The nonlinear association between ESG disclosure and firm performance: Evidence from Vietnam’s listed energy firms
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DOIhttp://dx.doi.org/10.21511/ee.17(4).2026.07
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Article InfoVolume 17 2026, Issue #4, pp. 92–108
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Type of the article: Research Article
Abstract
Vietnam’s energy sector faces growing tensions between production expansion and environmental protection, making the economic consequences of ESG disclosure particularly important. Accordingly, this study examines the nonlinear associations between composite and pillar-level ESG disclosure on 30 energy firms listed on Vietnam’s stock exchanges during 2015–2024. Annual report data were extracted by optical character recognition, with a large language model supporting paragraph classification into predefined E, S, and G categories, then quantified into an ESG score and each pillar based on frequency score and coverage score on 300 firm-year observations. The feasible generalized least squares regression results, with cautious interpretation for the relatively small sample size, indicate that ESG disclosure exhibits a statistically significant, though not uniformly robust, inverse U-shaped association with return on assets (ROA) (β = 0.012; β² = –0.002), Tobin’s Q (β = 0.025; β² = –0.004), and return on sales (ROS) (β = 0.003; β² = –0.001) although the statistical evidence is stronger for ROA. The E pillar shows a significant nonlinear association with ROA (β = 0.006, β² = –0.001) and ROS (β = 0.003[HT1.1], β² = –0.001), but no statistically significant association with Tobin’s Q. Governance disclosure shows comparatively stronger associations, particularly with Tobin’s Q (β = 0.034; β² = –0.005), while social disclosure is significantly associated with ROA and ROS but not with Tobin’s Q. These findings are associated with stronger governance signaling for market valuation, suggesting that energy firms may benefit from weighing governance strength alongside environmental disclosure aligned with operational capacity.
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JEL Classification (Paper profile tab)Q56, G34, G32, D22
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References40
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Tables12
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Figures0
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- Table 1. Descriptive statistics
- Table 2. Panel unit root test results
- Table 3. Matrix of correlations
- Table 4. Diagnostics
- Table 5. FGLS estimates of the associations between ESG disclosure and ROA (FGLS)
- Table 6. FGLS estimates of the associations between ESG disclosure and Tobin's Q (FGLS)
- Table 7. FGLS estimates of the associations between ESG disclosure and ROS (FGLS)
- Table 8. Summary of hypotheses testing results
- Table A1. Definitions of variables
- Table B1. Full FGLS estimation results for ROA: Linear and nonlinear specifications
- Table B2. Full FGLS estimation results for Tobin’s Q: Linear and nonlinear specifications
- Table B3. Full FGLS estimation results for ROS: Linear and nonlinear specifications
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