Green Finance Evaluation Based on Neural Network Model

Wang, Ke

doi:10.1155/2022/4803072

Cited by 6 publications

(6 citation statements)

References 20 publications

(20 reference statements)

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“…Regarding the negative responses and literature reviews, green finance also needs to evaluate its relatively slow development. Recent analysis results indicate that there are still various issues that need to be addressed within the green finance system (Wang, 2022). Furthermore, efforts are needed to establish a standardized evaluation system that can be universally applied to measure the level of development and stages in sustainable finance (Wang & Wang, 2021;Zheng & Ye, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Since the concept of sustainable finance was introduced, many economists have conducted in-depth research on the evaluation and implementation of sustainable finance (Wang, 2022). Oh & Kim (2018) and (Ozili, 2021) have highlighted the crucial role played by private companies, including commercial banks and private capital, in promoting sustainable finance.…”

Section: Discussionmentioning

confidence: 99%

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Twitter Sentiment Analysis on Green Finance

Syahdatul Maulida,

Bashir Ammar Hakim

2022

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This research aims to analyze public sentiment regarding the development of green finance worldwide using primary data extracted from Twitter tweets. The research methodology employed is a qualitative approach with the utilization of Python Library software known as VADER (Valence Aware Dictionary and Sentiment Reasoner) to classify sentiments within these tweets. The findings of this study indicate that the majority of the public holds a positive sentiment of 60.2% towards green finance, followed by a neutral sentiment at 26.7%, and a negative sentiment at 13.1%. Some frequently mentioned keywords in the tweets include green finance, finance minister, sustainable finance, green energy, and green bond. The objective of this research is to provide a comprehensive overview of public perceptions of green finance, encompassing its positive aspects, advantages, potentials, and benefits, while also identifying potential weaknesses and threats associated with negative perceptions of green finance. With a better understanding of public sentiment, it is hoped that relevant stakeholders can take appropriate actions to strengthen the green finance ecosystem and raise awareness and support for sustainable finance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Twitter Sentiment Analysis on Green Finance

Syahdatul Maulida,

Bashir Ammar Hakim

2022

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

Retracted: Green Finance Evaluation Based on Neural Network Model

Neuroscience¹

2023

Computational Intelligence and Neuroscience

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“…andZhou et al (2022) [68] divided green finance into five subcategories: green credit, green securities, green insurance, green investment, and carbon finance, and constructed indicator systems accordingly. Wang (2022)[69] andZheng (2022) [70] constructed evaluation indicator systems for green finance based on three macro aspects: environment, finance, and society Zeng et al (2022) [71]. utilized metrics such as carbon intensity as indicators for evaluating the performance of green supply chains, effectively measuring the manifestation of environmentally sustainable development Zhang et al (2023).…”

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confidence: 99%

Evaluation of coupling coordination development between digital economy and green finance: Evidence from 30 provinces in China

Liu,

Zhang,

Wang

et al. 2023

PLoS ONE

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The convergence of China’s digital economy and green finance holds great significance for fostering a sustainable and high-quality developmental path. However, existing studies have not explored the coupling coordination development between these two crucial subsystems. To bridge this gap, this paper employs a modified coupling coordination degree (CCD) model to assess and affirm the coupling coordination degree between the digital economy and green finance across 30 provinces in China from 2015–2021. Based on degree results, provinces are classified into three clusters by using K-means and hierarchical clustering algorithm. Our findings unveil that the current level of coupling coordination development in China is at a primary coordination stage. Although regional disparities significantly exist, the overall level of coordination remains steadily increasing, with the eastern region outperforming the western region. Additionally, we determine that the COVID-19 pandemic’s disruption on the coupling coordination development of these systems has been limited. This research sheds light on the evolution of coupling systems and offers practical recommendations for strengthening the coordinated development of the digital economy and green finance.

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Green Finance Evaluation Based on Neural Network Model

Cited by 6 publications

References 20 publications

Twitter Sentiment Analysis on Green Finance

Twitter Sentiment Analysis on Green Finance

Retracted: Green Finance Evaluation Based on Neural Network Model

Evaluation of coupling coordination development between digital economy and green finance: Evidence from 30 provinces in China

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