Build Sustainability of Data Sharing Based on Deep Learning in the ESG Environment
(2024) DABN01 20241Department of Economics
Department of Statistics
- Abstract
- Against the backdrop of increasing global emphasis on environmental, social, and
corporate governance (ESG), improving the efficiency of ESG data analysis and the
sustainability of its sharing has become particularly important. This study proposes a
comprehensive research framework, first demonstrating the advantages of deep models
for ESG data analysis, and then discussing the combination of ESG data sharing and
blockchain technology, aiming to achieve sustainable development of ESG data sharing. Firstly, we collected and processed ESG data from multiple companies, including annual
reports, third-party ratings, and survey data, and standardized and extracted features. In
terms of ESG data processing, K-nearest neighbor (KNN) models... (More) - Against the backdrop of increasing global emphasis on environmental, social, and
corporate governance (ESG), improving the efficiency of ESG data analysis and the
sustainability of its sharing has become particularly important. This study proposes a
comprehensive research framework, first demonstrating the advantages of deep models
for ESG data analysis, and then discussing the combination of ESG data sharing and
blockchain technology, aiming to achieve sustainable development of ESG data sharing. Firstly, we collected and processed ESG data from multiple companies, including annual
reports, third-party ratings, and survey data, and standardized and extracted features. In
terms of ESG data processing, K-nearest neighbor (KNN) models are used as
benchmarks, while multi-layer perceptrons (MLP) and long short-term memory networks
(LSTM) are adopted due to their excellent ability to handle complex ESG data. Due to
the excellent performance of LSTM in time series analysis, MLP can effectively extract
nonlinear features, thus outperforming KNN in ESG score prediction. Next, we are
considering using blockchain technology to design a decentralized ESG data sharing
platform to ensure data transparency, security, and privacy. This study is based on a comprehensive framework that combines deep learning models
with data sharing platforms. Through deep modeling, dynamic data updates, and
feedback mechanisms, continuously optimize model and platform design to ensure that
data sharing has practical significance for improving corporate ESG. The results of
model analysis can provide specific improvement suggestions for enterprises, forming a
virtuous cycle and promoting continuous improvement of ESG performance (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9155415
- author
- Jiang, Han LU
- supervisor
- organization
- course
- DABN01 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- ESG Data Analysis, Deep Learning Models, KNN Model, MLP, LSTM, Blockchain Technology, Data Sharing Platform, Sustainable Development, ESG Score Prediction
- language
- English
- id
- 9155415
- date added to LUP
- 2024-09-24 08:34:10
- date last changed
- 2024-09-24 08:34:10
@misc{9155415, abstract = {{Against the backdrop of increasing global emphasis on environmental, social, and corporate governance (ESG), improving the efficiency of ESG data analysis and the sustainability of its sharing has become particularly important. This study proposes a comprehensive research framework, first demonstrating the advantages of deep models for ESG data analysis, and then discussing the combination of ESG data sharing and blockchain technology, aiming to achieve sustainable development of ESG data sharing. Firstly, we collected and processed ESG data from multiple companies, including annual reports, third-party ratings, and survey data, and standardized and extracted features. In terms of ESG data processing, K-nearest neighbor (KNN) models are used as benchmarks, while multi-layer perceptrons (MLP) and long short-term memory networks (LSTM) are adopted due to their excellent ability to handle complex ESG data. Due to the excellent performance of LSTM in time series analysis, MLP can effectively extract nonlinear features, thus outperforming KNN in ESG score prediction. Next, we are considering using blockchain technology to design a decentralized ESG data sharing platform to ensure data transparency, security, and privacy. This study is based on a comprehensive framework that combines deep learning models with data sharing platforms. Through deep modeling, dynamic data updates, and feedback mechanisms, continuously optimize model and platform design to ensure that data sharing has practical significance for improving corporate ESG. The results of model analysis can provide specific improvement suggestions for enterprises, forming a virtuous cycle and promoting continuous improvement of ESG performance}}, author = {{Jiang, Han}}, language = {{eng}}, note = {{Student Paper}}, title = {{Build Sustainability of Data Sharing Based on Deep Learning in the ESG Environment}}, year = {{2024}}, }