Financing energy firms and catalyzing the energy transition are pivotal for achieving a sustainable future. In this era of increasing environmental consciousness, banks are incorporating environmental considerations into their credit rating methodologies, like the Partnership for Carbon Accounting Financial Guidelines. In the meantime, the advent of digital tokens offers new avenues for energy token creation. This study establishes a factor model as the fundamental framework for algorithmic energy tokens and employs gradient-boosting tree regression to examine energy price drivers in Italy and Austria. The results underscore the heightened motivation to invest in energy transition and security during periods of elevated energy prices. Conversely, the drive to invest in clean energy sources diminishes when operational profits are low or energy security must be maintained. This research elucidates on an innovative financing solution that handles these dynamics, produces momentum, and focuses special emphasis on its potential for implementing environmental policies by developing an algorithmic energy token mechanism based on environmental regulations and considerations. •Introduced: theoretical framework, employing linear regression energy pricing model based on Life Cycle Analysis, to establish algorithmic energy token.•Gradient-boosting tree model trained on historical energy price drivers in Italy and Austria.•Ranking of influential variables on energy tokens shows key parameters, highlighting potential for flexible financing.