Blockchain as a Driver for Smart City Development: Application Fields and a Comprehensive Research Agenda
Bibliographic record
Abstract
The term “Smart City” denotes a comprehensive concept to alleviate pending problems of modern urban areas which have developed into an important work field for practitioners and scholars alike. However, the question remains as to how cities can become “smart”. The application of information technology is generally considered a key driver in the “smartization” of cities. Detailed frameworks and procedures are therefore needed to guide, operationalize, and measure the implementation process as well as the impact of the respective technologies. In this paper, we discuss blockchain technology, a novel driver of technological transformation that comprises a multitude of underlying technologies and protocols, and its potential impact on smart cities. We specifically address the question of how blockchain technology may benefit the development of urban areas. Based on a comprehensive literature review, we present a framework and research propositions. We identify nine application fields of blockchain technology in the smartization of cities: (1) healthcare, (2) logistics and supply chains, (3) mobility, (4) energy, (5) administration and services, (6) e-voting, (7) factory, (8) home and (9) education. We discuss current developments in these fields, illustrate how they are affected by blockchain technology and derive propositions to guide future research endeavors.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".