Smart City Projects Financing
Bibliographic record
Abstract
The implementation of advanced digital technologies and innovations in smart cities for the provision of more efficient, sustainable, and inclusive city services, the development of infrastructure, and the improvement of citizens’ quality of life require appropriate financing technologies. In addition to purely public (for example, green bonds, social impact bonds, state subsidies, etc.) and purely private (venture investments, leasing, etc.) financial instruments, various options for public-private partnerships and financing with and against the participation of the community (participatory ), which increase the efficiency of budget financing due to the agreed distribution of risks and responsibilities between stakeholders. The bibliometric analysis of publications indexed by Scopus using the keywords “smart city” and “public-private partnership” using the VOSviwer tool allowed establishing the periods of growth (2013-2019 and 2022-2023) and decline (2019-2022) of the popularity of this topic among scientists, countries-leaders of research activity (USA, India, China, Italy, Spain, Great Britain, Canada and Germany), dominant directions of cross-sectoral research. The analysis of the Smart City Index Report (IMD Smart City Index Report) showed that in 2023, the top 5 leaders were Switzerland (Zurich), Norway (Oslo), Australia (Canberra), Denmark (Copenhagen), and Great Britain (London). With the help of the Google Trends toolkit, an analysis of the dynamics of Internet requests over the past 10 years from citizens of these countries, as well as Ukraine and the world, was carried out using identical keywords. The analysis proved that both in these countries and in the world as a whole, public interest in the development of the smart grid was recorded (in Ukraine, the highest peak (100 GT Scale) was in 2019; in Great Britain and Switzerland – in 2018, in Denmark – in 2017 and 2022, in Australia and Norway – in 2016) against the background of an almost absolute (except for Great Britain and Australia) lack of public interest in issues of public-private partnership. A regression model was developed to study the impact of participatory financing on the readiness level of Ukraine’s regions (as of 2020) to implement smart local development technologies. An integral indicator was used as the resulting variable, within which data on the assessment of the availability of electronic public services and the level of automation and digitization of public services (the number of state and local self-government bodies that provided the possibility of using electronic democracy tools), the level of use electronic platforms for communication with state bodies (the number of registered “E-appeals”, published “E-petitions” and reports on “E-consultations”), the availability of the Internet (the number of Internet subscribers and the share of households that have access to Internet services home). In the modeling process, adjustments were made for the gross regional product and the population of the respective region, the weighting factors were determined by the method of principal components, the estimation of the model parameters was carried out by the method of least squares, the calculations were made using the MS Excel spreadsheet and the Statistica application program package.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.078 |
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".