Climate policy uncertainty and regional innovation performance: New empirical evidence from the United States

This study aims to explore the empirical relationship between climate policy uncertainty (CPU) and regional innovation performance. The empirical analysis was conducted on 36 years of available data over the period 1987 to 2022 in the United States (US). By using an autoregressive distributed lag (ARDL) model for regression analysis, we examine the impact of CPU on two crucial indicators of innovation, namely, research and development (R&D) expenditures and patent applications (PTA). The empirical analysis reveals a significant negative effect of CPU on both R&D expenditures and PTA, suggesting that higher levels of climate uncertainty may hinder innovation activities in the regional context. A high CPU introduces hesitancy and risk aversion, impacting the allocation of resources, confidence in markets, and the willingness of businesses to pursue innovative endeavors. The negative effect remains consistent even after the inclusion of several control variables and robustness checks. The findings underscore the significance of a stable and supportive policy environment and economic conditions in promoting innovation. Overall, this research provides valuable empirical evidence on the interplay between CPU and innovation outcomes, offering insights for policymakers, businesses, and researchers seeking to navigate the complexities of climate-related challenges and their impact on innovation. We did not find any study exploring the comparable impact of CPU on innovation.