Akademik Məlumat İdarəetmə Sistemi
Applied Economics, 2025 (SSCI)
Understanding how sectoral emissions respond to climate and economic policy uncertainties is key to crafting effective mitigation strategies amid ever-evolving environmental and economic challenges. This study takes a groundbreaking approach by exploring how both climate and economic policy uncertainties influence sectoral CO2 emissions, specifically within the U.S., paving the way for more targeted and impactful climate action. The study also examines the role of renewable energy generation using data spanning from 8 January 2019, to 25 June 2024. To achieve this, the study introduces Multi-Frequency Quantile Regression (MFQR) and Multi-Frequency Quantile Granger Causality (MFQGC) techniques to capture causal effects across various frequencies and quantiles. The findings from the MFQR reveal that residential and industrial emissions respond differently to climate policy uncertainty, with mixed effects observed across frequencies and quantiles. Furthermore, economic policy uncertainty predominantly reduces emissions in most time-frequency domains, while renewable energy generation consistently lowers emissions. The MFQGC results further demonstrate that all variables significantly predict changes in emissions, with effects varying by frequency and quantile. Based on these findings, policymakers should adopt sector-specific strategies that address climate and economic policy uncertainties while prioritizing renewable energy generation to achieve consistent emissions reductions.