Tyler Hansen

Tyler's research explores the economics, policy, and governance of energy transitions, with a particular focus on offshore wind power. He employs a mixed-methods approach, including quantitative (econometrics, cash flow analysis, descriptive analysis), qualitative (interviews, focus groups, document analysis, case studies), and—most recently—computational methods (applied large language models).

His current work examines how large language models (LLMs)—such as ChatGPT and Claude—can be integrated into expert-in-the-loop approaches to scale qualitative research in complex social decision domains. Clean energy megaprojects—including offshore wind development and power transmission—are characterized by massive scale, high risks, and extensive regulatory processes. A single megaproject generates hundreds of thousands to millions of pages of documentation through development and approval processes, leading to information overload that hinders rapid, just, and efficient deployment. By leveraging LLMs, Tyler aims to transform these vast corpora into powerful analytical tools to investigate structural challenges, develop sector-wide solutions, and facilitate institutional learning.