Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms
Franck Ramaharo, Fitiavana Randriamifidy
TL;DR
This study addresses the determinants of renewable energy consumption in Madagascar by compiling 12 macroeconomic, financial, social, and environmental drivers and applying a comprehensive set of feature-selection algorithms to a linear $REC=f(\cdot)$ framework over 1990–2021. By classifying methods into filter, embedded, and wrapper types and using cross-validation, the authors identify robust drivers: domestic investment, foreign direct investment, and inflation consistently promote renewable energy adoption, while industrial development and trade openness exert negative effects. The results highlight macroeconomic factors as the primary determinants of renewable energy uptake and illustrate how different selection strategies converge on a core set of predictors, notably $DINV$, $FDI$, $TR$, and $URB$, with additional contributions from $FDI$, $IND$, and $INFL$. The findings inform policy by underscoring the importance of investment incentives, stable prices, and urban energy demand in accelerating Madagascar's energy transition, while cautioning against letting import-dependence and industrial growth raise energy intensity. Overall, the paper advances methodology for determinant analysis in small samples using a battery of feature-selection algorithms and provides practical insights for energy policy under Madagascar's NEP.
Abstract
The aim of this note is to identify the factors influencing renewable energy consumption in Madagascar. We tested 12 features covering macroeconomic, financial, social, and environmental aspects, including economic growth, domestic investment, foreign direct investment, financial development, industrial development, inflation, income distribution, trade openness, exchange rate, tourism development, environmental quality, and urbanization. To assess their significance, we assumed a linear relationship between renewable energy consumption and these features over the 1990-2021 period. Next, we applied different machine learning feature selection algorithms classified as filter-based (relative importance for linear regression, correlation method), embedded (LASSO), and wrapper-based (best subset regression, stepwise regression, recursive feature elimination, iterative predictor weighting partial least squares, Boruta, simulated annealing, and genetic algorithms) methods. Our analysis revealed that the five most influential drivers stem from macroeconomic aspects. We found that domestic investment, foreign direct investment, and inflation positively contribute to the adoption of renewable energy sources. On the other hand, industrial development and trade openness negatively affect renewable energy consumption in Madagascar.