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Manuscript ID : JEST-2007-5125 (R2) Visit : 137 Page: 285 - 300

10.22034/jest.2021.54309.5125

Article Type: Original Research

Comparison of Technical and Environmental Efficiencies of Energy Industries in Isfahan Province - using Stochastic Nonparametric Envelopment of Data Approach

Subject Areas : Environmental Economics

mansoureh jalayeri 1 * , mohmmad hasan vakilpoor 2 * , sadegh khalilian 3 , Hamed Najafi 4

1 - PhD student in Department of Agricultural Economics, Tarbiat Modares University .
2 - Department of Agricultural Economics Member, Tarbiat Modares University. *(Corresponding Author)
3 - Associate Professor, Department of Agricultural Economics, Tarbiat Modares University.
4 - Associate Professor, Department of Agricultural Economics, Tarbiat Modares University.

Received: 2020-07-28 Accepted : 2020-12-13 Published : 2022-04-21

Keywords: Undesirable Output, Eco Efficiency, StoNED Method,

Abstract :

Background and Objective: Sustainable development policies are expected to be designed in such a way that the production process and products are environmentally friendly and have the least negative side effects on the environment. The concern of developed countries these days is to achieve sustainable development, and this is measured by the tools of environmental efficiency. Environmental efficiency is the most well-known and widely used valuation indicator in relation to environmental development. Material and Methods: this study evaluated the technical and environmental efficiencies of energy industries in Isfahan province and compared the results using the data envelopment analysis and non-parametric random envelopment analysis approach(StoNED). Financial data including annual sales, labor force, cost of raw materials and capital stock were collected from the audited financial statements of companies for the year ending 2017, and the amount of energy consumption and the type of energy consumed and the percentage of each one, collected and Completed using a questionnaire from the technical department of the factory. Findings: One of the assumptions of this study was that the average environmental efficiency was lower than the average technical efficiency and expected a significant difference between environmental and technical efficiency in each group of industries. The results showed that the average environmental efficiency was significantly lower than the average technical efficiency of firms. The results also showed that the average technical efficiency in selected energy industries in Isfahan province, using DEA method, was calculated 67.4% and using StoNED method was 75.7% and the average environmental efficiency in StoNED method was 52.1%. Thus, the average environmental efficiency of energy industries in Isfahan province was 23.6% lower than their average technical efficiency. Among the three groups studied, non-metallic industries such as cement and refractory brick factories had the highest average technical efficiency with 87.35%, while the average environmental efficiency of this group with 48.79% was lower than the other two groups. The average technical efficiency of metal industries was 81.23% and the average environmental efficiency of this group was 55.61% and the average technical efficiency of chemical and petrochemical industries was 55.9% and the average environmental efficiency of this group was 49.54%. Discussion and Conclusion: Due to the difference in the average environmental efficiency calculated for selected industries, it is suggested that environmental standards appropriate to each industry be developed to have a greater impact on environmental efficiency.  

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_||_

  1. Emami Meybodi, A., KHoshkalam-kh, , mahdavi, R., (2013)" . Efficiency and productivity from an economic point of view" , Allameh Tabatabai University Publication. (In Persian)
    2.
  2. Amadhe,H, Rezaei,A (2011). " Environmental efficiency measurement using the efficiency model The universal is the desired and undesired result of the universal inseparable In the electric energy production department of regional electricity companies ", Quarterly Journal of Energy Economics Studies,1,125-154. (In Persian)
    3.
  3. Seifi ,A, Salimifar M, Fanudi ,H., 2012. Environmental Performance Measurement: A Case Study of Thermal Power Plants in South Khorasan, Razavi and North Khorasan Provinces. Iranian Energy Economics Quarterly.7,17-41. (In Persian)
    4.
  4. Shahiki Tash, M., Khajeh Hasani, M., Jafari, S., 2015. Assessment of the Environmental Performance in Energy Intensive Industries of Iran by Using Directional Distance Function Approach. Quarterly Journal of Applied Theories of Economics, 1, 99-120. (In Persian)
    5.
  5. Molaei, Hesari, N. Javanbakht, A, 2017 "Estimation of environmental efficiency of input-oriented agricultural products (case study environmental efficiency of rice production) " , Agricultural Economics Journal, 2, 157-172
    6.
  6. Mao j, Du Y, Xu L, Zeng Y,(2011), " Quantification of energy related industrial eco-effciency of China". Frontiers of Environmental & Science & Engineering in China, vol 5, Issue 4, 585-596.
    7.
  7. Picazo-Tadeo, A. J., Castillo-Giménez, J., & Beltrán-Esteve, M. (2014). An intertemporal approach to measuring environmental performance with directional distance functions: greenhouse gas emissions in the European Union. Ecological Economics, 100, 173-18.
    8.
  8. Vlontzos, G, Niavis, S, Manos, B (2014)." A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries",Renewable and Sustainable Energy Reviews 40, 91–96.
    9.
  9. Guide for calculating and reporting greenhouse gas emissions , . (2017) Oil Ministry
    10.
  10. Choi, Y and Lee H. S., 2016. Are Emissions Trading Policies Sustainable? A Study of the Petrochemical Industry in Korea. Global E-Governance Program, Inha University. mdpi.com/journal/sustainability
    11.
  11. Motafakker Azad, M., Pourebadollahan Covich, M., Fallahi, F., Ranj Pour, R., Sojoodi, S.,, 2014. Measuring the Technical Efficiency of Iranian Thermal Power Plants and Analysis of its Determinants: Application of Stochastic Nonparametric Data Envelopment Method. Journal of Economic Research (Tahghighat- E- Eghtesadi), 1, 93-113. (In Persian)
    12.
  12. Kuosmanen, T., & Kortelainen, M., 2012. Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis, 1, 11–28.
    13.
  13. Kuosmanen, T., & Johnson, A., 2008. Data Envelopment Analysis as Nonparametric Least Squares Regression. SSRN Electronic Journal, 1–30. doi:10.2139/ssrn.1158252
    14.
  14. Kuosmanen, T. and Johnson,A.L., 2010. Data envelopment analysis as nonparametric least squares regression, Operations Research.1, 149-160.
    15.
  15. Kuosmanen T., 2016. Stochastic Nonparametric Envelopment of Panel Data: Frontier Estimation with Fixed and Random Effects Approaches. EWEPA X.
    16.
  16. Baradaran V, yaghoubi N., 2016 . Valuate the Efficiency of Iranian Electric Power Distribution Companies by Stochastic Nonparametric Envelopment of Data (StoNED) Approach. Iranian Electric Industry Journal of Quality and Productivity. 2,15-26 . (In Persian)
    17.
  17. Mekaroonreung, M., & Johnson,A. L., 2012. Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach. Energy Economics. 3,723–732.
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    20.
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