Agricultural Productivity‐Driven Renewable Energy Adoption and Mechanisms in Developing Economies: A Double Machine Learning Approach

ABSTRACT Sub‐Saharan Africa is facing challenges surrounding energy access, as around 750 million people have limited access to electricity. Meanwhile, rural households rely mainly on agriculture as a major source of income. The relationship between energy demand and agricultural production can help us identify effective rural electrification methods. This study analyses whether improved productivity in agriculture contributes to decentralised renewable energy use in Ghana, utilising double machine learning (ML) methods to address concerns surrounding endogeneity. The study applied random forest techniques, accompanied by cross‐validation approaches, to estimate data from 21,583 area samples to identify market access, infrastructure improvement and wealth as probable causal channels in the market. The findings revealed that an increase in productivity by one unit increases energy demand by 1.127 kWh when market access becomes a leading channel. Significant relationships are observed among variables like distance to market centres, road network and grid accessibility, whereas correlations surrounding wealth and infrastructure show mixed results. Channel‐based relationships show robust strength using different ML procedures, including different cross‐validation setups. The study recommends targeted rural energy investments proportionate to levels of productivity in agriculture, the implementation of integrated infrastructure programmes to raise market accessibility and the restructuring of funding mechanisms within the entire marketing cycle of agriculture, rather than relying on household accumulation of wealth. These recommendations emphasise coordinated rural development initiatives aiming to push forward productivity in agriculture alongside increased access to renewable energy sources.