Do Sectoral AI Adoption Rates Reduce Carbon Intensity? Evidence from EU Industries, 2021–2023

We test whether higher sectoral adoption of artificial intelligence (AI) aligns with lower greenhouse-gas (GHG) intensity across EU country-by-industry cells. Using harmonised Eurostat sources—enterprise AI use (isoc_eb_ain2; share of firms ≥ 10 employees using ≥ 1 AI technology) and air-emissions intensities by NACE activity (env_ac_aeint_r2; kg CO₂e per chain-linked euro)—we assemble a 2021 and 2023 panel at Level-1 NACE sections and estimate two-way fixed-effects models with country×industry and year effects. On average, the AI–intensity association is statistically indistinguishable from zero over this short window. However, context-dependent patterns are consistent with a cybernetic feedback view: in energy/process-intensive manufacturing and selected information-intensive services with high baseline intensity, higher AI adoption correlates with economically meaningful reductions in GHG intensity. Results are robust to alternative outcomes (GVA-based intensity), functional forms, winsorisation, and difference-specifications. The findings imply that the returns of AI to decarbonisation are heterogeneous and likely stronger where energy flows are proximate and baseline intensity is high, underscoring the importance of sector-specific AI programmes and complementary investments in data pipelines, OT–IT integration, skills, and cleaner power mixes.