How Do Energy Prices, Financial Conditions, and ESG‐Related Uncertainty Shape Energy Efficiency in the United States? Evidence From Wavelet Quantile Regression

Abstract

Improving energy efficiency is a central pillar of climate mitigation strategies, energy security, and sustainable economic growth, particularly in energy‐intensive economies such as the United States. This study investigates the nonlinear and distribution‐dependent effects of energy prices, financial conditions, economic policy uncertainty, and ESG‐related uncertainty on energy efficiency in the United States. The study employs an integrated quantile‐based framework that combines wavelet quantile regression (WQR), quantile‐on‐quantile granger causality (QQGC), and quantile‐on‐quantile kernel regularized least squares (QQKRLS), enabling the analysis of distributional heterogeneity and time‐frequency dynamics. The findings reveal substantial heterogeneity across the conditional distribution of energy efficiency. Energy prices and financial conditions exert strong and persistent positive effects, particularly at lower and middle efficiency quantiles, indicating their critical role during periods of low or transitional efficiency. In contrast, economic policy uncertainty and ESG‐related uncertainty display pronounced state‐dependent effects. While their influence remains limited under normal conditions, uncertainty shocks become more persistent and detrimental at the upper and lower tails of the efficiency distribution. Robustness checks based on QQKRLS confirm that uncertainty transmits unevenly across efficiency regimes, whereas price and financial channels operate more systematically. These findings offer important implications for energy‐efficiency investments and policy design under volatile energy markets, changing financial conditions, and rising sustainability‐related uncertainty.