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Efficiency Of Biomass Production – Methodological Approaches

References AIGNER, D. J. – CHU, S. F. 1968. On Estimating the Industry Production Function. In American Economic Review, 1968, no. 58, p. 826–839 AIGNER, D. J. – LOVELL, C. A. – SCHMIDT, P. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. In Journal of Econometrics, vol. 6, 1977, no. 2, p. 1–37. BANKER, R. D. – MOREY, R. C. 1986. Efficiency Analysis for Exogenously Fixed Inputs and Outputs. In Operations Research, vol. 34, 1986, no. 5, p. 13–52. CHARNES, A. – COOPER, W. W. – RHODES, E. 1978. Measuring the

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Farm Structure And Efficiency In The Hungarian Agriculture

References AFRIAT, S. N. 1972. Efficiency estimation of production functions. In International Economic Review, vol. 13, 1972, no. 3, pp. 568–598. AIGNER, D. J. – LOVELL, C. A. K. – SCHMIDT, P. 1977. Formulation and estimation of stohastic frontier production functions. In Journal of Econometrics, vol. 6, 1977, pp. 21–37. ALLEN, R. – ATHANASSOPOULOS, A. – DYSON, R. G. – THANASSOULIS, E. 1997. Weights Restrictions and Value Judgements in Data Envelopment Analysis: Evolution, Development and Future Directions, Annals of Operational Research, vol

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Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

). Prediction of Biosorption efficiency for the removal of copper (II) using artifi cial neural networks, Journal of Hazardous Materials, 152, pp. 1268-1275. Ranade, V.V. & Bhandari, V.M. (2014). Industrial Wastewater Treatment, Recycling, and Reuse, Elsevier Ltd, ISBN: 978-0-08-099968-5. Sarkar, A. & Pandey, P. (2015). River Water Quality Modelling Using Artificial Neural Network Technique, Aquatic Procedia, 4, pp. 1070-1077. Yesilnacar, M.I. & Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an

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Change in dust collection efficiency of liquid collectors in conditions of dedusting liquid recirculation

(1), 45–49. 10. Byeon, S.H., Lee, B.K. & Mohan, B.R. (2012). Removal of ammonia and particulate matter using a modified turbulent wet scrubbing system. Sep. Purif. Technol. 98, 221–229. DOI: 10.1016/j.seppur.2012.07.014. 11. Mohan, Jain, R. & Meikap, B. (2008). Comprehensive analysis for prediction of dust removal efficiency using twin-fluid atomization in a spray scrubber. Sep. Purif. Technol. 63 (2), 269–277. DOI: 10.1016/j.seppur.2008.05.006. 12. Kim, H., Jung, C., Oh, S. & Lee K. (2001). Particle removal efficiency of gravitational wet scrubber

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Implementation of energy efficiency programs using cogeneration based on internal combustion engines

, Pages: 25-29, Accession Number: WOS:000263153400002, IDS Number: BIV45, ISBN: 978-960-474-016-1. Pătraşcu, R. and Minciuc, E. (2013a). Cogeneration plant for energy supply of a meat processing company: case study. UPB scientific bulletin, series C, vol. 75, no. 4, ISSN 2286-3540, pp. 269-276. Pătraşcu, R. and Minciuc, E. (2013b). Energy audit. Examples, Politehnica Press, Bucharest, ISBN 978-606-515-443-8, 113 pages. Pătraşcu, R., Minciuc, E. and Darie, G. (2013). Case study regarding the energy efficiency of cogeneration compared to separate

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Energy efficiency solutions for driers used in the glass manufacturing and processing industry

References Vuoristo, P. and Mäntylä, T. (2007). Basics of Glass Processes and Coated Glass. Workshop: Glass Performance Days , Tampere. Romanian Parliament, Law no. 121/2014 concerning energy efficiency. Mäntylä, T. (2007). Types of Coatings for Glass and their Properties. Workshop: Glass Performance Days, Tampere, Finland. Mäntylä, T. (2007). Chemical Deposition Methods, Workshop - Glass Performance Days , Tampere, Finland. Kobayashi, H., Wu, K. T., Switzer, L. H., Martinez, S., & Gludici, R. (2006). CO~ 2 reduction from glass melting

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Efficient Use of Water and Fertilizers in Irrigated Agriculture: Drip Irrigation and Fertigation

line spacing, irrigation regimes and planting geometries of tomato on yield, irrigation water use efficiency and net return. In Agric. Wate. Manage., vol. 95, 2008, no. 8, pp. 949–958. ÇETIN, O. – BILGEL, L. 2002. Effects of different irrigation methods on shedding and yield of cotton. In Agric. Wate. Manage, vol. 54, 2002, no. 1, pp. 1–15. CHARTZOULAKIS, K. – BERTAKI, M. 2015. Sustainable water management in agriculture under climate change. In Agriculture and Agricultural Science Procedia, 2015, no. 4, pp. 88–98. HAGIN, J. – SNEH, M. – LOWENGART

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The energy performance contract - key towards energy efficiency in Europe?

References Asociația Română pentru Promovarea Eficienței Energetice (ARPEE) (2013), Eficiența Energetică în România. Cartea Albă , Editura AGIR. BfEE (Presenter, Jonas Geissler) (2015), Energy Performance Contracting in Germany. The role of facilitators for the market , available at (last accessed 3 March 2017). Bleyl, J.W. (2011), Conservation first! The new integrated energy-contracting model to combine energy efficiency and renewable supply in large buildings

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Using non-parametric technical data envelopment analysis - DEA, for measuring productive technical efficiency

References Aigner, D. J., Chu, S.F. (1968), On estimating the industry production function. American Economic Review, 58, 226-239. Agrell, P.J., Hatami-Marbini, A. (2013), Frontier-based performance analysis models for supply chain management: State of the art and research directions, Computers & Industrial Engineering, 66, 567-583. Alene, A. D., Manyong, V.M., Gockowski, J. (2006), The production efficiency of intercropping annual and perennial crops in southern Ethiopia: A comparison of distance functions and

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A Research on the Social Responsibility Efficiency of Chinese Listed Food Companies


On the basis of establishing an input-output index system of listed food companies’ social responsibility, this paper uses the DEA model to assess 22 Chinese listed food companies’ social responsibility efficiency between 2014 and 2016. Results show that the social responsibility efficiency of Chinese listed food companies is generally lower and the average of the 22 companies is only 0.665. The social responsibility management in 81.80% of listed food companies is in a relatively ineffective state. There is a big room for improvement. In addition, the social responsibility efficiencies of listed companies with different sizes are quite different. The social responsibility efficiency of large-sized listed food company is obviously higher than that of the small and medium-sized. The study also shows that the social responsibility efficiencies of most listed food companies are in the stage of increasing returns to scale and more input would be necessary in order to achieve higher efficiency.

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