In modern conditions, boosting the energy efficiency of regional economies by reducing energy consumption by businesses and households stands as an imperative for their greening. Russia’s legal regulations establish concrete deadlines for reaching sustainable development targets. Against this backdrop, the study aims to evaluate the current energy efficiency status of Russian regions, thereby identifying prospects (by 2030) for achieving decarbonization and sustainable development goals in their economies. The author’s methodology, spanning multiple stages, centres on calculating growth rates for relevant energy efficiency indicators over 2016—2022, followed by their extrapolation to 2030. The findings indicate that few Russian regions can meet the established targets by the deadline. The reasons behind this projected shortfall are as diverse as the regions themselves. However, data analysis reveals a common trend: insufficient growth rates in reducing industrial energy intensity, energy consumption by economic entities, and atmospheric pollutant emissions across most Russian regions. This, in turn, underscores the need for regional authorities — accounting for each subject’s unique developmental specifics and features — to implement active regional policies whose tools align seamlessly with all sustainable development components. The practical value of this research lies in its preliminary energy efficiency estimates for regions, which not only spotlight emerging ‘energy’ issues but also enable authorities to adopt congruent, timely decisions based on their identification, fulfilling the immanent sustainability tasks set by national leadership.

The relevance of the study stems from the growing digital inequality among Russian regions amid the rapid development of the digital economy. Disparities in digitalization levels perpetuate existing interregional gaps and create risks of concentrating human and technological potential in a limited number of regions. The aim is to identify and quantify the dynamics and spatial structure of digital inequality in Russian regions (2011—2023), differentiating it into primary (infrastructural — internet access) and secondary (human capital and competency-based — ICT employment) levels. The methodology combines cartographic methods of quantile classification, the Gini index, kernel density estimation (KDE), and Moran’s index to verify neighborhood effects. The results indicate divergent dynamics: a steady reduction in the infrastructural gap in internet access is accompanied by an increasing concentration of human capital in ICT. Significant spatial autocorrelation is confirmed, manifested in the formation of stable clusters of leading and lagging regions. Conclusions. The key challenge for regional development is shifting towards overcoming the secondary divide, necessitating a transition from universal infrastructure policies to targeted measures that stimulate the diffusion of digital competencies and the development of human capital in peripheral regions.