainability Efficiency Analysismentioning

confidence: 99%

Emergy Analysis and Sustainability Efficiency Analysis of Different Crop‐Based Biodiesel in Life Cycle Perspective

Ren

Manzardo²,

Mazzi

et al. 2013

The Scientific World Journal

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Biodiesel as a promising alternative energy resource has been a hot spot in chemical engineering nowadays, but there is also an argument about the sustainability of biodiesel. In order to analyze the sustainability of biodiesel production systems and select the most sustainable scenario, various kinds of crop-based biodiesel including soybean-, rapeseed-, sunflower-, jatropha- and palm-based biodiesel production options are studied by emergy analysis; soybean-based scenario is recognized as the most sustainable scenario that should be chosen for further study in China. DEA method is used to evaluate the sustainability efficiencies of these options, and the biodiesel production systems based on soybean, sunflower, and palm are considered as DEA efficient, whereas rapeseed-based and jatropha-based scenarios are needed to be improved, and the improved methods have also been specified.

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“…This technique has been utilized to measure the relative energy and environmental performance of fossil fuel electricity generation. The related studies include Barros and Peypoch (), Maruyama and Eckelman (), Sozen and colleagues (), Liu and colleagues (), Sueyoshi and colleagues (), Sueyoshi and Goto (, ), and Zhou and colleagues (), among others.…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Performance of China's Power Industry: Regional Disparity and Spatial Analysis

Wang

2016

J of Industrial Ecology

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Summary This study employs an undesirable‐output–oriented data envelopment analysis model to measure the carbon emission performance of the power industry throughout China's 30 administrative regions during the period of 2003–2012. Also, it further studies the regional disparity and spatial correlation of the carbon emission performance of China's power industry. The main findings are as follows: (1) The carbon emission performance of China's power industry is at a relatively low level, but shows a rising trend. (2) The regional carbon emission performance of China's power industry is extremely unbalanced: The eastern area ranks first, with the highest performance of 0.851, followed by the central area, whereas the western area falls behind, with the lowest performance of 0.760. Provinces in the eastern area generally perform better than those in other areas. (3) According to spatial analysis, the global Moran's I values of carbon emission performance are significantly positive during the sample period, which indicates that the carbon emission performance is a positive spatial correlation and has an obvious clustering effect. The estimate of the local spatial autocorrelation index confirms the imbalance of spatial distribution of the power sector's carbon emission performance. Based on the above findings, several policy suggestions are presented in this article.

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Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis

Cited by 86 publications

References 22 publications

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

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

Emergy Analysis and Sustainability Efficiency Analysis of Different Crop‐Based Biodiesel in Life Cycle Perspective

Carbon Emission Performance of China's Power Industry: Regional Disparity and Spatial Analysis

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