Malte Toetzke: Mapping innovation networks in climate-tech based on LinkedIn data

Innovation networks are essential for advancing climate technologies, yet their structure and dynamics remain poorly understood. To address this gap, we use large language models (LLMs) to analyze 26 million LinkedIn posts, mapping a global network of 166,459 organizations and 442,250 collaborations (~1.4 million direct partnerships) across 189 countries. Our dataset spans 27 climate technologies and 17 collaboration types, including demonstration projects, product launches, adoption, and equity investments. We find that, following the recent wave of industrial policy, the structure of innovation networks across many climate technologies has changed substantially. Between 2020 and 2024, governmental organizations shifted from peripheral supporters to central orchestrators of emerging and scaling technologies characterized by high capital intensity, uncertain demand, and a lack of central incumbent industry (e.g., geothermal energy, direct air capture, green hydrogen). Increases in the centrality of governmental organizations are associated with substantial expansions in domestic partnerships, ranging from 2.7 (concentrated solar) to 9.2 (batteries) new domestic partnerships per additional governmental partnership. At the same time, 62% of all global partnerships now arise from collaborations with government participation (e.g., via public procurement and financing), revealing a substantial structural dependence which is highest for concentrated solar (87%) and lowest for electric vehicles (55%). Our LLM-based approach provides a scalable method for continuously monitoring the structure and dynamics of innovation networks beyond climate technologies.

Malte Toetzke is a PostDoc at TU Munich and a Senior Research Fellow at the Max Planck Institute for Innovation and Competition. His research centers on the global transition towards net-zero emissions, informing public policy with evidence generated via novel data science and machine learning approaches. Primary research areas are climate-tech innovation, climate finance, and green industrial policy with publications in leading academic journals (Nature Sustainability, Nature Climate Change) and machine learning conferences (NeurIPS, ICLR). Malte is also a co-founder of a tech-startup based on natural language processing and computer vision technology.