Beyond the Hubs: AI’s Surprising Regional Roots in Europe

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Author: Denis Avetisyan

New research reveals that artificial intelligence specialization isn’t limited to Europe‘s major tech centers, with significant activity emerging in peripheral regions.

A bibliometric analysis at the NUTS-3 level demonstrates AI research specialization in Eastern Europe and Spain, though regional focus doesn’t necessarily translate to higher citation impact.

Despite a prevailing focus on core metropolitan areas, artificial intelligence (AI) research is increasingly distributed across Europe’s regional landscape-a dynamic explored in ‘Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level’. Analyzing bibliometric data at the NUTS-3 level, this study reveals that peripheral regions, particularly in Eastern Europe and Spain, demonstrate unexpectedly high levels of relative AI specialization. However, specialization does not necessarily translate to increased citation impact, identifying diverse regional profiles ranging from high-impact niches to high-volume, low-impact outputs. What strategic implications do these findings hold for fostering equitable scientific development and maximizing the visibility of AI research across the European Union?

Decoding the European AI Landscape: Beyond the Usual Suspects

The uneven geographical distribution of Artificial Intelligence (AI) research presents both opportunities and challenges for the European Union. Concentrated innovation within established metropolitan hubs risks exacerbating regional disparities, potentially leaving valuable talent and economic growth untapped in other areas. A thorough understanding of where AI research is actually occurring – not just where it is published – is therefore vital for crafting effective policies that foster inclusive innovation. By pinpointing emerging centers of excellence and identifying regions with unrealized potential, the EU can strategically allocate resources, attract investment, and ensure that the benefits of AI are shared more equitably across its member states, ultimately bolstering the continent’s competitiveness on the global stage.

Current methods for evaluating the European AI research landscape frequently overlook crucial activity blossoming beyond well-established metropolitan centers. Analyses relying on broad national or regional data often mask the emergence of specialized expertise within smaller cities and university towns, creating a skewed perception of innovation distribution. This lack of granularity presents a significant challenge to effective investment strategies, as funding tends to concentrate in already-developed hubs, potentially stifling promising research and talent in under-recognized locations. Consequently, a more detailed understanding of these emerging centers is essential to unlock the full innovative potential across the continent and ensure a balanced, geographically diverse AI ecosystem.

Accurately charting the European AI landscape demands a shift beyond simple publication metrics; a comprehensive bibliometric analysis offers the necessary precision. This approach delves into the specifics of research citations, co-authorship networks, and the venues where AI research appears, revealing patterns of specialization at the regional level. By examining who cites whom, and where research is disseminated, analysts can pinpoint emerging AI hubs beyond established metropolitan centers and identify areas of unique regional strength. This detailed mapping moves beyond merely counting papers, instead offering a nuanced understanding of the actual impact and interconnectedness of AI research across Europe, allowing for more targeted investment and fostering a more equitable distribution of innovation.

Quantifying the Signal: A Methodology for Regional AI Assessment

Data for this analysis was sourced from Clarivate InCites, a bibliometric database providing comprehensive citation information for scholarly publications. This database allowed the construction of a regional dataset encompassing publications related to Artificial Intelligence (AI) between 2018 and 2022. The dataset was structured at the NUTS-3 regional level, a standard European Union geographical classification, enabling granular analysis of AI research activity across 239 regions. Data fields utilized included publication year, author affiliations, source titles, and citation counts, facilitating the calculation of regional research volumes and impact metrics. The selection criteria for AI-related publications involved keyword searches and topic categorization within the InCites database, ensuring a focused dataset for subsequent analysis.

The Relative Specialization Index (RSI) quantifies a region’s focus on Artificial Intelligence research relative to its overall research output. Calculated from the Activity Index (AIndx), the RSI normalizes the number of AI-related publications by the total number of publications originating from that region. This normalization process corrects for size biases; a region with high overall research output will not automatically have a higher RSI simply due to volume. The RSI is expressed as a ratio, where values greater than one indicate regional specialization in AI research, a value of one indicates research effort aligned with the global average, and values less than one indicate relative under-representation of AI research within that region’s overall research portfolio.

The Relative Citation Impact (RCI) is calculated by dividing a region’s average citations per publication in the field of Artificial Intelligence by the global average citations per publication in the same field, over a three-year period. This normalization process accounts for differences in citation practices across disciplines and allows for a direct comparison of research impact between regions. An RCI value greater than one indicates that the region’s AI publications are cited more frequently than the global average, suggesting a higher level of research influence. The data source for calculating RCI is Clarivate’s Web of Science Core Collection, ensuring a standardized and comprehensive citation dataset.

Unveiling the Hidden Nodes: Regional Patterns Beyond the Core

Regional analysis of AI activity reveals a differentiation in profiles beyond simple activity levels. Some regions demonstrate both a high degree of specialization in Artificial Intelligence, measured by the Relative Specialization Index (RSI), and a correspondingly high research impact, indicated by the Relative Citation Index (RCI). Conversely, other regions exhibit a high volume of AI-related publications and activity – suggesting a substantial research presence – but demonstrate a comparatively lower research impact as measured by citation metrics. This indicates that while certain regions are focused centers of AI expertise generating influential research, others are characterized by broader, but less impactful, AI-related activity. The divergence suggests that specialization, not simply scale, is a key determinant of research influence in the field.

Analysis indicates notable AI specialization is developing in Peripheral Regions, specifically within Eastern Europe and Spain, challenging the concentration of expertise in established technological hubs. These regions are demonstrating focused capabilities in AI, suggesting the emergence of new innovation centers beyond traditional core locations. This specialization is evidenced by Regional Specialization Indices (RSIs) exceeding expectations for peripheral areas, indicating a disproportionately high concentration of AI-related activity relative to overall research output. This trend suggests a broadening of the AI landscape and the potential for increased distributed innovation, rather than continued centralization.

Analysis indicates substantial AI specialization in specific peripheral regions of Spain, notably Granada and Jaén. Granada demonstrates a Relative Specialization Index (RSI) of 0.71, while Jaén achieves an RSI of 0.62, signifying a concentration of AI-related research within those locales. However, the regional impact of this specialization differs; Granada exhibits a Relative Citation Index (RCI) of 2.55, indicating a stronger influence on the broader field, compared to Jaén’s RCI of 2.05. These metrics suggest that while both regions are actively specializing in AI, Granada’s research output currently has a greater demonstrable impact based on citation patterns.

Analysis of Citation Topics, derived from publications originating in Electrical Engineering, Electronics, and Computer Science, allows for a detailed breakdown of the AI specializations present within specific regions. This methodology moves beyond broad classifications of ‘AI activity’ to identify focused areas of expertise, such as machine learning algorithms, computer vision techniques, or natural language processing applications, that are driving regional performance. By examining the thematic content of cited publications, it is possible to determine which specific AI sub-fields are concentrated in a given area, and to quantify the relative prominence of these sub-fields within the region’s overall AI profile. This granular approach facilitates a more nuanced understanding of regional strengths and potential areas for collaborative development.

Rewriting the Map: Implications for Innovation and Policy

The study reveals that innovation isn’t confined to established metropolitan centers; rather, specialized peripheral regions possess significant, and often untapped, potential for driving impactful advancements. These areas, characterized by concentrated expertise in specific technologies, represent fertile ground for geographically distributed innovation ecosystems. Recognizing this necessitates a shift in investment strategies, moving beyond solely funding dominant hubs to proactively supporting the unique strengths of these peripheral regions. Targeted investments – focusing on infrastructure, skill development, and fostering connections to broader networks – can unlock substantial economic and societal benefits, fostering a more balanced and resilient European innovation landscape. Ignoring these specialized regions risks overlooking crucial sources of future growth and limiting the overall impact of research and development efforts.

Effective resource allocation and impactful policy interventions hinge on a granular understanding of regional specialization, as quantified by the Regional Specialization Index (RSI) and Regional Citation Index (RCI). These indices move beyond broad geographical categorizations, revealing whether a region concentrates its research efforts – indicated by a high RSI – and whether that research achieves significant influence, reflected in a robust RCI. Policymakers can leverage this data to pinpoint areas where focused investment will yield the greatest returns, avoiding the pitfalls of uniformly applied strategies. Regions exhibiting high specialization but low citation impact might benefit from initiatives fostering knowledge transfer and collaboration, while those with broad research portfolios and strong citation rates could serve as models for diversification. Ultimately, utilizing these metrics allows for a move toward evidence-based policies tailored to the unique strengths and weaknesses of each regional innovation ecosystem.

The study reveals that regional impact, as measured by the Regional Citation Impact (RCI), isn’t always a direct result of deep specialization in a particular field. Regions such as Fyn in Denmark demonstrate this decoupling, achieving an RCI exceeding 4.0 – a significant indicator of research influence – despite exhibiting relatively low levels of regional specialization. This finding challenges conventional wisdom suggesting that concentrated expertise is the primary driver of innovation and impact. Instead, it points to the importance of broader factors, potentially including robust regional innovation ecosystems, effective knowledge transfer mechanisms, and strong cross-sectoral collaboration. Consequently, policy interventions should move beyond simply targeting specialized clusters and embrace more nuanced strategies that foster innovation across a wider range of regional capabilities and contexts.

Innovation across Europe is demonstrably strengthened by robust international collaboration networks. These connections function as critical conduits for the exchange of knowledge, best practices, and specialized expertise, accelerating the pace of discovery and application. Regions actively participating in these networks benefit not only from access to a wider pool of resources, but also from the cross-fertilization of ideas, fostering a more dynamic and resilient innovation ecosystem. This collaborative environment extends beyond academic circles, encompassing industry partnerships, governmental initiatives, and the sharing of technological advancements, ultimately driving economic growth and addressing complex societal challenges with greater efficiency and impact. The interconnectedness facilitated by these networks proves particularly crucial for regions seeking to overcome limitations in local resources or expertise, allowing them to leverage the collective intelligence of the European research and innovation landscape.

The study’s findings regarding regional AI specialization echo a fundamental principle of systems understanding: to truly know a system, one must probe its edges. It’s not sufficient to examine only the well-trodden paths of established research hubs; the periphery often holds the key to unexpected insights. As Ken Thompson famously stated, “Sometimes it’s the people who can’t read the map that find the most interesting things.” This sentiment perfectly encapsulates the research’s discovery that meaningful AI specialization exists outside of major European centers, challenging the conventional focus on established nodes and suggesting that innovation isn’t solely confined to the expected locations. The concentration of activity doesn’t necessarily equate to impact, a concept highlighted by the study’s observation that specialization at the NUTS-3 level doesn’t automatically translate to higher citation rates.

Beyond the Core

The observation that artificial intelligence research is not solely the domain of established European centers invites a necessary discomfort. It suggests specialization, as measured by publication patterns at the NUTS-3 level, is a surprisingly diffuse phenomenon. The apparent presence of focused effort in peripheral regions – particularly in Eastern Europe and Spain – is not, however, a guarantee of amplified impact, as reflected in citation metrics. This disconnect presents a crucial challenge: is relative specialization merely a statistical artifact, or does it represent nascent capacity struggling against systemic headwinds?

Future inquiry must move beyond simply mapping specialization. The architecture of knowledge transfer, the role of regional funding mechanisms, and the interplay between local expertise and global networks require meticulous dissection. One wonders if the current metrics prioritize incremental advancement within established paradigms, inadvertently obscuring genuinely novel contributions originating from the periphery. The study of citation impact, while convenient, may function as a self-fulfilling prophecy, rewarding conformity over disruption.

Ultimately, the task is not to optimize for citation counts, but to understand the conditions under which specialized knowledge can flourish, irrespective of geographical location. Chaos is not an enemy, but a mirror of architecture reflecting unseen connections. Perhaps the true innovation lies not in replicating the core, but in harnessing the unique strengths of the periphery – a proposition that demands a more nuanced, and frankly, less predictable, approach to measuring success.

Original article: https://arxiv.org/pdf/2602.15249.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-19 04:31