Energy Price Uncertainty and Renewable Energy Consumption: A Nonlinear Analysis

In this study, we conduct an in-depth examination of the intricate relationship between energy uncertainty and renewable energy consumption. Understanding this dynamic is crucial, as energy uncertainty-characterized by volatility in energy prices, policy shifts, and supply chain disruptions- can significantly influence the adoption and utilization of renewable energy sources. To explore this relationship, we employ a nonlinear Granger causality test based on artificial neural networks. This advanced methodology allows us to capture complex and nonlinear dependencies that traditional econometric models may overlook. Our empirical findings reveal that fluctuations in the energy uncertainty index play a pivotal role in forecasting changes in renewable energy usage within the industrial sector. Specifically, periods of heightened uncertainty correspond with noticeable shifts in renewable energy consumption, suggesting that businesses may adjust their energy strategies in response to uncertainty-driven risks. These insights hold significant implications for industry stakeholders, policymakers, and energy investors. A clearer understanding of the influence of energy uncertainty on renewable energy adoption can aid in formulating more resilient energy management strategies. Furthermore, policymakers can design regulatory frameworks that mitigate the adverse effects of uncertainty, fostering a more stable and predictable environment for industrial renewable energy investment.