Artificial intelligence, industrial robots, and green technological innovation: evidence on carbon intensity from five leading robotics economies
INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/13504509.2026.2732017
- Dergi Adı: INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, BIOSIS, Environment Index, Geobase, Greenfile, Index Islamicus, Public Affairs Index, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Hatay Mustafa Kemal Üniversitesi Adresli: Evet
Özet
Achieving low-carbon industrial development has become a central challenge for sustainable development and climate policy. Although artificial intelligence (AI) and industrial automation are widely regarded as key drivers of industrial decarbonization, empirical evidence on their environmental effectiveness remains limited and fragmented. This study examines how AI-related innovation, industrial robot deployment, and green technological innovation influence carbon intensity across the world's five leading robotics economies - China, Germany, Japan, South Korea, and the United States - over the period 2011-2022. Using the Augmented Mean Group (AMG) estimator and regularized Common Correlated Effects (rCCE), the analysis evaluates the long-run relationships between technological transformation and carbon efficiency. The findings reveal substantial heterogeneity across innovation pathways. Industrial robot deployment is associated with significantly lower carbon intensity, suggesting that production-integrated automation improves resource efficiency and supports cleaner manufacturing. By contrast, AI-related patents show no statistically significant direct relationship with carbon intensity, indicating that AI innovation alone does not automatically translate into environmental gains without effective industrial diffusion. Renewable energy and waste management innovations are negatively associated with carbon intensity, whereas fossil fuel technologies exhibit the opposite relationship, highlighting the environmental costs of carbon-intensive technological trajectories. These findings demonstrate that not all technological innovations contribute equally to sustainable industrial transformation. The study underscores the importance of complementing AI innovation with production-oriented automation and environmentally directed technological policies to accelerate progress toward climate action and sustainable industrial development.