Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies
This study examines the role of Green Innovation in predicting ESG ratings across developed and emerging economies. Among 292 firms, Green R&D Intensity is identified as a key predictor of ESG ratings. Results indicate that companies currently make minimal investments in Green Innovation...
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| Format: | Article |
| Language: | English |
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Pensoft
2025-07-01
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| Series: | MAB |
| Online Access: | https://mab-online.nl/article/135692/download/pdf/ |
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| _version_ | 1849714529324236800 |
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| author | Thomas Archer |
| author_facet | Thomas Archer |
| author_sort | Thomas Archer |
| collection | DOAJ |
| description | This study examines the role of Green Innovation in predicting ESG ratings across developed and emerging economies. Among 292 firms, Green R&D Intensity is identified as a key predictor of ESG ratings. Results indicate that companies currently make minimal investments in Green Innovation, meaning modest increases in investments could enhance ESG ratings. Findings support Signaling Theory, suggesting Green Innovation can immediately boost ratings, though long-term impacts may require time to mature. The study also shows integrating Green Innovation into ML models reduces prediction error by 2% rising to 11.5% for firms without prior ESG ratings. Ultimately, the study’s implications underscore the importance of ESG factors for firms, investors, and policymakers, as higher ESG ratings are linked to increased firm value, improved performance, and economic growth. |
| format | Article |
| id | doaj-art-5e1bff84b7ef41f88ba6537ea59a2ec8 |
| institution | DOAJ |
| issn | 2543-1684 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Pensoft |
| record_format | Article |
| series | MAB |
| spelling | doaj-art-5e1bff84b7ef41f88ba6537ea59a2ec82025-08-20T03:13:40ZengPensoftMAB2543-16842025-07-0199314515410.5117/mab.99.135692135692Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economiesThomas Archer0Rotterdam School of ManagementThis study examines the role of Green Innovation in predicting ESG ratings across developed and emerging economies. Among 292 firms, Green R&D Intensity is identified as a key predictor of ESG ratings. Results indicate that companies currently make minimal investments in Green Innovation, meaning modest increases in investments could enhance ESG ratings. Findings support Signaling Theory, suggesting Green Innovation can immediately boost ratings, though long-term impacts may require time to mature. The study also shows integrating Green Innovation into ML models reduces prediction error by 2% rising to 11.5% for firms without prior ESG ratings. Ultimately, the study’s implications underscore the importance of ESG factors for firms, investors, and policymakers, as higher ESG ratings are linked to increased firm value, improved performance, and economic growth.https://mab-online.nl/article/135692/download/pdf/ |
| spellingShingle | Thomas Archer Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies MAB |
| title | Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies |
| title_full | Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies |
| title_fullStr | Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies |
| title_full_unstemmed | Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies |
| title_short | Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies |
| title_sort | assessing the influence of green innovation on esg ratings a machine learning approach across developed and emerging economies |
| url | https://mab-online.nl/article/135692/download/pdf/ |
| work_keys_str_mv | AT thomasarcher assessingtheinfluenceofgreeninnovationonesgratingsamachinelearningapproachacrossdevelopedandemergingeconomies |