INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH (IJSSMR )
E-ISSN 2545-5303
P-ISSN 2695-2203
VOL. 10 NO. 1 2024
DOI: https://doi.org/10.56201/ijssmr.v10.no1.2024.pg101.107
Ohaji Evans Chukwudi Paulinus (B.Eng, M.Eng, PhD), Mahmud Hussaini (HND, B.Sc. PGD, M.Eng
This research explores optimal strategic decision-making for hydropower development in the Cross River Basin, with a focus on determining Expected Monetary Value (EMV) and Expected Opportunity Loss (EOL) through the use of Decision Modeling (BDM). The objective is to overcome challenges and optimize resource planning for sustainable development. Comprehensive data collection was facilitated through collaborative efforts with the Cross River Basin Development Authority (CRBDA), Parastatals, and Ministries. The collected data was validated using the Pearson moment coefficient (R2 = 0.9376).The research methodology encompasses dam project experiments, economic efficiency estimation, net benefit analysis, and the application of BDM. Key findings highlight hydropower with a Maximum EMV of 1.69 and a Minimum EOL of -0.79. The validation of models resulted in R2 = 0.999, establishing it as a preferred choice for development. Graphical representation illustrates the dynamics between EMV and EOL.The study underscores the significance of employing strategic decision-making models like BDM, providing insights to address challenges and optimize resource planning. Hydropower is identified as aligning with national goals and Sustainable Development Goals (SDGs). Recommendations advocate for strategic policy implementation, emphasizing the adoption of renewable energy. BDM's efficacy in drawing inferences from historical information addresses dimensionality challenges.The suggestion to deploy BDM by the Federal Government aligns with national SDG pursuits, integrating hydropower as a renewable energy source. The research solidifies BDM's effectiveness, offering valuable insights. References are provided to support the methodology and enrich the understanding of decision modeling and watershed management.
Expected Monetary Values, Expected Opportunity Loss, Bayelsa Decision Model, Hydropower, SDGs, Renewable Energy
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