Analysis of Factors Influencing Renewable Energy Consumption in Madagascar
In this study, the aim was to identify the factors that have influenced renewable energy consumption in Madagascar over the period of 1990 to 2021. The researchers focused on 12 features that covered various aspects including macroeconomic, financial, social, and environmental factors.
The features that were considered in this analysis are:
- Economic growth
- Domestic investment
- Foreign direct investment
- Financial development
- Industrial development
- Inflation
- Income distribution
- Trade openness
- Exchange rate
- Tourism development
- Environmental quality
- Urbanization
In order to assess the significance of these features, the researchers assumed a linear relationship between renewable energy consumption and the selected factors. They then applied different machine learning feature selection algorithms to determine the importance of each feature.
The machine learning algorithms used for feature selection were classified into three categories: filter-based methods, embedded methods, and wrapper-based methods.
Filter-based Methods
The researchers employed two filter-based methods: relative importance for linear regression and correlation method. Filter-based methods rank the features based on their individual importance rather than considering interactions between features. These methods provide a quick and efficient way to identify the most influential features.
Embedded Methods
The LASSO (Least Absolute Shrinkage and Selection Operator) method was used as an embedded method in this analysis. Embedded methods incorporate feature selection within the model training process. The LASSO method is known for its ability to perform both feature selection and regularization, which helps to prevent overfitting and improve model performance.
Wrapper-based Methods
Several wrapper-based methods were utilized in this study, including best subset regression, stepwise regression, recursive feature elimination, iterative predictor weighting partial least squares, Boruta, simulated annealing, and genetic algorithms. Wrapper-based methods evaluate subsets of features and select the one that achieves the best model performance. These methods are computationally intensive but often yield more accurate results compared to filter-based or embedded methods.
The findings of the analysis revealed that the five most influential drivers of renewable energy consumption in Madagascar are related to macroeconomic aspects.
Firstly, domestic investment was found to have a positive impact on the adoption of renewable energy sources. This suggests that increased domestic investment in renewable energy projects can contribute to the growth of the sector in Madagascar.
Secondly, foreign direct investment was identified as another positive driver. This implies that foreign financial inflows specifically targeted at renewable energy projects can stimulate the adoption and development of clean energy sources in the country.
Thirdly, inflation was found to positively contribute to renewable energy consumption. This result may indicate that higher inflation rates lead to increased investment in renewable energy as a hedge against inflationary pressures.
On the other hand, industrial development and trade openness were found to negatively affect renewable energy consumption in Madagascar. This suggests that as industrialization and trade activities increase, there may be a tendency to rely more on conventional energy sources rather than investing in renewable alternatives.
This analysis provides valuable insights into the factors influencing renewable energy consumption in Madagascar. Policymakers and stakeholders in the energy sector can use these findings to design effective strategies and policies that promote sustainable and renewable energy sources in the country. Future research could further explore the interactions between these factors and consider additional variables to enhance the understanding of renewable energy adoption in Madagascar.