Conference Agenda

Innovation is central to the transition to low-carbon production, yet empirical evidence on how the combination and interaction of multiple climate policy instrument relate to large accelerations in green technological activity remains limited. We study green innovation using patent family data covering 14 green technology fields across 22 countries from 2000 to 2019.

We develop an empirical framework that combines a difference-in-differences–type model with a machine-learning–based variable selection procedure to identify positive structural breaks in green patenting, defined as sustained increases that exceed model-implied trajectories. This approach identifies 108 positive breaks across countries and technologies.

We then link these break episodes to climate mitigation policies drawn from a comprehensive policy database. For individual policy instruments and broader policy categories, we evaluate whether their presence is associated with an increased probability of observing a positive break. In addition, we introduce information-theoretic measures to characterize the cohesion (how typical is the policy mix compared to other countries?) and proximity (novel vs. established combinations) of policy mixes, and show that these measures are predictive of break occurrence.

Across specifications, pricing-based policies are associated with systematically higher probabilities of observing strong increases in green patenting, while subsidy-based and regulatory instruments exhibit more heterogeneous associations across technologies and contexts. The results provide new evidence on the relationship between climate policy design and large, discrete changes in green innovation activity.