The Confluence of AI and Eco-Innovation: Shaping the Future of Multidisciplinary Research

Abstract

Abstract Artificial Intelligence (AI) and eco-innovation are increasingly converging to redefine how societies address environmental degradation, resource scarcity, and climate risk. This paper examines the intersection of these two domains as an emerging site of multidisciplinary inquiry, one that draws simultaneously on computer science, environmental economics, engineering, and policy studies. Rather than treating AI merely as a technical enabler bolted onto existing green technologies, the paper argues that AI is reshaping the very logic of eco-innovation—accelerating discovery cycles, enabling predictive resource management, and creating new business models built around circularity and efficiency. Drawing on a structured review of academic literature, industry reports, and policy documents published largely over the past decade, the study identifies recurring themes: AI-driven materials discovery, smart energy grids, precision agriculture, and algorithmic carbon accounting. It also surfaces tensions that are often underexplored, including the substantial energy footprint of AI systems themselves, questions of data governance in environmental monitoring, and the risk of techno-solutionism crowding out structural policy reform. Using a qualitative-interpretive methodology combined with thematic content analysis, the paper maps the current research landscape and proposes a framework for understanding AI-eco-innovation convergence across four layers: technological, organizational, institutional, and ecological. The paper concludes that meaningful progress in this space depends not on AI alone but on deliberate multidisciplinary collaboration among technologists, environmental scientists, economists, and regulators. It offers directions for future research, including longitudinal impact studies and frameworks for measuring the net environmental value of AI-enabled innovation.