Blockchain Platforms in Energy Markets—A Critical Assessment
Bibliographic record
Abstract
Compared to other applications of distributed ledger technologies, for example, in decentralized finance, non-fungible tokens, and logistics, Blockchain applications in the energy industry have not found widespread dissemination and fell short of market expectations during the Blockchain hype in the late 2010s. In semi-structured qualitative interviews with leading providers in the energy industry, conducted from 2019 to 2021, hurdles in energy applications are compared with a control group of additional interviews with representatives of companies operating in IT and FinTech. The analysis uses a framework covering technical feasibility, desirability, and economic viability, as well as the role of regulatory frameworks. The interviews reveal that the first Blockchain applications suffered from a combination of technological constraints and inter-platform competition. Due to the permissionless configuration of the early energy Blockchains, they were slow in terms of transaction speed compared to existing platforms and prices per transaction were high, in addition to high degrees of complexity related to requirements from both critical-infrastructure systems and financial market regulation. The analysis further points to the slow adoption of Blockchain applications in the energy sector being related to business models rather focusing on products and platforms as well as on transactional rather than procedural use cases, with a high degree of standardization of the offering and low levels of inclusiveness concerning processes. The move from transaction platforms to innovation platforms and the emergence of Blockchain as a service provider—plus technical advances with regards to high-frequency transactions combined with the increasing importance of use cases, such as proof of origin for fuels or e-charging—may induce a shift from pilot applications to commercialization within the larger innovation ecosystem. While the involvement of Blockchain solutions in energy markets increases with pilot projects and with this, the acceptance of players and stakeholders in the energy ecosystem, a big hurdle for innovation remains the regulation of energy markets to allow for peer-to-peer trading, a usage-driven distribution of network costs, and bottom-up pricing markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.044 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".