مقاله انگلیسی رایگان در مورد تاثیر عدم اطمینان خط مشی آب و هوایی بر تقاضای انرژی – الزویر 2022

Abstract

     This paper evaluates the impact of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. Economic growth and crude oil prices are added to the energy consumption functions as control variables. The paper considers several approaches to model both renewable and non-renewable energy demand. It is found that crude oil prices promote non-renewable energy demand and climate policy uncertainty reduces it. Surprisingly, the impact of economic growth on non-renewable energy consumption is positive but insignificant. It is also observed that economic growth promotes renewable energy demand, and crude oil prices reduce it. Furthermore, climate policy uncertainty positively affects renewable energy demand in the long run. Some policy implications are provided for reducing non-renewable energy consumption and promoting renewable energy use in the United States through climate policy implementation.

Introduction

     Climate change is one of the leading problems in the 21st century. Scholars have demonstrated that risks and uncertainties related to climate change can affect various dimensions of the global economic system [1,2]. Climate change is attributed to global warming, environmental pollution, and caused by greenhouse gas emissions. Also, CO2 emissions are driven by fossil fuels and non-renewable energy sources [3,4]. Therefore, the transition from fossil fuels to green renewable energy is an important policy tool to transfer economies to the low carbon economy. It is suggested that higher renewable energy consumption can slow down the negative consequences of climate change on the economic system [5,6]. Similarly [7], argued that promoting renewable energy usage in consumption and production activities can also reduce the adverse impact of climate change and global warming on living human beings and natural habitats in the long run. Therefore, the determinants of the non-renewable and renewable energy demand are crucial for designing climate change policies in developing and developed economies.

Conclusion

     The determinants of energy consumption patterns have been an important issue in achieving sustainable environmental quality in the current context of energy depletion, climate change, and global warming. The following facts motivated us to explore the topic. The first fact is that the concept of “green recovery” or “green economic growth” in 2020 has reignited the debate among scholars about the influence of climate policy uncertainty, economic growth and crude oil prices on the pattern of energy consumption (non-renewable and renewable) pressure. The second fact is that the relevant empirical findings are inconclusive when they are based on the influences of economic growth and crude oil prices on the energy consumption pattern. This evidence suggests that we should use climate policy uncertainty as to the key factor in non-renewable and renewable energy consumption functions, which need to be further investigated empirically using the right methods. The third fact is that advanced economies like the United States are the leading energy consumers and second-best pollution creating countries globally; experiencing the factual findings can provide effective policy recommendations for their authorities and departments to adopt energy sustainability measures. This result can promote sustainable environmental quality for the United States and other growing economies in the long run.

     In this connection, we argue that this is the first study that motivates us to examine the effects of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. As control variables, economic growth and crude oil prices are added to non-renewable and renewable energy functions. This objective is modelled by applying the ARDL bounds test to cointegration. This model helps us to estimate the long-run and short-run results. Moreover, the DOLS and the FMOLS techniques are used as results of robust checking purposes.