气候变化领域集成服务门户(CCPortal): Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique

CCPortal

DOI	10.1016/j.jmse.2023.10.001
	Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique
	Abudu, Hermas; Wesseh Jr, Presley K.; Lin, Boqiang
发表日期	2024
ISSN	2096-2320
EISSN	2589-5532
起始页码	9
结束页码	1
卷号	9 期号:1
英文摘要	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.(c) 2023 China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
英文关键词	Climate bonds funds utilization; Climate bonds policy text mining; Machine learning technique; Net zero emissions; Policy sentiment analysis
语种	英语
WOS研究方向	Business & Economics ; Operations Research & Management Science
WOS类目	Business, Finance ; Economics ; Management ; Operations Research & Management Science
WOS记录号	WOS:001150211700001
来源期刊	JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301252
作者单位	Chengdu University; Xiamen University
推荐引用方式 GB/T 7714	Abudu, Hermas,Wesseh Jr, Presley K.,Lin, Boqiang. Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique[J],2024,9(1).
APA	Abudu, Hermas,Wesseh Jr, Presley K.,&Lin, Boqiang.(2024).Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique.JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING,9(1).
MLA	Abudu, Hermas,et al."Climate bonds toward achieving net zero emissions and carbon neutrality: Evidence from machine learning technique".JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING 9.1(2024).