Короткий опис(реферат):
INTRODUCTION: Digital twin technology is at the head of smart city innovations; rapid urbanization and the rising
complexity of infrastructure management have driven this technology. Real time virtual replicas of the physical urban
systems - digital twins - simulate urban scenarios, monitor conditions, and improve resource allocation more accurately than
ever before. Rigorous empirical studies that quantify the impact of these technologies on the efficiency of urban planning
are rare, curbing evidence-based policy making and large-scale adoption strategies.
OBJECTIVES: This study estimates a panel fixed effects econometric model that quantifies the impact of integrating digital
twins on urban planning performance of five European countries (Estonia, Germany, Portugal, the UK, Poland) over the
period of 2020–2024.
METHODS: Using data from the OECD Smart Cities database, the European Commission Urban Data Platform, the World
Bank’s Worldwide Governance Indicators, and national municipal APIs, annual data were assembled on a composite urban
efficiency index, a digital twin integration score, real time data usage, per capita infrastructure investment, governance
quality metrics, and population density. The model employs city and time fixed effects and addresses heteroskedasticity and
serial correlation with clustered robust standard errors.
RESULTS: The efficiency index of Germany improved from 53,52 in 2020 to 62,57 in 2024; Poland from 45,51 to 58,73;
and Estonia dropped sharply from 70,22 to 39,44. Coefficients on digital twin integration (β₁ = 12,00) and governance quality
(β₄ = 8,00) are positive and statistically significant. Real-time data use (β₂ = 0,08) and infrastructure investment (β₃ = 0,004)
increase efficiency, while population density has a slight negative effect (β₅ = –0,005).
CONCLUSION: This underscores the importance of the network and governance relationships of digital twins in realizing
their potential. Future research should include non-European cities, apply spatial econometric techniques to account for
intercity spillovers, and use dynamic panel models to assess feedback loops from past efficiency gains and their influence
on future technology investment.
