Volume 9, Issue 31 (3-2018)                   jemr 2018, 9(31): 131-163 | Back to browse issues page


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Azami S, Poor-Karimi L, Sadri S. Total Factor CO2 Emission Performance in Iranian Manufacturing Industries: Meta-Frontier Non-Radial Malmquist Index Approach. jemr 2018; 9 (31) :131-163
URL: http://jemr.khu.ac.ir/article-1-1660-en.html
1- Razi University , sazami_econ@yahoo.com
2- Razi University
Abstract:   (5512 Views)
The purpose of this study is to evaluate environmental productivity changes in Iranian manufacturing industries, with two-digit ISIC codes, during 2003-2014. For this purpose, Meta-frontier Non-radial Malmquist CO_2 emission Performance Index (MNMCPI) is used. This index considers technological heterogeneities of industries. Empirical results indicate that, during 2003-2014, MNMCPI has grown, on average; the highest growth rate belongs to industries with medium technology. Also, all three indices of EC, BPC and TGC, as MNMCPI components, experienced growth, on average. TGC has the greatest impact in industries with medium technology while BPC has the greatest impact in industries with high and low technology. In general, BPC had the greatest effect on MNMCPI growth.The highest growth rate in EC index is observed in industries with low technology and the highest growth rates in BPC index, which shows the effect of innovation, and in TGC index are observed in industries with medium technology. Therefore, based on TGC index, industries with medium technology level are leading technological industries. Rregression analysis shows that energy intensity has a negative and significant effect and R&D has a positive significant effect on MNMCPI.
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Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2018/01/28 | Accepted: 2018/04/4 | Published: 2018/06/13

References
1. Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50(6), 1393-1414. [DOI:10.2307/1913388]
2. Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and Distance Functions. Journal of Economics Theory, 70(2), 407-419 [DOI:10.1006/jeth.1996.0096]
3. Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and Undesirable Outputs: A Directional Distance Function Approach. Journal of Environmental Management, 51(3), 229-240. [DOI:10.1006/jema.1997.0146]
4. Emami Meibodi, A., & Jaidari, F. (2015). Eco-efficiency Evaluation of Iran's Oil Refineries: Using Data Envelopment Analysis (DEA). Quarterly Journal of The Economic Research (Sustainable Growth and Development), 14(4), 79-96. (In Farsi)
5. Erickson, G. & Jacobson, R. (1992). Gaining comparative advantage through discretionary expenditures: The returns to R&D and Advertising. Management science, 38, 1264-1279. [DOI:10.1287/mnsc.38.9.1264]
6. Fan, M., Shao, S., & Yang, L. (2015). Combining global Malmquist-Luenberger index and generalized method of moments to investigate industrial total factor CO2 emission performance: A case of Shanghai (China). Energy Policy, 79, 189-201. [DOI:10.1016/j.enpol.2014.12.027]
7. Esmaeili, A., & Mohsenpoor, R. (2016). Productivity Analyses of Iranian Power Plant with Environmental Criterion. Quarterly Journal of Environmental Science and Technology, 17(4), 95-107. (In Farsi)
8. Färe, R., & Grosskopf, S. (2005). New Direction: Efficiency and Productivity. Springer, New York.
9. Fathi, B., Mahdavi Adeli, M. H., & Fotros, M. H (2015). Measuring industrial energy efficiency with CO2 emissions in developing countries using static and dynamic nonparametric models. Quartery Energy Economics Review, 11(46), 61-87. (In Farsi)
10. Fotros, M. H., & Barati, J. (2010). Analysis of Effective Factors Affecting Changes in 〖"CO" 〗_"2" Emissions of Power Plants Sector of Iran, 1997-2008. Quarterly Journal of Economic Modeling Research, 1(1), 135-153. (In Farsi)
11. Grabowski, H.G., & Mueller, D.C. (1978). Industrial research and development, intangible capital stock, and firm profit rates. Bell J. Econ, vol. 9, pp. 328-343. [DOI:10.2307/3003585]
12. IPCC 1996 Revised Guidelines for National Greenhouse Gas Inventories. Emission Factor from Fuel Combustion .
13. Kumar, S. (2006). Environmentally sensitive productivity growth: A global analysis using Malmquist-Luenberger index. Ecological Economics, 56(2), 280-293. [DOI:10.1016/j.ecolecon.2005.02.004]
14. Lin, B., & Du, K. (2015). Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach. Energy Economics, 49, 550-557. [DOI:10.1016/j.eneco.2015.03.028]
15. Lin, B., & Tan, R. (2016). China's CO2 emissions of a critical sector: Evidence from energy intensive industries. Journal of Cleaner Production, 142, 4270-4281. [DOI:10.1016/j.jclepro.2016.11.186]
16. Malmquist, S. (1953). Index Number and Indifference Surfaces. Trabajos de Estadistica, 4, 209-242. [DOI:10.1007/BF03006863]
17. Mamipour, S., & Najafzadeh, B. (2016). Environmental Efficiency Assessment of Iranian Electric Power Companies: Comparison between Radial and Non-Radial Models. Quarterly Journal of Applied Theories of Economics, 3(3), 153-178. (In Farsi)
18. Ministry of Energy (Office of Planning and Macroeconomics of Electricity and Energy). (2014). Energy Balance sheet. (In Farsi)
19. Molaei, M., & Sani, F. (2015). Estimating Environmental Efficiency of the Agricultural Sector. Journal of Agricultural Science and Sustainable Production, 25(2), 91-101. (In Farsi)
20. Moosavi Haghighi, M. H., & Rajabi, A. (2013). Modeling the Effect of Energy Intensity Changes in Industrial Sector on the Economic and Environmental Indices: A System Dynamics Approach. Quarterly Journal of Economic Modeling Research, 3(12), 103-134. (In Farsi)
21. Oh, D. hyun, & Lee, J. dong. (2010). A metafrontier approach for measuring Malmquist productivity index. Empirical Economics, 38(1), 47-64 [DOI:10.1007/s00181-009-0255-0]
22. Najafzadeh, B., & Mamipour, S. (2016). Measurement of Environmental Performance of Iranian Electric Power Companies (In the context of Contemporaneous and Sequential Frontiers of Slack-Based Measures and Directional Distance Functions). Quarterly Journal of Applied Economics Studies in Iran, 5(19), 211-240. (In Farsi)
23. Parsa, P., Sadeghi, Z., & Jalaee, A. (2016). Decomposition of Environmental Total Factor Productivity Growth Using Distance Function in the Provinces of Iran. Quarterly Journal of Applied Economic Studies in Iran, 4(16), 1-24. (In Farsi)
24. 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, 173-182. [DOI:10.1016/j.ecolecon.2014.02.004]
25. Rezaei, A., Amadeh, H., & Mohammadi, T. (2012). Analysis of Environmental Productivity and Efficiency in Selected Countries of the Importer and Exporter of Fossil Energy Resources: Directional Distance Function Approach. Quarterly Journal of Iranian Energy Economics (Quarterly Environment and Energy Economics), 1(2), 93-126. (In Farsi)
26. 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, 2(1), 99-120. (In Farsi)
27. Shao, S.,Yang, L. L.,Yu, M. B.,Yu, M.L. (2011).Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai(China), 1994-2009. Energy Policy 39(10),6476-6494. [DOI:10.1016/j.enpol.2011.07.049]
28. UNIDO. (2016). Industrial Development Report 2016: The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development.
29. Youssefi Hajiabad, R. (2016). The Evaluation of the Total Factor Productivity in Iran's Manufacturing Sector. The Journal of Economic Policy, 8(15), 153-175. (In Farsi)
30. Yu, Y., Choi, Y., Wei, X., & Chen, Z. (2017). Did China's regional transport industry enjoy better carbon productivity under regulations? Journal of Cleaner Production, 165, 777-787. [DOI:10.1016/j.jclepro.2017.07.105]
31. Zhang, N., & Choi, Y. (2013a). Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis. Energy Economics, 40, 549-559. [DOI:10.1016/j.eneco.2013.08.012]
32. Zhang, N., & Choi, Y. (2013b). A comparative study of dynamic changes in 〖"CO" 〗_"2" emission performance of fossil fuel power plants in china and korea. Energy Policy, 62, 324-332. [DOI:10.1016/j.enpol.2013.07.060]
33. Zhang, N., Zhou,P., & Choi, Y. (2013). Energy Efficiency CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea:a Meta frontier non-radial directional distance function analysis. Energy Policy,56,653-662. [DOI:10.1016/j.enpol.2013.01.033]
34. Zhang, N., & Choi, Y. (2014). A note on the evolution of directional distance function and its development in energy and environmental studies 1997-2013.Renewable and Sustainable Energy Review, 33,50-59. [DOI:10.1016/j.rser.2014.01.064]
35. Zhang, N., & Wei, X. (2015). Dynamic total factor carbon emissions performance changes in the Chinese transportation industry. Applied Energy, 146, 409-420. [DOI:10.1016/j.apenergy.2015.01.072]
36. Zhang, N., Zhou, P., & Kung, C. C. (2015). Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis. Renewable and Sustainable Energy Reviews, 41, 584-593. [DOI:10.1016/j.rser.2014.08.076]
37. Zhang, N., Wang, B., & Liu, Z. (2016). Carbon emissions dynamics, efficiency gains, and technological innovation in China's industrial sectors. Energy, 99, 10-19. [DOI:10.1016/j.energy.2016.01.012]
38. Zhou, P., Ang, B. W., & Han, J. Y. (2010). Total factor carbon emission performance: A Malmquist index analysis. Energy Economics, 32(1), 194-201. [DOI:10.1016/j.eneco.2009.10.003]
39. Zhou P, Ang BW, Wang H.(2012). Energy and CO2 emission performance in electricity generation: a non-radial directional distance function approach. Eur J Oper Res,221,625-35. [DOI:10.1016/j.ejor.2012.04.022]

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