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
Science and technology talent evaluation is an important part of the talent development system and mechanism,and the state's emphasis on science and technology talent evaluation is reflect-ed in the national polic...Science and technology talent evaluation is an important part of the talent development system and mechanism,and the state's emphasis on science and technology talent evaluation is reflect-ed in the national policy.This paper takes 49 policy documents on science and technology tal-ent evaluation issued by the state from 1978 to 2022 as the object,and uses policy text analysis,word frequency analysis and co-word network analysis to interpret the content of the policies.These policy texts are divided into four stages.200 high-frequency words were extracted from the policies in each stage after word segmentation was made,and the co-word matrixes and co-word networks were constructed for each stage.The research draws the following conclu-sions:1)The content of Chinese science and technology talent evaluation policies is gradually becoming concrete,and the concept of science and technology talent evaluation is gradually clear;2)The policies have transitioned from"scientific and technological development"to"sci-entific and technological innovation";3)The distribution of policies with different strengths is uneven;4)The cooperation between all departments involved in policy formulation is not e-nough.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.
文摘Science and technology talent evaluation is an important part of the talent development system and mechanism,and the state's emphasis on science and technology talent evaluation is reflect-ed in the national policy.This paper takes 49 policy documents on science and technology tal-ent evaluation issued by the state from 1978 to 2022 as the object,and uses policy text analysis,word frequency analysis and co-word network analysis to interpret the content of the policies.These policy texts are divided into four stages.200 high-frequency words were extracted from the policies in each stage after word segmentation was made,and the co-word matrixes and co-word networks were constructed for each stage.The research draws the following conclu-sions:1)The content of Chinese science and technology talent evaluation policies is gradually becoming concrete,and the concept of science and technology talent evaluation is gradually clear;2)The policies have transitioned from"scientific and technological development"to"sci-entific and technological innovation";3)The distribution of policies with different strengths is uneven;4)The cooperation between all departments involved in policy formulation is not e-nough.
