Modeling renewable energy market performance under climate policy uncertainty: A novel multivariate quantile causality analysis.

Abstract

The renewable energy market in the United States of America (USA) has experienced several crests and troughs owing to the changes in the climate policies. These changes in the climate policies have impacted the climate risk management scenario in the USA. This impact has changed the behavioral pattern of the renewable energy drivers, and a supply-side analysis of this aspect is largely ignored in the literature. In this pursuit, the present study aims at analyzing the moderating role of climate policy uncertainty in shaping the behavior of renewable energy drivers in the USA. Given the risk analysis perspective, a novel multivariate quantile-on-quantile causality test is introduced in the present study to address five aspects of risk analysis, i.e., tail dependence, co-movement, predictability, multivariate, and asymmetric impact. Moreover, this test also addresses the omitted variable bias and absence of ortho-partiality distribution, which were inherent to Granger causality test. Along with the analysis at the national level, a firm-level analysis is also done by taking the top-5 renewable energy generation firms of the USA. The results show that the climate policy uncertainty has a dampening effect on the renewable energy drivers, and this effect differs at the firm level. These impacts show a significant policy dimension for addressing the climatic risk management concerns in the USA, while achieving the Sustainable Development Goal (SDG) 7 objectives.