Unveiling the Impacts of Corporate Environmental, Social, and Governance Disclosure

Amidst heightened scrutiny of corporate environmental, social, and governance (ESG) practices, this study employs threshold techniques combined with artificial neural networks to examine the impact of ESG disclosure on companies, emphasizing its pivotal role in promoting sustainability. Analyzing data from Taiwan's 20 industries from 2012 to 2022, it finds that while ESG engagement positively influences financial performance, it also underscores the vital connection between corporate practices and sustainable development. This analysis explores the relationship between carbon emissions, operating expenses, and financial performance in the overall sample and a threshold sample based on a threshold variable. In the overall sample, carbon emissions significantly increase operating expenses, accompanied by other influential variables. Introducing a threshold value of firm size alters the dynamics, showing a positive and more pronounced impact in the threshold sample. The examination of financial performance metrics reveals significant positive associations with carbon emissions, particularly when the threshold is not met or exceeded. Intriguingly, subgroup analysis indicates a negative association between carbon emissions and financial performance within the larger-size subgroup, in stark contrast to a more pronounced positive relationship observed in the smaller-size subgroup. Furthermore, the developed ANN model exhibits robust learning capabilities, underscoring its efficacy in capturing complex patterns within the data. It suggests its potential as a reliable tool for accurately predicting carbon emissions across diverse scenarios, facilitating informed decision-making and policy formulation to mitigate environmental impact.