Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms

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.

The macroeconomic determinants of renewable energy consumption in Madagascar: Evidence from an Autoregressive Distributed Lag modeling approach

“…Our research findings are consistent with the study conducted by Ramaharo and Randriamifidy [84] regarding specific determinants. They employed feature selection algorithms to identify the primary factors influencing the adoption of renewable energy consumption.…”

“…increase in renewable energy consumption (REC). The results agree with those of Lawal [50] in the context of Sub-Saharan African economies but contradicts those of Ramaharo and Randriamifidy [84] who obtain negative relation in the context of Madagascar. The positive relation likely reflects the role of trade in facilitating the importation of renewable energy equipment and technologies, which in turn supports the expansion of renewable energy infrastructure in the country.…”

“…While previous research by Ramaharo and Randriamifidy [84] adopted feature selection algorithms to estimate (1), the present study employs the Autoregressive Distributed Lag (ARDL) bounds testing approach. This econometric method allows us to analyze both short-run and long-run dynamics while considering potential cointegration among the variables.…”