In last decades, many scholars have studied the cost of hydropower plants based on the capacity and head. The different correlation equations obtained depend mostly on geographical locations and electro-mechanical characteristics. As Sub-Saharan Africa remains the region with the largest untapped hydropower potential, coupled with the need of expansion of Chinese energy companies, this paper aims to estimate the cost of hydropower projects financed and constructed by Chinese companies in Sub-Saharan Africa. The data used in this study were rigorously selected. After refinement of the raw data, screening was performed to improve the quality of the database suitable for the log transformed linear regression. Furthermore, a bootstrap resampling with replacement was applied to assure the robustness of the model.
International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2020, 10(3), 136-146 Bootstrapping the Cost Modelling of Hydropower Projects in Sub-Saharan Africa: Case of Chinese Financed Projects Desire Wade Atchike1*, Zhen-Yu Zhao1, Geriletu Bao2 School of Economics and Management, North China Electric Power University, Beijing 102206, China, 2Inner Mongolia Technical College of Construction, Huimin, Hohhot, Inner Mongolia Autonomous Region 010070, P R China *E-mail: adesire3@yahoo.fr Received: 10 October 2019 Accepted: 15 February 2020 DOI: https://doi.org/10.32479/ijeep.8842 ABSTRACT In last decades, many scholars have studied the cost of hydropower plants based on the capacity and head The different correlation equations obtained depend mostly on geographical locations and electro-mechanical characteristics As Sub-Saharan Africa remains the region with the largest untapped hydropower potential, coupled with the need of expansion of Chinese energy companies, this paper aims to estimate the cost of hydropower projects financed and constructed by Chinese companies in Sub-Saharan Africa The data used in this study were rigorously selected After refinement of the raw data, screening was performed to improve the quality of the database suitable for the log transformed linear regression Furthermore, a bootstrap resampling with replacement was applied to assure the robustness of the model The results show a good accuracy of the model confirmed by the high value of the coefficient of determination and an average error