How Green Energy Patents, Energy Uncertainty, and Green Energy Quality Moves for the SDG13 in the US? A Wavelet Local Multiple Correlation Analysis

Abstract

This research aims to investigate the dynamic connectedness between green energy patents, energy uncertainty, energy quality, and climate change while considering the sample from the US economy. The study mainly focuses on an innovative approach entitled Wavelet Local Multiple Correlation (WLMC) analysis which helps to investigate the dynamic relationships between the variables. The study finds the quarterly observations from 1998Q1 to 2021Q4 by applying both the time and frequency-based empirical investigation. The findings of the WLMC reveal that green energy patents, energy uncertainty, and green energy quality significantly restrain carbon emissions in the US. This underlines their importance in meeting climate targets and supporting Sustainable Development Goal 13. Conversely, a significantly positive connection exists between green energy patents, energy uncertainty, and green energy quality, both in the short and long run. Moreover, the concerns related to the changing climate in the form of higher carbon emissions can substantially be controlled when accounted for on individual grounds. The findings reflect a few but vital policy implications for policymakers linked with the US departments, such as energy and climate change specific and generally for the rest of the similar global economies.  These insights can guide strategic planning for sustainable energy transitions and effective climate action.