A Study on the Influence Path of Corporate ESG Disclosure Quality and Sustainability Performance Based on Bayesian Network Modeling

Corporate ESG disclosure quality is a key condition to optimize industrial structure and a realistic path to reach sustainability performance.Based on the theoretical knowledge of Bayesian network model, the research program of corporate ESG disclosure quality and sustainability performance in luence path is designed.According to the current status of enterprise development, 11 research variables are set, which contain explanatory variables, interpreted variables, and control variables.Mathematical statistics and Bayesian network modeling are adopted to parse the mutual in luence mechanism between the two.In the forward Bayesian inference, the probability of enterprise sustainability performance being in a good state is 49.3%, and the probability of the explanatory variables being in a good state is increased to 58.7% by changing the state probability of other variables.In order to provide a comprehensive overview of the relationship, backward Bayesian inference was also performed, and when the probability of sustainability performance being in a good state was 100%, the probability of the board concurrent position being in a good state was the highest with a value of 72.3%.This study enhances the most effective corporate ESG disclosure quality control program for companies to maximize the possibility of sustainability performance.

Publication Details

Journal
Journal of Combinatorial Mathematics and Combinatorial Computing
Published
2025-04-16
DOI
https://doi.org/10.61091/jcmcc127a-261
Primary Topic
Regional Development and Environment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Study on the Influence Path of Corporate ESG Disclosure Quality and Sustainability Performance Based on Bayesian Network Modeling

Journal of Combinatorial Mathematics and Combinatorial Computing
Regional Development and Environment
article

A Study on the Influence Path of Corporate ESG Disclosure Quality and Sustainability Performance Based on Bayesian Network Modeling

article en

Abstract

Corporate ESG disclosure quality is a key condition to optimize industrial structure and a realistic path to reach sustainability performance.Based on the theoretical knowledge of Bayesian network model, the research program of corporate ESG disclosure quality and sustainability performance in luence path is designed.According to the current status of enterprise development, 11 research variables are set, which contain explanatory variables, interpreted variables, and control variables.Mathematical statistics and Bayesian network modeling are adopted to parse the mutual in luence mechanism between the two.In the forward Bayesian inference, the probability of enterprise sustainability performance being in a good state is 49.3%, and the probability of the explanatory variables being in a good state is increased to 58.7% by changing the state probability of other variables.In order to provide a comprehensive overview of the relationship, backward Bayesian inference was also performed, and when the probability of sustainability performance being in a good state was 100%, the probability of the board concurrent position being in a good state was the highest with a value of 72.3%.This study enhances the most effective corporate ESG disclosure quality control program for companies to maximize the possibility of sustainability performance.

Journal of Combinatorial Mathematics and Combinatorial ComputingVol. 127a
Responsible consumption and production, Industry, innovation and infrastructure
Openalex Percentile: Top 89%
Regional Development and Environment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.