Digital innovation for a greener future: the role of artificial intelligence in Europe's biofuel transition
BIOFUELS, BIOPRODUCTS AND BIOREFINING, cilt.1, sa.1, ss.1-25, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 1 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1002/bbb.70188
- Dergi Adı: BIOFUELS, BIOPRODUCTS AND BIOREFINING
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Scopus, Science Citation Index Expanded (SCI-EXPANDED), Chemical Abstracts Core, Compendex, Greenfile, INSPEC, CAB Abstracts
- Sayfa Sayıları: ss.1-25
- Anahtar Kelimeler: artificial intelligence, biofuel production, distributed lag model, European Union, FGLS, panel data, quantile regression, renewable energy, Renewable Energy Directive, technology diffusion
- Erciyes Üniversitesi Adresli: Evet
Özet
This study investigates the relationship between artificial intelligence (AI) development and biofuel production using a balanced panel dataset of 12 European Union (EU) countries over the 2008–2024 period. Employing feasible generalized least squares (FGLS) estimation with distributed lag specifications, the analysis controls for Renewable Energy Directive shocks and common cyclical effects through time fixed effects. The results reveal that AI-related scientific publication volume exerts a positive and statistically significant effect on biofuel production, with a cumulative elasticity of approximately 0.47 materializing predominantly through a 2-year lag, while venture capital investment in AI technologies generates a complementary but more modest effect (cumulative elasticity: 0.076) over a 1-year horizon. These findings suggest that AI influences biofuel production through two distinct yet complementary pathways: a research output channel and a commercial adoption channel, both of which are theoretically consistent with a multi-stage technology diffusion process. Quantile regression estimates further reveal pronounced asymmetry across the production distribution, with the AI effect being substantially stronger among low-production countries (Q10–Q25 elasticities: 0.58–0.61) and statistically insignificant among high-production countries, a pattern attributable to technological catch-up advantages, ceiling effects imposed by binding blending mandates, and disproportionate early-adopter gains. Mechanism analysis indicates that AI operates primarily through R&D absorption capacity, agricultural productivity improvements, and land resource optimization rather than through direct volumetric expansion. These findings carry implications for the integrated design of AI and biofuel support policies at both national and EU levels.