Policy Analysis of Low-Carbon Energy Transition in Senegal Using a Multi-Criteria Decision Approach Based on Principal Component Analysis

Herve Tevenim Mewenemesse (), Qiang Yan and Prince Foli Acouetey
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Herve Tevenim Mewenemesse: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Qiang Yan: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Prince Foli Acouetey: Faculté des Sciences & Technologies de Nancy-France, Mathematics Department, Université de Lorraine, 54000 Nancy, France

Sustainability, 2023, vol. 15, issue 5, 1-23

Abstract: Senegal has been investing in the development of its energy sector for decades. By using a novel multi-criteria decision analysis (MCDA) based on the principal component analysis (PCA) method, this paper develops an approach to determine the effectiveness of Senegal’s policies in supporting low-carbon development. This was determined using six criteria (C1 to C6) and 17 policies selected from the review of Senegal’s energy system. In order to determine the optimal weighting of the six criteria, a PCA is performed. In our approach, the best weighted factor is the normalized version of the best linear combination of the initial criteria with the maximum summarized information. Proper weighted factors are determined through the percentage of the information provided by the six criteria kept by the principal components. The percentage of information is statistically a fit of goodness of a principal component. The higher it is, the more statistically important the corresponding principal component is. Among the six principal components obtained, the first principal component (comp1) best summarizes the values of criteria C1 to C6 for each policy. It contains 81.15% of the information on energy policies presented by the six criteria and was used to rank the policies. Future research should take into account that when the number of criteria is high, the share of information explained by the first principal component could be lower (less than 50% of the total variance). In this case, the use of a single principal component would be detrimental to the analysis. For such cases, we recommend a higher dimensional visualization (using two or three components), or a new PCA should be performed on the principal components. This approach presented in our study can serve as an important benchmark for energy projects and policy evaluation.

Keywords: Senegal; low-carbon development; MCDA; PCA; criteria; optimal weighting; energy policies (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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