ESG Practices, Green Innovation, and Financial Performance: Panel Evidence from ASEAN Firms
Bibliographic record
Abstract
This study examines the impact of environmental, social, and governance (ESG) practices on green innovation and financial performance among 174 publicly listed firms across ASEAN countries over the period from 2019 to 2023. Utilizing an unbalanced panel dataset of firms from key ASEAN economies, the analysis employs panel regression techniques. Green innovation performance is measured through innovation disclosures related to environmental technologies, while financial success is assessed via return on assets (ROA) and Tobin’s Q. The findings reveal that environmental and governance disclosure scores positively influence green innovation, whereas social scores exert a more immediate impact on financial performance. Moreover, green innovation is found to partially mediate the relationship between overall ESG practices and long-term market valuation. These results highlight the strategic role of ESG transparency in enhancing innovation-driven competitiveness, responsible business conduct, and sustainable employment across Southeast Asian markets. Implications are discussed for corporate managers, policymakers, and socially responsible investors. The study reinforces the case for ESG-aligned strategy as a pathway to both innovation, inclusive economic growth, and long-term competitiveness in ASEAN markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".