اثر آستانه‌ای جهانی شدن بر همگرایی شدت انرژی در منطقه خاورمیانه: تحلیل انتقال ملایم پانلی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار اقتصاد کشاورزی دانشگاه شیراز

2 دانشجوی دکتری اقتصاد کشاورزی دانشگاه شیراز

چکیده

با توجه به اهمیت جهانی شدن و ارتباط آن با شدت انرژی، در این مطالعه نقش جهانی­شدن با تاکید بر اثر آستانه­­ای آن بر همگرایی شدت انرژی در کشورهای منطقه خاورمیانه طی دوره زمانی 2019-1980 مورد بررسی قرار گرفت. جهت بررسی رابطه غیرخطی، مدل رگرسیون انتقال ملایم پانلی (PSTR) بکار گرفته شد. پس انجام آزمون وابستگی مقطعی و ایستایی متغیرهای مدل، با در نظر گرفتن جهانی­شدن به عنوان متغیر انتقال، همزمان ویژگی ناهمگنی، غیرخطی بودن و همبستگی مقطعی متغیرها مورد بررسی قرار گرفت. نتایج آزمون خطی بودن، وجود رابطه غیرخطی بین متغیرها را تأیید نمود. همچنین، در نظر گرفتن یک تابع انتقال با یک پارامتر آستانه­ای که بیانگر یک مدل دو رژیمی است، برای تصریح رابطة غیرخطی بین متغیرهای مدل تعیین شد. در نهایت، حد آستانه­ای 697/1 و 874/1 و پارامتر شیب یا سرعت انتقال نیز برابر با 997/226 برآورد شد. همچنین نتایج حاکی از عدم همگرایی شدت انرژی بین کشورها منطقه خاورمیانه ­می­باشد. بنابراین نمی­توان یک سیاست مشترک و یکسان را برای تمام کشورهای منطقه خاورمیانه اجرا نمود

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Threshold Effects of Globalization on Energy Intensity Convergence in the Middle East Region: A Panel Smooth Transition Analysis

نویسندگان [English]

  • Mohammad Hassan Tarazkar 1
  • Masoumeh Arabollah Firozjah 2
1 Associate Professor, Department of Agricultural Economics, School of Agriculture, Shiraz University
2 Ph. D. Candidate, Department of Agricultural Economics, School of Agriculture, Shiraz University
چکیده [English]

Given the importance of globalization and its relationship with energy intensity, this study examines the role of globalization with an emphasis on its threshold effect on energy intensity convergence in the Middle East region during 1980-2019. The Panel Smooth Transition Regression (PSTR) model was used to investigate the non-linear relationship. The simultaneous characteristics of heterogeneity, non-linearity, and cross-sectional correlation of variables were examined, considering globalization as a transmission variable. The results of the linearity test confirmed the existence of a non-linear relationship between the variables. Moreover, considering a threshold transfer function with a threshold parameter indicating a two-regime model was determined to clarify the non-linear relationship between variables of the model. Finally, threshold limits of 1.697 and 1.874 and a slope parameter or transfer rate of 226.997 were estimated. The results also indicate a lack of convergence in energy intensity among countries in the Middle East region. Therefore, it is not possible to create a single energy policy that can be implemented simultaneously for all countries in the Middle East region

کلیدواژه‌ها [English]

  • Energy Intensity Convergence
  • PSTR
  • Globalization
  1. خداویسی، حسن، نجار قابل، سمیه، و عزتی شورگلی، احمد (1396). بررسی رابطه بین جهانی شدن و نابرابری درآمدی: کاربردی از مدل انتقال ملایم آستانه‌ای پانلی. فصلنامه تحقیقات اقتصادی، 52(1)، 61-87. https://jte.ut.ac.ir/article_59581.html
  2. سیف, اله‌، مراد، و حمیدی رزی, داود (1395). بررسی تأثیر شاخص‌های منتخب اقتصاد دانش‌بنیان بر شدت انرژی استان‌های کشور. پژوهشنامه اقتصاد انرژی ایران، 5(18)، 101-145. https://doi.org/10.22054/jiee.2016.7194
  3. شهبازی کیومرث، و حمیدی رزی داود (1393). همگرایی شدت انرژی بین کشورهای عضو اوپک (یک رویکرد دوجانبه). فصلنامه پژوهش ها و سیاست های اقتصادی، 22(71)، 173-198. http://qjerp.ir/article-1-719-fa.html
  4. طاهری ریکنده، عمران، رفیعی، حامد، کلهری، صابر، و مهرپرورحسینی، الهام (1395). بررسی همگرایی بلندمدت شدت مصرف انرژی و سیاست‌های حمایتی دولت در بخش کشاورزی (ارسال مجدد)، تحقیقات اقتصاد و توسعه کشاورزی ایران، 47(3)، 599-608. https://ijaedr.ut.ac.ir/article_60228.html
  5. علیزاده، سمانه (1393). بررسی تاثیر جهانی شدن بر شدت انرژی در ایران (پایان نامه کارشناسی ارشد). دانشکده مدیریت و حسابداری، دانشگاه تبریز، ایران. https://www.virascience.com/thesis/786479/
  6. فلاحی، فیروز؛ رنج پور، رضا، و شکری، توحید، (1396). بررسی وجود همگرایی تصادفی و بتا در مصرف سرانه‌ی انرژی کشورهای عضو اوپک، تحقیقات مدلسازی اقتصادی، 8(29)،169-200. https://jemr.khu.ac.ir/article-1-1185-fa.html
  7. منظور، داود، و نیاکان، لیلی (1393). رابطه توسعه اقتصادی و شدت انرژی در کشورهای عضو اکو: مدل رگرسیون داده های تابلویی آستانه ای یکنواخت، فصلنامه پؤوهش ها و سیاست­های اقتصادی،22(69)، 83-106. https://sid.ir/paper/89424/fa.html

 

  1. Azaliah, R., Kurniawan, H., Hartono, D., & Widyastaman, P. A. (2023). The convergence of energy intensity in developing countries: a spatial econometric analysis with Indonesia’s provincial panel data. Environment, Development and Sustainability, 1-25. https://doi.org/10.1007/s10668-023-03227-8
  2. Alizadeh, S.(2014). Investigating the impact of globalization on energy intensity in Iran. Tabriz University, Faculty of Management and Accounting, Tabriz, Iran. (In Persian).
  3. Barro, R. J., Sala-i-Martin, X., Blanchard, O. J., & Hall, R. E. (1991). Convergence across states and regions. Brookings papers on economic activity, 107-182. https://doi.org/10.2307/2534639
  4. Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of political Economy, 100(2), 223-251.
  5. Bigerna, S., Bollino, C. A., & Polinori, P. (2021). Convergence in renewable energy sources diffusion worldwide. Journal of Environmental Management, 292, 112784. https://doi.org/10.1016/j.jenvman.2021.112784
  6. Bist, J. P. (2018). Financial development and economic growth: Evidence from a panel of 16 African and non-African low-income countries. Cogent Economics & Finance, 6(1), 1449780. https://doi.org/10.1080/23322039.2018.1449780
  7. Burnett, J. W., & Madariaga, J. (2017). The convergence of US state-level energy intensity. Energy Economics, 62, 357-370. https://doi.org/10.1016/j.eneco.2016.03.029
  8. Bulut, U., & Durusu-Ciftci, D. (2018). Revisiting energy intensity convergence: new evidence from OECD countries. Environmental Science and Pollution Research, 25, 12391-12397. https://doi.org/10.1007/s11356-018-1551-z
  9. De Groot, H., & Mulder, P. (2012). Structural change and convergence of energy intensity across OECD countries. Energy Economics, 34(6), 1910-1921. https://doi.org/10.1016/j.eneco.2012.07.023
  10. Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied economics, 38(10), 1091-1110. https://doi.org/10.1080/00036840500392078
  11. Eberhardt, M., & Presbitero, A. F. (2015). Public debt and growth: Heterogeneity and non-linearity. Journal of international Economics, 97(1), 45-58. https://doi.org/10.1016/j.jinteco.2015.04.005
  12. Fallahi, F., & Rodríguez, G. (2015). Structural breaks and labor market disparities in the Canadian provinces. Journal of Economic Studies, 42(2), 322-342. https://doi.org/10.1108/JES-04-2013-0057
  13. Fallahi, F., Ranjpour, R., & Shokri, T. (2017). Stochastic and Beta Convergence in Per Capita Energy Use among OPEC Member Countries [Applicable]. Journal of Economic Modeling Research, 8(29), 169-200 (In Persian). https://doi.org/10.29252/jemr.8.29.169  
  14. Foroughi pour, E. (2006). A survey of sigma and beta convergence across opec countries (the test of solow-swan hypothesis). Iranian journal of trade studies (ijts), 10(39), 135-156. https://sid.ir/paper/7175/en
  15. Gonzalez, A., Teräsvirta, T., Van Dijk, D., & Yang, Y. (2017). Panel smooth transition regression models.
  16. Granger, C. W., & Teräsvirta, T. (1993). Modelling nonlinear economic relationships. oxford university Press. https://doi.org/10.1093/oso/9780198773191.001.0001
  17. Gygli, S., Haelg, F., Potrafke, N., & Sturm, J. E. (2019). The KOF globalisation index–revisited. The Review of International Organizations, 14, 543-574. https://doi.org/10.1007/s11558-019-09344-2
  18. Herzer, D. (2024). The impact of domestic R&D and North–South R&D spillovers on energy intensity in developing countries. Economic Change and Restructuring, 57(2), 33. https://doi.org/10.1007/s10644-024-09591-3
  19. Huang, Z., Zhang, H., & Duan, H. (2019). Nonlinear globalization threshold effect of energy intensity convergence in Belt and Road countries. Journal of cleaner production, 237, 117750. https://doi.org/10.1016/j.jclepro.2019.117750
  20. Jude, E. C. (2010). Financial development and growth: A panel smooth regression approach. Journal of Economic Development, 35(1), 15.
  21. Khodavaisi, H., najari, s., & ezzati, A. (2017). Investigating the relationship between globalization and income inequality: an application for Panel Smooth Transition Threshold Regression Model. Journal of Economic Research (Tahghighat- E- Eghtesadi), 52(1), 61-87 (In Persian). https://doi.org/10.22059/jte.2017.59581
  22. Koompans, T. C. (1965). On the Concept of Optimal Economic Growth,[w:] The Econometric Approach to Development Planning.
  23. Lee, D. Y., & Park, S. Y. (2023). Global energy intensity convergence using a spatial panel growth model. Applied Economics, 55(41), 4745-4764. https://doi.org/10.1080/00036846.2022.2131715
  24. Liddle, B. (2010). Revisiting world energy intensity convergence for regional differences. Applied energy, 87(10), 3218-3225. https://doi.org/10.1016/j.apenergy.2010.03.030
  25. Mohammadi, H., & Ram, R. (2012). Cross-country convergence in energy and electricity consumption, 1971–2007. Energy economics, 34(6), 1882-1887. https://doi.org/10.1016/j.eneco.2012.08.001
  26. Mohammadi, H., & Ram, R. (2017). Convergence in energy consumption per capita across the US states, 1970–2013: An exploration through selected parametric and non-parametric methods. Energy Economics, 62, 404-410. https://doi.org/10.1016/j.eneco.2016.07.002
  27. Manzoor, D., & Niakan, L. (2014). The relationship between economic development and energy intensity in ECO member countries: a uniform threshold panel data regression model. Journal of Economic Research and Policies, 22(69), 83-106 (In Persian).
  28. Omay, T., Van Eyden, R., & Gupta, R. (2018). Inflation–growth nexus: evidence from a pooled CCE multiple-regime panel smooth transition model. Empirical Economics, 54, 913-944. https://doi.org/10.1007/s00181-017-1237-2
  29. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers. Economics, 1240(1), 1.
  30. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
  31. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross‐section dependence. Journal of applied econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951
  32. Perron, P., & Yabu, T. (2009). Testing for shifts in trend with an integrated or stationary noise component. Journal of Business & Economic Statistics, 27(3), 369-396. https://doi.org/10.1198/jbes.2009.07268
  33. Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric reviews, 34(6-10), 1089-1117. https://doi.org/10.1080/07474938.2014.956623
  34. Saba, C. S., & Ngepah, N. (2022). Convergence in renewable energy consumption and their influencing factors across regions: evidence from convergence algorithm Environ Sci Pollut Res Int, 29(40), 61412-61445. https://doi.org/10.1007/s11356-022-19731-9
  35. Seif, A. M., & Hamidi Razi, D. (2016). The Knowledge-Based Economy Structure and Energy Intensity Index Nexus: Evidence from Iran Provinces. Iranian Energy Economics, 5(18), 101-145 (In Persian).
  36. Sim, K. Y., & Sek, S. K. (2024, January). Does economic development affect energy intensity level and vice versa? A comparison of nonstationary panel models with nonlinearity. In AIP Conference Proceedings (Vol. 2905, No. 1). AIP Publishing. https://doi.org/10.1063/5.0171914
  37. Solow, R. M. (1956). A contribution to the theory of economic growth. The quarterly journal of economics, 70(1), 65-94. https://doi.org/10.2307/1884513
  38. Shahbazi, K., & Hamidi Razi, D. (2014). Investigation of Energy Intensity Convergence among OPEC Countries (A Pair-Wise Approach) [Research]. Quarterly Journal of Economic Research and Policies, 22(71), 173-198 (In Persian). http://qjerp.ir/article-1-719-fa.html
  39. Tarazkar, M. H., Dehbidi, N. K., Ozturk, I., & Al-Mulali, U. (2021). The impact of age structure on carbon emission in the Middle East: the panel autoregressive distributed lag approach.Environmental Science and Pollution Research, 28, 33722-33734. https://doi.org/10.1007/s11356-020-08880-4
  40. Taheri reykande, e. (2016). Evaluation of long-term convergence in energy intensity and supporting government policies in agriculture. Iranian Journal of Agricultural Economics and Development Research, 47(3), 599-608 (In Persian). https://www.magiran.com/p1646765
  41. Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the american Statistical association, 89(425), 208-218. https://doi.org/10.1080/01621459.1994.10476462
  42. Wang, Q., Zhang, C., & Cai, W. (2017). Factor substitution and energy productivity fluctuation in China: a parametric decomposition analysis. Energy Policy, 109, 181-190. https://doi.org/10.1016/j.enpol.2017.07.003
  43. Wang, Q., Hang, Y., Su, B., & Zhou, P. (2018). Contributions to sector-level carbon intensity change: an integrated decomposition analysis|. Energy Economics, 70, 12-25. https://doi.org/10.1016/j.eneco.2017.12.014
  44. Wu, D., Xie, Y., & Liu, D. (2023). Rethinking the complex effects of the clean energy transition on air pollution abatement: Evidence from China's coal-to-gas policy. Energy, 283, https://doi.org/10.1016/j.energy.2023.128413
  45. Xin-gang, Z., Yuan-feng, Z., & Yan-bin, L. (2019). The spillovers of foreign direct investment and the convergence of energy intensity. Journal of cleaner production, 206, 611-621. https://doi.org/10.1016/j.jclepro.2018.09.225
  46. Yu, B., Fang, D., Xiao, K., & Pan, Y. (2023). Drivers of renewable energy penetration and its role in power sector's deep decarbonization towards carbon peak. Renewable and Sustainable Energy Reviews, 178, 113247. https://doi.org/10.1016/j.rser.2023.113247