The impact of green finance development on ecological protection based on machine learning

• Autorzy: Zhang, Ting
• Języki publikacji: EN

Abstrakty

EN

In the context of today’s green development, it is the core task of the financial sector at all levels to enhance the utilisation of resources and to guide the high-quality development of industries, especially to channel funds originally gathered in high-pollution and energy-intensive industries to sectors with green and high-technology, to achieve the harmonious development of the economy and the resources and environment. This paper proposes a green financial text classification model based on machine learning. The model consists of four modules: the input module, the data analysis module, the data category module, and the classification module. Among them, the data analysis module and the data category module extract the data information of the input information and the green financial category information respectively, and the two types of information are finally fused by the attention mechanism to achieve the classification of green financial data in financial data. Extensive experiments are conducted on financial text datasets collected from the Internet to demonstrate the superiority of the proposed green financial text classification method.

Słowa kluczowe

PL

zielone finanse; klasyfikacja tekstu; mechanizm uwagi; uczenie maszynowe

EN

green finance; text classification; attention mechanism; machine learning

• Czasopismo: Ecological Chemistry and Engineering. S
• Rocznik: 2023
• Tom: Vol. 30, nr 1
• Strony: 103--110
• Opis fizyczny: Bibliogr. 16 poz., tab.

Twórcy

autor

Zhang, Ting

• Zhongyuan Institute of Science and Technology, Zhengzhou 450000, China,
• Henan Pivotal Port and Economy Research Centre, Zhengzhou 450000, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Identyfikatory

DOI

10.2478/eces-2023-0008