Technological Impact and Financial Rewards: Patent Citations and Market Valuation in the Twin Transition

This thesis examines how forward patent citations predict market-based patent value, comparing Artificial Intelligence (AI) and Green (cleantech) technologies. Drawing on patent-level data from Kogan et al. (2017), I constructed a dataset covering 16 industries and applied scatterplots and regression models, with patent age included to control for exposure to citations. The findings show that patent age consistently reduces market value. In cleantech, forward citations are weakly but significantly linked to lower value, while in AI, citations display a modestly positive association. A pooled model confirms that the citation–value relationship is significantly stronger for AI than for cleantech. Overall, the study demonstrates that although citations are widely used as indicators of patent importance, their predictive power varies: markets reward citations in AI but tend to undervalue green innovation.