Ms Jaewon Park (p), Prof Donghyun Kim

Large-scale digital infrastructure disruptions have revealed the depth of contemporary urban dependence on centralized digital layers. When core data facilities malfunction, cascading interruptions across administrative platforms, mobility management systems, financial transactions, and public services make visible the systemic dependencies underlying urban operations. These events raise a fundamental question: to what extent have regional urban systems become structurally dependent on digital infrastructures? This study conceptualizes urban systems as regionally defined functional configurations composed of interrelated social, economic, and infrastructural subsystems. Digital infrastructures operate as coordination and control layers that mediate transactions, governance processes, mobility flows, and service provision. Digital dependence is defined as the structural dependence of core regional services on digital infrastructures.
The empirical analysis draws on multi-source regional operational data collected between 2016 and 2025, including ICT network failure logs, administrative system interruptions, mobility management disruptions, energy supply incidents, and platform-service outages. These datasets enable identification of simultaneous digital-system interruptions and service-level malfunction events across sectors within regions. Methodologically, the study adapts probabilistic dependency metrics developed in engineering research on complex technical infrastructures to regional systems analysis. Regional service sectors are modeled as system nodes, and operational time-series records are converted into binary state matrices representing normal operation and disruption events. Functional dependence is computed using conditional co-occurrence probabilities between digital-system interruptions and service disruptions, normalized by marginal occurrence rates to distinguish structural dependence from stochastic coincidence. This produces a digital structural dependence measure derived from probabilistic dependency metrics, calculated at the regional level.
The results show that sector-specific configurations of structural dependence are observed across regional systems. Transport control and administrative information platforms exhibit particularly strong functional dependence on digital infrastructures, while energy management systems show intermediate levels and certain localized services remain comparatively less dependent. Asymmetric dependence structures are observed: service sectors rely heavily on digital infrastructures, whereas digital infrastructures do not rely to the same extent on individual service domains. Notably, the intensity and configuration of digital dependence vary across regions, indicating territorially differentiated patterns of structural reliance. These findings demonstrate that regional urban systems have become structurally digital-dependent. As digital transformation advances, operational reliance becomes increasingly centralized within digital coordination layers, intensifying cross-sector vulnerability to digital disruption. By introducing a probability-based dependency measurement framework into regional analysis, this study provides a structural foundation for assessing digital resilience across regions and for identifying territorially differentiated concentrations of digital vulnerability.

Mr Jihan Park (p), Prof. Donghyun Kim

Data centers have become essential urban infrastructure in the era of digital and artificial intelligence (AI) transformation, yet systematic assessments of their urban and regional economic impacts remain limited. Existing policy discussions tend to focus narrowly on burdens related to land, energy, and grid capacity, rather than the economic implications of data centers. Even if they do not generate large-scale direct employment, data centers have substantial potential to function as foundational infrastructure that triggers indirect and structural industrial effects.
This study aims to empirically identify the ways in which data centers influence the growth of high-income jobs and high-tech industries. We used the Data Center KnowledgeBase provided by S&P Global 451 Research, containing information on the location, history, and facility characteristics of data centers across the US. The analysis focuses on the period 2010–2024 and employs US metropolitan statistical areas as the unit of analysis. To estimate the economic effects of data center development within urban areas, we apply the synthetic difference-in-differences method. We used growth in AI-exposed high-tech jobs and increases in related start-up activity as the key outcome variables.
Preliminary findings indicate that while the direct employment effects of data centers are very limited, regions with strong productivity and technological capacity experience significant gains in job upgrading and digital specialization. Conversely, regions with weaker human-capital foundations show little evidence of spillover effects, suggesting that the digital economy may exacerbate existing spatial inequalities. The estimated effects emerge within a few years of data center entry and are concentrated in AI-exposed and knowledge-intensive sectors. These patterns indicate that digital infrastructure tends to reinforce pre-existing regional advantages rather than produce economy-wide local spillovers.
Our findings challenge the conventional view of data centers as resource-heavy, low-value land uses. Instead, we argue that when paired with the right regional conditions, these centers serve as vital catalysts for AI-driven urban growth and industrial transformation. Therefore, regional planning should shift its focus from the binary choice of hosting to the strategic alignment of complementary innovation assets. Without these supporting capacities, digital infrastructure risks exacerbating spatial disparities rather than fostering balanced development.

Dr. Torben Dall Schmidt (p)

Digitalization has in the literature most often been considered from the perspective of potential social or economic benefits. This contribution considers one of the potential drawbacks of digitalisation: data and cybersecurity issues in establishments. While literature on this issue is scarce, particularly with regard to regional perspectives, it is important to gain a better understanding of the potential issues raised by digitalisation. Specifically, two mechanisms are investigated that form part of the digital ecosystem in which establishments operate. The first is the use of suppliers in digitalisation, often labelled ITOs in the literature. These suppliers are part of the digital ecosystem and may provide establishments with a cost and competence advantage in digitalisation. However, the literature also highlights potential hazards associated with using such ITOs, as this involves integrating external partners into the establishment's digital infrastructure, which increases the risk of data and cybersecurity breaches. While digitalisation may increase productivity, it may also increase the risk of major adverse shocks to production. Secondly, a small body of literature highlights the importance of technological know-how, information sharing and knowledge in mitigating such data and cybersecurity risks. It has been argued that such sharing is driven by the availability of digital infrastructure, which may depend on the location of the establishment. These are therefore aspects of the digital ecosystem of firms to consider when investigating data and cybersecurity problems.

These mechanisms associated with the digital ecosystem are investigated in terms of the likelihood of establishments experiencing data and cybersecurity problems, based on three waves of the German SOEP-LEE2 representative establishment survey for the years 2021, 2023 and 2024, comprising a total of 2519 establishments. Probability modelling is used for this analysis, including an inverse Mills ratio for selection into the final sample, taking into account selection due to item non-response in the surveys. Fixed effects are included for time, business sector, and establishment location to account for unobservable heterogeneity. The findings show that establishments that depend heavily on external partners for digitalisation are more vulnerable to data and cybersecurity problems, while high-speed local digital infrastructure in the establishment location has the opposite effect. Furthermore, high-speed local digital infrastructure that facilitates knowledge sharing mitigates the effects of external partnerships on digitalisation. This arguably point to an IV estimation strategy in support of external partners increasing likelihood of data and cybersecurity problems. The flip side of digitalization relates to such aspects of digital ecosystem.

Ms Lamia Enab (p)

This research aims to study the impacts of urban sprawl on soil resources in the Tours metropolitan area and the possibility of integrating artificial intelligence with the conventional mitigation methods to reduce its impact. The research will focus on the long-term study of improving the urban planning, with the objective of understanding the important role of applying AI applications in reducing the major impacts of urban sprawl, especially on the soil resources. The study involves a combination of AI applications; for example, it analyzes various factors by using AI algorithms, such as soil quality, land topography, and ecological sensitivity, to optimize land-use planning. It also uses AI-powered predictive modelling to assess the potential impact of urban development on soil resources. This involves considering factors like population growth, economic trends, and environmental conditions to predict where sprawl might occur and identifying areas with vulnerable soil resources, moreover implementing AI-driven systems to assist in designing smart zoning regulations that prioritize the conservation of valuable soil resources. AI can help identify areas suitable for development while safeguarding critical soil and environmental assets. The collected data will be analyzed using analytical and descriptive methods and models. The expected outcomes of the research are significant contributions to the field of urban planning in general and urban sprawl in particular, including a deeper understanding of the benefits and the important role of applying AI in minimizing soil degradation, monitoring and predicting soil erosion, and developing new AI applications that can be a part of Tours' urban sprawl. This research addresses important questions that are currently crucial for the Tours metropolitan area, such as soil mitigation and environmental surveys, and addresses several scientific questions related to the study of reducing urban sprawl impacts and improving soil resources using AI technologies. AI technologies will be used to produce a dynamic mapping, which will help local authorities to implement sustainable land management.