Journal Screenshot

International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

Impact of Foreign direct investment on Power production: Evidence from Nepal

Mohammad Rabbani

http://dx.doi.org/10.6007/IJARBSS/v8-i11/4824

Open access

Foreign direct investment can lead to strategic aims in power industry as well as economic growth. The aim of the current study is to measure the relationship between foreign direct investment and power production in Nepal while the energy consumption, economic growth, imports and exports has been used as macroeconomic variables. The results show that FDI has significant positive impact on power production. With the increase in FDI power consumption and power production will also increase. GDP also has positive impact on Power consumption. GDP is significant at 10 percent level of significance. Greater the GDP greater will be the consumption of electricity. Results of auto regressive distributive lag model in short run is found to be 0.98 which shows that 98 % variations the model is good fit. The findings of short run ARDL model. While the Kurtosis and Skewness defines the shape of the distribution. It is understandable from the table 1 that the variables are only positively skewed excluding in the analysis. Kurtosis is an indicator to show the shape of distribution. And if the value greater then 3, it’s called leptokurtic and the probability distribution is not normally distributed. The study provides a valuable guideline for decision and policy makers.

Agency, I. E. (2007). Climate Policy Uncertainty and Investment Risk. Energy, 17(7), 144. https://doi.org/http://dx.doi.org/10.1016/j.bbr.2011.03.031
Ahmad, A., Zhao, Y., Shahbaz, M., Bano, S., Zhang, Z., Wang, S., & Liu, Y. (2016). Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy. Energy Policy, 96, 131–143. https://doi.org/10.1016/j.enpol.2016.05.032
Al-Sulaiti, K. I., Baker, M. J., Bryman, A., Baker, M. J., Ballington, L., Bilkey, W. J., … Saunders, M. N. K. K. (2010). Research Methods for Business Students. International Marketing Review, 14(2), 656. https://doi.org/10.4135/9781412986182
Alkhathlan, K., & Javid, M. (2013). Energy consumption, carbon emissions and economic growth in saudi arabia: An aggregate and disaggregate analysis. Energy Policy, 62, 1525–1532. https://doi.org/10.1016/j.enpol.2013.07.068
BP Statistics. (2017). BP Statistics.
Chen, Y. (2010). Autoregressive Distributed Lag ( ADL ) Model. Interpretation A Journal Of Bible And Theology, 1–3.
Dogan, E. (2016). Analyzing the linkage between renewable and non-renewable energy consumption and economic growth by considering structural break in time-series data. Renewable Energy, 99, 1126–1136. https://doi.org/10.1016/j.renene.2016.07.078
Galvao, A. F., Montes-Rojas, G., & Park, S. Y. (2013). Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns. Oxford Bulletin of Economics and Statistics, 75(2), 307–321. https://doi.org/10.1111/j.1468-0084.2011.00683.x
Habib-Mintz, N. (2010). Biofuel investment in Tanzania: Omissions in implementation. Energy Policy, 38(8), 3985–3997. https://doi.org/10.1016/j.enpol.2010.03.023
Harris, R., & Sollis, R. (2003). Applied Time Series Modelling and Forecasting. Book.
Hassler, U., & Wolters, J. (2006). Autoregressive distributed lag models and cointegration. In Modern Econometric Analysis: Surveys on Recent Developments (pp. 57–72). https://doi.org/10.1007/3-540-32693-6_5
IEA. (2015). Energy and Climate Change. World Energy Outlook Special Report, 1–200. https://doi.org/10.1038/479267b
Khan, M. T. I., Ali, Q., & Ashfaq, M. (2018). The nexus between greenhouse gas emission, electricity production, renewable energy and agriculture in Pakistan. Renewable Energy, 118, 437–451. https://doi.org/10.1016/j.renene.2017.11.043
Komal, R., & Abbas, F. (2015). Linking financial development, economic growth and energy consumption in Pakistan. Renewable and Sustainable Energy Reviews, 44, 211–220. https://doi.org/10.1016/j.rser.2014.12.015
Kripfganz, S., & Schneider, D. C. (2016). ardl?: Stata module to estimate autoregressive distributed lag models. Retrieved from http://www.stata.com/meeting/chicago16/slides/chicago16_kripfganz.pdf
Lee, I., & Lee, B. C. (2010). An investment evaluation of supply chain RFID technologies: A normative modeling approach. International Journal of Production Economics, 125(2), 313–323. https://doi.org/10.1016/j.ijpe.2010.02.006
Mbaga, M., & Coyle, B. T. (2003). Beef Supply Response Under Uncertainty: An Autoregressive Distributed Lag Model. Journal of Agricultural and Resource Economics, 28(3), 519–539.
Mohsin, M., Rasheed, A. K., & Saidur, R. (2018). Economic viability and production capacity of wind generated renewable hydrogen. International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2017.12.113
Mohsin, M., Zhou, P., Iqbal, N., & Shah, S. A. A. (2018). Assessing oil supply security of South Asia. Energy, 155, 438–447. https://doi.org/10.1016/J.ENERGY.2018.04.116
Nasir, M., & Ur Rehman, F. (2011). Environmental Kuznets Curve for carbon emissions in Pakistan: An empirical investigation. Energy Policy, 39(3), 1857–1864. https://doi.org/https://doi.org/10.1016/j.enpol.2011.01.025
Nehler, T., & Rasmussen, J. (2016). How do firms consider non-energy benefits? Empirical findings on energy-efficiency investments in Swedish industry. Journal of Cleaner Production, 113, 472–482. htt

In-Text Citation: (Rabbani, 2018)
To Cite this Article: Rabbani, M. (2018). Impact of Foreign direct investment on Power production: Evidence from Nepal. International Journal of Academic Research in Business and Social Sciences, 8(11), 1–12.