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This paper discusses the emergence of blockchain technology in the energy sector in the light of ongoing energy market transformation. The study builds on literature research and expert interviews, and provides insights into the future energy landscape in the context of the blockchain advent. While the interviewees acknowledge the great, though disruptive, potential of blockchain technology for the primary activities in the electricity sector, there is agreement that inflexible regulatory frameworks impose the biggest challenge. The widest impact which blockchain technology will have in the short-term will be in electric vehicle integration, while in the long-term blockchain will enable peer-to-peer microgrids. The role that the blockchain will play, though, relies mainly on the business model innovation in energy. While a growing body of literature discusses specific blockchain applications and solutions in an advanced technological set-up, this paper presents a holistic picture of the blockchain applicability in the energy sector and thematises this very powerful and versatile technology against the background of two emerging economies: South Africa and Russia. • The electricity sector is testing the blockchain technology in an attempt to support the decentralisation of energy production, while the oil & gas sector uses the blockchain for business process optimisation. • The near-term outlook is mainly associated with the blockchain-enabled electric vehicle integration. The long-term impact is uncertain. • Although the blockchain is very applicable to decentralised systems and could help overcome a lot of energy-related issues, their regulatory frameworks are very inflexible. • For the blockchain technology to play a bigger role in the energy field, more business model innovations for the energy sector are needed. • Most likely those systems will play a wider role for energy markets across the globe. [ABSTRACT FROM AUTHOR]

This paper argues that innovation behavior roots in specific socio-psychological set-ups that crystallize in daily practices and routines. The latter are easy to observe and have great potential for the identification of user-innovation behavior. We study the practices and routines of Russian user-innovators around media consumption, internet and technology-usage, consumer preferences and civic engagement in comparison with a sample of mere users. The derived model correctly classified 73% of the original grouped cases of user-innovators. We conclude that a set of practices relative to the certain economic, social and cultural background explains user-innovation engagement and how support could be provided. Although some of our findings are probably specific to Russia, the results are encouraging for further research into the importance of practices and routines in identifying user-innovators in various environments. [ABSTRACT FROM AUTHOR]

• Russia's extractive industry seeks local technologies to replace expensive technology-intense imports. • Russia sees opportunities to become a prominent provider of specialized equipment and services for the production of oil, gas. • To identify technological developments, Russia is increasingly building competencies around foresight activities. • Presented foresight study combines the subjective opinions gathered through expert workshops with text-mining techniques. • The presented methodology helped to link the technologies to dominant professional discussions and to flag key trends. The world economy relies on access to industrial metals, oil and gas for maintaining its critical industrial infrastructure. Although demand is likely to remain high, the most accessible deposits have been depleted. Future capacity growth will be facilitated through further technological developments. Russia as a leading producer is paying great attention to strengthening its competitive edge in global markets. This paper reports on a large-scale technology foresight study of the Russian extractive sector (including oil and gas), which combined expert-based foresight activities with statistical analyses and text-mining techniques based on artificial intelligence and machine learning technologies. The presented methodology helped to link the technologies to dominant discussions (e.g. climate change vs rural development) and to flag key trends. Furthermore, quantitative estimates can be identified quickly. The study's methodology should function as an example for similar studies to support policy planning and investment decisions based on text-mining techniques. [ABSTRACT FROM AUTHOR]