Integrating Artificial Intelligence into ESG Practices: Opportunities, Challenges, and Strategic Solutions for Corporate Sustainability.
Bibliographic record
Abstract
Environmental, social, and governance (ESG) practices have become increasingly important in corporate strategy in recent years, while the rapid development of artificial intelligence (AI) has created new opportunities and challenges for corporate sustainability. AI technology driving companies for ESG time is getting more and more attention. This study examines the application of AI technologies in environmental management, social responsibility, and corporate governance, demonstrating their potential to optimize resource utilization, reduce carbon emissions, improve recruitment fairness, and prevent fraud. However, integrating AI with ESG faces many challenges, including technological complexity, high costs, data privacy and ethical issues, and organizational and cultural resistance. To address these challenges, this study proposes solutions to reduce financial burdens, secure data, and enhance cultural buy-in through strategies such as technology partnerships, open-source tools, and employee training. By delving into the convergence of AI and ESG, this study provides companies with a guiding direction to fully utilize the potential of AI while maintaining long-term sustainability.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".