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This paper extends the Regional Knowledge Production Function framework by introducing an artificial intelligence dimension into the analysis of regional innovation across Europe. AI dimension is captured through firm-level adoption and a novel Regional AI Exposure (AIRE) indicator. To address spatial dependence and heterogeneity, a Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) model is used. Results show significant spatial heterogeneity in the AI-innovation nexus. Firm-level AI adoption is strongly linked to higher innovation, especially in Northern and Western Europe. AIRE plays a dual role, reinforcing innovation in technologically advanced regions while acting as a catalyst for catch-up in less developed areas, especially in Eastern Europe.

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New Approaches to Measuring AI Contribution to Innovation Across European Regions

  • 会议论文

  • 发表日期：

    2025年

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  • Emma Bruno,
  • Rosalia Castellano,
  • Gennaro Punzo

摘要

This paper extends the Regional Knowledge Production Function framework by introducing an artificial intelligence dimension into the analysis of regional innovation across Europe. AI dimension is captured through firm-level adoption and a novel Regional AI Exposure (AIRE) indicator. To address spatial dependence and heterogeneity, a Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) model is used. Results show significant spatial heterogeneity in the AI-innovation nexus. Firm-level AI adoption is strongly linked to higher innovation, especially in Northern and Western Europe. AIRE plays a dual role, reinforcing innovation in technologically advanced regions while acting as a catalyst for catch-up in less developed areas, especially in Eastern Europe.