绿色与灰色:可再生能源和不可再生能源的使用在内部和外部力量的背景下对加拿大增长轨迹的影响

IF 3.3 2区 社会学 Q2 ENVIRONMENTAL SCIENCES
Md. Idris Ali , Md. Monirul Islam , Brian Ceh
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引用次数: 0

摘要

为有效应对气候变化,全球经济必须从依赖化石燃料的灰色模式过渡到以可再生能源为基础的绿色系统,从而努力减少全球变暖。然而,关于内部和外部宏观经济动态如何影响灰色能源和绿色能源的比较研究仍然严重不足。本研究探讨了可再生能源和不可再生能源对经济增长的综合影响。因此,本研究通过纳入内部和外部宏观经济决定因素以及制度质量(1990-2022 年期间的变量),研究加拿大综合能源消耗和分类能源消耗(可再生能源和不可再生能源)与经济增长之间的联系。利用动态自回归分布滞后(DARDL)方法,研究结果表明,在存在内部和外部动态因素的情况下,无论从短期还是长期来看,可再生能源和非可再生能源的总能源消耗和分类能源消耗都会刺激经济增长。不过,在内部动力的情况下,这种关系要强于外部动力。此外,我们还进行了反事实分析,对回归变量显示了 1%(±)和 5%(±)的冲击,并考察了它们对回归变量的影响。最后,我们使用基于核的正则化最小二乘法(KRLS)机器学习算法来检验变量之间的因果联系。根据研究结果,本研究建议通过采用多元化能源组合战略来优化内部和外部动力。这种方法将使加拿大过渡到一个可持续和有弹性的经济未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Green versus grey: Impact of renewable and non-renewable energy usage on Canada's growth trajectory in the context of internal and external forces

To effectively address climate change, economies worldwide must transition from grey, fossil fuel–dependant models to green, renewable energy–based systems, thereby striving to reduce global warming. However, a comparative study of how internal and external macro-economic dynamics influence both grey and green energy sources remains significantly underexplored. This study examines the effects of aggregated, renewable and non-renewable energy on economic growth. Therefore, this study investigates the connection between aggregated and disaggregated energy consumption (renewables and non-renewables) and economic growth in Canada by incorporating internal and external macro-economic determinants, along with institutional quality, which are variables during the period of 1990–2022. Using the dynamic autoregressive distributed lag (DARDL) approach, the study's results reveal that both aggregated and disaggregated energy consumption of renewable and non-renewable sources stimulate economic growth in the presence of both internal and external dynamics in both the short and long terms. However, this relationship is stronger in the context of internal dynamics than external ones. In addition, we conduct a counterfactual analysis by displaying 1 % (±) and 5 % (±) shocks to regressors and examining their effects on the regressed variable. Finally, we use the kernel-based regularised least squares (KRLS) machine learning algorithm to examine the cause-and-effect connectedness amongst variables. On the basis of the findings, this study recommends optimising both internal and external dynamics by adopting a diversified energy mix strategy. This approach will enable Canada to transition towards a sustainable and resilient economic future.

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来源期刊
Sustainable Futures
Sustainable Futures Social Sciences-Sociology and Political Science
CiteScore
9.30
自引率
1.80%
发文量
34
审稿时长
71 days
期刊介绍: Sustainable Futures: is a journal focused on the intersection of sustainability, environment and technology from various disciplines in social sciences, and their larger implications for corporation, government, education institutions, regions and society both at present and in the future. It provides an advanced platform for studies related to sustainability and sustainable development in society, economics, environment, and culture. The scope of the journal is broad and encourages interdisciplinary research, as well as welcoming theoretical and practical research from all methodological approaches.
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