实现净零排放和碳中和的气候债券:来自机器学习技术的证据

IF 5.4 2区 管理学 Q1 BUSINESS, FINANCE Journal of Management Science and Engineering Pub Date : 2023-11-10 DOI:10.1016/j.jmse.2023.10.001
Hermas Abudu , Presley K. Wesseh Jr. , Boqiang Lin
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引用次数: 0

摘要

缔约方大会(COP26 和 27)十分重视以实现净零排放和碳中和为目标的气候融资政策。然而,有关这一政策主张实施情况的研究十分有限。针对这一文献空白,本研究采用机器学习技术,特别是自然语言处理技术(NLP),在气候融资背景下研究了 32 个国家的 77 项气候债券(CB)政策。研究结果表明,"可持续性 "和 "碳排放控制 "是这些气候债券政策中概述最多的政策目标。此外,研究还强调,大多数气候债券资金都投向了能源项目(即可再生、清洁和高效项目)。然而,在 2015 年至 2019 年期间,CB 资金的分配出现了明显的变化,从气候友好型能源项目转向了建筑部门。这一转变令人担忧资金可能会从以气候为重点的投资转向房地产业,从而有可能导致气候基金被 "洗绿"。此外,政策情绪分析表明,少数政策对气候变化持怀疑态度,这可能会对气候行动产生负面影响。因此,研究结果突出表明,CB 政策的有效实施取决于政策目标、目的和情绪。最后,本研究通过使用 NLP 技术了解气候融资中的政策情感,为相关文献做出了贡献。
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Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique

The Conference of the Parties (COP26 and 27) placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality. However, studies on the implementation of this policy proposition are limited. To address this gap in the literature, this study employs machine learning techniques, specifically natural language processing (NLP), to examine 77 climate bond (CB) policies from 32 countries within the context of climate financing. The findings indicate that “sustainability” and “carbon emissions control” are the most outlined policy objectives in these CB policies. Additionally, the study highlights that most CB funds are invested toward energy projects (i.e., renewable, clean, and efficient initiatives). However, there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019. This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry, potentially leading to the greenwashing of climate funds. Furthermore, policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change, which may negatively influence climate actions. Thus, the findings highlight that the effective implementation of CB policies depends on policy goals, objectives, and sentiments. Finally, this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

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来源期刊
Journal of Management Science and Engineering
Journal of Management Science and Engineering Engineering-Engineering (miscellaneous)
CiteScore
9.30
自引率
3.00%
发文量
37
审稿时长
108 days
期刊介绍: The Journal of Engineering and Applied Science (JEAS) is the official journal of the Faculty of Engineering, Cairo University (CUFE), Egypt, established in 1816. The Journal of Engineering and Applied Science publishes fundamental and applied research articles and reviews spanning different areas of engineering disciplines, applications, and interdisciplinary topics.
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