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<p>The justice dimension of energy within the “energy trilemma”, policy framework and its inherent cross-cutting qualities relative to the Sustainable Development Goals (SDGs), make it an important driver for socio-economic development. Despite these, there are widespread energy inequalities around the world, not least, in India despite it being the world’s fifth largest economy in terms of nominal GDP. In seeking to help address the multifaceted and complex issues of intermittent power supply and energy inequality and to bridge the gap between electrification and consistent power supply, the paper elucidates the intricate factors contributing to this challenge and discusses strategic solutions and nuanced insights to address this problem. The study utilizes various Machine Learning techniques such as Softmax Regression, Artificial Neural Networks, Gradient Boosting, Random Forest classification techniques, and a qualitative data analysis technique; Analytic Hierarchy Process (AHP) to analyze survey data obtained from 9072 participants from six Indian states. This helped in establishing the factors exacerbating the problem and in articulating its implications, thereby informing the decision-making process. The study also provides recommendations for effective policy formulation and implementation to successfully attain 24/7 electricity access in India.</p>

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Decoding India’s struggle for energy trilemma balance: a mixed-method approach using machine learning and AHP

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

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  • Pratyush Kumar Patro,
  • Enoch Quaye,
  • Timileyin Aworinde,
  • Prince Aduama,
  • Adolf Acquaye

摘要

The justice dimension of energy within the “energy trilemma”, policy framework and its inherent cross-cutting qualities relative to the Sustainable Development Goals (SDGs), make it an important driver for socio-economic development. Despite these, there are widespread energy inequalities around the world, not least, in India despite it being the world’s fifth largest economy in terms of nominal GDP. In seeking to help address the multifaceted and complex issues of intermittent power supply and energy inequality and to bridge the gap between electrification and consistent power supply, the paper elucidates the intricate factors contributing to this challenge and discusses strategic solutions and nuanced insights to address this problem. The study utilizes various Machine Learning techniques such as Softmax Regression, Artificial Neural Networks, Gradient Boosting, Random Forest classification techniques, and a qualitative data analysis technique; Analytic Hierarchy Process (AHP) to analyze survey data obtained from 9072 participants from six Indian states. This helped in establishing the factors exacerbating the problem and in articulating its implications, thereby informing the decision-making process. The study also provides recommendations for effective policy formulation and implementation to successfully attain 24/7 electricity access in India.