Balanced Knowledge for Smarter Technological Convergence: Insights from AI-Green Technology Integration

• In Event: AI, Innovation Policy, and Global Sustainability

Abstract

The integration of artificial intelligence (AI) and green technologies presents a transformative opportunity to address global challenges such as climate change and sustainability (Kaack et al., 2022; Vinuesa et al., 2020). AI-driven solutions could reduce global CO₂ emissions by 3–6 gigatons by 2035 (Stern & Romani, 2025) and cut building-sector energy use by 8–19% by 2050 (Ding et al., 2024). Despite rapid adoption—evidenced by a USD 10 billion global market for AI in renewables (Precedence Research, 2025)—the mechanisms driving this convergence remain understudied, particularly how knowledge structures shape integration across such distinct domains.

Technology convergence fosters cross-disciplinary innovation (Giordano et al., 2021), yet prior research has focused on macro-level trends (Curran & Leker, 2011) or institutional factors (Caviggioli, 2016), neglecting inventor-level knowledge dynamics. We argue that the balance between fast-evolving AI and slow-moving green technologies (Kaack et al., 2022) is critical for convergence, as disequilibrium in knowledge may hinder integration (Lee, 2015). Leveraging ambidexterity and absorptive capacity theories, we hypothesize that inventors with balanced knowledge across both domains achieve deeper convergence by enabling exploration-exploitation synergies. We further propose that external collaboration networks moderate this relationship, while convergence itself enhances innovation value.

Using a dataset of 2.1 million global patents, we employ co-classification analysis (Karvonen et al., 2012) to measure convergence. Results show that balanced knowledge accumulation significantly boosts convergence likelihood and depth, while external collaboration strengthens this link. Surprisingly, deeper convergence reduces technological value (measured by citations), likely due to integration complexity and niche-market limitations, whereas broader convergence increases it.

Our study offers three contributions. First, we shift focus from institutional or technological factors to inventor-level knowledge balance, revealing its pivotal role in bridging AI and green tech. Policymakers and firms should prioritize cross-domain expertise to accelerate convergence. Second, we highlight the moderating effect of collaboration networks, suggesting that cross-sector partnerships are vital for integration. Third, we advance understanding of convergence’s impact on innovation value, showing that breadth—not depth—drives market applicability.

Keywords: AI, green technology, technological convergence, knowledge balance, innovation value

Author

• Fang Zhang, Tsinghua University
  Presenting Author