Journal
RENEWABLE ENERGY
Volume 182, Issue -, Pages 1103-1125Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.10.103
Keywords
Community capitals; Alternative energy technologies; Development predicting models; Weighting models; Multi-criteria analysis techniques
Funding
- Ministry of Foreign Affairs and Trade, New Zealand
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This study presents a multi-criteria analysis of alternative energy technologies based on the sustainable livelihoods framework. It proposes a time bound development predicting model and compares it with a time unbound development predicting model. The results reveal that rooftop solar PV is the best-choice technology and predict a high level of development for Uganda in the next 50 years.
A multi-criteria analysis of alternative energy technologies based on sustainable livelihoods framework is presented. A time bound development predicting model derived from a modified heterogenous mean field approach is proposed. The time bound development predicting model and time unbound development predicting model were validated as well as compared. A decision-makers' weighting model for the VIKOR technique was also proposed and compared with the existent weighting models. For the case of Uganda, the results revealed that the development predicting models have statistical errors in the range of 0.2-13.7% when validated with the historical data for human development indices. The multi-criteria analysis revealed that rooftop solar PV is the best-choice technology to implement. By using the time bound development predicting model, development forecasts by the time unbound development predicting model for rooftop solar PV would be achieved between 8.8 and 16.7 years. The study predicts that Uganda would attain a development level of 0.756 in a period of 50 years from its current status of 0.401. The VIKOR technique gap distance recorded statistical errors in the range of 0.64-0.74% when the proposed decision-makers' weighting model was used in comparison to both the interdependent weighting and entropy weighting models. (C) 2021 Elsevier Ltd. All rights reserved.
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