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The rapid increase in the amount of greenhouse gas emissions resulting from the utilization of fossil fuels to meet global energy needs has made the transition to cleaner energy sources imperative. Past studies show that energy transition not only improves the environment that we live in but is also a means to fulfill many of the United Nation's sustainable development goals. Even though studies as early as the 1980s focused on energy transition, there has been a spurt in the volume of literature in the mentioned area over the last five years (post-2018). Consequently, researchers face a daunting task in scanning through this large literature body and identifying the key research trends and gray areas in energy transition research. The current study tries to address this problem by the use of an unsupervised machine learning technique “topic modeling via latent Dirichlet allocation model” implemented on the abstracts of 1221 research articles (related to energy transition) extracted from the Scopus database. The topic modeling approach reveals eight meaningfully interpretable unique topics. T he use of technology and models for energy transition, energy transition and policy, environmental impacts of energy transition, and the impact of transition on energy markets are the most researched topics. However, benefits from energy transition, energy distribution, importance, and socio-economic impacts of energy transition are largely understudied. The study not only conducts a comprehensive analysis of the energy transition literature but also provides lots of implications and future research directions for the benefit of various stakeholders.
Trivedi et al. (Mon,) studied this question.