This research analyzes the dynamic relationship between green bonds, energy prices, geopolitical risk, and CO₂ emissions. In doing so, the study examines the global scale at disaggregated (i.e., sectoral) level, applies a novel time and frequency-based approach (i.e., wavelet local multiple correlation-WLMC), and uses high-frequency daily data between 1st January 2020 and 28th April 2023. In doing so, the study considers the potential differences among sectors. So, aggregated and disaggregated level CO₂ emissions on sectoral bases are investigated. Hence, the study comprehensively uncovers the effect of the aforementioned indicators on global CO₂ emissions. The results reveal that on CO₂ emissions (i) the most influential factor is the geopolitical risk (2020/1–2021/5), green bonds (2021/5–2021/7), energy prices (2021/7–2023/1), and green bonds (2023/1–2023/4); (ii) the effects of the influential factors are much weaker (stronger) at lower (higher) frequencies; (iii) the effect of the influential factors change based on times and frequencies; (iv) however, the effects of the influential factors on CO₂ emissions do not differ at aggregated or disaggregated levels. Overall, the results present novel insights for time and frequency-varying effects as well as both aggregated and disaggregated level analyses of global CO₂ emissions.

_{本研究分析了绿色债券、能源价格、地缘政治风险和CO 2}排放之间的动态关系。为此，该研究在分类（即部门）层面上考察了全球规模，应用了一种新颖的基于时间和频率的方法（即小波局部多重相关 - WLMC），并使用了 2020 年 1 月 1 日之间的高频每日数据2023 年 4 月 28 日。在此过程中，该研究考虑了行业之间的潜在差异。因此，我们对部门基础上的汇总和分类的 CO _{2排放量进行了调查。因此，本研究全面揭示了上述指标对全球CO 2}排放的影响。结果表明，对CO ₂排放影响最大的因素是地缘政治风险（2020/1-2021/5）、绿色债券（2021/5-2021/7）、能源价格（2021/7-2023/）。 1)、绿色债券(2023/1–2023/4)；(ii) 影响因素的影响在频率较低（较高）时较弱（较强）；(iii) 影响因素的影响随时间和频率而变化；(iv) 然而，影响因素对CO ₂排放的影响在汇总或分类水平上没有差异。总体而言，研究结果对随时间和频率变化的影响以及全球 CO ₂排放的汇总和分类分析提供了新颖的见解。