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 th...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.展开更多
基金supported by the funding of Belt and Road Research Institute,Xiamen University(No:1500-X2101200)National Natural Science Foundation of China(Key Program,No:72133003).
文摘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.
