Financial and environmental efficiency assessment model of seven Iranian chemical industries
Abstract
The chemical industries commercially emerged to be the type of prominent techniques in handling and generating a variety of valuable products. The present research included the description of technologies, energy and materials streams, facilities exploited in generation lines of industries. The main source of the present study refers to the screening of industrial projects in project identification steps of the Environmental Impact Assessment (EIA) plan in Iran. The initial data of assessment estimated by the evaluator team was taken into further processing in both environmental and financial issues via the Data Envelopment Analysis (DEA) model. The conventional DEA model was integrated with 4 weighing systems of Multi-Criteria Decision-Making (MCDM) models to assess the performance of seven Iranian chemical industries in both environmental and currency issues empirically. The findings classified the industries in financial and environmental efficiency scores. The conclusion of the research can be summarized in developing two types of classification based on the screening step of project identification and with good compliance between findings in the integration of DEA-weighing systems.
Downloads
References
Li, Y., Chiu, Y., Liu, Y., Lin, T-Y., Chang, T-H. (2020). The Impact of the Media and Environmental Pollution on the Economy and Health Using a Modified Meta 2-Stage EBM Malmquist Model. The Journal of Health Care Organization, Provision, and Financing. 57, 1–24. DOI: 10.1177/0046958020921070.
Singh, S., Olugu, E.U., Musa, S.N. (2016). Development of sustainable manufacturing performance evaluation expert system for small and medium enterprises. Procedia CIRP, 40, 608 – 613. Doi: 10.1016/j.procir.2016.01.142.
Compton, M., Willis, S., Rezaie, B., Humes, K. (2018). Food processing industry energy and water consumption in the Pacific Northwest. Innovative Food Science and Emerging Technologies, 47, 371–383. https://doi.org/10.1016/j.ifset.2018.04.001.
Rahmani, M. (2017). A productivity analysis of Iranian industries using an additive data envelopment analysis. Management Science Letters, 7(4), 197–204. Doi: 10.5267/j.msl.2016.12.007.
Shakeri, M., Zarei, A., Azar, A., Minbash Razgah, M.M. (2020). Green Supply Chain Risk Network Management and Performance Analysis: Bayesian Belief Network Modeling. Environmental Energy and Economic Research, 4(3), 165-183. DOI 10.22097/eeer.2020.215399.1134.
Hassanpour, M. (2020). Evaluation of Iranian small and medium-sized industries using the DEA based on additive ratio model-a review. FACTA UNIVERSITATIS Series: Mechanical Engineering. 18(3), 491 – 511 https://doi.org/10.22190/FUME200426030H.
Nemati, M., Saen, R.F., Matin, R.K. (2020). A data envelopment analysis approach by partial impacts between inputs and desirable undesirable outputs for sustainable supplier selection problem. Industrial Management & Data Systems, 121(4), 809-838. DOI 10.1108/IMDS-12-2019-0653.
Al-Mezeini, N.K., Oukil, A., Al-Ismaili, A.M. (2020). Investigating the efficiency of greenhouse production in Oman: A twostage approach based on Data Envelopment Analysis and double bootstrapping, 247, 119160, 1-29. https://doi.org/10.1016/j.jclepro.2019.119160.
Rahimpour, K., Shirouyehzad, H, Asadpour, M., Karbasian M. (2020). A PCA-DEA method for organizational performance evaluation based on intellectual capital and employee loyalty a case study. Journal of Modelling in Management, 15(4), 1479-1513. DOI 10.1108/JM2-03-2019-0060.
Mozaffari, M.R., Mohammadi, S., Wanke, P.F., Correa, H.L. (2021). Towards greener petrochemical production: Two-stage network data envelopment analysis in a fully fuzzy environment in the presence of undesirable outputs. Expert Systems with Applications, 164, 113903. https://doi.org/10.1016/j.eswa.2020.113903.
Ren, J., Gao, B., Zhang, J., Chen, C. (2020). Measuring the Energy and Carbon Emission Efficiency of Regional Transportation Systems in China: Chance Constrained DEA Models. Hindawi Mathematical Problems in Engineering. Article ID 9740704, 1-12. https://doi.org/10.1155/2020/9740704.
Poordavoodia, A., Moazami Goudarzi, M.R., Javadic, H.H.S., Rahmani, A.M. (2021). Measuring Efficiency of Financial Cloud Services in Banking Industry Using Modified Dynamic DEA with Network Structure (Case study: Iran E-Banking). Advances in Mathematical Finance & Applications, 6(1), 1-28. DOI: 10.22034/amfa.2020.1898567.1416.
Abbas, A., Waseem, M., Yang, M. (2020). An ensemble approach for assessment of energy efficiency of agriculture system in Pakistan. Energy Efficiency, 13, 683-696. https://doi.org/10.1007/s12053-020-09845-9.
Li, F., Emrouznejad, A., Yang, G.L., Li, Y. (2020). Carbon emission abatement quota allocation in Chinese manufacturing industries: An integrated cooperative game data envelopment analysis approach. Journal of the operational research society, 71(8), 1259-1288. https://doi.org/10.1080/01605682.2019.1609892.
Li, Y., Chiu, Y.H., Wu, M., Li, Y., Lin, T.Y. (2020). Chinese liquor company management efficiency from a social responsibility perspective: a two stage dynamic directional distance function. Economic Research-Ekonomska Istraživanj, 1-31. DOI: 10.1080/1331677X.2020.1860797.
Oghani, A.V., Khiyabani, F.M., Farahmand, F.H.N. (2020). Data envelopment analysis technique to measure the management ability: Evidence from Iran Cement industry. Cogent Business & Management, 7(1), 1801960, 1-13. DOI: 10.1080/23311975.2020.1801960.
Fathi, B., Ashena, M., Bahari, A.R. (2021). Energy, environmental, and economic efficiency in fossil fuel exporting countries: A modified data envelopment analysis approach. Sustainable Production and Consumption 26, 588–596. https://doi.org/10.1016/j.spc.2020.12.030.
Haia, A.T.N., Speelman, S. (2020). Economic-environmental trade-offs in marine aquaculture: The case of lobster farming in Vietnam. Aquaculture, 516, 734593. https://doi.org/10.1016/j.aquaculture.2019.734593.
Sun, H., Ikram, M., Mohsin, M., Abbas, Q. (2021). Energy security and environmental efficiency: evidence from OECD countries. The Singapore Economic Review, 66(2), 489-506. DOI: 10.1142/S0217590819430033.
Munn, R.E. (1979). Environmental Impact Assessment, Principles and Procedures. Scope 5. John Wiley and Sons, New York.
Lohani, B.N., Evans, J.W., Everitt, R.R., Ludwig, H., Richard, A.C., Shih-Liang, T.U. (1997). Environmental Impact Assessment for Developing Countries in Asia. Asian Development Bank. Volume 1 – Overview, 1-356.
Biju, V.G., Prashanth, C.M. (2017). Friedman and Wilcoxon evaluations comparing SVM, bagging, boosting, K-NN and decision tree classifiers. Journal of Applied Computer Science Methods, 9(1), 23-47. DOI 10.1515/jacsm-2017-0002.
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.
Ijadi Maghsoodi, A., Hafezalkotob, A., Aziz Ari, I., Ijadi Maghsoodi, S., Hafezalkotob, A. (2018). Selection of Waste Lubricant Oil Regenerative Technology Using Entropy-Weighted Risk-Based Fuzzy Axiomatic Design Approach. INFORMATICA, 29(1), 41–7441. DOI: http://dx.doi.org/10.15388/Informatica.2018.157.
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. DOI: http://dx.doi.org/10.17582/journal.pjz/2019.51.3.903.912.
Pamucar, D., Stevic, Z., Sremac, S. (2018). A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393, 1-22. Doi: 10.3390/sym10090393.
Dewangan, D.K., Agrawal, R., Sharma, V. (2015). Enablers for Competitiveness of Indian Manufacturing Sector: An ISM-Fuzzy MICMAC Analysis. XVIII Annual International Conference of the Society of Operations Management (SOM-14). Procedia - Social and Behavioral Sciences, 189, 416 – 432. DOI: 10.1016/j.sbspro.2015.03.200.
Ranjbar Nia B., Murgante, B., Qelichi, M.M., Rustaei, S. (2017). A Comparative Study Employing CIA Methods in Knowledge-Based Urban Development with Emphasis on Affordable Housing in Iranian Cities (Case: Tabriz). International Conference on Computational Science and Its Applications, Part IV, LNCS 10407, pp. 485–501. DOI: 10.1007/978-3-319-62401-3_35.
This work is licensed under a Creative Commons Attribution 4.0 International License.
