The Impact of Environmental Costs and Corporate Governance Mechanisms on Financial Performance
DOI:
https://doi.org/10.32939/fbv2c845Keywords:
Environmental Accounting, Environmental Costs , Corporate Governance Mechanisms, Return on Assets, Food and Beverage ManufacturingAbstract
Purpose: This investigation assesses the extent to which financial performance is influenced by environmental costs and corporate governance mechanisms, specifically audit committee meeting frequency and the proportion of independent commissioners, within an industry marked by substantial environmental impact.
Design/Methodology/Approach: Companies in the food and beverage manufacturing sub-sector were examined for the 2022-2024 period. Purposive sampling with pre-set criteria determined the sample, resulting in 15 companies and 45 firm-year observations. Multiple linear regression, conducted via SPSS 22, served to assess the partial and simultaneous associations of the independent variables with financial performance.
Findings: Financial performance is significantly negatively associated with environmental costs (B = −4.040; p = 0.017) and the proportion of independent commissioners (B = −389.723; p = 0.001), while audit committee meeting frequency shows no significant association with financial performance (B = 100.376; p = 0.081). Simultaneously, the three variables are significantly associated with financial performance (F = 5.558; p = 0.003 The model captures 23.7% of the variation manifest in financial performance, as indicated by the adjusted R² of 0.237.
Research Implications: These outcomes offer further insight into environmental accounting and corporate governance by offering empirical evidence from the food and beverage manufacturing sub-sector. Practically, companies are advised to prioritize environmental cost management and strengthen board independence, as increasing audit committee meeting frequency alone appears insufficient to improve financial performance. Future research is encouraged to incorporate additional governance variables, expand the observation window, or consider other industry sectors to enhance the generalizability of these results.
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