An investigation of five generation and regeneration industries using DEA
The data envelopment analysis (DEA) has employed to figure out the efficiency of various engineering projects in the Environment Impact Assessment (EIA) plan and Post-EIA. The procedure allocated to comprise the input and output variables within industries by the present study. The study was used both weighing systems of the Friedman test and the CRiteria Importance Through Intercriteria Correlation (CRITIC) model in the estimation of DEA. The objective of the research sought to find the efficiency of industries for the time interval before the establishment of industries and in the screening step of identification of projects. The findings manifested a classification of industries based on the DEA model and in both weighing systems. Using different weighing systems creates different categories via DEA. Overall, the DEA model is an essential decision-making model in the screening step of EIA.
Anthony, P., Behnoee, B., Hassanpour, M., Pamucar, D. (2019). Financial performance evaluation of seven Indian chemical companies, Decision Making: Applications in Management and Engineering, 2(1), 19-37.
Arab, RO., Masoumi, SS., Asoumi, BA. (2015). Financial Performance of the Steel Industry in India: A Critical Analysis. Middle-East Journal of Scientific Research, 23(6), 1085-1090.
Andrejić, M., Kilibarda, M. (2016). A framework for measuring and improving efficiency in distribution channels. International Journal for Traffic and Transport Engineering, 6(2), 137 – 148. DOI: https://doi.org/10.7708/ijtte.2016.6(2).02
Bharathi, KS., Ramesh, ST. (2013). Removal of dyes using agricultural waste as low-cost adsorbents: a review. Appl Water Sci, 3, 773–790. DOI: https://doi.org/10.1007/s13201-013-0117-y
Bahrami, A., Soltani, N., Pech-Canul, MI., Gutierrez, CA. (2016). Development of metal-matrix composites from industrial/agricultural waste materials and their derivatives. Critical reviews in environmental science and technology, 0(0), 1-66. DOI: https://doi.org/10.1080/10643389.2015.1077067
Bulak, ME., Turkyilmaz, A. (2014). Performance assessment of manufacturing SMEs: a frontier approach. Industrial Management & Data Systems, 114(5), 797-816. DOI: https://doi.org/10.1108/IMDS-11-2013-0475
Bayyurt, N., Duzu, G. (2008). Performance Measurement of Turkish and Chinese Manufacturing Firms: A Comparative Analysis. Eurasian Journal of Business and Economics, 1 (2), 71-83.
Bagh, T., Nazir, MI., Khan, MA., Khan, MA., Razzaq, S. (2016). The Impact of Working Capital Management on Firms Financial Performance: Evidence from Pakistan. International Journal of Economic and Finance, 6(3), 1097-1105.
Blagojević, A., Vesković, S., Kasalica, S., Gojić, A., Allamani, A. (2020). The application of the fuzzy AHP and DEA for measuring the efficiency of freight transport railway undertakings. Operational Research in Engineering Sciences: Theory and Applications, 3(2), 1-23.
Blagojevic, A., Stevic, Z., Marinkovic, D., Kasalica, S., Rajili, S. (2020). A Novel Entropy-Fuzzy PIPRECIA-DEA Model for Safety Evaluation of Railway Traffic. Symmetry, 12, 1479, 1-23.
Biju VG, Prashanth CM. (2017). Friedman and Wilcoxon's evaluations comparing SVM, bagging, boosting, K-NN and decision tree classifiers, JACSM, 9(1), 23-47. DOI: https://doi.org/10.1515/jacsm-2017-0002
Difallah, W., Benahmed, K., Bounnama, F., Draoui B, Saaidi A. (2018). Intelligent Irrigation Management System. International Journal of Advanced Computer Science and Applications, 9(9), 429-433.
Dubey, VN., Dai, JS. (2006). Complex Carton Packaging with Dexterous Robot Hands", Chapter 29 the book: Industrial Robotics: Programming, Simulation and Application, 583-594. Germany.
De, D., Chowdhury. S., Dey, PK., Ghosh, SK. (2020). Impact of Lean and Sustainability Oriented Innovation on Sustainability Performance of Small and Medium-Sized Enterprises: A Data Envelopment Analysis-based framework, 219, 416-430.
Eisinga, R., Heskes, T., Pelzer, B., Grotenhuis MT. (2017). Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinformatics, 18, (68), 2-18. DOI: https://doi.org/10.1186/s12859-017-1486-2
Fenyves, V., Tarnóczi, T., Zsidó, K. (2015). Financial Performance Evaluation of agricultural enterprises with DEA Method. Procedia Economics and Finance, 32, 423 – 431. DOI: https://doi.org/10.1016/S2212-5671(15)01413-6
Gutiérrez, J., Villa-Medina, JF., Nieto-Garibay, A., Porta-Gándara, MÁ. (2013). Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans Instrum Measur, 63(1), 166–176. DOI: https://doi.org/10.1109/TIM.2013.2276487
Hossain, ABMS., Saleh, AA., Aishah, SS., Boyce, AN., Chowdhury, PP., Naqiuddin, M. (2008). Bioethanol Production from Agricultural Waste Biomass as a Renewable Bioenergy Resource in Biomaterials. Biomed Proceedings, 21, 300–305. DOI: https://doi.org/10.1007/978-3-540-69139-6_77
Hassanpour, M. (2020). Evaluation of Iranian Chemical Industries. International journal of chemistry materials research, 8(1), 26-48.
Jha, G. (2016). A Review on Drip Irrigation using Saline Irrigation Water in Potato (Solanum tuberosum L.). Journal of Agro-ecology and Natural Resource Management, 3(1), 43-46.
Jumanne, MK. (2016). Applicability of drip irrigation for smallholder farmers: A case study of the Horticultural industry in Tanzania. Presented in Partial Fulfillment of the Requirement for the Degree Master of Science in the Graduate School of the Ohio State University, 1-70.
IEEM. (2006). Guidelines for ecological impact assessment in the United Kingdom. Institute of Ecology and Environmental Management, 1-67.
Kado, S., Sekine, Y., Nozaki, T., Okazaki, K. (2004). Diagnosis of atmospheric pressure low-temperature plasma and application to high efficient methane conversion. Catalysis Today, 89(1-2), 47–55. DOI: https://doi.org/10.1016/j.cattod.2003.11.036
Kettiramalingam, AY., Sowmiya, K., Sangeetha, P. (2017). Financial performance analysis of select cement companies. Intercontinental journal of finance research, 5(4), 15-27.
Küçükönder, H., Demirarslan, CP., Burgut, A., Boğa, M. (2019). A Hybrid Approach of Data Envelopment Analysis Based Grey Relational Analysis: A Study on Egg Yield. Pakistan J. Zool, 51(3), 903-912.
Lepchak, A., Voese, SB. (2020). Evaluation of the efficiency of logistics activities using Data Envelopment Analysis (DEA). Gestão & Produção, 27(1), e3371, 1-20.
Mansour, LB., Kesentini, I. (2008). Treatment of effluents from cardboard industry by coagulation–electro flotation. J. Hazard. Mater, 153(3), 1067-1070. DOI: https://doi.org/10.1016/j.jhazmat.2007.09.061
Niu, D., Song, Z., Xiao, X., Wang, Y. (2018). Analysis of wind turbine micro siting efficiency: An application of two-subprocess data envelopment analysis method. Journal of Cleaner Production, 170, 193-204. DOI: https://doi.org/10.1016/j.jclepro.2017.09.113
Peeters, JR., Vanegas, P., Tange, L., Houwelingen, JV., Duflou, JR. (2014). Closed-loop recycling of plastics containing Flame Retardants. Resources, Conservation and Recycling, 84, 35– 43. DOI: https://doi.org/10.1016/j.resconrec.2013.12.006
Papouskova, K., Telecky, M., Cejka, J. (2020). Process efficiency analysis of selected automotive companies in Europe. Communications, 22(4) 20-27.
Peoples, J., Abdullah, MA., Satar, NM. (2020). COVID-19 and Airline Performance in the Asia Pacific region. Version 1; peer review: 1 approved with reservations]. Emerald Open Research, 2:62, 1-17.
Raju, GU., Kumarappa, S., Gaitonde, VN. (2012). Mechanical and physical characterization of agricultural waste reinforced polymer composites. J. Mater. Environ. Sci, 3 (5), 907-916.
Raithatha, M., Komera, S. (2016). Executive compensation and firm performance: Evidence from Indian firms. IIMB Management Review, 28, 160–169. DOI: https://doi.org/10.1016/j.iimb.2016.07.002
Sueyoshi, T., Yuan, Y., Goto, M. (2017). A literature study for DEA applied to energy and environment. Energy Economics, 62, 104–124. DOI: https://doi.org/10.1016/j.eneco.2016.11.006
Shermeh, HE., Najafi, SE., Alavidoost, MH. (2016). A novel fuzzy network SBM model for data envelopment analysis: A case study in Iran regional power companies. Energy, 112, 686-697. DOI: https://doi.org/10.1016/j.energy.2016.06.087
Santos, SP., Belton, V., Howick, S., Pilkington, M. (2018). Measuring organizational performance using a mix of OR methods. Technological Forecasting & Social Change, 131, 18–30. DOI: https://doi.org/10.1016/j.techfore.2017.07.028
Sergi, SB., D’Aleo, V., Arbolinoc, R., Carlucci, F., Barilla, D., Ioppolo, G. (2020). Evaluation of the Italian transport infrastructures: A technical and economic efficiency analysis. Land Use Policy, 99, 104961.
Song, M., Jia, G., Zhang, P. (2020). An Evaluation of Air Transport Sector Operational Efficiency in China based on a Three-Stage DEA Analysis. Sustainability, 12, 4220, 1-16.
Taylor, R., Zilberman, D. (2017). Diffusion of Drip Irrigation: The Case of California. Applied Economic Perspectives and Policy, 39(1), 16–40. DOI: https://doi.org/10.1093/aepp/ppw026
Taboada, GL, Han L. (2020). Exploratory data analysis and data envelopment analysis of urban rail transit. Electronics, 9, 1270, 1-29.
Usman, KM., Muhammad, T., Majid, M., Ali, SM., Shilan, R., Alireza, M., Sergey, P. (2016). Drip irrigation in Pakistan: status, challenges and future prospects. Russian Journal of Agricultural and Socio-Economic Sciences, 8(56), 114-126.
Vujicic, MD., Papic, M., Blagojevic, MD. (2017). Comparative Analysis of Objective Techniques for Criteria Weighing in Two MCDM Methods on Example of an Air Conditioner Selection. TEHNIKA –MENADŽMENT, 67, 3, 422-429. DOI: https://doi.org/10.5937/tehnika1703422V
Vallero, DA. (2004). Environmental Contaminants: Assessment and Control. Elsevier Academic Press, Burlington, MA, 1-820.
Wang, B., Dong, F., Chen, M., Zhu, J., Tan, J., Fu, X., Wang, Y., Chen, S. (2016). Advances in recycling and utilization of agricultural wastes in China: Based on environmental risk, crucial pathways, influencing factors, policy mechanism. The Tenth International Conference on Waste Management and Technology (ICWMT), Procedia Environmental Sciences, 31, 12 – 17. DOI: https://doi.org/10.1016/j.proenv.2016.02.002
Xiong, B., Li, Y., Song, M. (2017). Eco-efficiency measurement and improvement of Chinese industry using a new closest target method. International Journal of Climate Change Strategies and Management, 9(5), 666-681. DOI: https://doi.org/10.1108/IJCCSM-08-2016-0112
Zhou, X., Pedrycz, W., Kuang, Y., Zhang, Z. (2016). Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation. Applied Soft Computing, 46, 424–440. DOI: https://doi.org/10.1016/j.asoc.2016.04.038
Zurano-Cervello, P., Pozo, C., Mateo-Sanz, JM., Jimenez, L., Guillen-Gosalbez, G. (2018). Eco-efficiency assessment of EU manufacturing sectors combining input-output tables and data envelopment analysis following production and consumption-based accounting approaches. Journal of Cleaner Production, 174, 1161-1189. DOI: https://doi.org/10.1016/j.jclepro.2017.10.178