Document Type : Research Paper

Authors

1 Department of Economics, Shiraz University.Shiraz .Iran.

2 shiraz university

3 M.A. in Economics, Shiraz University, Shiraz, Iran

Abstract

Air pollution concerns, climate change, and sustainable development necessitate the discussion of the dynamics of energy intensity. Nowadays, energy convergence is widely used as a tool for considering the dynamics of energy intensity. The energy intensity convergence in manufacturing industries is suitable to assess whether there is a knowledge spillover between manufacturing industries and whether government policies have been effective for reducing energy intensity in manufacturing industries. The purpose of the study is to examine energy intensity convergence in Iranian manufacturing industries. To do that, we collected data from nine manufacturing industries from 1995 to 2015 and employed the generalized method of moments in panel data (GMM) technique. The results of the model estimation show that there is  convergence of energy intensity in the manufacturing industries.

Keywords

Main Subjects

ابریشمی، حمید، مهرآرا، محسن، غنیمی‌فر، حجت‌اله و کشاورزیان، مریم. (1387). اثرات نامتقارن قیمت نفت بر رشد کشور‌های OECD. تحقیقات اقتصادی، شماره (2)43، صفحات 16-1.
بهبودی، داوود و اصلانی‌نیا، نسیم و سجودی، سکینه. (1389). تجزیه شدت انرژی و بررسی عوامل مؤثر بر آن در اقتصاد ایران. مطالعات اقتصاد انرژی، شماره 26، صفحات 130-105.
حمیدی‌رزی، داوود. (1392). بررسی همگرایی شدت انرژی در بین کشور‌های اوپک در حضور شکست‌های ساختاری (یک رویکرد دوجانبه). پایان‌نامه کارشناسی ارشد علوم اقتصادی دانشگاه ارومیه.
کفایی، محمدعلی، خسروی، عاطفه. (1396). بررسی همگرایی بهره‌وری انرژی استان‌های ایران: رویکرد اقتصادسنجی فضایی. پژوهش‌های رشد و توسعه پایدار. شماره (2)17. صفحات 197-177.
نماز‌پور، منصوره. (1395). بررسی همگرایی شدت انرژی بین کشور‌های عضو اکو. پایان‌نامه کارشناسی ارشد علوم اقتصادی/گرایش توسعه اقتصادی و برنامه‌ریزی دانشگاه تبریز.
Akram, V., Rath, B. N. & Sahoo, P. K. (2020). Stochastic conditional convergence in per capita energy consumption in India. Economic Analysis and Policy, Vol. 65, pp. 224-240.‏
Barro, R. J. (1991). Economic growth in a cross section of countries. The quarterly journal of economics, Vol. 106(2), pp. 407-443.
Barro, R. J. & Sala-i-Martin, X. (1990). Public finance in models of economic growth. No. 3362, May 1990, Economic Growth and Convergence Across the United States, (3419).
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, Vol. 87(1), pp.115-143.
Blundell, R., Bond, S., & Windmeijer, F. (2001). Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. In Nonstationary panels, panel cointegration, and dynamic panels (pp. 53-91). Emerald Group Publishing Limited.
Breitung, J. & Das, S. (2005). Panel unit root tests under cross‐sectional dependence. Statistica Neerlandica, Vol. 59(4), pp. 414-433.
Breusch, T. S. & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, Vol. 47(1), pp. 239-253.
Fisher-Vanden, K., Jefferson, G. H., Liu, H. & Tao, Q. (2004). What is driving China’s decline in energy intensity?. Resource and Energy economics, 26(1), pp. 77-97.
Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, Vol. 3(2), pp. 148-161.
Hadri, K. & Rao, Y. (2008). Panel stationarity test with structural breaks. Oxford Bulletin of Economics and statistics, Vol. 70(2), pp.245-269.
Hajko, V. (2014). The energy intensity convergence in the transport sector. Procedia Economics and Finance, Vol. 12, pp. 199-205.‏
Herrerias, M. J. (2012). World energy intensity convergence revisited: A weighted distribution dynamics approach. Energy policy, Vol. 49, pp. 383-399.‏
Huang, J., Zheng, X., Wang, A. & Cai, X. (2019). Convergence analysis of China’s energy intensity at the industrial sector level. Environmental Science and Pollution Research, Vol. 26(8), pp. 7730-7742.
Im, K. S., Pesaran, M. H. & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, Vol. 115(1), pp. 53-74.
Ivanovski, K., Churchill, S. A. & Smyth, R. (2018). A club convergence analysis of per capita energy consumption across Australian regions and sectors. Energy Economics, Vol. 76, pp. 519-531.‏
Karimu, A., Brännlund, R., Lundgren, T. & Söderholm, p. (2017). Energy intensity and convergence in Swedish industry: A combined econometric and decomposition analysis. Energy Economics, Vol. 62, pp. 347-356.
Levin, A., Lin, C. F. & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, Vol.108(1), pp. 1-24.
Miketa, A., & Mulder, p. (2005). Energy productivity across developed and developing countries in 10 manufacturing sectors: patterns of growth and convergence. Energy Economics, Vol.27(3), pp. 429-453.‏
Mishra, V., & Smyth, R. (2017). Conditional convergence in Australia's energy consumption at the sector level. Energy Economics, Vol. 62, pp. 396-403.‏
Mulder, p., & de Groot, H. L. (2012). Structural change and convergence of energy intensity across OECD countries, 1970-2005. Energy Economics, Vol. 34(6), pp. 1910-1921.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels.
Shaozhou, Q., & Li, K. (2011). The Convergence Analysis on the Economic Growth and Energy Intensity Gap between Regional Sectors. Chinese Journal of Population Resources and Environment, Vol. 9(3), pp. 33-46.‏