Energy security and CO2 emissions: New evidence from time-varying and quantile-varying aspects.

This paper examines the long-term effects and related short-term movements of energy security (ENS), economic policy uncertainty (EPU), and government ecological expenditure (GEE) on China's carbon dioxide (CO 2) emissions by applying the quantile autoregressive distributed lag approach. The empirical findings demonstrate that ENS and EPU have prominent negative and positive impacts, respectively, on CO 2 emissions in the long term for the majority of quantiles, while their short-term influences are less obvious. In addition, the effect of GEE is short-term and concentrated in high quantiles. The following are the article's main contributions. This study builds special energy security indicators based on the actual situation in China, providing a basis for better understanding the interaction mechanism between ENS and CO 2. Considering that China is the largest oil importer and greenhouse gas emitter, this study explores for the first time the linkages between ENS and CO 2. Both short- and long-term impacts are observed, and long-run effects are dominant in the linkages among variables, suggesting that the level of CO 2 emissions is mainly driven by long-term shocks. In addition, to clarify the nexus between ENS and CO 2 , time- and quantile-varying analyses are used, which take into account not only varied CO 2 emission levels but also distinct time periods for events. The findings emphasize the importance of market participants to form a better understanding of how ENS, EPU, and GEE affect CO 2 under different emission levels. Some detailed policies, including optimizing the energy consumption structure, making prudent economic policy adjustments, and strengthening ecological protection expenditures, are provided to curb pollution. • Energy security and carbon neutralization are main targets for China. • Energy security negatively affects carbon dioxide in the long-term. • Time-varying and quantile-varying features are captured for Energy security. • The quantile autoregressive distributed lag model is utilized in this paper. [ABSTRACT FROM AUTHOR]