Climate policy uncertainty and its impact on energy demand: An empirical evidence using the Fourier augmented ARDL model

Abstract

Global climate change and its subsequent impact on energy demand present pressing issues for policymakers. Existing literature presents various determinants of energy demand, but the intricate relationship between energy demand and climate policy uncertainty (CPU) remains underexplored. Utilizing the Fourier-augmented ARDL (FA-ARDL) model and drawing from monthly data spanning March 1995 to August 2022, we investigate the impact of CPU on energy demand in China. Our study finds a significant long-run co-integration between climate policy uncertainty (CPU) and renewable energy demand. The FA-ARDL analysis shows that CPU negatively impacts renewable energy demand in both the short and long term, as it leads to higher renewable energy prices. These increased prices deter stakeholders from investing in or adopting renewable technologies, making renewables less competitive compared to traditional energy sources. Our findings are helpful for policymakers to communicate the climate objectives since mitigating climate-related uncertainties would substantially drive renewable energy consumption.