<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The pervasive shift towards carbon neutrality is fundamentally transforming the trajectory of the financial sector and concurrently poses novel challenges for the development of multi-skilled talent in green finance. This paper adopts the sustainable development strategy of green finance as a theoretical framework, critically examines the specific talent needs within this sector, and proposes a model for cultivating composite talents accordingly. Employing the Koch assessment model, this study constructs a robust evaluation system for composite talent training. The entropy weighting method is utilized to allocate weights to various indicators, subsequently forming an assessment cloud model. Moreover, a multiple linear regression analysis is introduced to identify and analyze the determinants influencing the effectiveness of talent training initiatives. This research centers on University A, where a meticulously designed questionnaire was deployed to gather empirical data pertinent to the evaluation of talent training. The findings reveal that the dual-wheel drive approach to talent development primarily accentuates the action level, assigning it a weight coefficient of 0.3246. The overall assessment score for the effectiveness of talent cultivation stands at 72.42, indicating a favorable outcome. Among the variables studied, the pedagogical content and methods implemented by higher education institutions exhibit the highest influence coefficient of 0.492. Additionally, it is observed that a 1% increase in the scale of financial institutions correlates with a 0.358% enhancement in the effectiveness of talent cultivation. Given these insights, universities must enhance their collaborative efforts with financial entities to significantly boost the quality of composite talent cultivation in green finance.</p>
</abstract>

The pervasive shift towards carbon neutrality is fundamentally transforming the trajectory of the financial sector and concurrently poses novel challenges for the development of multi-skilled talent in green finance. This paper adopts the sustainable development strategy of green finance as a theoretical framework, critically examines the specific talent needs within this sector, and proposes a model for cultivating composite talents accordingly. Employing the Koch assessment model, this study constructs a robust evaluation system for composite talent training. The entropy weighting method is utilized to allocate weights to various indicators, subsequently forming an assessment cloud model. Moreover, a multiple linear regression analysis is introduced to identify and analyze the determinants influencing the effectiveness of talent training initiatives. This research centers on University A, where a meticulously designed questionnaire was deployed to gather empirical data pertinent to the evaluation of talent training. The findings reveal that the dual-wheel drive approach to talent development primarily accentuates the action level, assigning it a weight coefficient of 0.3246. The overall assessment score for the effectiveness of talent cultivation stands at 72.42, indicating a favorable outcome. Among the variables studied, the pedagogical content and methods implemented by higher education institutions exhibit the highest influence coefficient of 0.492. Additionally, it is observed that a 1% increase in the scale of financial institutions correlates with a 0.358% enhancement in the effectiveness of talent cultivation. Given these insights, universities must enhance their collaborative efforts with financial entities to significantly boost the quality of composite talent cultivation in green finance.

Using big data analysis to optimize the two-wheel-drive model of green finance composite talent cultivation

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The pervasive shift towards carbon neutrality is fundamentally transforming the trajectory of the financial sector and concurrently poses novel challenges for...

Using big data analysis to optimize the two-wheel-drive model of green finance composite talent cultivation

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 01 apr 2024

Accettato: 30 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2002

Parole chiave
Ke assessment model, Entropy weight method, Assessment cloud model, Multiple linear regression, Green finance

© 2024 Rui Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Using big data analysis to optimize the two-wheel-drive model of green finance composite talent cultivation

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 01 apr 2024

Accettato: 30 giu 2024

DOI: https://doi.org/10.2478/amns-2024-2002

Parole chiaveKe assessment model, Entropy weight method, Assessment cloud model, Multiple linear regression, Green finance

© 2024 Rui Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Ke assessment model, Entropy weight method, Assessment cloud model, Multiple linear regression, Green finance