School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
Benazir Bhutto Shaheed University, Lyari, Karachi, Pakistan.
Environ Sci Pollut Res Int. 2022 Oct;29(48):73241-73261. doi: 10.1007/s11356-022-20906-7. Epub 2022 May 27.
This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (EC) and non-renewable (REN) energies), economic globalization (GLO), and economic growth (GDP) on carbon dioxide (CO) emissions in Argentina over the period 1970-2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs EC-CO and GDP-CO are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina's environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO emissions on the non-renewable energy and economic growth are time-varying, while the pairs REN-CO and GLO-CO possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO emissions.
本文试图建立静态和动态相依结构模型，并测度阿根廷 1970-2020 年期间能源消耗（包括可再生能源和非可再生能源）、经济全球化和经济增长对二氧化碳排放的影响。为了分析目的，本研究采用了新的静态和动态基于 copula 的 ARIMA-fGARCH 与不同子模型。静态二元 copula 结果表明，EC-CO 和 GDP-CO 增长率之间存在非对称正协同变动和高左尾（极端）相依性，这意味着非可再生能源和经济增长的增加会对环境恶化产生重大影响，而非可再生能源消费水平的降低也会相应地减少 CO 排放。基于几种基于 copula 的相依性测度，我们记录到，在这两个因素中，非可再生能源对 CO 排放的影响比经济增长更强。另一方面，经济全球化和可再生能源的增长率与 CO 排放的增长率呈对称负协同变动，但它们没有极端相依性，这表明这些因素有助于提高阿根廷的环境质量，其中可再生能源的因素具有更大的影响。此外，动态 copula 结果表明，CO 排放对非可再生能源和经济增长的（尾部）相依性是时变的，而 REN-CO 和 GLO-CO 对则仅具有动态相依性，而没有动态尾部相依性。此外，通过动态 copula 相依性，可以估计和明确说明环境库兹涅茨曲线（EKC）假说。此外，我们利用多元 vine copulas 同时对五个变量的相依结构进行建模，这可以揭示相关变量之间条件关联的丰富信息。还提出了一些缓解 CO 排放的政策建议。
