工业采用和向可持续可再生能源过渡

Nishesh Garg, N. Gupta
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引用次数: 0

摘要

各国呼吁进行一场全球革命,以加快从化石燃料向可再生能源的过渡,成为具有自我承诺时间表的净零碳排放国。本研究将二氧化碳排放量和可再生能源在能源供应总量中的参与确定为两个主要维度,并通过时间序列和国家的多面板回归定量讨论了各种自变量的影响,进行比较相关性分析。基于主题的行业专家访谈为研究增加了定性维度。研究结果表明,该国的能源需求和国内化石燃料的可用性决定了可再生能源的采用率,而高度依赖煤炭和原油的国家正在转向可再生能源。对氢、燃料电池和存储的研发投资尚未朝着这个方向产生影响,因为它们是最近在这场漫长的能源转型游戏中进行的。
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Industrial adoption and transition towards sustainable renewable energy
A global revolution is called upon to transition into renewable sources of energy from fossil fuels in an accelerated manner by various countries to become net zero carbon emitter with self-committed timelines. This study identified carbon dioxide emissions and participation of renewable energy in total energy supply as two major dimensions and discusses the impact of various independent variables quantitively by multipanel regression in timeseries and countries for comparative correlation analysis. Theme based industry expert interviews adds qualitative dimension to the study. The results suggest that country’s energy need and availability of fossil fuels within, determines the RE adoption rate, while countries with high reliance on coal and crude are shifting towards RE sources. Investments in research and development in hydrogen, fuel cells and storage are yet to make an impact in this direction since they are recent in this long game of energy transition.
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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