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.

DESIGN AND ANALYSIS OF THE GREEN CLIMATE FUND

The Green Climate Fund （GCF） has been one of the core issues of the world climate summits under the United Nations Framework Convention on Climate Change （UNFCCC） in recent years. However, the GCF has not progressed smoothly, and currently there are no satisfactory schemes for raising and distributing the fund. This paper first discusses how to finance the GCF among Annex II countries. It introduces the ＇preference score compromises＇ （PSC） approach which is based on environmental responsibility and economic capacity, with historical emissions as an indicator for environmental responsibility and GDP as indicator for economic capacity. The results show that the United States and the European Union are the two largest contributors to the GCF, sponsoring more than 80% of the funds. Second, we discuss how to allocate the funds among non-Annex II parties. The ＇adaptation needs＇ （AN） approach, which takes account of economic strength and climate damages, is proposed to achieve the adaptation purpose of the GCF, and the results reveal that African countries with high levels of climate vulnerability could get most funds, with a share of almost 30%. Regarding the mitigation purpose of the GCF, this research introduces two approaches： the ＇carbon reduction contribution＇ （CC） approach and the ＇incremental cost＇ （IC） approach. Both approaches could achieve significant reductions in carbon emissions in non-Annex II parties, whereas the latter may provide limited adaptation finance but result in more mitigation effects. This paper also develops a method to combine abatement efficiency and adaptation fairness of the GCF, and we find that with an equal split between the AN and CC （or AN and IC） approaches, the amount of USD 100 billion could finance an emissions reduction of 1613 MtCO2 （2477 MtCO2）, while allocating USD 16 （or USD 9） per capita for adaptation in non-Annex II parties. The schemes proposed may be useful for promoting the development of the GCF in the future.